WO2018155960A1 - Method for diagnosing ovarian cancer through microbial metagenome analysis - Google Patents

Method for diagnosing ovarian cancer through microbial metagenome analysis Download PDF

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WO2018155960A1
WO2018155960A1 PCT/KR2018/002280 KR2018002280W WO2018155960A1 WO 2018155960 A1 WO2018155960 A1 WO 2018155960A1 KR 2018002280 W KR2018002280 W KR 2018002280W WO 2018155960 A1 WO2018155960 A1 WO 2018155960A1
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ovarian cancer
bacteria
derived
extracellular vesicles
decrease
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PCT/KR2018/002280
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French (fr)
Korean (ko)
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김윤근
박태성
송용상
김세익
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주식회사 엠디헬스케어
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Priority claimed from KR1020180021197A external-priority patent/KR101940446B1/en
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Priority to US16/629,360 priority Critical patent/US20200199655A1/en
Publication of WO2018155960A1 publication Critical patent/WO2018155960A1/en

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/6895—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for plants, fungi or algae

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  • the present invention relates to a method for diagnosing ovarian cancer through a microbial metagenome analysis, and more specifically, by performing a microbial metagenomic analysis of bacteria, archaea, etc., using a sample derived from a subject,
  • the present invention relates to a method for diagnosing ovarian cancer by analyzing the increase or decrease in content.
  • Ovarian cancer is the second most common cancer of the genitals. However, 70% of women are diagnosed at an advanced stage, so the treatment rate is only 20-30%. The cause of ovarian cancer, as with other cancers, is not yet known exactly. For some factors, there is a high risk of ovarian cancer if there is ovarian cancer in the family, but 95% of ovarian cancer patients have no family history. If you have a past or family history of breast cancer, endometrial cancer, or rectal cancer, breast cancer will double your chances of developing ovarian cancer. Persistent ovulation and menstruation are known to increase the risk of ovarian cancer.
  • ovarian cancer On the other hand, pregnancy tends to prevent the occurrence of ovarian cancer, so once a child is born, the risk of ovarian cancer is about 10% less than that of a woman who never gives birth. Feeding after birth also reduces the number of ovulation to reduce the occurrence of ovarian cancer. Due to environmental factors, ovarian cancers occur in advanced countries and urban women. In addition, obesity and infectivity of various viral diseases are known to be related to the occurrence of ovarian cancer.
  • Vaginal ultrasound and tumor markers are mainly used to diagnose ovarian cancer, and CA125, CA19-9, AFP, CEA, SA, and CA72.4 are used as tumor indicators.
  • the CA125 is widely used for screening, diagnostics, monitoring, and follow-up. However, the specificity and sensitivity are limited in stage 1 and 2 of ovarian cancer.
  • the symbiosis of the human body reaches 100 trillion times 10 times more than human cells, the number of genes of the microorganism is known to be more than 100 times the number of human genes.
  • a microbiota is a microbial community, including bacteria, archaea, and eukarya that exist in a given settlement.
  • the intestinal microbiota plays an important role in human physiology.
  • it is known to have a great effect on human health and disease through interaction with human cells.
  • the symbiotic bacteria secrete nanometer-sized vesicles to exchange information about genes and proteins in other cells.
  • the mucous membrane forms a physical protective film that particles larger than 200 nanometers (nm) in size can't pass through, so that the symbiotic bacteria cannot pass through the mucosa, but bacterial-derived vesicles are usually less than 100 nanometers in size. It freely speaks to the mucous membrane and is absorbed by our body.
  • Metagenomics also called environmental genomics, can be said to be an analysis of metagenomic data obtained from samples taken from the environment (Korean Patent Publication No. 2011-0073049). Recently, it has become possible to list the bacterial composition of the human microflora by a method based on 16s ribosomal RNA (16s rRNA) sequencing. Next generation sequencing of 16s rDNA sequencing gene of 16s ribosomal RNA is performed. , NGS) platform to analyze.
  • NGS 16s ribosomal RNA
  • the present inventors In order to diagnose ovarian cancer, the present inventors separated extracellular vesicles using blood and urine, a sample derived from a subject, extracted genes from vesicles, and performed a metagenome analysis on the vesicles. Bacteria and archaea-derived extracellular vesicles that can act were identified, and the present invention was completed.
  • an object of the present invention is to provide an information providing method for diagnosing ovarian cancer through metagenome analysis of extracellular vesicles derived from bacteria and archaea.
  • the present invention provides a method for providing information for diagnosing ovarian cancer, comprising the following steps:
  • the present invention provides a method for diagnosing ovarian cancer, comprising the following steps:
  • the present invention also provides a method for predicting the risk of developing ovarian cancer, comprising the following steps:
  • the subject sample may be blood or urine.
  • At least one class selected from the group consisting of Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 isolated from the subject blood sample in step (c). ) May be compared to increase or decrease the content of bacterial-derived extracellular vesicles.
  • Rhizobiaceae Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Burcobacteria coccaceae It may be to compare the increase or decrease in the content of one or more family bacteria-derived extracellular vesicles selected from the group consisting of, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae.
  • family bacteria-derived extracellular vesicles selected from the group consisting of, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae,
  • At least one phylum selected from the group consisting of Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, and TM6 isolated from the subject urine sample in step (c) It may be to compare the increase or decrease in the content of bacterial-derived extracellular vesicles.
  • Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycidadee , Gemmatimonadetes, Flavobacteriia, ML635J-21, and SJA-4 may be compared to increase or decrease the content of one or more class bacteria-derived extracellular vesicles selected from the group consisting of.
  • the blood may be whole blood, serum, plasma, or blood monocytes.
  • Extracellular vesicles secreted by the microorganisms present in the environment can be absorbed directly into the body and directly affect the development of cancer, and ovarian cancer is difficult to diagnose early because symptoms are difficult, so effective treatment is difficult.
  • Metagenome analysis of extracellular vesicles derived from bacteria using a sample predicts the risk of developing ovarian cancer in advance, so that risk groups of ovarian cancer can be diagnosed and predicted early and appropriate management can be delayed or prevented. It can be diagnosed early, reducing the incidence of ovarian cancer and improving the therapeutic effect.
  • metagenome analysis in patients diagnosed with ovarian cancer can be used to avoid the causative agent and improve the course of the cancer or prevent recurrence.
  • Figure 1a is a photograph of the distribution of bacteria and vesicles by time after the oral administration of enteric bacteria and bacterial derived vesicles (EV) to the mouse
  • Figure 1b is 12 hours after oral administration, blood And several organs were extracted to evaluate the distribution of bacteria and vesicles in the body.
  • FIG. 2 is a result showing the distribution of bacterial vesicles (EVs) with significant diagnostic performance at the class level by separating bacterial vesicles from ovarian cancer patients and normal blood, and performing a metagenome analysis.
  • EVs bacterial vesicles
  • Figure 3 shows the distribution of bacteria-derived vesicles from ovarian cancer patients and normal blood, and shows the distribution of bacterial-derived vesicles (EVs) of significant diagnostic performance at the order level by performing a metagenome analysis.
  • EVs bacterial-derived vesicles
  • Figure 4 shows the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the family level by separating bacteria-derived vesicles from ovarian cancer patients and normal blood, and performing a metagenome analysis.
  • EVs bacteria-derived vesicles
  • FIG. 5 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the genus level after separating the bacteria-derived vesicles from ovarian cancer patients and normal blood.
  • EVs bacteria-derived vesicles
  • FIG. 6 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the phylum level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine.
  • EVs bacteria-derived vesicles
  • FIG. 7 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the class level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine, and performing a metagenome analysis.
  • EVs bacteria-derived vesicles
  • FIG. 8 shows the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the order level after separation of bacteria-derived vesicles from ovarian cancer patients and normal urine.
  • EVs bacteria-derived vesicles
  • FIG. 9 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the family level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine, and performing a metagenome analysis.
  • EVs bacteria-derived vesicles
  • FIG. 10 shows the distribution of bacterial vesicles (EVs) with significant diagnostic performance at the genus level after isolation of bacterial vesicles from ovarian cancer patients and normal urine.
  • EVs bacterial vesicles
  • the present invention relates to a method for diagnosing ovarian cancer through microbial metagenome analysis, and the present inventors separated extracellular vesicles using a sample derived from a subject, extracted genes from vesicles, and performed a metagenome analysis. , Extracellular vesicles derived from bacteria that can act as a causative agent of ovarian cancer were identified.
  • the present invention comprises the steps of (a) extracting DNA from the extracellular vesicles isolated from the subject sample;
  • (C) provides an information providing method for diagnosing ovarian cancer comprising the step of comparing the increase and decrease of the content of bacteria and archaea-derived extracellular vesicles and the normal-derived sample through the sequencing of the PCR product.
  • the term "diagnosed ovarian cancer” means to determine whether a ovarian cancer is likely to develop, whether the ovarian cancer is relatively high, or whether an ovarian cancer has already occurred in a patient. .
  • the method of the present invention can be used to prevent or delay the onset of the disease through special and appropriate management as a patient at high risk of developing ovarian cancer for any particular patient.
  • the methods of the present invention can be used clinically to determine treatment by early diagnosis of ovarian cancer and selecting the most appropriate treatment regimen.
  • metagenome used in the present invention, also referred to as “metagenome”, refers to the total of the genome including all viruses, bacteria, fungi, etc. in an isolated area such as soil, animal intestine, It is mainly used as a concept of genome explaining the identification of many microorganisms at once using sequencer to analyze microorganisms which are not cultured.
  • metagenome does not refer to one species of genome or genome, but refers to a kind of mixed dielectric as the genome of all species of one environmental unit. This is a term from the point of view of defining a species in the course of the evolution of biology in terms of functional species as well as various species that interact with each other to create a complete species.
  • metagenome analysis was preferably performed using bacteria-derived extracellular vesicles isolated from blood and urine.
  • bacterial vesicle is a concept including not only bacteria but also extracellular vesicles secreted by archaea, but is not limited thereto.
  • the subject sample may be blood or urine, and the blood may preferably be whole blood, serum, plasma, or blood monocytes, but is not limited thereto.
  • the metagenome analysis of the extracellular vesicles derived from bacteria and archaea was performed, and at the level of phylum, class, order, family, and genus, Each analysis identified bacterial vesicles that could actually cause ovarian cancer.
  • the analysis of the bacterial metagenome at the level of the vesicles present in the blood samples from the subject Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 strong bacteria
  • the bacterial metagenome was analyzed at the neck level for vesicles present in a blood sample derived from a subject, Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, and Methylophilales Neck bacteria-derived extracellular vesicles were significantly different between ovarian cancer patients and normal subjects (see Example 4).
  • Rhizobiaceae as a result of analyzing the bacterial metagenome at the level of the vesicles present in the blood samples from the subject, Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae Fusobacteriaceae, Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae were significantly different between ovarian cancer patients and normal individuals. (See Example 4).
  • the present invention as a result of analyzing the bacterial metagenome at the genus level for the vesicles present in the blood samples from the subject, Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, The contents of extracellular vesicles derived from bacteria belonging to Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, and Collinsella were significantly different between ovarian cancer patients and normal individuals (see Example 4).
  • the bacterial metagenome of the vesicles present in the subject-derived urine sample at the gate level Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, TM6 door bacteria-derived cells
  • the bacterial metagenome was analyzed at the neck level for vesicles present in the urine sample derived from the subject, Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iiiccales Rhocolate Flaco, iii1-15 The contents of MLE1-12, Methylophilales, and Ellin6067 throat bacterial extracellular vesicles were significantly different between ovarian cancer patients and normal
  • the bacterial metagenome of the vesicles present in the subject-derived urine sample at the excess level Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaceae, Pseudonocardiaceae, Leuconostocaceae, Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclfoaceae, Saceae, Deaceae Was significantly different between ovarian
  • the bacterial metagenome of the vesicles present in the subject-derived urine sample at the genus level Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyoccephactus Docebacci Ocedociocce phactus Erwinia, Staphylococcus, Citrobacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Bre
  • the present invention through the results of the above embodiment, by identifying the bacteria-derived extracellular vesicles isolated from blood and urine by metagenomic analysis of bacteria-derived vesicles significantly changed in ovarian cancer patients compared to normal people Meta-genomic analysis confirmed that ovarian cancer can be diagnosed by analyzing the increase and decrease of the contents of the bacteria-derived vesicles at each level.
  • the fluorescently labeled 50 ⁇ g of bacteria and bacteria-derived vesicles were administered in the same manner as above 12 hours.
  • Blood, Heart, Lung, Liver, Kidney, Spleen, Adipose tissue, and Muscle were extracted from mice.
  • the intestinal bacteria (Bacteria) were not absorbed into each organ, whereas the intestinal bacteria-derived extracellular vesicles (EV) were detected in the tissues, as shown in FIG. And distribution in liver, kidney, spleen, adipose tissue, and muscle.
  • PCR was performed using the 16S rDNA primer shown in Table 1 to amplify the gene and perform sequencing (Illumina MiSeq sequencer). Output the result as a Standard Flowgram Format (SFF) file, convert the SFF file into a sequence file (.fasta) and a nucleotide quality score file using GS FLX software (v2.9), check the credit rating of the lead, and window (20 bps) The part with the average base call accuracy of less than 99% (Phred score ⁇ 20) was removed.
  • SFF Standard Flowgram Format
  • the Operational Taxonomy Unit performed UCLUST and USEARCH for clustering according to sequence similarity. Specifically, the clustering is based on 94% genus, 90% family, 85% order, 80% class, and 75% sequence similarity. OTU's door, river, neck, family and genus level classifications were performed, and bacteria with greater than 97% sequence similarity were analyzed using BLASTN and GreenGenes' 16S DNA sequence database (108,453 sequences) (QIIME).
  • Example 3 By the method of Example 3, the vesicles were isolated from the blood of 137 patients with ovarian cancer and 139 normal-matched age and sex, followed by metagenome sequencing. In the development of the diagnostic model, the strains whose p-value between the two groups is 0.05 or less and more than two times different between the two groups are selected in the t-test. under curve), sensitivity, and specificity.
  • vesicle-derived vesicles in the blood at the class level revealed a diagnostic model for ovarian cancer when a diagnostic model was developed using Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 strong bacterial biomarkers. Performance was significant (see Table 2 and FIG. 2).
  • Control Ovarian Cancer Training Testing name Mean SD Mean SD p value Ratio AUC sensitivity specificity AUC sensitivity specificity o__Erysipelotrichales 0.0094 0.0136 0.0019 0.0026 0.0000 0.20 0.69 0.81 0.54 0.67 0.79 0.54 o__Rhizobiales 0.0268 0.0319 0.0060 0.0052 0.0000 0.22 0.79 0.89 0.62 0.78 0.88 0.62 o__Caulobacterales 0.0064 0.0109 0.0017 0.0027 0.0000 0.27 0.62 0.80 0.42 0.60 0.80 0.41 o__Pseudomonadales 0.1657 0.1328 0.0647 0.0386 0.0000 0.39 0.79 0.84 0.60 0.78 0.83 0.60 o__Coriobacteriales 0.0064 0.0086 0.0183 0.0111 0.0000 2.84 0.83 0.74 0.79 0.82 0.74 0.79 o__Flavobacteriales 0.0057 0.0121 0.0177 0.0226 0.0000
  • Rhizobiaceae Rhizobiaceae
  • Bradyrhizobiaceae Peptostreptococcaceae
  • Oxalobacteraceae Erysipelotrichaceae
  • Pseudomonadaceae Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Planococcaceae, Burkccaceae, Cocoaceae, Burkholderaceae Cocoaceae
  • diagnostic models were developed with F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae and bacterial biomarkers
  • diagnostic performance for ovarian cancer was significant (see Table 4 and FIG. 4).
  • Bacterial-derived vesicles in the blood were analyzed at the genus level and found to be Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, and Collinsella genus
  • the diagnostic performance for ovarian cancer was significant (see Table 5 and FIG. 5).
  • Control Ovarian Cancer Training Testing Mean SD Mean SD p value Ratio AUC sensitivity specificity AUC sensitivity specificity g__Morganella 0.0012 0.0032 0.0000 0.0002 0.0000 0.02 0.66 0.95 0.29 0.64 0.94 0.28 g__Hydrogenophilus 0.0012 0.0048 0.0000 0.0003 0.0042 0.04 0.62 0.89 0.27 0.58 0.86 0.27 g__Cupriavidus 0.0242 0.0432 0.0011 0.0017 0.0000 0.04 0.75 0.90 0.55 0.73 0.90 0.55 g __ [Eubacterium] 0.0026 0.0055 0.0003 0.0008 0.0000 0.11 0.68 0.86 0.41 0.65 0.86 0.40 g__Catenibacterium 0.0050 0.0105 0.0007 0.0015 0.0000 0.13 0.68 0.82 0.44 0.68 0.81 0.44 g___Micrococcus 0.0115 0.0185 0.0019 0.0034 0.0000 0.17 0.66 0.84 0.44 0.
  • Example 3 By the method of Example 3, the vesicles were isolated from the urine of 135 patients with ovarian cancer and 136 normal people with age and sex matched with metagenome sequencing. In the development of the diagnostic model, the strains whose p-value between the two groups is 0.05 or less and more than two times different between the two groups are selected in the t-test. under curve), sensitivity, and specificity.
  • Cardiobacteriaceae Analysis of bacteria-derived vesicles in urine at the family level revealed Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaocae, Pseeu When developing diagnostic models with Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, and Bacterial Biomarkers, Significant Diagnostic Performance for Ovarian Cancer (See Table 9 and FIG. 9).
  • Control Ovarian Cancer Training Testing Mean SD Mean SD p value Ratio AUC sensitivity specificity AUC sensitivity specificity g__Morganella 0.0091 0.0243 0.0000 0.0002 0.0000 0.00 0.80 0.98 0.54 0.79 0.98 0.53 g__Rhizobium 0.0034 0.0036 0.0000 0.0001 0.0000 0.00 0.96 0.99 0.88 0.95 0.99 0.88 g___Exiguobacterium 0.0017 0.0077 0.0000 0.0001 0.0134 0.01 0.70 0.92 0.43 0.70 0.89 0.43 g__Cupriavidus 0.0333 0.0988 0.0016 0.0028 0.0002 0.05 0.73 0.78 0.58 0.73 0.77 0.56 g__Ralstonia 0.0165 0.0483 0.0012 0.0026 0.0003 0.07 0.70 0.75 0.54 0.69 0.73 0.53 g___Cellulomonas 0.0007 0.0017 0.0001 0.0003 0.0000 0.08 0.67 0.80 0.43 0.67
  • the method for providing information on the diagnosis of ovarian cancer through the bacterial metagenomic analysis according to the present invention is carried out by performing a bacterial metagenomic analysis using a sample derived from a subject to analyze the increase or decrease in the content of specific bacterial-derived extracellular vesicles. It can be used to predict risk and diagnose ovarian cancer.
  • Extracellular vesicles secreted by the bacteria present in the environment can be absorbed directly into the body and directly affect the development of cancer, and ovarian cancer is difficult to diagnose effectively because of the early diagnosis of symptoms before the symptoms appear, the human-derived according to the present invention Metagenome analysis of bacterial-derived extracellular vesicles using a sample predicts the risk of developing ovarian cancer in advance, allowing early diagnosis and prediction of risk groups for ovarian cancer, and delaying the onset or preventing the onset through proper management. Early diagnosis is possible even after the onset of cancer, which can lower the incidence of ovarian cancer and increase the therapeutic effect.
  • the bacterial metagenomic analysis according to the present invention in patients diagnosed with ovarian cancer can be used to improve the progression of ovarian cancer or to prevent recurrence by avoiding causal agent exposure.

