CN111424091A - Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof - Google Patents
Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof Download PDFInfo
- Publication number
- CN111424091A CN111424091A CN202010311494.0A CN202010311494A CN111424091A CN 111424091 A CN111424091 A CN 111424091A CN 202010311494 A CN202010311494 A CN 202010311494A CN 111424091 A CN111424091 A CN 111424091A
- Authority
- CN
- China
- Prior art keywords
- methylation
- promoter
- malignant
- thyroid
- benign
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- 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
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/112—Disease subtyping, staging or classification
-
- 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
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/154—Methylation markers
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Organic Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Engineering & Computer Science (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Zoology (AREA)
- Genetics & Genomics (AREA)
- Wood Science & Technology (AREA)
- Physics & Mathematics (AREA)
- Biotechnology (AREA)
- Microbiology (AREA)
- Molecular Biology (AREA)
- Hospice & Palliative Care (AREA)
- Biophysics (AREA)
- Oncology (AREA)
- Biochemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a group of methylation markers for differential diagnosis and/or prediction of benign and malignant thyroid follicular tumors; the invention further provides application of the methylation marker in preparation of a differential diagnosis tool for benign and malignant thyroid follicular tumors. According to the invention, by using a paraffin tissue specimen with 128 thyroid follicular lesions, 70 thyroid follicular tumor related methylation markers are found, and the sensitivity and specificity are very high when judging whether the thyroid follicular tumor is benign or malignant (the specificity is 100%, the sensitivity is 92.3%, the accuracy is 96.2%, and the AUC value is 0.994); meanwhile, the 70 markers can predict the benign and malignant degree of the borderline thyroid follicular tumor, guide clinical doctors to make a correct diagnosis and treatment strategy and avoid over diagnosis and treatment.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a group of markers for differential diagnosis of benign and malignant thyroid follicular tumors and application thereof.
Background
Thyroid Cancer (TC) is the most common endocrine malignancy, accounting for over 95% of cases. Thyroid cancer mainly comprises four pathological types (papillary thyroid cancer, follicular thyroid cancer, medullary thyroid cancer and anaplastic thyroid cancer, respectively). Thyroid follicular cancer (FTC) accounts for approximately 15% of all TCs, and is a rare type of thyroid cancer. FTC is commonly found in older patients and is more aggressive than Papillary Thyroid Carcinoma (PTC). Pathological diagnosis of FTC depends on typical envelope and/or vascular infiltration, while tumor cells lack the characteristics of papillary carcinoma nuclei.
Thyroid Adenomas (FTA) are the most common benign follicular thyroid lesions, and refer to a group of benign tumors with follicular differentiation, intact envelope, no envelope and vascular infiltration. FTA and FTC cannot be identified from a cytological perspective, and identification of both requires finding the presence or absence of envelopes and/or vascular infiltrates from surgically excised paraffin specimens. Thyroid tumors with uncertain malignant potential (TT-UMP), which are a group of intercalary thyroid follicular lesions with envelopes, have uncertain or suspicious envelopes and/or vascular infiltrates and can not reach the standard for diagnosing malignancy; TT-UMP includes follicular tumors of undetermined malignant potential (FT-UMP) and well-differentiated tumors of undetermined malignant potential of the thyroid (WT-UMP).
Since the treatment strategies of FTA, FTC and TT-UMP are different, correct pathological diagnosis is very important, but in daily pathological diagnosis work, the three are accurately distinguished only by morphology, sometimes the three are very difficult to make accurate diagnosis. Moreover, the good and the malignant of TT-UMP still lack of understanding, and if the good and the malignant of the group of diseases can be effectively judged, clinical doctors can be better guided to make diagnosis and treatment strategies.
Many studies have attempted to find an index for identifying the three by immunohistochemistry, genetic mutation or methylation. However, the existing indexes mainly aim at papillary thyroid carcinoma, the biological indexes aiming at other follicular tumors are very limited, and experimental researches for applying large sample volumes are lacked. In addition, the sensitivity and specificity of the existing benign and malignant judgment indexes are not high; furthermore, the benign and malignant properties of the borderline tumor cannot be predicted by the existing indexes.
Therefore, there is an urgent need in the art to find methylation markers that can aid in the pathological diagnosis of thyroid follicular lesions, and at the same time can be used to predict the malignancy and malignancy of junctional lesions.