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Abstract

The present invention relates to a method for diagnosing ovarian cancer through microbial metagenome analysis and, more specifically, to a method for performing metagenomic analysis by using a sample derived from a subject so as to analyze increases and decreases in the amount of extracellular vesicles derived from specific bacteria or archaea, thereby diagnosing ovarian cancer. The extracellular vesicles secreted from microbes present in the environment are absorbed into the body so as to directly influence the occurrence of cancer, and since early diagnosis of ovarian cancer before symptoms occur is difficult, effective treatment has been difficult. Through the metagenome analysis using a human-derived sample, according to the present invention, the risk of onset of ovarian cancer can be predicted in advance such that ovarian cancer risk groups are diagnosed and predicted in an early stage, thereby enabling the time of onset of the disease to be delayed or the onset of the disease to be prevented through appropriate management, and early diagnosis is enabled even after the onset of the disease, thereby enabling the incidence of ovarian cancer to be lowered and therapeutic effects to be increased.

Description

미생물 메타게놈 분석을 통한 난소암 진단방법Method for Diagnosing Ovarian Cancer by Microbial Metagenome Analysis
본 발명은 미생물 메타게놈 분석을 통해 난소암을 진단하는 방법에 관한 것으로서, 보다 구체적으로는 피검체 유래 샘플을 이용해 세균, 고세균 등의 미생물 메타게놈 분석을 수행하여 특정 세균 및 고세균 유래 세포밖 소포의 함량 증감을 분석함으로써 난소암을 진단하는 방법에 관한 것이다.The present invention relates to a method for diagnosing ovarian cancer through a microbial metagenome analysis, and more specifically, by performing a microbial metagenomic analysis of bacteria, archaea, etc., using a sample derived from a subject, The present invention relates to a method for diagnosing ovarian cancer by analyzing the increase or decrease in content.
난소암(ovarian cancer)은 생식기암중 두번째로 빈번한 암이다. 그러나 70%의 여성이 진행된 단계에서 진단되기에 치료율은 20~30%에 불과하다. 난소암의 원인은 다른 암과 마찬가지로 아직 정확하게 밝혀진 바는 없다. 몇 가지 요인으로, 가족 중에 난소암 환자가 있는 경우 난소암에 걸릴 위험이 높으나 난소암 환자의 경우 95%는 가족력이 없다. 유방암, 자궁내막암, 직장암의 과거력이나 가족력이 있는 경우 유방암이 생기면 난소암이 생길 가능성이 2배 높아진다. 지속적인 배란 및 월경은 난소암의 확률을 높인다고 알려져 있다. 반면, 임신은 난소암의 발생을 방지하는 경향이 있어 출산횟수가 한 번이면 난소암 위험은 전혀 출산하지 않는 여성에 비해 약 10%, 출산횟수가 3번이면 50%나 줄어든다. 출산 후 수유를 하는 것도 배란 횟수를 줄여 난소암의 발생을 감소시키는 역할을 한다. 환경적 요인으로 선진국이나 도시 여성에서 난소암이 많이 발생하고, 이 밖에도 비만, 여러 바이러스 질환의 감염력이 난소암의 발생과 관계있다고 알려져 있다. Ovarian cancer is the second most common cancer of the genitals. However, 70% of women are diagnosed at an advanced stage, so the treatment rate is only 20-30%. The cause of ovarian cancer, as with other cancers, is not yet known exactly. For some factors, there is a high risk of ovarian cancer if there is ovarian cancer in the family, but 95% of ovarian cancer patients have no family history. If you have a past or family history of breast cancer, endometrial cancer, or rectal cancer, breast cancer will double your chances of developing ovarian cancer. Persistent ovulation and menstruation are known to increase the risk of ovarian cancer. On the other hand, pregnancy tends to prevent the occurrence of ovarian cancer, so once a child is born, the risk of ovarian cancer is about 10% less than that of a woman who never gives birth. Feeding after birth also reduces the number of ovulation to reduce the occurrence of ovarian cancer. Due to environmental factors, ovarian cancers occur in advanced countries and urban women. In addition, obesity and infectivity of various viral diseases are known to be related to the occurrence of ovarian cancer.
난소암 진단을 위해서는 질내 초음파와 종양 지표가 주로 이용되고 있으며, 종양지표로는 CA125, CA19-9, AFP, CEA, SA, CA72.4 등이 사용된다. 이중 CA125는 스크리닝, 진단, 모니터링, 추적 검사시 널리 이용된다. 그러나 난소암 1,2단계에서 특이도, 민감도가 낮다는 한계를 가지고 있다. Vaginal ultrasound and tumor markers are mainly used to diagnose ovarian cancer, and CA125, CA19-9, AFP, CEA, SA, and CA72.4 are used as tumor indicators. The CA125 is widely used for screening, diagnostics, monitoring, and follow-up. However, the specificity and sensitivity are limited in stage 1 and 2 of ovarian cancer.
한편, 인체에 공생하는 미생물은 100조에 이르러 인간 세포보다 10배 많으며, 미생물의 유전자수는 인간 유전자수의 100배가 넘는 것으로 알려지고 있다. 미생물총(microbiota)은 주어진 거주지에 존재하는 세균(bacteria), 고세균(archaea), 진핵생물(eukarya)을 포함한 미생물 군집(microbial community)을 말하고, 장내 미생물총은 사람의 생리현상에 중요한 역할을 하며, 인체 세포와 상호작용을 통해 인간의 건강과 질병에 큰 영향을 미치는 것으로 알려져 있다. 우리 몸에 공생하는 세균은 다른 세포로의 유전자, 단백질 등의 정보를 교환하기 위하여 나노미터 크기의 소포(vesicle)를 분비한다. 점막은 200 나노미터(nm) 크기 이상의 입자는 통과할 수 없는 물리적인 방어막을 형성하여 점막에 공생하는 세균인 경우에는 점막을 통과하지 못하지만, 세균 유래 소포는 크기가 대개 100 나노미터 크기 이하라서 비교적 자유롭게 점막을 통화하여 우리 몸에 흡수된다.On the other hand, the symbiosis of the human body reaches 100 trillion times 10 times more than human cells, the number of genes of the microorganism is known to be more than 100 times the number of human genes. A microbiota is a microbial community, including bacteria, archaea, and eukarya that exist in a given settlement. The intestinal microbiota plays an important role in human physiology. In addition, it is known to have a great effect on human health and disease through interaction with human cells. The symbiotic bacteria secrete nanometer-sized vesicles to exchange information about genes and proteins in other cells. The mucous membrane forms a physical protective film that particles larger than 200 nanometers (nm) in size can't pass through, so that the symbiotic bacteria cannot pass through the mucosa, but bacterial-derived vesicles are usually less than 100 nanometers in size. It freely speaks to the mucous membrane and is absorbed by our body.
환경 유전체학이라고도 불리는 메타게놈학은 환경에서 채취한 샘플에서 얻은 메타게놈 자료에 대한 분석학이라고 할 수 있다(국내공개특허 제2011-0073049호). 최근 16s 리보솜 RNA(16s rRNA) 염기서열을 기반으로 한 방법으로 인간의 미생물총의 세균 구성을 목록화하는 것이 가능해졌으며, 16s 리보솜 RNA의 유전자인 16s rDNA 염기서열을 차세대 염기서열분석 (next generation sequencing, NGS) platform을 이용하여 분석한다. 그러나 난소암 발병에 있어서, 혈액 또는 소변 등의 인체 유래물에서 미생물 유래 소포에 존재하는 메타게놈 분석을 통해 난소암의 원인인자를 동정하고 난소암을 예측하는 방법에 대해서는 보고된 바가 없다.Metagenomics, also called environmental genomics, can be said to be an analysis of metagenomic data obtained from samples taken from the environment (Korean Patent Publication No. 2011-0073049). Recently, it has become possible to list the bacterial composition of the human microflora by a method based on 16s ribosomal RNA (16s rRNA) sequencing. Next generation sequencing of 16s rDNA sequencing gene of 16s ribosomal RNA is performed. , NGS) platform to analyze. However, in the development of ovarian cancer, there has been no report on a method for identifying ovarian cancer and predicting ovarian cancer through metagenomic analysis present in microbial-derived vesicles in human derivatives such as blood or urine.
본 발명자들은 난소암을 진단하기 위하여, 피검체 유래 샘플인 혈액 및 소변을 이용해 세포밖 소포를 분리하여, 소포로부터 유전자를 추출하고 이에 대하여 메타게놈 분석을 수행하였으며, 그 결과 난소암의 원인인자로 작용할 수 있는 세균 및 고세균 유래 세포밖 소포를 동정하였는바, 이에 기초하여 본 발명을 완성하였다.In order to diagnose ovarian cancer, the present inventors separated extracellular vesicles using blood and urine, a sample derived from a subject, extracted genes from vesicles, and performed a metagenome analysis on the vesicles. Bacteria and archaea-derived extracellular vesicles that can act were identified, and the present invention was completed.
이에, 본 발명은 세균 및 고세균 유래 세포밖 소포에 대한 메타게놈 분석을 통해 난소암을 진단하기 위한 정보제공방법을 제공하는 것을 목적으로 한다.Accordingly, an object of the present invention is to provide an information providing method for diagnosing ovarian cancer through metagenome analysis of extracellular vesicles derived from bacteria and archaea.
그러나 본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 과제에 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 당업자에게 명확하게 이해될 수 있을 것이다.However, the technical problem to be achieved by the present invention is not limited to the above-mentioned problem, another task that is not mentioned will be clearly understood by those skilled in the art from the following description.
상기와 같은 본 발명의 목적을 달성하기 위하여, 본 발명은 하기의 단계를 포함하는, 난소암 진단을 위한 정보제공방법을 제공한다:In order to achieve the object of the present invention as described above, the present invention provides a method for providing information for diagnosing ovarian cancer, comprising the following steps:
(a) 피검체 샘플에서 분리한 세포밖 소포로부터 DNA를 추출하는 단계;(a) extracting DNA from extracellular vesicles isolated from a subject sample;
(b) 상기 추출한 DNA에 대하여 서열번호 1 및 서열번호 2의 프라이머 쌍을 이용하여 PCR을 수행하는 단계; 및(b) performing PCR using the primer pairs of SEQ ID NO: 1 and SEQ ID NO: 2 on the extracted DNA; And
(c) 상기 PCR 산물의 서열분석을 통하여 정상인 유래 샘플과 세균 및 고세균 유래 세포밖 소포의 함량 증감을 비교하는 단계.(c) comparing the increase and decrease of the contents of the normal sample and the bacterial and archaea-derived extracellular vesicles by sequencing the PCR product.
그리고, 본 발명은 하기의 단계를 포함하는, 난소암 진단방법을 제공한다:In addition, the present invention provides a method for diagnosing ovarian cancer, comprising the following steps:
(a) 피검체 샘플에서 분리한 세포밖 소포로부터 DNA를 추출하는 단계;(a) extracting DNA from extracellular vesicles isolated from a subject sample;
(b) 상기 추출한 DNA에 대하여 서열번호 1 및 서열번호 2의 프라이머 쌍을 이용하여 PCR을 수행하는 단계; 및(b) performing PCR using the primer pairs of SEQ ID NO: 1 and SEQ ID NO: 2 on the extracted DNA; And
(c) 상기 PCR 산물의 서열분석을 통하여 정상인 유래 샘플과 세균 및 고세균 유래 세포밖 소포의 함량 증감을 비교하는 단계.(c) comparing the increase and decrease of the contents of the normal sample and the bacterial and archaea-derived extracellular vesicles by sequencing the PCR product.
또한, 본 발명은 하기의 단계를 포함하는, 난소암의 발병 위험도 예측방법을 제공한다:The present invention also provides a method for predicting the risk of developing ovarian cancer, comprising the following steps:
(a) 피검체 샘플에서 분리한 세포밖 소포로부터 DNA를 추출하는 단계;(a) extracting DNA from extracellular vesicles isolated from a subject sample;
(b) 상기 추출한 DNA에 대하여 서열번호 1 및 서열번호 2의 프라이머 쌍을 이용하여 PCR을 수행하는 단계; 및(b) performing PCR using the primer pairs of SEQ ID NO: 1 and SEQ ID NO: 2 on the extracted DNA; And
(c) 상기 PCR 산물의 서열분석을 통하여 정상인 유래 샘플과 세균 및 고세균 유래 세포밖 소포의 함량 증감을 비교하는 단계.(c) comparing the increase and decrease of the contents of the normal sample and the bacterial and archaea-derived extracellular vesicles by sequencing the PCR product.
본 발명의 일구현예로, 상기 피검체 샘플은 혈액 또는 소변일 수 있다.In one embodiment of the present invention, the subject sample may be blood or urine.
본 발명의 다른 구현예로, 상기 (c) 단계에서 상기 피검체 혈액 샘플에서 분리한 Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, 및 ML635J-21로 이루어진 군으로부터 선택되는 1종 이상의 강(class) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment of the present invention, at least one class selected from the group consisting of Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 isolated from the subject blood sample in step (c). ) May be compared to increase or decrease the content of bacterial-derived extracellular vesicles.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 혈액 샘플에서 분리한 Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, 및 Methylophilales로 이루어진 군으로부터 선택되는 1종 이상의 목(order) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment, Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, and Methylophilales isolated from the subject blood sample in step (c) It may be to compare the increase or decrease in the content of one or more order bacteria-derived extracellular vesicles.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 혈액 샘플에서 분리한 Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, 및 Methylophilaceae로 이루어진 군으로부터 선택되는 1종 이상의 과(family) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment, Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Burcobacteria coccaceae It may be to compare the increase or decrease in the content of one or more family bacteria-derived extracellular vesicles selected from the group consisting of, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 혈액 샘플에서 분리한 Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, 및 Collinsella로 이루어진 군으로부터 선택되는 1종 이상의 속(genus) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment, Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter isolated from the subject blood sample in step (c) It may be to compare the increase and decrease of the content of one or more genus bacteria-derived extracellular vesicles selected from the group consisting of, and Collinsella.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 소변 샘플에서 분리한 Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, 및 TM6로 이루어진 군으로부터 선택되는 1종 이상의 문(phylum) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment, at least one phylum selected from the group consisting of Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, and TM6 isolated from the subject urine sample in step (c) It may be to compare the increase or decrease in the content of bacterial-derived extracellular vesicles.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 소변 샘플에서 분리한 Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycideae, 4C0d-2, Gemmatimonadetes, Flavobacteriia, ML635J-21, 및 SJA-4로 이루어진 군으로부터 선택되는 1종 이상의 강(class) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment of the present invention, Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycidadee , Gemmatimonadetes, Flavobacteriia, ML635J-21, and SJA-4 may be compared to increase or decrease the content of one or more class bacteria-derived extracellular vesicles selected from the group consisting of.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 소변 샘플에서 분리한 Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chroococcales, CW040, EW055, Gemmatimonadales, Flavobacteriales, Rhodocyclales, Desulfovibrionales, MLE1-12, Methylophilales, 및 Ellin6067로 이루어진 군으로부터 선택되는 1종 이상의 목(order) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment, Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonamonadales, Erysudo Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chroococcales, CW040, EW055, Gemmatimonadales, Flavobacteriales, Rhodobrales, Desulophilo It may be to compare the increase or decrease of the content of one or more order bacteria-derived extracellular vesicles selected from the group consisting of.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 소변 샘플에서 분리한 Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaceae, Pseudonocardiaceae, Leuconostocaceae, Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, 및 Coxiellaceae로 이루어진 군으로부터 선택되는 1종 이상의 과(family) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment of the present invention, Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonaocaceae, Pisoeuceococaceae , A family of more than one species of the family selected from the group consisting of: Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, It may be to compare the increase and decrease of the content of the extracellular vesicles.
본 발명의 또 다른 구현예로, 상기 (c) 단계에서 상기 피검체 소변 샘플에서 분리한 Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turicibacter, Enterococcus, Dorea, Lactobacillus, Erwinia, Staphylococcus, Citrobacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Brevibacillus, Aerococcus, Salinicoccus, Jeotgalicoccus, Desulfovibrio, Burkholderia, Novosphingobium, Comamonas, Cloacibacterium, Dechloromonas, Thermomonas, Diaphorobacter, Pedomicrobium, KD1-23, Zoogloea, Methylophaga, 및 Haererehalobacter로 이루어진 군으로부터 선택되는 1종 이상의 속(genus) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것일 수 있다.In another embodiment of the present invention, Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella isolated from the subject urine sample in step (c) , Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turactbacter, Enter Eococcus, Ciocococosacis, Docu Locococococococium, Docu Lococococococococactus Selected from Gordonia, Adlercreutzia, Brevibacillus, Aerococcus, Salinicoccus, Jeotgalicoccus, Desulfovibrio, Burkholderia, Novosphingobium, Comamonas, Cloacibacterium, Dechloromonas, Thermomonas, Diaphorobacter, Pedomicrobium, KD1-23, Zoogloga, Methyler, and Halo Genus bacteria-derived extracellular vesicles It may be to compare the amount of increase or decrease.
본 발명의 또 다른 구현예로, 상기 혈액은 전혈, 혈청, 혈장, 또는 혈액 단핵구일 수 있다.In another embodiment of the invention, the blood may be whole blood, serum, plasma, or blood monocytes.
환경에 존재하는 미생물에서 분비되는 세포밖 소포는 체내에 흡수되어 암 발생에 직접적인 영향을 미칠 수 있으며, 난소암은 증상이 나타나기 전 조기진단이 어려워 효율적인 치료가 어려운 실정이므로, 본 발명에 따른 인체 유래 샘플을 이용한 세균 유래 세포밖　소포의 메타게놈 분석을 통해 난소암 발병의 위험도를 미리 예측함으로써 난소암의 위험군을 조기에 진단 및 예측하여 적절한 관리를 통해 발병 시기를 늦추거나 발병을 예방할 수 있으며, 발병 후에도 조기진단 할 수 있어 난소암의 발병률을 낮추고 치료효과를 높일 수 있다. 또한, 난소암으로 진단받은 환자에서 메타게놈 분석을 통해 원인인자 노출을 피함으로써 암의 경과를 좋게 하거나, 재발을 막을 수 있다.Extracellular vesicles secreted by the microorganisms present in the environment can be absorbed directly into the body and directly affect the development of cancer, and ovarian cancer is difficult to diagnose early because symptoms are difficult, so effective treatment is difficult. Metagenome analysis of extracellular vesicles derived from bacteria using a sample predicts the risk of developing ovarian cancer in advance, so that risk groups of ovarian cancer can be diagnosed and predicted early and appropriate management can be delayed or prevented. It can be diagnosed early, reducing the incidence of ovarian cancer and improving the therapeutic effect. In addition, metagenome analysis in patients diagnosed with ovarian cancer can be used to avoid the causative agent and improve the course of the cancer or prevent recurrence.
도 1a은, 마우스에 장내 세균과 세균유래 소포 (EV)를 구강으로 투여한 후, 시간별로 세균과 소포의 분포양상을 촬영한 사진이고, 도 1b는 구강으로 투여한 후 12시간째에, 혈액 및 여러 장기를 적출하여, 세균과 소포의 체내 분포양상을 평가한 그림이다.Figure 1a is a photograph of the distribution of bacteria and vesicles by time after the oral administration of enteric bacteria and bacterial derived vesicles (EV) to the mouse, Figure 1b is 12 hours after oral administration, blood And several organs were extracted to evaluate the distribution of bacteria and vesicles in the body.
도 2는 난소암환자 및 정상인 혈액에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 강(class) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.FIG. 2 is a result showing the distribution of bacterial vesicles (EVs) with significant diagnostic performance at the class level by separating bacterial vesicles from ovarian cancer patients and normal blood, and performing a metagenome analysis.
도 3은 난소암환자 및 정상인 혈액에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 목(order) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.Figure 3 shows the distribution of bacteria-derived vesicles from ovarian cancer patients and normal blood, and shows the distribution of bacterial-derived vesicles (EVs) of significant diagnostic performance at the order level by performing a metagenome analysis.