Disclosure of Invention
The invention aims to provide a group of methylation markers related to benign and malignant thyroid tumors and application thereof in preparing tools for differential diagnosis and prediction of benign and malignant thyroid tumors.
The above object of the present invention is achieved by the following technical solutions:
the invention carries out methylation detection on paraffin samples of 128 cases (comprising 46 cases of FTA,36 cases of TT-UMP and 46 cases of FTC) thyroid follicular tumors by a bisulfite sequencing technology (RRBS). Firstly, the inventor finds 70 thyroid follicular tumor related methylation markers in a training set (consisting of 33 FTAs and 33 FTCs), then constructs an evaluation model of malignant thyroid cancer potential according to the 70 markers, and verifies the evaluation model of malignant thyroid cancer potential constructed on the basis of the 70 markers in a test set (consisting of 13 FTAs and 13 FTCs). Further, the inventors utilized an evaluation model constructed based on 70 markers for predicting benign and malignant of the borderline thyroid follicular tumor.
First, the present invention provides a set of methylation markers for differentially diagnosing and/or predicting benign and malignant thyroid follicular tumors, said methylation markers comprising a combination of methylation of genomic regions of TBX intergenic region, HX intron, MYOG exon, MYOG' UTR, HMX promoter, KRT intergenic region, GPC promoter, FOXA intergenic region, FOXA exon region, SIX promoter, GSE intergenic region, WNK promoter, xb hopromoter, GATA-AS promoter, CB N promoter, POU3F promoter, KIAA2012 intron, 0RRTM promoter, FAM95 intergenic region, SIM intron, PITX intron, CRIPAK intergenic region, RBM promoter, TMEM174 intergenic region, FRMD intergenic region, MY 110 intergenic region, MIR153-2 intron promoter, 2INC 89 promoter, HOXA 0061 promoter, HOXA 3 promoter, sax-arg promoter, sax-t exon region, sax promoter, sax-promoter, sax-887 exon promoter, sax 8851, sax exon regions, sax promoter.
In some of these embodiments, the methylation marker comprises 70 methylation haplotype regions (MHB), the 70 methylation haplotype regions being shown in table 2.
In some of these embodiments, the thyroid follicular tumor comprises thyroid adenoma, thyroid follicular cancer, and thyroid follicular tumor of undetermined malignant potential.
The second aspect of the invention provides an application of the methylation marker in preparing a differential diagnosis tool for benign and malignant thyroid follicular tumors.
In some embodiments, the methylation marker is used for preparing a differential diagnosis tool for benign and malignant thyroid follicular tumors by constructing an evaluation model of malignant thyroid cancer potential.
The third aspect of the invention provides the application of the methylation marker in preparing a tool for evaluating the malignant potential of the thyroid follicular tumor with uncertain malignant potential.
In some embodiments, the methylation marker is used for preparing a tool for evaluating the malignant potential of the thyroid follicular tumor with uncertain malignant potential by constructing an evaluation model of the malignant thyroid cancer potential.
In some of these embodiments, the tools may include reagents, kits, and systems.
In some of these embodiments, the kit comprises reagents for detecting the methylation level of the methylation marker in a sample from a subject.
In some of these embodiments, the reagents are based on reagents used using pyrosequencing, bisulfite conversion sequencing, methylation chip methods, qPCR methods, digital PCR methods, second generation sequencing methods, third generation sequencing methods, whole genome methylation sequencing methods, DNA enrichment assays, simplified bisulfite sequencing techniques, HP L C methods, MassArray, methylation specific PCR, or combinations thereof.
Preferably, the reagents are those employed to simplify bisulfite sequencing techniques.
In the present invention, the system comprises a thyroid follicular tumor benign and malignant diagnosis and evaluation system.
In some of these embodiments, the diagnostic or assessment system comprises a substance that detects the methylation level of the methylation marker in table 2; the diagnosis or evaluation system comprises a data processing device, wherein a diagnosis or evaluation module is arranged in the data processing device, and the diagnosis or evaluation module judges the benign and malignant risk of the thyroid follicular tumor of the subject according to the methylation level of the marker in the sample of the subject.