도 4는 난소암환자 및 정상인 혈액에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 과(family) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.Figure 4 shows the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the family level by separating bacteria-derived vesicles from ovarian cancer patients and normal blood, and performing a metagenome analysis.
도 5는 난소암환자 및 정상인 혈액에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 속(genus) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.5 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the genus level after separating the bacteria-derived vesicles from ovarian cancer patients and normal blood.
도 6은 난소암환자 및 정상인 소변에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 문(phylum) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.6 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the phylum level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine.
도 7은 난소암환자 및 정상인 소변에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 강(class) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.7 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the class level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine, and performing a metagenome analysis.
도 8은 난소암환자 및 정상인 소변에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 목(order) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.FIG. 8 shows the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the order level after separation of bacteria-derived vesicles from ovarian cancer patients and normal urine.
도 9는 난소암환자 및 정상인 소변에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 과(family) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.9 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the family level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine, and performing a metagenome analysis.
도 10은 난소암환자 및 정상인 소변에서 세균 유래 소포를 분리한 후, 메타게놈 분석을 수행하여 속(genus) 수준에서 진단적 성능이 유의한 세균 유래 소포(EVs)의 분포를 나타낸 결과이다.FIG. 10 shows the distribution of bacterial vesicles (EVs) with significant diagnostic performance at the genus level after isolation of bacterial vesicles from ovarian cancer patients and normal urine.
본 발명은 미생물 메타게놈 분석을 통해 난소암을 진단하는 방법에 관한 것으로서, 본 발명자들은 피검체 유래 샘플을 이용해 세포밖 소포를 분리한 후, 소포로부터 유전자를 추출하고 이에 대하여 메타게놈 분석을 수행하였으며, 난소암의 원인인자로 작용할 수 있는 세균 유래 세포밖 소포를 동정하였다. The present invention relates to a method for diagnosing ovarian cancer through microbial metagenome analysis, and the present inventors separated extracellular vesicles using a sample derived from a subject, extracted genes from vesicles, and performed a metagenome analysis. , Extracellular vesicles derived from bacteria that can act as a causative agent of ovarian cancer were identified.
이에, 본 발명은 (a) 피검체 샘플에서 분리한 세포밖 소포로부터 DNA를 추출하는 단계;Thus, the present invention comprises the steps of (a) extracting DNA from the extracellular vesicles isolated from the subject sample;
(b) 상기 추출한 DNA에 대하여 서열번호 1 및 서열번호 2의 프라이머 쌍을 이용하여 PCR을 수행하는 단계; 및(b) performing PCR using the primer pairs of SEQ ID NO: 1 and SEQ ID NO: 2 on the extracted DNA; And
(c) 상기 PCR 산물의 서열분석을 통하여 정상인 유래 샘플과 세균 및 고세균 유래 세포밖 소포의 함량 증감을 비교하는 단계를 포함하는 난소암을 진단하기 위한 정보제공방법을 제공한다.(C) provides an information providing method for diagnosing ovarian cancer comprising the step of comparing the increase and decrease of the content of bacteria and archaea-derived extracellular vesicles and the normal-derived sample through the sequencing of the PCR product.
본 발명에서 사용되는 용어, "난소암 진단" 이란 환자에 대하여 난소암이 발병할 가능성이 있는지, 난소암이 발병할 가능성이 상대적으로 높은지, 또는 난소암이 이미 발병하였는지 여부를 판별하는 것을 의미한다. 본 발명의 방법은 임의의 특정 환자에 대한 난소암 발병 위험도가 높은 환자로써 특별하고 적절한 관리를 통하여 발병 시기를 늦추거나 발병하지 않도록 하는데 사용할 수 있다. 또한, 본 발명의 방법은 난소암을 조기에 진단하여 가장 적절한 치료방식을 선택함으로써 치료를 결정하기 위해 임상적으로 사용될 수 있다.As used herein, the term "diagnosed ovarian cancer" means to determine whether a ovarian cancer is likely to develop, whether the ovarian cancer is relatively high, or whether an ovarian cancer has already occurred in a patient. . The method of the present invention can be used to prevent or delay the onset of the disease through special and appropriate management as a patient at high risk of developing ovarian cancer for any particular patient. In addition, the methods of the present invention can be used clinically to determine treatment by early diagnosis of ovarian cancer and selecting the most appropriate treatment regimen.
본 발명에서 사용되는 용어, "메타게놈(metagenome)"이란 "군유전체"라고도 하며, 흙, 동물의 장 등 고립된 지역 내의 모든 바이러스, 세균, 곰팡이 등을 포함하는 유전체의 총합을 의미하는 것으로, 주로 배양이 되지 않는 미생물을 분석하기 위해서 서열분석기를 사용하여 한꺼번에 많은 미생물을 동정하는 것을 설명하는 유전체의 개념으로 쓰인다. 특히, 메타게놈은 한 종의 게놈 또는 유전체를 말하는 것이 아니라, 한 환경단위의 모든 종의 유전체로서 일종의 혼합유전체를 말한다. 이는 오믹스적으로 생물학이 발전하는 과정에서 한 종을 정의할 때 기능적으로 기존의 한 종뿐만 아니라, 다양한 종이 서로 상호작용하여 완전한 종을 만든다는 관점에서 나온 용어이다. 기술적으로는 빠른 서열분석법을 이용해서, 종에 관계없이 모든 DNA, RNA를 분석하여, 한 환경 내에서의 모든 종을 동정하고, 상호작용, 대사작용을 규명하는 기법의 대상이다. 본 발명에서는 바람직하게 혈액 및 소변에서 분리한 세균 유래 세포밖　소포를 이용하여 메타게놈 분석을 실시하였다. The term "metagenome" used in the present invention, also referred to as "metagenome", refers to the total of the genome including all viruses, bacteria, fungi, etc. in an isolated area such as soil, animal intestine, It is mainly used as a concept of genome explaining the identification of many microorganisms at once using sequencer to analyze microorganisms which are not cultured. In particular, metagenome does not refer to one species of genome or genome, but refers to a kind of mixed dielectric as the genome of all species of one environmental unit. This is a term from the point of view of defining a species in the course of the evolution of biology in terms of functional species as well as various species that interact with each other to create a complete species. Technically, rapid sequencing is used to analyze all DNA and RNA, regardless of species, to identify all species in one environment, and to identify interactions and metabolism. In the present invention, metagenome analysis was preferably performed using bacteria-derived extracellular vesicles isolated from blood and urine.
본 발명에서 사용되는 용어, "세균 유래 소포"란 세균뿐만 아니라 고세균에서 분비되는 세포밖 소포를 포함하는 개념이며, 이것으로 제한되는 것은 아니다.As used herein, the term “bacterial vesicle” is a concept including not only bacteria but also extracellular vesicles secreted by archaea, but is not limited thereto.
본 발명에 있어서, 상기 피검체 샘플은 혈액 또는 소변일 수 있고, 상기 혈액은 바람직하게 전혈, 혈청, 혈장, 또는 혈액 단핵구일 수 있으나, 이것으로 제한되는 것은 아니다. In the present invention, the subject sample may be blood or urine, and the blood may preferably be whole blood, serum, plasma, or blood monocytes, but is not limited thereto.
본 발명의 실시예에서는 상기 세균 및 고세균 유래 세포밖 소포에 대한 메타게놈 분석을 실시하였으며, 문(phylum), 강(class), 목(order), 과(family), 및 속(genus) 수준에서 각각 분석하여 실제로 난소암 발생의 원인으로 작용할 수 있는 세균 유래 소포를 동정하였다.In an embodiment of the present invention, the metagenome analysis of the extracellular vesicles derived from bacteria and archaea was performed, and at the level of phylum, class, order, family, and genus, Each analysis identified bacterial vesicles that could actually cause ovarian cancer.
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 혈액 샘플에 존재하는 소포에 대하여 세균 메타게놈을 강 수준에서 분석한 결과, Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, 및 ML635J-21 강 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 4 참조). More specifically, in one embodiment of the present invention, the analysis of the bacterial metagenome at the level of the vesicles present in the blood samples from the subject, Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 strong bacteria There was a significant difference in the content of derived extracellular vesicles between ovarian cancer patients and normal individuals (see Example 4).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 혈액 샘플에 존재하는 소포에 대하여 세균 메타게놈을 목 수준에서 분석한 결과, Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, 및 Methylophilales 목 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 4 참조). More specifically, in an embodiment of the present invention, the bacterial metagenome was analyzed at the neck level for vesicles present in a blood sample derived from a subject, Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, and Methylophilales Neck bacteria-derived extracellular vesicles were significantly different between ovarian cancer patients and normal subjects (see Example 4).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 혈액 샘플에 존재하는 소포에 대하여 세균 메타게놈을 과 수준에서 분석한 결과, Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, 및 Methylophilaceae 과 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 4 참조). More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the level of the vesicles present in the blood samples from the subject, Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae Fusobacteriaceae, Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae were significantly different between ovarian cancer patients and normal individuals. (See Example 4).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 혈액 샘플에 존재하는 소포에 대하여 세균 메타게놈을 속 수준에서 분석한 결과, Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, 및 Collinsella 속 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 4 참조). More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the genus level for the vesicles present in the blood samples from the subject, Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, The contents of extracellular vesicles derived from bacteria belonging to Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, and Collinsella were significantly different between ovarian cancer patients and normal individuals (see Example 4).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 소변 샘플에 존재하는 소포에 대하여 세균 메타게놈을 문 수준에서 분석한 결과, Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, 및 TM6 문 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 5 참조). More specifically, in one embodiment of the present invention, the bacterial metagenome of the vesicles present in the subject-derived urine sample at the gate level, Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, TM6 door bacteria-derived cells There was a significant difference in the content of external vesicles between ovarian cancer patients and normal individuals (see Example 5).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 소변 샘플에 존재하는 소포에 대하여 세균 메타게놈을 강 수준에서 분석한 결과, Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycideae, 4C0d-2, Gemmatimonadetes, Flavobacteriia, ML635J-21, 및 SJA-4 강 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 5 참조). More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the level of the vesicles present in the urine sample derived from the subject, Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, The contents of acidimicrobiia, Deltaproteobacteria, Oscillatoriophycideae, 4C0d-2, Gemmatimonadetes, Flavobacteriia, ML635J-21, and SJA-4 strong bacteria were significantly different between ovarian cancer patients and normal individuals (see Example 5). .
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 소변 샘플에 존재하는 소포에 대하여 세균 메타게놈을 목 수준에서 분석한 결과, Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chroococcales, CW040, EW055, Gemmatimonadales, Flavobacteriales, Rhodocyclales, Desulfovibrionales, MLE1-12, Methylophilales, 및 Ellin6067 목 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 5 참조). More specifically, in one embodiment of the present invention, the bacterial metagenome was analyzed at the neck level for vesicles present in the urine sample derived from the subject, Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iiiccales Rhocolate Flaco, iii1-15 The contents of MLE1-12, Methylophilales, and Ellin6067 throat bacterial extracellular vesicles were significantly different between ovarian cancer patients and normal individuals (see Example 5).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 소변 샘플에 존재하는 소포에 대하여 세균 메타게놈을 과 수준에서 분석한 결과, Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaceae, Pseudonocardiaceae, Leuconostocaceae, Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, 및 Coxiellaceae 과 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 5 참조). More specifically, in one embodiment of the present invention, the bacterial metagenome of the vesicles present in the subject-derived urine sample at the excess level, Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaceae, Pseudonocardiaceae, Leuconostocaceae, Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclfoaceae, Saceae, Deaceae Was significantly different between ovarian cancer patients and normal subjects (see Example 5).
보다 구체적으로 본 발명의 일실시예에서는, 피검자 유래 소변 샘플에 존재하는 소포에 대하여 세균 메타게놈을 속 수준에서 분석한 결과, Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turicibacter, Enterococcus, Dorea, Lactobacillus, Erwinia, Staphylococcus, Citrobacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Brevibacillus, Aerococcus, Salinicoccus, Jeotgalicoccus, Desulfovibrio, Burkholderia, Novosphingobium, Comamonas, Cloacibacterium, Dechloromonas, Thermomonas, Diaphorobacter, Pedomicrobium, KD1-23, Zoogloea, Methylophaga, 및 Haererehalobacter 속 세균 유래 세포밖 소포의 함량이 난소암환자와 정상인에 사이에 유의한 차이가 있었다(실시예 5 참조). More specifically, in one embodiment of the present invention, the bacterial metagenome of the vesicles present in the subject-derived urine sample at the genus level, Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyoccephactus Docebacci Ocedociocce phactus Erwinia, Staphylococcus, Citrobacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Brevibacillus, Aerococcus, Salinicoccus, Jeotgalicoccus, Desulfovibrio, Burkholderia, Novosphingobium, Comamonas, Cloacibacterium, Dechloromonadobacerophadobacerophadobacerophadobacterium Extracellular cattle derived from the genus bacteria The content of there was a significant difference between the ovarian cancer patients and normal subjects (see Example 5).
본 발명은 상기와 같은 실시예 결과를 통해, 혈액 및 소변으로부터 분리한 세균 유래 세포밖 소포에 대하여 메타게놈 분석을 실시함으로써 정상인과 비교하여 난소암환자에서 함량이 유의하게 변화한 세균 유래 소포들을 동정하였으며, 메타게놈 분석을 통해 상기 각 수준에서 세균 유래 소포들의 함량 증감을 분석함으로써 난소암을 진단할 수 있음을 확인하였다.The present invention, through the results of the above embodiment, by identifying the bacteria-derived extracellular vesicles isolated from blood and urine by metagenomic analysis of bacteria-derived vesicles significantly changed in ovarian cancer patients compared to normal people Meta-genomic analysis confirmed that ovarian cancer can be diagnosed by analyzing the increase and decrease of the contents of the bacteria-derived vesicles at each level.
이하, 본 발명의 이해를 돕기 위하여 바람직한 실시예를 제시한다. 그러나 하기의 실시예는 본 발명을 보다 쉽게 이해하기 위하여 제공되는 것일 뿐, 하기 실시예에 의해 본 발명의 내용이 한정되는 것은 아니다. Hereinafter, preferred examples are provided to aid in understanding the present invention. However, the following examples are merely provided to more easily understand the present invention, and the contents of the present invention are not limited by the following examples.
[실시예]EXAMPLE
실시예 1. 장내 세균 및 세균 유래 소포의 체내 흡수, 분포, 및 배설 양상 분석Example 1 Analysis of Uptake, Distribution, and Excretion of Intestinal Bacteria and Bacterial-Derived Vesicles
장내 세균과 세균 유래 소포가 위장관을 통해 전신적으로 흡수되는 지를 평가하기 위하여 다음과 같은 방법으로 실험을 수행하였다. 마우스의 위장에 형광으로 표지한 장내세균과 장내 세균 유래 소포를 각각 50 μg의 용량으로 위장관으로 투여하고 0분, 5분, 3시간, 6시간, 12시간 후에 형광을 측정하였다. 마우스 전체 이미지를 관찰한 결과, 도 1a에 나타낸 바와 같이, 상기 세균(Bacteria)인 경우에는 전신적으로 흡수되지 않았지만, 세균 유래 소포(EV)인 경우에는, 투여 후 5분에 전신적으로 흡수되었고, 투여 3시간 후에는 방광에 형광이 진하게 관찰되어, 소포가 비뇨기계로 배설됨을 알 수 있었다. 또한, 소포는 투여 12시간까지 체내에 존재함을 알 수 있었다. In order to evaluate whether the intestinal bacteria and bacteria-derived vesicles are absorbed systemically through the gastrointestinal tract, experiments were performed as follows. Fluorescently labeled enterobacteriaceae and enteric bacteria-derived vesicles were administered to the gastrointestinal tract at doses of 50 μg, respectively, and the fluorescence was measured after 0, 5, 3, 6 and 12 hours. As a result of observing the entire image of the mouse, as shown in FIG. 1A, the bacteria (Bacteria) were not absorbed systemically, but in the case of bacteria-derived vesicles (EV), they were absorbed systemically 5 minutes after administration and administered. After 3 hours, the bladder was strongly observed, indicating that the vesicles were excreted by the urinary system. In addition, the vesicles were found to exist in the body until 12 hours of administration.
장내세균과 장내 세균유래 소포가 전신적으로 흡수된 후, 여러 장기로 침윤된 양상을 평가하기 위하여, 형광으로 표지한 50 μg의 세균과 세균유래 소포를 상기의 방법과 같이 투여한 다음 12시간째에 마우스로부터 혈액(Blood), 심장(Heart), 폐(Lung), 간(Liver), 신장(Kidney), 비장(Spleen), 지방조직(Adipose tissue), 및 근육(Muscle)을 적출하였다. 상기 적출한 조직들에서 형광을 관찰한 결과, 도1b에 나타낸 바와 같이, 상기 장내 세균(Bacteria)은 각 장기에 흡수되지 않은 반면, 상기 장내 세균 유래 세포밖 소포(EV)는 혈액, 심장, 폐, 간, 신장, 비장, 지방조직, 및 근육에 분포하는 것을 확인하였다.After the systemic absorption of enterobacteriaceae and enteric bacteria-derived vesicles systemically, in order to assess the invasion of various organs, the fluorescently labeled 50 μg of bacteria and bacteria-derived vesicles were administered in the same manner as above 12 hours. Blood, Heart, Lung, Liver, Kidney, Spleen, Adipose tissue, and Muscle were extracted from mice. As shown in FIG. 1B, the intestinal bacteria (Bacteria) were not absorbed into each organ, whereas the intestinal bacteria-derived extracellular vesicles (EV) were detected in the tissues, as shown in FIG. And distribution in liver, kidney, spleen, adipose tissue, and muscle.
실시예 2. 혈액 및 소변으로부터 소포 분리 및 DNA 추출Example 2. Vesicle Separation and DNA Extraction from Blood and Urine
혈액 및 소변으로부터 소포를 분리하고 DNA를 추출하기 위해, 먼저 10 ㎖ 튜브에 혈액 또는 소변을 넣고 원심분리(3,500 x g, 10min, 4℃)를 실시하여 부유물을 가라앉혀 상등액만을 회수한 후 새로운 10 ㎖ 튜브에 옮겼다. 0.22 ㎛ 필터를 사용하여 상기 회수한 상등액으로부터 세균 및 이물질을 제거한 후, 센트리프랩튜브(centripreigugal filters 50 kD)에 옮기고 1500 x g, 4℃에서 15분간 원심분리하여 50 kD 보다 작은 물질은 버리고 10 ㎖까지 농축 시켰다. 다시 한 번 0.22 ㎛ 필터를 사용하여 박테리아 및 이물질을 제거한 후, Type 90ti 로터로 150,000 x g, 4℃에서 3시간 동안 초고속원심분리방법을 사용하여 상등액을 버리고 덩어리진 pellet을 생리식염수(PBS)로 녹여 소포를 수득하였다. To separate the vesicles from the blood and urine and extract the DNA, first put the blood or urine in a 10 ml tube and centrifuge (3,500 xg, 10min, 4 ° C) to settle the suspended solids to recover only the supernatant and then to the new 10 ml. Transferred to the tube. After removing the bacteria and foreign substances from the recovered supernatant using a 0.22 ㎛ filter, transfer to centripreigugal filters (50 kD) and centrifuged at 1500 xg, 4 ℃ for 15 minutes to discard the material smaller than 50 kD and 10 ml Concentrated until. Once again, remove the bacteria and foreign substances using a 0.22 ㎛ filter, discard the supernatant using ultra-fast centrifugation for 3 hours at 150,000 xg, 4 ℃ with a Type 90ti rotor and dissolve the agglomerated pellet in physiological saline (PBS) Vesicles were obtained.