In some embodiments, the diagnosis or evaluation module obtains the methylation levels corresponding to the 70 methylation markers in the thyroid tumor sample to be tested, forms a sample-marker numerical matrix, obtains a score by inputting an evaluation model of malignant thyroid cancer potential, and identifies benign malignancy of the thyroid tumor or evaluates malignancy potential of the thyroid tumor with uncertain malignancy potential according to the score.
Specifically, the diagnosis module obtains methylation levels corresponding to the 70 methylation markers in the thyroid tumor sample to be detected to form a sample-marker numerical matrix, obtains the probability that each sample is malignant tumor by inputting an evaluation model of malignant thyroid cancer potential, and judges that the model is malignant tumor FTC when the probability of methylation prediction malignant tumor is greater than 0.5, otherwise, the model is benign tumor FTA.
Specifically, the evaluation module detects TT-UMP samples through an RRBS sequencing technology and calculates methylation values corresponding to the 70 markers to form a sample-marker numerical matrix, inputs an evaluation model of malignant thyroid cancer potential to obtain a malignant potential score, and can divide all UMP samples into three categories (three risk levels) according to the methylation score result: 1-low risk, methylation score 0-0.4; 2-intermediate risk, methylation score 0.4-0.6; 3-high risk, methylation score 0.6-1.
In a fourth aspect of the invention, a kit for the differential diagnosis of benign and malignant thyroid follicular tumor is provided, the kit comprising reagents for detecting the methylation level of a marker in a sample of a subject, wherein the marker is a methylation marker shown in table 2.
In some of these embodiments, the subject sample comprises a tissue sample of a thyroid follicular tumor.
In some of these embodiments, the reagents are based on reagents used using pyrosequencing, bisulfite conversion sequencing, methylation chip methods, qPCR methods, digital PCR methods, second generation sequencing methods, third generation sequencing methods, whole genome methylation sequencing methods, DNA enrichment assays, simplified bisulfite sequencing techniques, HP L C methods, MassArray, methylation specific PCR, or combinations thereof.
In some of these embodiments, the reagents are reagents employed to simplify bisulfite sequencing techniques.
In some embodiments, the reagents include enzymatic cleavage reagents, reagents required for methylated linker ligation, bisulfite conversion treatment reagents, primers, and primer amplification reagents, among others.
The cleavage reagent includes methylation-sensitive or non-sensitive restriction enzymes, preferably, MSP I, HaeIII, BanII, HpyCH4V, AluI, SphI and BssSI restriction enzymes. Reagents required for ligation of the methylated linker include buffers, DNA ligase, and ATP. The reagents required by the primer amplification comprise an amplification buffer solution, 4 dNTPs, a primer and DNA polymerase. Both the primers and the reagents are commercially available from commercial companies.
Further, the present invention also provides a kit for predicting the malignant potential of a thyroid follicular tumor with undetermined malignant potential, the kit comprising reagents for detecting the methylation level of a marker in a sample of a subject, the marker being a methylation marker as shown in table 2.
The fifth aspect of the invention provides application of the methylation marker in constructing an evaluation model of malignant thyroid cancer potential.
In some embodiments, the model for assessing the potential of malignant thyroid cancer can be used to identify benign and malignant thyroid tumors, and can also be used to assess the malignant potential of thyroid follicular tumors with uncertain malignant potential.
The invention discloses application of an evaluation model constructed based on the methylation marker in preparation of products for judging benign and malignant thyroid follicular tumor.
Advantageous effects
According to the invention, paraffin tissue specimens of 128 thyroid follicular lesions are used for finding 70 thyroid follicular tumor related methylation markers, and an evaluation model constructed based on the 70 markers has very high sensitivity and specificity (the specificity is 100%, the sensitivity is 92.3%, the accuracy is 96.2%, and the AUC value is 0.994) when judging whether thyroid follicular tumor is benign or malignant; meanwhile, the evaluation model constructed based on the 70 markers can predict the benign and malignant degree of the junctional thyroid follicular tumor, guide clinical doctors to make a correct diagnosis and treatment strategy and avoid excessive diagnosis and treatment. The inventor further utilizes the biomarkers to develop tools for identifying and diagnosing benign and malignant thyroid follicular tumors and tools (reagents, kits and diagnosis or evaluation systems) for evaluating the malignant potential of thyroid follicular tumors with uncertain malignant potential, so that the tools provide reference values for diagnosing or evaluating the benign and malignant thyroid follicular tumors.