상기 방법에 따라 혈액 및 소변으로부터 분리한 소포 100 ㎕를 100℃에서 끓여서 내부의 DNA를 지질 밖으로 나오게 한 후 얼음에 5분 동안 식혔다. 다음으로 남은 부유물을 제거하기 위하여 10,000 x g, 4℃에서 30분간 원심분리하고 상등액 만을 모은 후 Nanodrop을 이용하여 DNA 양을 정량하였다. 이후 상기 추출된 DNA에 세균 유래 DNA가 존재하는지 확인하기 위하여 하기 표 1에 나타낸 16s rDNA primer로 PCR을 수행하여 상기 추출된 유전자에 세균 유래 유전자가 존재하는 것을 확인하였다.According to the above method, 100 μl of the vesicles isolated from blood and urine were boiled at 100 ° C. to let the internal DNA come out of the lipid and then cooled on ice for 5 minutes. Next, in order to remove the remaining suspended matter, centrifugation at 10,000 x g, 4 ℃ for 30 minutes, and collected only the supernatant and quantified the DNA amount using Nanodrop. Thereafter, PCR was performed with the 16s rDNA primer shown in Table 1 to confirm whether the bacteria-derived DNA exists in the extracted DNA, and it was confirmed that the bacteria-derived gene exists in the extracted gene.
primerprimer 서열order 서열번호SEQ ID NO:
16S rDNA16S rDNA 16S_V3_F16S_V3_F 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3'5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3 ' 1One
16S_V4_R16S_V4_R 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-35'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3 22
실시예 3. 혈액 및 소변에서 추출한 DNA를 이용한 메타게놈 분석Example 3 Metagenomic Analysis Using DNA Extracted from Blood and Urine
상기 실시예 2의 방법으로 유전자를 추출한 후, 상기 표1에 나타낸 16S rDNA 프라이머를 사용하여 PCR을 실시하여 유전자를 증폭시키고 시퀀싱(Illumina MiSeq sequencer)을 수행하였다. 결과를 Standard Flowgram Format(SFF) 파일로 출력하고 GS FLX software(v2.9)를 이용하여 SFF 파일을 sequence 파일(.fasta)과 nucleotide quality score 파일로 변환한 다음 리드의 신용도 평가를 확인하고, window(20 bps) 평균 base call accuracy가 99% 미만(Phred score <20)인 부분을 제거하였다. 질이 낮은 부분을 제거한 후, 리드의 길이가 300 bps 이상인 것만 이용하였으며(Sickle version 1.33), 결과 분석을 위해 Operational Taxonomy Unit(OTU)은 UCLUST와 USEARCH를 이용하여 시퀀스 유사도에 따라 클러스터링을 수행하였다. 구체적으로 속(genus)은 94%, 과(family)는 90%, 목(order)은 85%, 강(class)은 80%, 문(phylum)은 75% 시퀀스 유사도를 기준으로 클러스터링을 하고 각 OTU의 문, 강, 목, 과, 속 레벨의 분류를 수행하고, BLASTN와 GreenGenes의 16S DNA 시퀀스 데이터베이스(108,453 시퀀스)를 이용하여 97% 이상의 시퀀스 유사도 갖는 박테리아를 분석하였다(QIIME).After the gene was extracted by the method of Example 2, PCR was performed using the 16S rDNA primer shown in Table 1 to amplify the gene and perform sequencing (Illumina MiSeq sequencer). Output the result as a Standard Flowgram Format (SFF) file, convert the SFF file into a sequence file (.fasta) and a nucleotide quality score file using GS FLX software (v2.9), check the credit rating of the lead, and window (20 bps) The part with the average base call accuracy of less than 99% (Phred score <20) was removed. After removing the low quality part, only the lead length was 300 bps or more (Sickle version 1.33), and the Operational Taxonomy Unit (OTU) performed UCLUST and USEARCH for clustering according to sequence similarity. Specifically, the clustering is based on 94% genus, 90% family, 85% order, 80% class, and 75% sequence similarity. OTU's door, river, neck, family and genus level classifications were performed, and bacteria with greater than 97% sequence similarity were analyzed using BLASTN and GreenGenes' 16S DNA sequence database (108,453 sequences) (QIIME).
실시예 4. 혈액에서 분리한 세균유래 소포 메타게놈 분석 기반 난소암 진단모형Example 4 Ovarian Cancer Diagnostic Model Based on Bacterial-Derived Vesicle Metagenome Analysis Isolated from Blood
상기 실시예 3의 방법으로, 난소암환자 137명과 나이와 성별을 매칭한 정상인 139명의 혈액에서 소포를 분리한 후 메타게놈 시퀀싱을 수행하였다. 진단모형 개발은 먼저 t-test에서 두 군 사이의 p값이 0.05 이하이고, 두 군 사이에 2배 이상 차이가 나는 균주를 선정하고 난 후, logistic regression analysis 방법으로 진단적 성능 지표인 AUC(area under curve), 민감도, 및 특이도를 산출하였다.By the method of Example 3, the vesicles were isolated from the blood of 137 patients with ovarian cancer and 139 normal-matched age and sex, followed by metagenome sequencing. In the development of the diagnostic model, the strains whose p-value between the two groups is 0.05 or less and more than two times different between the two groups are selected in the t-test. under curve), sensitivity, and specificity.
혈액 내 세균유래 소포를 강(class) 수준에서 분석한 결과, Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, 및 ML635J-21 강 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 2 및 도 2 참조).The analysis of vesicle-derived vesicles in the blood at the class level revealed a diagnostic model for ovarian cancer when a diagnostic model was developed using Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 strong bacterial biomarkers. Performance was significant (see Table 2 and FIG. 2).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
c__Erysipelotrichic__Erysipelotrichi 0.00940.0094 0.01360.0136 0.00190.0019 0.00260.0026 0.00000.0000 0.200.20 0.690.69 0.820.82 0.540.54 0.680.68 0.800.80 0.540.54
c__Alphaproteobacteriac__Alphaproteobacteria 0.07440.0744 0.05870.0587 0.03340.0334 0.02310.0231 0.00000.0000 0.450.45 0.720.72 0.800.80 0.580.58 0.710.71 0.800.80 0.570.57
c__Coriobacteriiac__Coriobacteriia 0.00640.0064 0.00860.0086 0.01830.0183 0.01110.0111 0.00000.0000 2.842.84 0.830.83 0.750.75 0.800.80 0.830.83 0.750.75 0.790.79
c__Flavobacteriiac__Flavobacteriia 0.00570.0057 0.01210.0121 0.01770.0177 0.02260.0226 0.00000.0000 3.083.08 0.740.74 0.530.53 0.820.82 0.710.71 0.500.50 0.800.80
c__Oscillatoriophycideaec__Oscillatoriophycideae 0.00030.0003 0.00190.0019 0.00100.0010 0.00370.0037 0.05200.0520 3.543.54 0.610.61 0.410.41 0.780.78 0.570.57 0.370.37 0.780.78
c__Deltaproteobacteriac__Deltaproteobacteria 0.00210.0021 0.00860.0086 0.01170.0117 0.01100.0110 0.00000.0000 5.515.51 0.840.84 0.670.67 0.900.90 0.830.83 0.660.66 0.890.89
c__ML635J-21c__ML635J-21 0.00000.0000 0.00000.0000 0.00050.0005 0.00130.0013 0.00000.0000 　 0.600.60 0.240.24 0.940.94 0.560.56 0.220.22 0.930.93
혈액 내 세균유래 소포를 목(order) 수준에서 분석한 결과, Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, 및 Methylophilales 목 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 3 및 도 3 참조).Analysis of bacterial vesicles in the blood at the order level showed that the diagnostic model was developed with Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, and Methylophilales neck bacterial biomarkers. Diagnostic performance for ovarian cancer was significant (see Table 3 and FIG. 3).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
o__Erysipelotrichaleso__Erysipelotrichales 0.00940.0094 0.01360.0136 0.00190.0019 0.00260.0026 0.00000.0000 0.200.20 0.690.69 0.810.81 0.540.54 0.670.67 0.790.79 0.540.54
o__Rhizobialeso__Rhizobiales 0.02680.0268 0.03190.0319 0.00600.0060 0.00520.0052 0.00000.0000 0.220.22 0.790.79 0.890.89 0.620.62 0.780.78 0.880.88 0.620.62
o__Caulobacteraleso__Caulobacterales 0.00640.0064 0.01090.0109 0.00170.0017 0.00270.0027 0.00000.0000 0.270.27 0.620.62 0.800.80 0.420.42 0.600.60 0.800.80 0.410.41
o__Pseudomonadaleso__Pseudomonadales 0.16570.1657 0.13280.1328 0.06470.0647 0.03860.0386 0.00000.0000 0.390.39 0.790.79 0.840.84 0.600.60 0.780.78 0.830.83 0.600.60
o__Coriobacterialeso__Coriobacteriales 0.00640.0064 0.00860.0086 0.01830.0183 0.01110.0111 0.00000.0000 2.842.84 0.830.83 0.740.74 0.790.79 0.820.82 0.740.74 0.790.79
o__Flavobacterialeso__Flavobacteriales 0.00570.0057 0.01210.0121 0.01770.0177 0.02260.0226 0.00000.0000 3.083.08 0.740.74 0.530.53 0.820.82 0.720.72 0.510.51 0.820.82
o__YS2o__YS2 0.00020.0002 0.00150.0015 0.00050.0005 0.00120.0012 0.02660.0266 3.383.38 0.610.61 0.380.38 0.810.81 0.560.56 0.360.36 0.760.76
o__Chroococcaleso__Chroococcales 0.00030.0003 0.00180.0018 0.00090.0009 0.00370.0037 0.05740.0574 3.523.52 0.600.60 0.420.42 0.750.75 0.540.54 0.380.38 0.710.71
o__CW040o__CW040 0.00100.0010 0.00560.0056 0.00380.0038 0.00440.0044 0.00000.0000 3.923.92 0.760.76 0.560.56 0.910.91 0.750.75 0.530.53 0.910.91
o__Desulfovibrionaleso__Desulfovibrionales 0.00150.0015 0.00840.0084 0.01130.0113 0.01090.0109 0.00000.0000 7.377.37 0.860.86 0.690.69 0.930.93 0.860.86 0.700.70 0.930.93
o__Methylophilaleso__Methylophilales 0.00000.0000 0.00020.0002 0.00200.0020 0.00350.0035 0.00000.0000 95.2595.25 0.760.76 0.490.49 0.980.98 0.720.72 0.490.49 0.980.98
혈액 내 세균유래 소포를 과(family) 수준에서 분석한 결과, Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, 및 Methylophilaceae 과 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 4 및 도 4 참조).Analysis of bacteria-derived vesicles in the blood at the family level revealed that Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Planococcaceae, Burkccaceae, Cocoaceae, Burkholderaceae Cocoaceae When diagnostic models were developed with F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae and bacterial biomarkers, diagnostic performance for ovarian cancer was significant (see Table 4 and FIG. 4).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
f__Rhizobiaceaef__Rhizobiaceae 0.01160.0116 0.02370.0237 0.00070.0007 0.00190.0019 0.00000.0000 0.060.06 0.810.81 0.900.90 0.560.56 0.790.79 0.900.90 0.550.55
f__Bradyrhizobiaceaef__Bradyrhizobiaceae 0.00390.0039 0.01190.0119 0.00030.0003 0.00100.0010 0.00050.0005 0.090.09 0.670.67 0.870.87 0.380.38 0.630.63 0.850.85 0.370.37
f__Peptostreptococcaceaef__Peptostreptococcaceae 0.00730.0073 0.01910.0191 0.00070.0007 0.00160.0016 0.00010.0001 0.100.10 0.670.67 0.820.82 0.430.43 0.660.66 0.810.81 0.420.42
f__Oxalobacteraceaef__Oxalobacteraceae 0.03350.0335 0.04820.0482 0.00430.0043 0.01270.0127 0.00000.0000 0.130.13 0.810.81 0.930.93 0.620.62 0.800.80 0.920.92 0.610.61
f__Erysipelotrichaceaef__Erysipelotrichaceae 0.00940.0094 0.01360.0136 0.00190.0019 0.00260.0026 0.00000.0000 0.200.20 0.690.69 0.820.82 0.540.54 0.670.67 0.800.80 0.540.54
f__Pseudomonadaceaef__Pseudomonadaceae 0.10010.1001 0.10870.1087 0.02560.0256 0.01860.0186 0.00000.0000 0.260.26 0.870.87 0.860.86 0.720.72 0.870.87 0.850.85 0.700.70
f__Caulobacteraceaef__Caulobacteraceae 0.00640.0064 0.01090.0109 0.00170.0017 0.00270.0027 0.00000.0000 0.270.27 0.620.62 0.800.80 0.420.42 0.610.61 0.790.79 0.410.41
f__Methylobacteriaceaef__Methylobacteriaceae 0.00740.0074 0.01040.0104 0.00230.0023 0.00300.0030 0.00000.0000 0.310.31 0.610.61 0.780.78 0.460.46 0.600.60 0.770.77 0.460.46
f__[Paraprevotellaceae]f __ [Paraprevotellaceae] 0.00250.0025 0.00510.0051 0.00080.0008 0.00180.0018 0.00040.0004 0.320.32 0.630.63 0.750.75 0.440.44 0.610.61 0.730.73 0.390.39
f__Fusobacteriaceaef__Fusobacteriaceae 0.00430.0043 0.00850.0085 0.00150.0015 0.00290.0029 0.00030.0003 0.350.35 0.610.61 0.740.74 0.410.41 0.550.55 0.700.70 0.380.38
f__Planococcaceaef__Planococcaceae 0.00700.0070 0.01150.0115 0.00260.0026 0.00280.0028 0.00000.0000 0.370.37 0.640.64 0.730.73 0.450.45 0.620.62 0.710.71 0.460.46
f__Burkholderiaceaef__Burkholderiaceae 0.00270.0027 0.00700.0070 0.00540.0054 0.00570.0057 0.00060.0006 2.002.00 0.730.73 0.520.52 0.810.81 0.720.72 0.530.53 0.790.79
f__Aerococcaceaef__Aerococcaceae 0.00500.0050 0.00970.0097 0.01170.0117 0.01070.0107 0.00000.0000 2.352.35 0.750.75 0.580.58 0.800.80 0.740.74 0.560.56 0.800.80
f__Lactobacillaceaef__Lactobacillaceae 0.03400.0340 0.03260.0326 0.08630.0863 0.05630.0563 0.00000.0000 2.542.54 0.810.81 0.650.65 0.830.83 0.800.80 0.630.63 0.840.84
f__Coriobacteriaceaef__Coriobacteriaceae 0.00640.0064 0.00860.0086 0.01830.0183 0.01110.0111 0.00000.0000 2.842.84 0.830.83 0.740.74 0.790.79 0.820.82 0.740.74 0.790.79
f__[Weeksellaceae]f __ [Weeksellaceae] 0.00440.0044 0.00990.0099 0.01650.0165 0.02230.0223 0.00000.0000 3.743.74 0.750.75 0.510.51 0.840.84 0.740.74 0.520.52 0.830.83
f__Xenococcaceaef__Xenococcaceae 0.00020.0002 0.00180.0018 0.00090.0009 0.00370.0037 0.04280.0428 4.154.15 0.610.61 0.410.41 0.780.78 0.580.58 0.400.40 0.750.75
f__F16f__F16 0.00060.0006 0.00290.0029 0.00370.0037 0.00440.0044 0.00000.0000 6.206.20 0.790.79 0.560.56 0.920.92 0.750.75 0.560.56 0.920.92
f__Desulfovibrionaceaef__Desulfovibrionaceae 0.00150.0015 0.00840.0084 0.01130.0113 0.01090.0109 0.00000.0000 7.377.37 0.860.86 0.690.69 0.930.93 0.860.86 0.690.69 0.930.93
f__Comamonadaceaef__Comamonadaceae 0.00850.0085 0.01540.0154 0.07270.0727 0.08020.0802 0.00000.0000 8.518.51 0.910.91 0.750.75 0.860.86 0.900.90 0.730.73 0.850.85
f__S24-7f__S24-7 0.00140.0014 0.00380.0038 0.06890.0689 0.05160.0516 0.00000.0000 49.8349.83 0.970.97 0.890.89 0.960.96 0.980.98 0.870.87 0.960.96
f__Methylophilaceaef__Methylophilaceae 0.00000.0000 0.00020.0002 0.00200.0020 0.00350.0035 0.00000.0000 95.2595.25 0.750.75 0.490.49 0.980.98 0.720.72 0.490.49 0.980.98
혈액 내 세균유래 소포를 속(genus) 수준에서 분석한 결과, Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, 및 Collinsella 속 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 5 및 도 5 참조).Bacterial-derived vesicles in the blood were analyzed at the genus level and found to be Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, and Collinsella genus When the diagnostic model was developed, the diagnostic performance for ovarian cancer was significant (see Table 5 and FIG. 5).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
g__Morganellag__Morganella 0.00120.0012 0.00320.0032 0.00000.0000 0.00020.0002 0.00000.0000 0.02 0.02 0.660.66 0.950.95 0.290.29 0.640.64 0.940.94 0.280.28
g__Hydrogenophilusg__Hydrogenophilus 0.00120.0012 0.00480.0048 0.00000.0000 0.00030.0003 0.00420.0042 0.04 0.04 0.620.62 0.890.89 0.270.27 0.580.58 0.860.86 0.270.27
g__Cupriavidusg__Cupriavidus 0.02420.0242 0.04320.0432 0.00110.0011 0.00170.0017 0.00000.0000 0.04 0.04 0.750.75 0.900.90 0.550.55 0.730.73 0.900.90 0.550.55
g__[Eubacterium]g __ [Eubacterium] 0.00260.0026 0.00550.0055 0.00030.0003 0.00080.0008 0.00000.0000 0.11 0.11 0.680.68 0.860.86 0.410.41 0.650.65 0.860.86 0.400.40
g__Catenibacteriumg__Catenibacterium 0.00500.0050 0.01050.0105 0.00070.0007 0.00150.0015 0.00000.0000 0.13 0.13 0.680.68 0.820.82 0.440.44 0.680.68 0.810.81 0.440.44
g__Micrococcusg__Micrococcus 0.01150.0115 0.01850.0185 0.00190.0019 0.00340.0034 0.00000.0000 0.17 0.17 0.660.66 0.840.84 0.440.44 0.640.64 0.830.83 0.430.43
g__Coprococcusg__Coprococcus 0.01050.0105 0.01410.0141 0.00220.0022 0.00290.0029 0.00000.0000 0.21 0.21 0.670.67 0.810.81 0.500.50 0.660.66 0.790.79 0.480.48
g__Pseudomonasg__Pseudomonas 0.09760.0976 0.10870.1087 0.02090.0209 0.01420.0142 0.00000.0000 0.21 0.21 0.890.89 0.880.88 0.750.75 0.890.89 0.870.87 0.740.74
g__Paraprevotellag__Paraprevotella 0.00070.0007 0.00240.0024 0.00020.0002 0.00070.0007 0.01740.0174 0.24 0.24 0.620.62 0.810.81 0.330.33 0.580.58 0.790.79 0.300.30
g__Sphingomonasg__Sphingomonas 0.01760.0176 0.02400.0240 0.00430.0043 0.00430.0043 0.00000.0000 0.25 0.25 0.730.73 0.810.81 0.550.55 0.720.72 0.790.79 0.540.54
g__Faecalibacteriumg__Faecalibacterium 0.03640.0364 0.03890.0389 0.01060.0106 0.00880.0088 0.00000.0000 0.29 0.29 0.690.69 0.830.83 0.560.56 0.680.68 0.810.81 0.550.55
g__Blautiag__Blautia 0.00840.0084 0.00960.0096 0.00250.0025 0.00340.0034 0.00000.0000 0.30 0.30 0.670.67 0.800.80 0.530.53 0.650.65 0.780.78 0.520.52
g__Serratiag__Serratia 0.00020.0002 0.00070.0007 0.00010.0001 0.00040.0004 0.04510.0451 0.36 0.36 0.630.63 0.790.79 0.370.37 0.590.59 0.740.74 0.330.33
g__Citrobacterg__Citrobacter 0.00450.0045 0.00640.0064 0.00160.0016 0.00240.0024 0.00000.0000 0.36 0.36 0.650.65 0.770.77 0.490.49 0.630.63 0.760.76 0.480.48
g__Collinsellag__Collinsella 0.00370.0037 0.00590.0059 0.00150.0015 0.00270.0027 0.00010.0001 0.40 0.40 0.620.62 0.760.76 0.460.46 0.590.59 0.740.74 0.450.45
g__Sutterellag__Sutterella 0.00050.0005 0.00250.0025 0.00090.0009 0.00180.0018 0.08070.0807 2.00 2.00 0.610.61 0.460.46 0.740.74 0.550.55 0.420.42 0.710.71
g__Finegoldiag__Finegoldia 0.00050.0005 0.00290.0029 0.00120.0012 0.00200.0020 0.02510.0251 2.27 2.27 0.650.65 0.480.48 0.780.78 0.610.61 0.460.46 0.750.75
g__Comamonasg__Comamonas 0.00270.0027 0.00760.0076 0.00660.0066 0.00910.0091 0.00010.0001 2.49 2.49 0.700.70 0.490.49 0.840.84 0.670.67 0.460.46 0.850.85
g__Lactobacillusg__Lactobacillus 0.03370.0337 0.03240.0324 0.08490.0849 0.05680.0568 0.00000.0000 2.52 2.52 0.800.80 0.640.64 0.830.83 0.790.79 0.620.62 0.830.83
g__Sphingobiumg__Sphingobium 0.00130.0013 0.00580.0058 0.00360.0036 0.00540.0054 0.00090.0009 2.70 2.70 0.720.72 0.490.49 0.840.84 0.690.69 0.480.48 0.840.84
g__Klebsiellag__Klebsiella 0.00060.0006 0.00160.0016 0.00240.0024 0.00390.0039 0.00000.0000 4.12 4.12 0.710.71 0.470.47 0.860.86 0.700.70 0.460.46 0.860.86
g__Aerococcusg__Aerococcus 0.00100.0010 0.00550.0055 0.00640.0064 0.00850.0085 0.00000.0000 6.54 6.54 0.820.82 0.590.59 0.940.94 0.790.79 0.570.57 0.950.95
g__Burkholderiag__Burkholderia 0.00030.0003 0.00190.0019 0.00420.0042 0.00480.0048 0.00000.0000 13.77 13.77 0.900.90 0.770.77 0.950.95 0.890.89 0.760.76 0.950.95
g__Cloacibacteriumg__Cloacibacterium 0.00100.0010 0.00620.0062 0.01450.0145 0.02070.0207 0.00000.0000 14.79 14.79 0.870.87 0.630.63 0.950.95 0.860.86 0.630.63 0.940.94
g__Salinicoccusg__Salinicoccus 0.00010.0001 0.00080.0008 0.00190.0019 0.00240.0024 0.00000.0000 16.53 16.53 0.800.80 0.590.59 0.970.97 0.770.77 0.580.58 0.980.98
g__Adlercreutziag__Adlercreutzia 0.00070.0007 0.00200.0020 0.01240.0124 0.00930.0093 0.00000.0000 18.55 18.55 0.920.92 0.820.82 0.940.94 0.910.91 0.800.80 0.940.94
g__Jeotgalicoccusg__Jeotgalicoccus 0.00050.0005 0.00180.0018 0.01350.0135 0.01500.0150 0.00000.0000 28.95 28.95 0.920.92 0.800.80 0.950.95 0.910.91 0.800.80 0.950.95
g__Novosphingobiumg__Novosphingobium 0.00030.0003 0.00140.0014 0.00840.0084 0.01190.0119 0.00000.0000 29.38 29.38 0.870.87 0.680.68 0.950.95 0.870.87 0.670.67 0.950.95
g__Enterobacterg__Enterobacter 0.00010.0001 0.00030.0003 0.00420.0042 0.00600.0060 0.00000.0000 40.30 40.30 0.800.80 0.620.62 0.940.94 0.760.76 0.610.61 0.940.94
g__Anaerofustisg__Anaerofustis 0.00000.0000 0.00010.0001 0.00060.0006 0.00140.0014 0.00000.0000 64.74 64.74 0.630.63 0.280.28 0.950.95 0.590.59 0.270.27 0.930.93
g__Desulfovibriog__Desulfovibrio 0.00010.0001 0.00040.0004 0.01110.0111 0.01090.0109 0.00000.0000 110.89 110.89 0.900.90 0.790.79 0.970.97 0.900.90 0.790.79 0.970.97
g__Dechloromonasg__Dechloromonas 0.00000.0000 0.00010.0001 0.00100.0010 0.00230.0023 0.00000.0000 129.91 129.91 0.660.66 0.320.32 0.980.98 0.640.64 0.320.32 0.980.98
g__Diaphorobacterg__Diaphorobacter 0.00000.0000 0.00000.0000 0.00170.0017 0.00230.0023 0.00000.0000 1769.58 1769.58 0.840.84 0.650.65 0.990.99 0.800.80 0.640.64 0.980.98
실시예 5. 소변에서 분리한 세균유래 소포 메타게놈 분석 기반 난소암 진단모형Example 5 Ovarian Cancer Diagnostic Model Based on Bacterial-Derived Vesicle Metagenome Analysis Isolated from Urine
상기 실시예 3의 방법으로, 난소암환자 135명과 나이와 성별을 매칭한 정상인 136명의 소변에서 소포를 분리한 후 메타게놈 시퀀싱을 수행하였다. 진단모형 개발은 먼저 t-test에서 두 군 사이의 p값이 0.05 이하이고, 두 군 사이에 2배 이상 차이가 나는 균주를 선정하고 난 후, logistic regression analysis 방법으로 진단적 성능 지표인 AUC(area under curve), 민감도, 및 특이도를 산출하였다.By the method of Example 3, the vesicles were isolated from the urine of 135 patients with ovarian cancer and 136 normal people with age and sex matched with metagenome sequencing. In the development of the diagnostic model, the strains whose p-value between the two groups is 0.05 or less and more than two times different between the two groups are selected in the t-test. under curve), sensitivity, and specificity.
소변 내 세균유래 소포를 문(phylum) 수준에서 분석한 결과, Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, 및 TM6 문 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 6 및 도 6 참조).Analysis of vesicle-derived vesicles in the urine at the phylum level showed that diagnostic performance for ovarian cancer was improved when the diagnostic model was developed with Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, and TM6 portal bacterial biomarkers. Significant (see Table 6 and FIG. 6).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
p__Tenericutesp__Tenericutes 0.00630.0063 0.00860.0086 0.00100.0010 0.00220.0022 0.00000.0000 0.150.15 0.790.79 0.800.80 0.590.59 0.780.78 0.790.79 0.590.59
p__Deferribacteresp__Deferribacteres 0.00030.0003 0.00080.0008 0.00010.0001 0.00040.0004 0.00040.0004 0.170.17 0.680.68 0.760.76 0.500.50 0.670.67 0.750.75 0.490.49
p__Fusobacteriap__Fusobacteria 0.00400.0040 0.00570.0057 0.00100.0010 0.00230.0023 0.00000.0000 0.260.26 0.740.74 0.840.84 0.540.54 0.730.73 0.800.80 0.530.53
p__Armatimonadetesp__Armatimonadetes 0.00030.0003 0.00080.0008 0.00010.0001 0.00040.0004 0.00250.0025 0.260.26 0.630.63 0.720.72 0.460.46 0.620.62 0.700.70 0.460.46
p__SR1p__SR1 0.00010.0001 0.00030.0003 0.00000.0000 0.00020.0002 0.18340.1834 0.270.27 0.610.61 0.650.65 0.490.49 0.610.61 0.650.65 0.480.48
p__Gemmatimonadetesp__Gemmatimonadetes 0.00010.0001 0.00040.0004 0.00040.0004 0.00120.0012 0.00850.0085 3.993.99 0.610.61 0.530.53 0.630.63 0.610.61 0.520.52 0.610.61
p__TM6p__TM6 0.00000.0000 0.00000.0000 0.00010.0001 0.00030.0003 0.02360.0236 0.600.60 0.550.55 0.580.58 0.600.60 0.560.56 0.570.57
소변 내 세균유래 소포를 강(class) 수준에서 분석한 결과, Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycideae, 4C0d-2, Gemmatimonadetes, Flavobacteriia, ML635J-21, 및 SJA-4 강 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 7 및 도 7 참조).Analysis of bacteria-derived vesicles in the urine at the class level revealed that Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycites, and 4C635dade, When diagnostic models were developed with 21, and SJA-4 strong bacterial biomarkers, diagnostic performance for ovarian cancer was significant (see Table 7 and FIG. 7).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
c__Mollicutesc__Mollicutes 0.0063 0.0063 0.0086 0.0086 0.0010 0.0010 0.0022 0.0022 0.0000 0.0000 0.15 0.15 0.790.79 0.800.80 0.600.60 0.780.78 0.780.78 0.580.58
c__Deferribacteresc__Deferribacteres 0.0003 0.0003 0.0008 0.0008 0.0001 0.0001 0.0004 0.0004 0.0004 0.0004 0.17 0.17 0.680.68 0.750.75 0.520.52 0.670.67 0.740.74 0.500.50
c__Fusobacteriiac__Fusobacteriia 0.0040 0.0040 0.0057 0.0057 0.0010 0.0010 0.0023 0.0023 0.0000 0.0000 0.26 0.26 0.740.74 0.830.83 0.540.54 0.730.73 0.810.81 0.510.51
c__[Fimbriimonadia]c __ [Fimbriimonadia] 0.0003 0.0003 0.0008 0.0008 0.0001 0.0001 0.0004 0.0004 0.0025 0.0025 0.26 0.26 0.630.63 0.690.69 0.480.48 0.620.62 0.690.69 0.470.47
c__Erysipelotrichic__Erysipelotrichi 0.0067 0.0067 0.0113 0.0113 0.0018 0.0018 0.0032 0.0032 0.0000 0.0000 0.27 0.27 0.780.78 0.800.80 0.570.57 0.770.77 0.800.80 0.570.57
c__Chloroplastc__Chloroplast 0.0147 0.0147 0.0318 0.0318 0.0054 0.0054 0.0065 0.0065 0.0010 0.0010 0.37 0.37 0.630.63 0.710.71 0.460.46 0.620.62 0.710.71 0.460.46
c__Gammaproteobacteriac__Gammaproteobacteria 0.3013 0.3013 0.1828 0.1828 0.1392 0.1392 0.0497 0.0497 0.0000 0.0000 0.46 0.46 0.810.81 0.900.90 0.700.70 0.810.81 0.890.89 0.700.70
c__Betaproteobacteriac__Betaproteobacteria 0.0653 0.0653 0.1524 0.1524 0.1323 0.1323 0.1238 0.1238 0.0001 0.0001 2.03 2.03 0.730.73 0.630.63 0.740.74 0.730.73 0.610.61 0.760.76
c__Bacillic__Bacilli 0.1031 0.1031 0.0551 0.0551 0.2242 0.2242 0.1162 0.1162 0.0000 0.0000 2.17 2.17 0.830.83 0.740.74 0.850.85 0.830.83 0.740.74 0.850.85
c__Acidimicrobiiac__Acidimicrobiia 0.0001 0.0001 0.0002 0.0002 0.0003 0.0003 0.0008 0.0008 0.0028 0.0028 5.11 5.11 0.620.62 0.530.53 0.630.63 0.610.61 0.520.52 0.610.61
c__Deltaproteobacteriac__Deltaproteobacteria 0.0016 0.0016 0.0026 0.0026 0.0104 0.0104 0.0115 0.0115 0.0000 0.0000 6.49 6.49 0.850.85 0.710.71 0.870.87 0.850.85 0.700.70 0.860.86
c__Oscillatoriophycideaec__Oscillatoriophycideae 0.0002 0.0002 0.0007 0.0007 0.0010 0.0010 0.0026 0.0026 0.0003 0.0003 6.61 6.61 0.640.64 0.490.49 0.690.69 0.640.64 0.490.49 0.680.68
c__4C0d-2c__4C0d-2 0.0002 0.0002 0.0005 0.0005 0.0013 0.0013 0.0027 0.0027 0.0000 0.0000 6.68 6.68 0.670.67 0.440.44 0.760.76 0.670.67 0.430.43 0.750.75
c__Gemmatimonadetesc__Gemmatimonadetes 0.0000 0.0000 0.0002 0.0002 0.0003 0.0003 0.0009 0.0009 0.0064 0.0064 6.81 6.81 0.610.61 0.540.54 0.600.60 0.610.61 0.540.54 0.600.60
c__Flavobacteriiac__Flavobacteriia 0.0034 0.0034 0.0053 0.0053 0.0325 0.0325 0.0406 0.0406 0.0000 0.0000 9.68 9.68 0.870.87 0.680.68 0.880.88 0.870.87 0.680.68 0.870.87
c__ML635J-21c__ML635J-21 0.0000 0.0000 0.0000 0.0000 0.0007 0.0007 0.0023 0.0023 0.0006 0.0006 610.44 610.44 0.640.64 0.470.47 0.730.73 0.640.64 0.460.46 0.730.73
c__SJA-4c__SJA-4 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0003 0.0003 0.0236 0.0236 0.600.60 0.550.55 0.590.59 0.600.60 0.540.54 0.580.58
소변 내 세균유래 소포를 목(order) 수준에서 분석한 결과, Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chroococcales, CW040, EW055, Gemmatimonadales, Flavobacteriales, Rhodocyclales, Desulfovibrionales, MLE1-12, Methylophilales, 및 Ellin6067 목 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 8 및 도 8 참조).Analysis of vesicle-derived vesicles in the urine at the order level showed Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipetoterials, St. Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chroococcales, CW040, EW055, Gemmatimonadales, Flavobacteriales, Rhodocyclales, Desulfovibrionales, MLE1-12, Methyllinphile, M. When developed, the diagnostic performance for ovarian cancer was significant (see Table 8 and FIG. 8).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
o__Desulfuromonadaleso__Desulfuromonadales 0.00020.0002 0.00110.0011 0.00000.0000 0.00000.0000 0.06040.0604 0.00 0.00 0.620.62 0.800.80 0.360.36 0.620.62 0.780.78 0.360.36
o__Desulfobacteraleso__Desulfobacterales 0.00040.0004 0.00150.0015 0.00000.0000 0.00000.0000 0.00760.0076 0.00 0.00 0.620.62 0.790.79 0.380.38 0.590.59 0.780.78 0.350.35
o__Gallionellaleso__Gallionellales 0.00000.0000 0.00000.0000 0.00000.0000 0.00000.0000 0.02480.0248 0.00 0.00 0.610.61 0.650.65 0.470.47 0.610.61 0.650.65 0.470.47
o__Cardiobacterialeso__Cardiobacteriales 0.00010.0001 0.00040.0004 0.00000.0000 0.00000.0000 0.01400.0140 0.00 0.00 0.610.61 0.700.70 0.440.44 0.610.61 0.690.69 0.440.44
o__Stramenopileso__Stramenopiles 0.00250.0025 0.00540.0054 0.00000.0000 0.00010.0001 0.00000.0000 0.00 0.00 0.710.71 0.970.97 0.380.38 0.670.67 0.960.96 0.370.37
o__[Marinicellales]o __ [Marinicellales] 0.00010.0001 0.00060.0006 0.00000.0000 0.00000.0000 0.01130.0113 0.02 0.02 0.610.61 0.710.71 0.430.43 0.620.62 0.710.71 0.420.42
o__Halanaerobialeso__Halanaerobiales 0.00010.0001 0.00060.0006 0.00000.0000 0.00000.0000 0.16150.1615 0.03 0.03 0.600.60 0.660.66 0.470.47 0.600.60 0.650.65 0.460.46
o__RF39o__RF39 0.00620.0062 0.00860.0086 0.00060.0006 0.00130.0013 0.00000.0000 0.10 0.10 0.810.81 0.830.83 0.630.63 0.810.81 0.830.83 0.610.61
o__Deferribacteraleso__Deferribacterales 0.00030.0003 0.00080.0008 0.00010.0001 0.00040.0004 0.00040.0004 0.17 0.17 0.680.68 0.750.75 0.510.51 0.670.67 0.750.75 0.490.49
o__Pirellulaleso__Pirellulales 0.00010.0001 0.00040.0004 0.00000.0000 0.00030.0003 0.12460.1246 0.25 0.25 0.600.60 0.650.65 0.490.49 0.580.58 0.630.63 0.460.46
o__Fusobacterialeso__Fusobacteriales 0.00400.0040 0.00570.0057 0.00100.0010 0.00230.0023 0.00000.0000 0.26 0.26 0.740.74 0.820.82 0.520.52 0.730.73 0.820.82 0.520.52
o__[Fimbriimonadales]o __ [Fimbriimonadales] 0.00030.0003 0.00080.0008 0.00010.0001 0.00040.0004 0.00250.0025 0.26 0.26 0.630.63 0.700.70 0.460.46 0.620.62 0.700.70 0.460.46
o__Erysipelotrichaleso__Erysipelotrichales 0.00670.0067 0.01130.0113 0.00180.0018 0.00320.0032 0.00000.0000 0.27 0.27 0.780.78 0.810.81 0.570.57 0.770.77 0.800.80 0.550.55
o__Pseudomonadaleso__Pseudomonadales 0.19960.1996 0.15910.1591 0.07370.0737 0.03270.0327 0.00000.0000 0.37 0.37 0.800.80 0.860.86 0.660.66 0.790.79 0.850.85 0.640.64
o__Streptophytao__Streptophyta 0.01220.0122 0.02980.0298 0.00530.0053 0.00650.0065 0.00940.0094 0.44 0.44 0.610.61 0.660.66 0.480.48 0.590.59 0.640.64 0.470.47
o__Turicibacteraleso__Turicibacterales 0.00250.0025 0.00370.0037 0.00110.0011 0.00250.0025 0.00040.0004 0.45 0.45 0.650.65 0.690.69 0.520.52 0.650.65 0.680.68 0.500.50
o__Burkholderialeso__Burkholderiales 0.06130.0613 0.15320.1532 0.12280.1228 0.11570.1157 0.00020.0002 2.00 2.00 0.730.73 0.620.62 0.740.74 0.710.71 0.610.61 0.740.74
o__Sphingomonadaleso__Sphingomonadales 0.01330.0133 0.01460.0146 0.02960.0296 0.02740.0274 0.00000.0000 2.23 2.23 0.720.72 0.550.55 0.780.78 0.700.70 0.520.52 0.770.77
o__Myxococcaleso__Myxococcales 0.00030.0003 0.00080.0008 0.00070.0007 0.00210.0021 0.04290.0429 2.38 2.38 0.600.60 0.550.55 0.570.57 0.600.60 0.570.57 0.560.56
o__Thermaleso__Thermales 0.00020.0002 0.00100.0010 0.00070.0007 0.00170.0017 0.00720.0072 3.22 3.22 0.630.63 0.530.53 0.640.64 0.620.62 0.510.51 0.610.61
o__YS2o__YS2 0.00020.0002 0.00040.0004 0.00060.0006 0.00210.0021 0.01660.0166 3.62 3.62 0.600.60 0.520.52 0.630.63 0.600.60 0.510.51 0.610.61
o__Bacillaleso__Bacillales 0.02020.0202 0.01810.0181 0.07890.0789 0.07240.0724 0.00000.0000 3.91 3.91 0.870.87 0.700.70 0.860.86 0.860.86 0.690.69 0.850.85
o__Acidimicrobialeso__Acidimicrobiales 0.00010.0001 0.00020.0002 0.00030.0003 0.00080.0008 0.00280.0028 5.11 5.11 0.620.62 0.520.52 0.630.63 0.610.61 0.530.53 0.620.62
o__Oceanospirillaleso__Oceanospirillales 0.00060.0006 0.00280.0028 0.00330.0033 0.00510.0051 0.00000.0000 5.31 5.31 0.790.79 0.620.62 0.840.84 0.780.78 0.590.59 0.830.83
o__Legionellaleso__Legionellales 0.00010.0001 0.00060.0006 0.00030.0003 0.00080.0008 0.00410.0041 5.48 5.48 0.650.65 0.540.54 0.680.68 0.650.65 0.520.52 0.680.68
o__iii1-15o__iii1-15 0.00000.0000 0.00040.0004 0.00030.0003 0.00160.0016 0.14250.1425 5.67 5.67 0.600.60 0.590.59 0.540.54 0.600.60 0.590.59 0.560.56
o__Chroococcaleso__Chroococcales 0.00020.0002 0.00070.0007 0.00100.0010 0.00260.0026 0.00040.0004 6.44 6.44 0.630.63 0.480.48 0.690.69 0.630.63 0.480.48 0.690.69
o__CW040o__CW040 0.00050.0005 0.00180.0018 0.00350.0035 0.00440.0044 0.00000.0000 6.49 6.49 0.780.78 0.590.59 0.850.85 0.780.78 0.590.59 0.850.85
o__EW055o__EW055 0.00000.0000 0.00020.0002 0.00040.0004 0.00170.0017 0.02990.0299 7.63 7.63 0.600.60 0.590.59 0.550.55 0.600.60 0.570.57 0.550.55
o__Gemmatimonadaleso__Gemmatimonadales 0.00000.0000 0.00010.0001 0.00020.0002 0.00070.0007 0.02880.0288 8.08 8.08 0.610.61 0.550.55 0.580.58 0.610.61 0.550.55 0.590.59
o__Flavobacterialeso__Flavobacteriales 0.00340.0034 0.00530.0053 0.03250.0325 0.04060.0406 0.00000.0000 9.68 9.68 0.870.87 0.690.69 0.880.88 0.860.86 0.680.68 0.860.86
o__Rhodocyclaleso__Rhodocyclales 0.00030.0003 0.00070.0007 0.00380.0038 0.00560.0056 0.00000.0000 13.55 13.55 0.770.77 0.580.58 0.890.89 0.770.77 0.580.58 0.890.89
o__Desulfovibrionaleso__Desulfovibrionales 0.00060.0006 0.00130.0013 0.00960.0096 0.01120.0112 0.00000.0000 15.98 15.98 0.900.90 0.750.75 0.910.91 0.890.89 0.740.74 0.920.92
o__MLE1-12o__MLE1-12 0.00000.0000 0.00020.0002 0.00070.0007 0.00160.0016 0.00000.0000 21.03 21.03 0.660.66 0.450.45 0.770.77 0.640.64 0.420.42 0.750.75
o__Methylophilaleso__Methylophilales 0.00000.0000 0.00030.0003 0.00310.0031 0.00430.0043 0.00000.0000 84.22 84.22 0.790.79 0.560.56 0.960.96 0.790.79 0.540.54 0.960.96
o__Ellin6067o__Ellin6067 0.00000.0000 0.00000.0000 0.00010.0001 0.00050.0005 0.02990.0299 113.57 113.57 0.610.61 0.600.60 0.540.54 0.600.60 0.600.60 0.540.54
소변 내 세균유래 소포를 과(family) 수준에서 분석한 결과, Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaceae, Pseudonocardiaceae, Leuconostocaceae, Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, 및 Coxiellaceae 과 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 9 및 도 9 참조).Analysis of bacteria-derived vesicles in urine at the family level revealed Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaocae, Pseeu When developing diagnostic models with Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, and Bacterial Biomarkers, Significant Diagnostic Performance for Ovarian Cancer (See Table 9 and FIG. 9).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
namename MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
f__Cardiobacteriaceaef__Cardiobacteriaceae 0.00010.0001 0.00170.0017 0.00000.0000 0.00130.0013 0.01400.0140 0.00 0.00 0.760.76 0.800.80 0.570.57 0.750.75 0.790.79 0.560.56
f__Acidobacteriaceaef__Acidobacteriaceae 0.00000.0000 0.00070.0007 0.00000.0000 0.00050.0005 0.03150.0315 0.00 0.00 0.730.73 0.750.75 0.590.59 0.720.72 0.750.75 0.560.56
f__Oxalobacteraceaef__Oxalobacteraceae 0.05540.0554 0.00270.0027 0.00440.0044 0.00510.0051 0.00010.0001 0.08 0.08 0.820.82 0.630.63 0.900.90 0.810.81 0.630.63 0.880.88
f__Prevotellaceaef__Prevotellaceae 0.06950.0695 0.01310.0131 0.00930.0093 0.06840.0684 0.00000.0000 0.13 0.13 0.900.90 0.730.73 0.890.89 0.900.90 0.720.72 0.890.89
f__Leptotrichiaceaef__Leptotrichiaceae 0.00160.0016 0.00590.0059 0.00030.0003 0.00310.0031 0.00000.0000 0.20 0.20 0.670.67 0.690.69 0.500.50 0.660.66 0.690.69 0.480.48
f__Christensenellaceaef__Christensenellaceae 0.00100.0010 0.00230.0023 0.00020.0002 0.00190.0019 0.00000.0000 0.21 0.21 0.740.74 0.710.71 0.650.65 0.730.73 0.700.70 0.650.65
f__[Barnesiellaceae]f __ [Barnesiellaceae] 0.00040.0004 0.00090.0009 0.00010.0001 0.00250.0025 0.00640.0064 0.23 0.23 0.680.68 0.480.48 0.810.81 0.680.68 0.480.48 0.790.79
f__[Fimbriimonadaceae]f __ [Fimbriimonadaceae] 0.00030.0003 0.00220.0022 0.00010.0001 0.00020.0002 0.00250.0025 0.26 0.26 0.960.96 0.970.97 0.840.84 0.960.96 0.970.97 0.830.83
f__Erysipelotrichaceaef__Erysipelotrichaceae 0.00670.0067 0.00860.0086 0.00180.0018 0.00130.0013 0.00000.0000 0.27 0.27 0.810.81 0.840.84 0.620.62 0.810.81 0.830.83 0.610.61
f__[Mogibacteriaceae]f __ [Mogibacteriaceae] 0.00050.0005 0.00170.0017 0.00010.0001 0.00440.0044 0.00010.0001 0.28 0.28 0.800.80 0.610.61 0.870.87 0.790.79 0.590.59 0.850.85
f__Pseudomonadaceaef__Pseudomonadaceae 0.10540.1054 0.03860.0386 0.03050.0305 0.04110.0411 0.00000.0000 0.29 0.29 0.690.69 0.640.64 0.640.64 0.670.67 0.640.64 0.640.64
f__Fusobacteriaceaef__Fusobacteriaceae 0.00240.0024 0.00430.0043 0.00070.0007 0.00290.0029 0.00010.0001 0.29 0.29 0.670.67 0.690.69 0.490.49 0.650.65 0.680.68 0.500.50
f__Pseudonocardiaceaef__Pseudonocardiaceae 0.00060.0006 0.00410.0041 0.00020.0002 0.00080.0008 0.00200.0020 0.31 0.31 0.790.79 0.830.83 0.590.59 0.790.79 0.830.83 0.570.57
f__Leuconostocaceaef__Leuconostocaceae 0.00410.0041 0.01070.0107 0.00140.0014 0.00360.0036 0.00000.0000 0.35 0.35 0.790.79 0.800.80 0.630.63 0.780.78 0.790.79 0.620.62
f__Moraxellaceaef__Moraxellaceae 0.09410.0941 0.00580.0058 0.04300.0430 0.00600.0060 0.00000.0000 0.46 0.46 0.730.73 0.600.60 0.750.75 0.700.70 0.570.57 0.730.73
f__Methylobacteriaceaef__Methylobacteriaceae 0.00450.0045 0.00070.0007 0.00210.0021 0.00560.0056 0.00000.0000 0.47 0.47 0.770.77 0.590.59 0.890.89 0.760.76 0.580.58 0.880.88
f__[Paraprevotellaceae]f __ [Paraprevotellaceae] 0.00260.0026 0.00060.0006 0.00120.0012 0.00290.0029 0.00320.0032 0.48 0.48 0.670.67 0.460.46 0.800.80 0.670.67 0.450.45 0.780.78
f__Sphingomonadaceaef__Sphingomonadaceae 0.01290.0129 0.01380.0138 0.02930.0293 0.01190.0119 0.00000.0000 2.27 2.27 0.700.70 0.610.61 0.710.71 0.680.68 0.590.59 0.690.69
f__Nocardioidaceaef__Nocardioidaceae 0.00040.0004 0.00230.0023 0.00110.0011 0.00040.0004 0.01440.0144 2.39 2.39 0.690.69 0.810.81 0.440.44 0.690.69 0.790.79 0.420.42
f__Lactobacillaceaef__Lactobacillaceae 0.03020.0302 0.07790.0779 0.08830.0883 0.00850.0085 0.00000.0000 2.92 2.92 0.850.85 0.880.88 0.670.67 0.840.84 0.860.86 0.680.68
f__Burkholderiaceaef__Burkholderiaceae 0.00170.0017 0.00610.0061 0.00780.0078 0.04050.0405 0.00000.0000 4.55 4.55 0.960.96 0.820.82 0.920.92 0.950.95 0.810.81 0.910.91
f__Aerococcaceaef__Aerococcaceae 0.00260.0026 0.04640.0464 0.02020.0202 0.02160.0216 0.00000.0000 7.79 7.79 0.720.72 0.710.71 0.610.61 0.720.72 0.710.71 0.610.61
f__Nocardiopsaceaef__Nocardiopsaceae 0.00000.0000 0.00060.0006 0.00010.0001 0.00050.0005 0.12130.1213 8.02 8.02 0.650.65 0.700.70 0.510.51 0.640.64 0.690.69 0.470.47
f__Rhodocyclaceaef__Rhodocyclaceae 0.00030.0003 0.00390.0039 0.00380.0038 0.04080.0408 0.00000.0000 13.55 13.55 0.900.90 0.720.72 0.900.90 0.890.89 0.710.71 0.900.90
f__S24-7f__S24-7 0.00370.0037 0.07350.0735 0.05420.0542 0.03690.0369 0.00000.0000 14.82 14.82 0.700.70 0.730.73 0.550.55 0.680.68 0.710.71 0.520.52
f__Eubacteriaceaef__Eubacteriaceae 0.00000.0000 0.00050.0005 0.00020.0002 0.00300.0030 0.00150.0015 15.51 15.51 0.780.78 0.590.59 0.890.89 0.770.77 0.580.58 0.880.88
f__Desulfovibrionaceaef__Desulfovibrionaceae 0.00060.0006 0.03320.0332 0.00960.0096 0.06790.0679 0.00000.0000 15.96 15.96 0.840.84 0.730.73 0.840.84 0.830.83 0.720.72 0.840.84
f__Comamonadaceaef__Comamonadaceae 0.00220.0022 0.08530.0853 0.10930.1093 0.01610.0161 0.00000.0000 50.44 50.44 0.860.86 0.880.88 0.730.73 0.860.86 0.870.87 0.720.72
f__Methylophilaceaef__Methylophilaceae 0.00000.0000 0.04270.0427 0.00310.0031 0.01040.0104 0.00000.0000 85.34 85.34 0.770.77 0.760.76 0.630.63 0.770.77 0.760.76 0.630.63
f__Coxiellaceaef__Coxiellaceae 0.00000.0000 0.00540.0054 0.00020.0002 0.00010.0001 0.00180.0018 284.80 284.80 0.700.70 0.970.97 0.370.37 0.680.68 0.960.96 0.360.36
소변 내 세균유래 소포를 속(genus) 수준에서 분석한 결과, Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turicibacter, Enterococcus, Dorea, Lactobacillus, Erwinia, Staphylococcus, Citrobacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Brevibacillus, Aerococcus, Salinicoccus, Jeotgalicoccus, Desulfovibrio, Burkholderia, Novosphingobium, Comamonas, Cloacibacterium, Dechloromonas, Thermomonas, Diaphorobacter, Pedomicrobium, KD1-23, Zoogloea, Methylophaga, 및 Haererehalobacter 속 세균 바이오마커로 진단모형을 개발하였을 때, 난소암에 대한 진단적 성능이 유의하게 나타났다 (표 10 및 도 10 참조).Analysis of bacteria-derived vesicles in the urine at genus level showed Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Muciobterium, Muciobterium Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turicibacter, Enterococcus, Dorea, Lactobacillus, Erwinia, Stabacloccicus, Dioccus, Dioccus, Dioccus, Dioccus, Dioccus, Dioccus, Dioccus, Dioccus, Dioccus, Dioccus, Obacoc Bacterial ovarian cancers for the development of aerobic cancers were diagnosed as a genus of ovarian cancers for the diagnosis of aerobic cancers in the genus of bacterial biomarkers for the development of aerobic bacterium against Aerococcus, Salinicoccus, Jeotgalicoccus, Desulfovibrio, Burkholderia, Novosphingobium, Comamonas, Cloacibacterium, Dechloromonas, Thermomonas, Diaphorobacter, Pedomicrobium, KD1-23, Zoogloea, Methylophaga, and Haererehalobacter. Diagnostic performance was significant (Table 10) And FIG. 10).
대조군Control 난소암Ovarian Cancer TrainingTraining TestingTesting
MeanMean SDSD MeanMean SDSD p valuep value RatioRatio AUCAUC sensitivitysensitivity specificityspecificity AUCAUC sensitivitysensitivity specificityspecificity
g__Morganellag__Morganella 0.00910.0091 0.02430.0243 0.00000.0000 0.00020.0002 0.00000.0000 0.000.00 0.800.80 0.980.98 0.540.54 0.790.79 0.980.98 0.530.53
g__Rhizobiumg__Rhizobium 0.00340.0034 0.00360.0036 0.00000.0000 0.00010.0001 0.00000.0000 0.000.00 0.960.96 0.990.99 0.880.88 0.950.95 0.990.99 0.880.88
g__Exiguobacteriumg__Exiguobacterium 0.00170.0017 0.00770.0077 0.00000.0000 0.00010.0001 0.01340.0134 0.010.01 0.700.70 0.920.92 0.430.43 0.700.70 0.890.89 0.430.43
g__Cupriavidusg__Cupriavidus 0.03330.0333 0.09880.0988 0.00160.0016 0.00280.0028 0.00020.0002 0.050.05 0.730.73 0.780.78 0.580.58 0.730.73 0.770.77 0.560.56
g__Ralstoniag__Ralstonia 0.01650.0165 0.04830.0483 0.00120.0012 0.00260.0026 0.00030.0003 0.070.07 0.700.70 0.750.75 0.540.54 0.690.69 0.730.73 0.530.53
g__Cellulomonasg__Cellulomonas 0.00070.0007 0.00170.0017 0.00010.0001 0.00030.0003 0.00000.0000 0.080.08 0.670.67 0.800.80 0.430.43 0.670.67 0.790.79 0.420.42
g__Sporosarcinag__Sporosarcina 0.00100.0010 0.00250.0025 0.00010.0001 0.00040.0004 0.00000.0000 0.080.08 0.710.71 0.800.80 0.530.53 0.710.71 0.770.77 0.510.51
g__Proteusg__Proteus 0.01120.0112 0.02130.0213 0.00100.0010 0.00210.0021 0.00000.0000 0.090.09 0.820.82 0.870.87 0.600.60 0.820.82 0.880.88 0.600.60
g__Leptotrichiag__Leptotrichia 0.00120.0012 0.00260.0026 0.00010.0001 0.00060.0006 0.00000.0000 0.120.12 0.700.70 0.810.81 0.450.45 0.690.69 0.810.81 0.430.43
g__SMB53g__SMB53 0.00410.0041 0.00530.0053 0.00050.0005 0.00110.0011 0.00000.0000 0.120.12 0.860.86 0.850.85 0.690.69 0.850.85 0.830.83 0.670.67
g__Prevotellag__Prevotella 0.06950.0695 0.07790.0779 0.00930.0093 0.00850.0085 0.00000.0000 0.130.13 0.850.85 0.880.88 0.670.67 0.840.84 0.880.88 0.670.67
g__Oribacteriumg__Oribacterium 0.00040.0004 0.00130.0013 0.00010.0001 0.00040.0004 0.00180.0018 0.140.14 0.650.65 0.740.74 0.470.47 0.630.63 0.730.73 0.460.46
g__Pediococcusg__Pediococcus 0.00050.0005 0.00120.0012 0.00010.0001 0.00050.0005 0.00040.0004 0.160.16 0.690.69 0.770.77 0.510.51 0.680.68 0.750.75 0.490.49
g__Paraprevotellag__Paraprevotella 0.00080.0008 0.00200.0020 0.00010.0001 0.00060.0006 0.00030.0003 0.160.16 0.720.72 0.750.75 0.570.57 0.710.71 0.740.74 0.540.54
g__Methylobacteriumg__Methylobacterium 0.00350.0035 0.00500.0050 0.00060.0006 0.00150.0015 0.00000.0000 0.170.17 0.780.78 0.840.84 0.540.54 0.770.77 0.830.83 0.530.53
g__Mucispirillumg__Mucispirillum 0.00030.0003 0.00080.0008 0.00010.0001 0.00040.0004 0.00040.0004 0.170.17 0.680.68 0.750.75 0.500.50 0.670.67 0.740.74 0.510.51
g__Catenibacteriumg__Catenibacterium 0.00390.0039 0.00990.0099 0.00070.0007 0.00170.0017 0.00020.0002 0.180.18 0.740.74 0.800.80 0.500.50 0.720.72 0.780.78 0.490.49
g__Parabacteroidesg__Parabacteroides 0.00750.0075 0.01020.0102 0.00160.0016 0.00290.0029 0.00000.0000 0.210.21 0.830.83 0.820.82 0.680.68 0.820.82 0.820.82 0.670.67
g__Collinsellag__Collinsella 0.00730.0073 0.01000.0100 0.00160.0016 0.00290.0029 0.00000.0000 0.220.22 0.760.76 0.790.79 0.570.57 0.750.75 0.780.78 0.570.57
g__Anaerostipesg__Anaerostipes 0.00070.0007 0.00150.0015 0.00020.0002 0.00130.0013 0.00240.0024 0.260.26 0.700.70 0.780.78 0.510.51 0.690.69 0.760.76 0.480.48
g__Pseudomonasg__Pseudomonas 0.09990.0999 0.08290.0829 0.02690.0269 0.01430.0143 0.00000.0000 0.270.27 0.870.87 0.900.90 0.740.74 0.860.86 0.900.90 0.730.73
g__Butyricimonasg__Butyricimonas 0.00110.0011 0.00210.0021 0.00030.0003 0.00160.0016 0.00110.0011 0.300.30 0.710.71 0.740.74 0.550.55 0.690.69 0.720.72 0.530.53
g__Fusobacteriumg__Fusobacterium 0.00230.0023 0.00460.0046 0.00070.0007 0.00200.0020 0.00020.0002 0.300.30 0.680.68 0.760.76 0.540.54 0.670.67 0.740.74 0.520.52
g__Weissellag__Weissella 0.00160.0016 0.00270.0027 0.00050.0005 0.00130.0013 0.00000.0000 0.310.31 0.700.70 0.740.74 0.560.56 0.680.68 0.710.71 0.530.53
g__[Eubacterium]g __ [Eubacterium] 0.00150.0015 0.00200.0020 0.00050.0005 0.00110.0011 0.00000.0000 0.310.31 0.740.74 0.770.77 0.560.56 0.730.73 0.750.75 0.560.56
g__Dialisterg__Dialister 0.00520.0052 0.01050.0105 0.00170.0017 0.00300.0030 0.00020.0002 0.330.33 0.680.68 0.680.68 0.560.56 0.670.67 0.690.69 0.540.54
g__Actinomycesg__Actinomyces 0.00390.0039 0.00480.0048 0.00140.0014 0.00240.0024 0.00000.0000 0.350.35 0.720.72 0.750.75 0.530.53 0.700.70 0.730.73 0.520.52
g__Odoribacterg__Odoribacter 0.00040.0004 0.00090.0009 0.00020.0002 0.00060.0006 0.00270.0027 0.360.36 0.670.67 0.700.70 0.550.55 0.660.66 0.690.69 0.530.53
g__Sphingomonasg__Sphingomonas 0.01020.0102 0.01390.0139 0.00390.0039 0.00390.0039 0.00000.0000 0.380.38 0.740.74 0.750.75 0.580.58 0.720.72 0.740.74 0.580.58
g__Bacteroidesg__Bacteroides 0.04340.0434 0.04270.0427 0.01760.0176 0.01040.0104 0.00000.0000 0.400.40 0.780.78 0.770.77 0.640.64 0.760.76 0.750.75 0.620.62
g__Turicibacterg__Turicibacter 0.00250.0025 0.00370.0037 0.00110.0011 0.00250.0025 0.00040.0004 0.450.45 0.650.65 0.680.68 0.510.51 0.650.65 0.680.68 0.500.50
g__Enterococcusg__Enterococcus 0.00870.0087 0.01030.0103 0.00410.0041 0.00490.0049 0.00000.0000 0.470.47 0.670.67 0.720.72 0.500.50 0.640.64 0.710.71 0.480.48
g__Doreag__Dorea 0.00200.0020 0.00240.0024 0.00100.0010 0.00230.0023 0.00040.0004 0.500.50 0.710.71 0.710.71 0.630.63 0.700.70 0.700.70 0.620.62
g__Lactobacillusg__Lactobacillus 0.02930.0293 0.03310.0331 0.08770.0877 0.06790.0679 0.00000.0000 3.003.00 0.840.84 0.730.73 0.840.84 0.840.84 0.720.72 0.840.84
g__Erwiniag__Erwinia 0.00050.0005 0.00100.0010 0.00160.0016 0.00280.0028 0.00000.0000 3.403.40 0.660.66 0.510.51 0.700.70 0.650.65 0.500.50 0.690.69
g__Staphylococcusg__Staphylococcus 0.00980.0098 0.01270.0127 0.04730.0473 0.05260.0526 0.00000.0000 4.814.81 0.870.87 0.700.70 0.880.88 0.860.86 0.690.69 0.870.87
g__Citrobacterg__Citrobacter 0.00110.0011 0.00420.0042 0.00570.0057 0.01830.0183 0.00520.0052 5.025.02 0.760.76 0.630.63 0.830.83 0.750.75 0.590.59 0.800.80
g__Halomonasg__Halomonas 0.00040.0004 0.00270.0027 0.00210.0021 0.00300.0030 0.00000.0000 5.085.08 0.780.78 0.590.59 0.850.85 0.780.78 0.580.58 0.830.83
g__Sphingobiumg__Sphingobium 0.00110.0011 0.00200.0020 0.00590.0059 0.00780.0078 0.00000.0000 5.405.40 0.730.73 0.550.55 0.810.81 0.720.72 0.540.54 0.800.80
g__Gordoniag__Gordonia 0.00010.0001 0.00050.0005 0.00090.0009 0.00220.0022 0.00000.0000 6.516.51 0.690.69 0.500.50 0.770.77 0.680.68 0.490.49 0.750.75
g__Adlercreutziag__Adlercreutzia 0.00140.0014 0.00230.0023 0.01230.0123 0.01080.0108 0.00000.0000 8.508.50 0.910.91 0.750.75 0.890.89 0.910.91 0.730.73 0.880.88
g__Brevibacillusg__Brevibacillus 0.00010.0001 0.00050.0005 0.00140.0014 0.00320.0032 0.00000.0000 16.9116.91 0.670.67 0.460.46 0.790.79 0.670.67 0.460.46 0.790.79
g__Aerococcusg__Aerococcus 0.00050.0005 0.00160.0016 0.00850.0085 0.01550.0155 0.00000.0000 17.9217.92 0.830.83 0.630.63 0.920.92 0.820.82 0.630.63 0.920.92
g__Salinicoccusg__Salinicoccus 0.00010.0001 0.00030.0003 0.00110.0011 0.00190.0019 0.00000.0000 18.4718.47 0.730.73 0.480.48 0.880.88 0.710.71 0.470.47 0.840.84
g__Jeotgalicoccusg__Jeotgalicoccus 0.00070.0007 0.00200.0020 0.01470.0147 0.01930.0193 0.00000.0000 20.5020.50 0.910.91 0.760.76 0.920.92 0.900.90 0.750.75 0.920.92
g__Desulfovibriog__Desulfovibrio 0.00030.0003 0.00110.0011 0.00960.0096 0.01120.0112 0.00000.0000 30.2930.29 0.920.92 0.770.77 0.940.94 0.920.92 0.770.77 0.930.93
g__Burkholderiag__Burkholderia 0.00020.0002 0.00080.0008 0.00680.0068 0.00780.0078 0.00000.0000 33.5233.52 0.870.87 0.710.71 0.960.96 0.860.86 0.710.71 0.950.95
g__Novosphingobiumg__Novosphingobium 0.00040.0004 0.00130.0013 0.01610.0161 0.02060.0206 0.00000.0000 44.0144.01 0.850.85 0.720.72 0.960.96 0.840.84 0.710.71 0.950.95
g__Comamonasg__Comamonas 0.00020.0002 0.00050.0005 0.01040.0104 0.01200.0120 0.00000.0000 59.7159.71 0.890.89 0.770.77 0.960.96 0.890.89 0.760.76 0.950.95
g__Cloacibacteriumg__Cloacibacterium 0.00030.0003 0.00120.0012 0.02730.0273 0.03750.0375 0.00000.0000 81.1081.10 0.930.93 0.810.81 0.960.96 0.930.93 0.800.80 0.950.95
g__Dechloromonasg__Dechloromonas 0.00000.0000 0.00000.0000 0.00130.0013 0.00260.0026 0.00000.0000 285.02285.02 0.730.73 0.470.47 0.880.88 0.730.73 0.460.46 0.860.86
g__Thermomonasg__Thermomonas 0.00000.0000 0.00000.0000 0.00070.0007 0.00140.0014 0.00000.0000 430.36430.36 0.700.70 0.440.44 0.860.86 0.700.70 0.420.42 0.850.85
g__Diaphorobacterg__Diaphorobacter 0.00000.0000 0.00000.0000 0.00260.0026 0.00360.0036 0.00000.0000 848.13848.13 0.850.85 0.670.67 0.980.98 0.840.84 0.660.66 0.970.97
g__Pedomicrobiumg__Pedomicrobium 0.00000.0000 0.00000.0000 0.00090.0009 0.00230.0023 0.00000.0000 0.700.70 0.420.42 0.860.86 0.700.70 0.420.42 0.850.85
g__KD1-23g__KD1-23 0.00000.0000 0.00000.0000 0.00040.0004 0.00080.0008 0.00000.0000 0.690.69 0.470.47 0.820.82 0.690.69 0.450.45 0.800.80
g__Zoogloeag__Zoogloea 0.00000.0000 0.00000.0000 0.00050.0005 0.00110.0011 0.00000.0000 0.680.68 0.460.46 0.840.84 0.680.68 0.430.43 0.810.81
g__Methylophagag__Methylophaga 0.00000.0000 0.00000.0000 0.00040.0004 0.00120.0012 0.00060.0006 0.660.66 0.430.43 0.820.82 0.660.66 0.420.42 0.800.80
g__Haererehalobacterg__Haererehalobacter 0.00000.0000 0.00000.0000 0.00090.0009 0.00270.0027 0.00020.0002 0.660.66 0.470.47 0.760.76 0.660.66 0.450.45 0.760.76
상기 진술한 본 발명의 설명은 예시를 위한 것이며, 본 발명이 속하는 기술분야의 통상의 지식을 가진 자는 본 발명의 기술적 사상이나 필수적인 특징을 변경하지 않고서 다른 구체적인 형태로 쉽게 변형이 가능하다는 것을 이해할 수 있을 것이다. 그러므로 이상에서 기술한 실시예들은 모든 면에서 예시적인 것이며 한정적이 아닌 것으로 이해해야만 한다. The description of the present invention set forth above is for illustrative purposes, and one of ordinary skill in the art may understand that the present invention may be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. There will be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.
본 발명에 따른 세균 메타게놈 분석을 통해 난소암 진단에 대한 정보를 제공하는 방법은 피검체 유래 샘플을 이용해 세균 메타게놈 분석을 수행하여 특정 세균 유래 세포밖 소포의 함량 증감을 분석함으로써 난소암의 발병 위험도를 예측하고 난소암을 진단하는데 이용할 수 있다. 환경에 존재하는 세균에서 분비되는 세포밖 소포는 체내에 흡수되어 암 발생에 직접적인 영향을 미칠 수 있으며, 난소암은 증상이 나타나기 전 조기진단이 어려워 효율적인 치료가 어려운 실정이므로, 본 발명에 따른 인체 유래 샘플을 이용한 세균 유래 세포밖　소포의 메타게놈 분석을 통해 난소암 발병의 위험도를 미리 예측함으로써 난소암의 위험군을 조기에 진단 및 예측하여 적절한 관리를 통해 발병 시기를 늦추거나 발병을 예방할 수 있으며, 난소암의 발병 후에도 조기진단 할 수 있어 난소암의 발병률을 낮추고 치료효과를 높일 수 있다. 또한, 난소암으로 진단받은 환자에서 본 발명에 따른 세균 메타게놈 분석은 원인인자 노출을 피함으로써 난소암의 경과를 좋게 하거나 재발을 막는데 이용할 수 있다.The method for providing information on the diagnosis of ovarian cancer through the bacterial metagenomic analysis according to the present invention is carried out by performing a bacterial metagenomic analysis using a sample derived from a subject to analyze the increase or decrease in the content of specific bacterial-derived extracellular vesicles. It can be used to predict risk and diagnose ovarian cancer. Extracellular vesicles secreted by the bacteria present in the environment can be absorbed directly into the body and directly affect the development of cancer, and ovarian cancer is difficult to diagnose effectively because of the early diagnosis of symptoms before the symptoms appear, the human-derived according to the present invention Metagenome analysis of bacterial-derived extracellular vesicles using a sample predicts the risk of developing ovarian cancer in advance, allowing early diagnosis and prediction of risk groups for ovarian cancer, and delaying the onset or preventing the onset through proper management. Early diagnosis is possible even after the onset of cancer, which can lower the incidence of ovarian cancer and increase the therapeutic effect. In addition, the bacterial metagenomic analysis according to the present invention in patients diagnosed with ovarian cancer can be used to improve the progression of ovarian cancer or to prevent recurrence by avoiding causal agent exposure.

Claims (16)

  1. (a) 피검체 샘플에서 분리한 세포밖 소포로부터 DNA를 추출하는 단계;(a) extracting DNA from extracellular vesicles isolated from a subject sample;
    (b) 상기 추출한 DNA에 대하여 서열번호 1 및 서열번호 2의 프라이머 쌍을 이용하여 PCR을 수행하는 단계; 및(b) performing PCR using the primer pairs of SEQ ID NO: 1 and SEQ ID NO: 2 on the extracted DNA; And
    (c) 상기 PCR 산물의 서열분석을 통하여 정상인 유래 샘플과 세균 유래 세포밖 소포의 함량 증감을 비교하는 단계를 포함하는, 난소암 진단을 위한 정보제공방법.(c) comparing the increase and decrease of the content of the normal-derived sample and the bacterial-derived extracellular vesicles by sequencing the PCR product, information providing method for ovarian cancer diagnosis.
  2. 제1항에 있어서,The method of claim 1,
    상기 (c) 단계에서 테네리쿠테스(Tenericutes), 탈철간균문(Deferribacteres), 푸조박테리아(Fusobacteria), 아르마티모나스문(Armatimonadetes), SR1, 젬마티모나데테스(Gemmatimonadetes), 및 TM6로 이루어진 군으로부터 선택되는 1종 이상의 문(phylum) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 정보제공방법.In step (c), Tenericutes, Deferribacteres, Fuzobacteria, Armatimonadetes, SR1, Gemmatimonadetes, and TM6 from the group consisting of An information providing method, characterized by comparing the increase or decrease in the content of one or more phylum bacteria-derived extracellular vesicles selected.
  3. 제1항에 있어서,The method of claim 1,
    상기 (c) 단계에서 에리시펠로트리치(Erysipelotrichi), 알파프로테오박테리아(Alphaproteobacteria), 코리오박테리아(Coriobacteriia), 플라보박테리아(Flavobacteriia), 오실라토리오피시디에(Oscillatoriophycideae), 델타프로테오박테리아(Deltaproteobacteria), ML635J-21, 몰리쿠테스강(Mollicutes), 탈철간균강(Deferribacteres), 푸조박테리아(Fusobacteriia), 핌브리모나디아(Fimbriimonadia), 에리시펠로트리치(Erysipelotrichi), 클로로플라스트(Chloroplast), 감마프로테오박테리아(Gammaproteobacteria), 베타프로테오박테리아(Betaproteobacteria), 간균강(Bacilli), 아시디마이크로비아(Acidimicrobiia), 델타프로테오박테리아(Deltaproteobacteria), 오실라토리오피시디에(Oscillatoriophycideae), 4C0d-2, 젬마티모나데테스(Gemmatimonadetes), 플라보박테리아(Flavobacteriia), ML635J-21, 및 SJA-4로 이루어진 군으로부터 선택되는 1종 이상의 강(class) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 정보제공방법.In the step (c) Erysipelotrichi (Erysipelotrichi), Alpha proteobacteria (Alphaproteobacteria), Coriobacteria (Coriobacteriia), Flavoacteriia, Oscillatoriophycideae (Oscillatoriophycideae), Delta proteo Deltaproteobacteria, ML635J-21, Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplasm (Chloroplast), Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycidea ), 4C0d-2, Gemmatimonadetes, Flavoacteriia, ML635J-21, and one or more class three selected from the group consisting of SJA-4 An information providing method, characterized by comparing the increase and decrease of the content of the bacteria-derived extracellular vesicles.
  4. 제1항에 있어서,The method of claim 1,
    상기 (c) 단계에서 에리시펠로트리찰레스(Erysipelotrichales), 리조비움목(Rhizobiales), 카울로박테라레스(Caulobacterales), 슈도모나달레스(Pseudomonadales), 코리오박테리아레스(Coriobacteriales), 플라보박테리아레스(Flavobacteriales), YS2, 남구슬말목(Chroococcales), CW040, 데설포비브리오날레스(Desulfovibrionales), 메틸로필라레스(Methylophilales), 데설퍼로모나달레스(Desulfuromonadales), 데설포박테라레스(Desulfobacterales), 갈리오넬라레스(Gallionellales), 카디오박테리알레스(Cardiobacteriales), 스트라메노필레스(Stramenopiles), 마리니셀라레스(Marinicellales), 할라나에로비알레스(Halanaerobiales), RF39, 탈철간균목(Deferribacterales), 피렐룰라레스(Pirellulales), 푸조박테리알레스(Fusobacteriales), 핌브리모나달레스(Fimbriimonadales), 에리시펠로트리찰레스(Erysipelotrichales), 슈도모나달레스(Pseudomonadales), 스트렙토피타(Streptophyta), 터리시박테랄레스(Turicibacterales), 벌크홀데리알레스(Burkholderiales), 스핑고모나달레스(Sphingomonadales), 믹소코칼레스(Myxococcales), 써말레스(Thermales), YS2, 바실라레스(Bacillales), 아시디마이크로비알레스(Acidimicrobiales), 오세아노스피릴랄레스(Oceanospirillales), 레지오넬라레스(Legionellales), iii1-15, 남구슬말목(Chroococcales), CW040, EW055, 젬마티모나달레스(Gemmatimonadales), 플라보박테리움목(Flavobacteriales), 로도사이클러스(Rhodocyclales), 데설포비브리오날레스(Desulfovibrionales), MLE1-12, 메틸로필라레스(Methylophilales), 및 엘린6067(Ellin6067)로 이루어진 군으로부터 선택되는 1종 이상의 목(order) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 정보제공방법.Erysipelotrichales, Rhizobiales, Caulolobacterales, Pseudomonadales, Coriobacteriales, Flavo in step (c) Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, Methylophilales, Desulfuromonadales, Desulfobacterares , Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirrell Pirellulales, Fuzobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptopita Streptophyta, Turicibacterales, Bulkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales , Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chrococcales, CW040, EW055, Gemmatimonadales, Flavo At least one member selected from the group consisting of Flavobacteriales, Rhodocyclales, Desulfovibrionales, MLE1-12, Methylophilales, and Ellin6067 An information providing method, characterized by comparing the increase or decrease in the content of the order bacteria-derived extracellular vesicles.
  5. 제1항에 있어서,The method of claim 1,
    상기 (c) 단계에서 리조비움과(Rhizobiaceae), 브라디리조비아시에(Bradyrhizobiaceae), 펩토스트렙토코카시에(Peptostreptococcaceae), 옥살로박테라시에(Oxalobacteraceae), 에리시펠로트리차시에(Erysipelotrichaceae), 슈도모나다시에(Pseudomonadaceae), 카우로박테라시에(Caulobacteraceae), 메틸로박테리아시에(Methylobacteriaceae), 파라프레보텔라시에(Paraprevotellaceae), 푸조박테리아시에(Fusobacteriaceae), 플라노코카시에(Planococcaceae), 버크홀데리아시에(Burkholderiaceae), 아이로콕쿠스과(Aerococcaceae), 유산균과(Lactobacillaceae), 코리오박테리움과(Coriobacteriaceae), 위크셀라시에(Weeksellaceae), 제노코카시에(Xenococcaceae), F16, 데설포비브리오나시에(Desulfovibrionaceae), 코마모나다시에(Comamonadaceae), S24-7, 메틸로필라시에(Methylophilaceae), 카디오박테리아시에(Cardiobacteriaceae), 아시도박테리아시에(Acidobacteriaceae), 옥살로박테라시에(Oxalobacteraceae), 프레보텔라과(Prevotellaceae), 렙토트리치아시에(Leptotrichiaceae), 크리스텐세넬라시에(Christensenellaceae), 바르네시엘라시에(Barnesiellaceae), 핌브리모나다시에(Fimbriimonadaceae), 에리시펠로트리차시에(Erysipelotrichaceae), 모지박테리아시에(Mogibacteriaceae), 슈도모나다시에(Pseudomonadaceae), 푸조박테리아시에(Fusobacteriaceae), 슈도노카르디아시에(Pseudonocardiaceae), 류코노스토카시에(Leuconostocaceae), 모락셀라시에(Moraxellaceae), 메틸로박테리아시에(Methylobacteriaceae), 파라프레보텔라시에(Paraprevotellaceae), 스핑고모나다시에(Sphingomonadaceae), 노카르디오이다시에(Nocardioidaceae), 유산균과(Lactobacillaceae), 버크홀데리아시에(Burkholderiaceae), 아이로콕쿠스과(Aerococcaceae), 노카디옵사시에(Nocardiopsaceae), 로도사이클라시에(Rhodocyclaceae), S24-7, 에우박테리아시에(Eubacteriaceae), 데설포비브리오나시에(Desulfovibrionaceae), 코마모나다시에(Comamonadaceae), 메틸로필라시에(Methylophilaceae), 및 콕시엘라시에(Coxiellaceae)로 이루어진 군으로부터 선택되는 1종 이상의 과(family) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 정보제공방법.In step (c) Rizobiaceae (Rhizobiaceae), Bradyrhizobiaceae (Peptostreptococcaceae), Oxalobacteraceae (Oxalobacteraceae), Erysipelotrichaceae (Erysipelotrichaceae) ), Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fuzobacteriaceae, Planococica Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae , F16, Desulfovibrionaceae, Comamonadaceae, S24-7, Methylophilaceae, Cardiobacteriaceae, Acidobacteriaceae, Oxal Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae , Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fuzobacteriaceae, Pseudonocardiaceae, Leukonostokcie (Leuconostocaceae), Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillus ( Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, One or more family bacteria-derived cells selected from the group consisting of Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, and Coxiellaceae. An information providing method, characterized by comparing the increase and decrease of the content of the outer vesicles.
  6. 제1항에 있어서,The method of claim 1,
    상기 (c) 단계에서 모르가넬라(Morganella), 하이드로제노필러스(Hydrogenophilus), 쿠프리아비두스(Cupriavidus), 에우박테리움(Eubacterium), 카테니박테리움(Catenibacterium), 마이크로코쿠스(Micrococcus), 코프로코커스(Coprococcus), 페칼리박테리움(Faecalibacterium), 블라우티아(Blautia), 세라티아(Serratia), 시트로박터(Citrobacter), 콜린셀라(Collinsella), 리조비움(Rhizobium), 엑시구오박데리움(Exiguobacterium), 랄스토니아(Ralstonia), 셀룰로모나스(Cellulomonas), 스포로사르시나(Sporosarcina), 프로테우스(Proteus), 렙토트리키아(Leptotrichia), SMB53, 프레보텔라(Prevotella), 오리박테리움(Oribacterium), 페디오코커스(Pediococcus), 파라프레보텔라(Paraprevotella), 메틸로박테리움(Methylobacterium), 뮤시스피릴룸(Mucispirillum), 파라박테로이데스(Parabacteroides), 콜린셀라(Collinsella), 아내로스티페스(Anaerostipes), 슈도모나스(Pseudomonas), 부티리시모나스(Butyricimonas), 푸조박테리움(Fusobacterium), 위셀라(Weissella), 에우박테리움(Eubacterium), 디알리스터(Dialister), 엑티노마이세스(Actinomyces), 오도리박터(Odoribacter), 스핑고모나스(Sphingomonas), 박테로이데스(Bacteroides), 터리시박터(Turicibacter), 엔테로코커스(Enterococcus), 도레아(Dorea), 유산균속(Lactobacillus), 얼위니아(Erwinia), 스타필로코커스(Staphylococcus), 시트로박터(Citrobacter), 할로모나스(Halomonas), 스핑고비움(Sphingobium), 고르도니아(Gordonia), 아들러크레우치아(Adlercreutzia), 브레비바실러스(Brevibacillus), 아에로코커스(Aerococcus), 살리니코커스(Salinicoccus), 제오트갈리코커스(Jeotgalicoccus), 데설포비브리오(Desulfovibrio), 벌크홀데리아(Burkholderia), 노보스핑고비움(Novosphingobium), 코마모나스(Comamonas), 클로시박테리움(Cloacibacterium), 데클로로모나스(Dechloromonas), 데르모모나스(Thermomonas), 디아포로박터(Diaphorobacter), 페도마이크로비움(Pedomicrobium), KD1-23, 주글로에(Zoogloea), 메틸로파가(Methylophaga), 및 해레레할로박터(Haererehalobacter)로 이루어진 군으로부터 선택되는 1종 이상의 속(genus) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 정보제공방법.In the step (c) Morganella (Morganella), Hydrogenenophilus (Hydrogenophilus), Cupriavidus (Cupriavidus), Eubacterium (Eubacterium), Catenibacterium (Catenibacterium), Micrococcus , Coprococcus, Pecalibacterium, Blautia, Serratia, Citrobacter, Collinsella, Rizobium, Exhiguo Exiguobacterium, Ralstonia, Cellulomonas, Sporosrcina, Proteus, Leptotrichia, SMB53, Prevotella, Duck Bacterium (Oribacterium), Pediococcus (Pediococcus), Paraprevotella, Methylobacterium, Mucispirillum, Parabacacteroides, Collinsella, Anaerostipes and Pseeudom onas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialial, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turicibacter, Enterococcus, Dorea, Lactobacillus, Erwinia, Staphylococcus ), Citroacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Brevibacillus, Aerococcus , Salinicoccus, Zeotgalicoccus, Desulfovibrio, Bulkholderia, Novosphingobium, Comamonas, Clomobacterium (Cloaci) Dechloromonas, dermo Group consisting of Nass (Thermomonas), Diaphorobacter, Pedomicrobium, KD1-23, Zogloa, Methylophaga, and Haererehalobacter An information providing method, characterized by comparing the increase or decrease in the content of one or more genus bacteria-derived extracellular vesicles selected from.
  7. 제1항에 있어서,The method of claim 1,
    상기 피검체 샘플은 혈액 또는 소변인 것을 특징으로 하는, 정보제공방법.The subject sample is blood or urine, characterized in that the information providing method.
  8. 제7항에 있어서,The method of claim 7, wherein
    상기 혈액은 전혈, 혈청, 혈장, 또는 혈액 단핵구인 것을 특징으로 하는, 정보제공방법.The blood is characterized in that the whole blood, serum, plasma, or blood monocytes, information providing method.
  9. (a) 피검체 샘플에서 분리한 세포밖 소포로부터 DNA를 추출하는 단계;(a) extracting DNA from extracellular vesicles isolated from a subject sample;
    (b) 상기 추출한 DNA에 대하여 서열번호 1 및 서열번호 2의 프라이머 쌍을 이용하여 PCR을 수행하는 단계; 및(b) performing PCR using the primer pairs of SEQ ID NO: 1 and SEQ ID NO: 2 on the extracted DNA; And
    (c) 상기 PCR 산물의 서열분석을 통하여 정상인 유래 샘플과 세균 유래 세포밖 소포의 함량 증감을 비교하는 단계를 포함하는, 난소암 진단방법.(C) ovarian cancer diagnostic method comprising the step of comparing the increase and decrease of the content of the normal-derived sample and bacterial-derived extracellular vesicles through the sequencing of the PCR product.
  10. 제9항에 있어서,The method of claim 9,
    상기 (c) 단계에서 테네리쿠테스(Tenericutes), 탈철간균문(Deferribacteres), 푸조박테리아(Fusobacteria), 아르마티모나스문(Armatimonadetes), SR1, 젬마티모나데테스(Gemmatimonadetes), 및 TM6로 이루어진 군으로부터 선택되는 1종 이상의 문(phylum) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 진단방법.In step (c), Tenericutes, Deferribacteres, Fuzobacteria, Armatimonadetes, SR1, Gemmatimonadetes, and TM6 from the group consisting of A diagnostic method, characterized by comparing the increase or decrease in the content of one or more phylum bacteria-derived extracellular vesicles selected.
  11. 제9항에 있어서,The method of claim 9,
    상기 (c) 단계에서 에리시펠로트리치(Erysipelotrichi), 알파프로테오박테리아(Alphaproteobacteria), 코리오박테리아(Coriobacteriia), 플라보박테리아(Flavobacteriia), 오실라토리오피시디에(Oscillatoriophycideae), 델타프로테오박테리아(Deltaproteobacteria), ML635J-21, 몰리쿠테스강(Mollicutes), 탈철간균강(Deferribacteres), 푸조박테리아(Fusobacteriia), 핌브리모나디아(Fimbriimonadia), 에리시펠로트리치(Erysipelotrichi), 클로로플라스트(Chloroplast), 감마프로테오박테리아(Gammaproteobacteria), 베타프로테오박테리아(Betaproteobacteria), 간균강(Bacilli), 아시디마이크로비아(Acidimicrobiia), 델타프로테오박테리아(Deltaproteobacteria), 오실라토리오피시디에(Oscillatoriophycideae), 4C0d-2, 젬마티모나데테스(Gemmatimonadetes), 플라보박테리아(Flavobacteriia), ML635J-21, 및 SJA-4로 이루어진 군으로부터 선택되는 1종 이상의 강(class) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 진단방법.In the step (c) Erysipelotrichi (Erysipelotrichi), Alpha proteobacteria (Alphaproteobacteria), Coriobacteria (Coriobacteriia), Flavoacteriia, Oscillatoriophycideae (Oscillatoriophycideae), Delta proteo Deltaproteobacteria, ML635J-21, Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplasm (Chloroplast), Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycidea ), 4C0d-2, Gemmatimonadetes, Flavoacteriia, ML635J-21, and one or more class three selected from the group consisting of SJA-4 A diagnostic method, characterized by comparing the increase and decrease of the content of the bacteria-derived extracellular vesicles.
  12. 제9항에 있어서,The method of claim 9,
    상기 (c) 단계에서 에리시펠로트리찰레스(Erysipelotrichales), 리조비움목(Rhizobiales), 카울로박테라레스(Caulobacterales), 슈도모나달레스(Pseudomonadales), 코리오박테리아레스(Coriobacteriales), 플라보박테리아레스(Flavobacteriales), YS2, 남구슬말목(Chroococcales), CW040, 데설포비브리오날레스(Desulfovibrionales), 메틸로필라레스(Methylophilales), 데설퍼로모나달레스(Desulfuromonadales), 데설포박테라레스(Desulfobacterales), 갈리오넬라레스(Gallionellales), 카디오박테리알레스(Cardiobacteriales), 스트라메노필레스(Stramenopiles), 마리니셀라레스(Marinicellales), 할라나에로비알레스(Halanaerobiales), RF39, 탈철간균목(Deferribacterales), 피렐룰라레스(Pirellulales), 푸조박테리알레스(Fusobacteriales), 핌브리모나달레스(Fimbriimonadales), 에리시펠로트리찰레스(Erysipelotrichales), 슈도모나달레스(Pseudomonadales), 스트렙토피타(Streptophyta), 터리시박테랄레스(Turicibacterales), 벌크홀데리알레스(Burkholderiales), 스핑고모나달레스(Sphingomonadales), 믹소코칼레스(Myxococcales), 써말레스(Thermales), YS2, 바실라레스(Bacillales), 아시디마이크로비알레스(Acidimicrobiales), 오세아노스피릴랄레스(Oceanospirillales), 레지오넬라레스(Legionellales), iii1-15, 남구슬말목(Chroococcales), CW040, EW055, 젬마티모나달레스(Gemmatimonadales), 플라보박테리움목(Flavobacteriales), 로도사이클러스(Rhodocyclales), 데설포비브리오날레스(Desulfovibrionales), MLE1-12, 메틸로필라레스(Methylophilales), 및 엘린6067(Ellin6067)로 이루어진 군으로부터 선택되는 1종 이상의 목(order) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 진단방법.Erysipelotrichales, Rhizobiales, Caulolobacterales, Pseudomonadales, Coriobacteriales, Flavo in step (c) Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, Methylophilales, Desulfuromonadales, Desulfobacterares , Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirrell Pirellulales, Fuzobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptopita Streptophyta, Turicibacterales, Bulkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales , Acidimicrobiales, Oceanospirillales, Legionellales, iii1-15, Chrococcales, CW040, EW055, Gemmatimonadales, Flavo At least one member selected from the group consisting of Flavobacteriales, Rhodocyclales, Desulfovibrionales, MLE1-12, Methylophilales, and Ellin6067 A diagnostic method, characterized by comparing the increase and decrease of the content of the order bacteria-derived extracellular vesicles.
  13. 제9항에 있어서,The method of claim 9,
    상기 (c) 단계에서 리조비움과(Rhizobiaceae), 브라디리조비아시에(Bradyrhizobiaceae), 펩토스트렙토코카시에(Peptostreptococcaceae), 옥살로박테라시에(Oxalobacteraceae), 에리시펠로트리차시에(Erysipelotrichaceae), 슈도모나다시에(Pseudomonadaceae), 카우로박테라시에(Caulobacteraceae), 메틸로박테리아시에(Methylobacteriaceae), 파라프레보텔라시에(Paraprevotellaceae), 푸조박테리아시에(Fusobacteriaceae), 플라노코카시에(Planococcaceae), 버크홀데리아시에(Burkholderiaceae), 아이로콕쿠스과(Aerococcaceae), 유산균과(Lactobacillaceae), 코리오박테리움과(Coriobacteriaceae), 위크셀라시에(Weeksellaceae), 제노코카시에(Xenococcaceae), F16, 데설포비브리오나시에(Desulfovibrionaceae), 코마모나다시에(Comamonadaceae), S24-7, 메틸로필라시에(Methylophilaceae), 카디오박테리아시에(Cardiobacteriaceae), 아시도박테리아시에(Acidobacteriaceae), 옥살로박테라시에(Oxalobacteraceae), 프레보텔라과(Prevotellaceae), 렙토트리치아시에(Leptotrichiaceae), 크리스텐세넬라시에(Christensenellaceae), 바르네시엘라시에(Barnesiellaceae), 핌브리모나다시에(Fimbriimonadaceae), 에리시펠로트리차시에(Erysipelotrichaceae), 모지박테리아시에(Mogibacteriaceae), 슈도모나다시에(Pseudomonadaceae), 푸조박테리아시에(Fusobacteriaceae), 슈도노카르디아시에(Pseudonocardiaceae), 류코노스토카시에(Leuconostocaceae), 모락셀라시에(Moraxellaceae), 메틸로박테리아시에(Methylobacteriaceae), 파라프레보텔라시에(Paraprevotellaceae), 스핑고모나다시에(Sphingomonadaceae), 노카르디오이다시에(Nocardioidaceae), 유산균과(Lactobacillaceae), 버크홀데리아시에(Burkholderiaceae), 아이로콕쿠스과(Aerococcaceae), 노카디옵사시에(Nocardiopsaceae), 로도사이클라시에(Rhodocyclaceae), S24-7, 에우박테리아시에(Eubacteriaceae), 데설포비브리오나시에(Desulfovibrionaceae), 코마모나다시에(Comamonadaceae), 메틸로필라시에(Methylophilaceae), 및 콕시엘라시에(Coxiellaceae)로 이루어진 군으로부터 선택되는 1종 이상의 과(family) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 진단방법.In step (c) Rizobiaceae (Rhizobiaceae), Bradyrhizobiaceae (Peptostreptococcaceae), Oxalobacteraceae (Oxalobacteraceae), Erysipelotrichaceae (Erysipelotrichaceae) ), Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fuzobacteriaceae, Planococica Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae , F16, Desulfovibrionaceae, Comamonadaceae, S24-7, Methylophilaceae, Cardiobacteriaceae, Acidobacteriaceae, Oxal Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae , Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fuzobacteriaceae, Pseudonocardiaceae, Leukonostokcie (Leuconostocaceae), Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillus ( Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, One or more family bacteria-derived cells selected from the group consisting of Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, and Coxiellaceae. A diagnostic method, characterized by comparing the increase and decrease of the content of the outer vesicles.
  14. 제9항에 있어서,The method of claim 9,
    상기 (c) 단계에서 모르가넬라(Morganella), 하이드로제노필러스(Hydrogenophilus), 쿠프리아비두스(Cupriavidus), 에우박테리움(Eubacterium), 카테니박테리움(Catenibacterium), 마이크로코쿠스(Micrococcus), 코프로코커스(Coprococcus), 페칼리박테리움(Faecalibacterium), 블라우티아(Blautia), 세라티아(Serratia), 시트로박터(Citrobacter), 콜린셀라(Collinsella), 리조비움(Rhizobium), 엑시구오박데리움(Exiguobacterium), 랄스토니아(Ralstonia), 셀룰로모나스(Cellulomonas), 스포로사르시나(Sporosarcina), 프로테우스(Proteus), 렙토트리키아(Leptotrichia), SMB53, 프레보텔라(Prevotella), 오리박테리움(Oribacterium), 페디오코커스(Pediococcus), 파라프레보텔라(Paraprevotella), 메틸로박테리움(Methylobacterium), 뮤시스피릴룸(Mucispirillum), 파라박테로이데스(Parabacteroides), 콜린셀라(Collinsella), 아내로스티페스(Anaerostipes), 슈도모나스(Pseudomonas), 부티리시모나스(Butyricimonas), 푸조박테리움(Fusobacterium), 위셀라(Weissella), 에우박테리움(Eubacterium), 디알리스터(Dialister), 엑티노마이세스(Actinomyces), 오도리박터(Odoribacter), 스핑고모나스(Sphingomonas), 박테로이데스(Bacteroides), 터리시박터(Turicibacter), 엔테로코커스(Enterococcus), 도레아(Dorea), 유산균속(Lactobacillus), 얼위니아(Erwinia), 스타필로코커스(Staphylococcus), 시트로박터(Citrobacter), 할로모나스(Halomonas), 스핑고비움(Sphingobium), 고르도니아(Gordonia), 아들러크레우치아(Adlercreutzia), 브레비바실러스(Brevibacillus), 아에로코커스(Aerococcus), 살리니코커스(Salinicoccus), 제오트갈리코커스(Jeotgalicoccus), 데설포비브리오(Desulfovibrio), 벌크홀데리아(Burkholderia), 노보스핑고비움(Novosphingobium), 코마모나스(Comamonas), 클로시박테리움(Cloacibacterium), 데클로로모나스(Dechloromonas), 데르모모나스(Thermomonas), 디아포로박터(Diaphorobacter), 페도마이크로비움(Pedomicrobium), KD1-23, 주글로에(Zoogloea), 메틸로파가(Methylophaga), 및 해레레할로박터(Haererehalobacter)로 이루어진 군으로부터 선택되는 1종 이상의 속(genus) 세균 유래 세포밖 소포의 함량 증감을 비교하는 것을 특징으로 하는, 진단방법.In the step (c) Morganella (Morganella), Hydrogenenophilus (Hydrogenophilus), Cupriavidus (Cupriavidus), Eubacterium (Eubacterium), Catenibacterium (Catenibacterium), Micrococcus , Coprococcus, Pecalibacterium, Blautia, Serratia, Citrobacter, Collinsella, Rizobium, Exhiguo Exiguobacterium, Ralstonia, Cellulomonas, Sporosrcina, Proteus, Leptotrichia, SMB53, Prevotella, Duck Bacterium (Oribacterium), Pediococcus (Pediococcus), Paraprevotella, Methylobacterium, Mucispirillum, Parabacacteroides, Collinsella, Anaerostipes and Pseeudom onas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialial, Actinomyces, Odoribacter, Sphingomonas, Bacteroides, Turicibacter, Enterococcus, Dorea, Lactobacillus, Erwinia, Staphylococcus ), Citroacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Brevibacillus, Aerococcus , Salinicoccus, Zeotgalicoccus, Desulfovibrio, Bulkholderia, Novosphingobium, Comamonas, Clomobacterium (Cloaci) Dechloromonas, dermo Group consisting of Nass (Thermomonas), Diaphorobacter, Pedomicrobium, KD1-23, Zogloa, Methylophaga, and Haererehalobacter A diagnostic method, characterized by comparing the increase or decrease in the content of one or more genus bacteria-derived extracellular vesicles selected from.
  15. 제9항에 있어서,The method of claim 9,
    상기 피검체 샘플은 혈액 또는 소변인 것을 특징으로 하는, 진단방법.The subject sample is a diagnostic method, characterized in that the blood or urine.
  16. 제15항에 있어서,The method of claim 15,
    상기 혈액은 전혈, 혈청, 혈장, 또는 혈액 단핵구인 것을 특징으로 하는, 진단방법.Wherein said blood is whole blood, serum, plasma, or blood monocytes.
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