Drawings
FIG. 1: the ROC curve shows the accuracy and specificity of the methylation index in the verification set;
FIG. 2: 36 UMP samples were compared for gene mutation and methylation scores in different risk groups.
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art, and the materials and devices used in the present invention are commercially available unless otherwise specified.
The present invention provides methylation detection of paraffin specimens from 128 (including 46 FTA,36 TT-UMP and 46 FTC) thyroid follicular tumors by bisulfite sequencing technology (RRBS). Firstly, the inventor finds 70 thyroid follicular tumor related methylation markers in a training set (consisting of 33 FTA and 33 FTC), and utilizes 70 differential methylation markers to construct an evaluation model of malignant thyroid cancer potential; the constructed methylation marker model is used for identifying the benign and malignant of 26 thyroid follicular tumor samples (pathological detection results of 13 FTA and 13 FTC), and the high sensitivity and specificity of the methylation marker-based model for thyroid benign and malignant differential diagnosis are further verified. Finally, the inventors used an evaluation model constructed by methylation markers to predict benign and malignant status of 36 borderline (malignant potential indeterminate) thyroid follicular tumors, and found that no malignant mutation was detected in any of 7 low-risk samples (0%), no malignant mutation was detected in 5 of 14 cases (35.7%), and no mutation was detected in 11 of 15 high-risk samples (73.3%). The mutation sample proportion of the high-risk group is obviously higher than that of the low-risk group (p is 0.004) and is obviously higher than that of the medium-risk group, which indicates that thyroid tumors belong to the high-risk group, the probability of carrying gene mutation is higher, the malignant potential of the thyroid tumors is obviously higher than that of the medium-risk group and the low-risk group, and patients in the group should be closely followed to avoid developing malignant tumors; and the possibility of carrying gene mutation by patients in a low-risk group is low, and follow-up diagnosis can be carried out to avoid over-treatment.
Example 1: methylation marker screening for benign and malignant thyroid tumors
The inventor collects paraffin wax samples of thyroid follicular tumor diagnosed by Beijing coordination hospital pathology department from 2010 to 2016, and extracts DNA (including 46 FTA,36 TT-UMP and 46 FTC) in 128 cases, and the DNA is purified by magnetic beads (Backman Ampure Xp beads); methylation detection analysis was performed by bisulfite sequencing technique (RRBS), first looking for differentially methylated molecules in a training set (consisting of 33 FTAs and 33 FTCs).
Methylation detection and analysis are completed by Shanghai Kun Biotech Co., Ltd. 100ng of purified sample DNA is subjected to enzyme digestion reaction, the reaction system is shown in Table 1, the reaction temperature is as follows, 37 ℃ for 3 hours, 80 ℃ for 20 minutes, and 4 ℃ for storage, wherein 37 ℃ is the enzyme digestion reaction temperature, 80 ℃ is the enzyme inactivation temperature, the mixed enzyme comprises 7 restriction enzymes, namely MSP I (NEB, R0106L), HaeIII (NEB, R0108L), BanII (Thermo Scientific)TM，ER0281)、HpyCH4V(NEB，R0620L)、AluI(Thermo ScientificTMER0011), SphI (NEB, R0182L) and BssSI (Thermo Scientific)TMER 1841); connecting the enzyme digestion products by a methylation joint, and purifying the connection products by magnetic beads; performing bisulfite conversion treatment using EZ DNA Methylation-Gold Kit from ZYMOresearch; carrying out index primer amplification and carrying out magnetic bead purification on an amplification product; quality control of the library: determining the concentration of the library by using a method of Qubit and qPCR, and analyzing the size of the fragment of the library by using a labHip fragment analyzer of PerkinElmer company; sequencing: sequencing was performed with the Illumina platform instrument HiseqX Ten.
TABLE 1 digestion reaction System
| Reaction mixture | Volume/microliter |
| 10x reaction buffer | 4.0 |
| Mixed restriction enzymes | 1.0 |
| 1.2 pg/. mu. L non-methylated lambda DNA | 0.8 |
| DNA + nuclease-free Water | 34.2 |
| Total of | 40 |
The present invention follows (Guo S, et al. (2017) Identification of Methylation signatures of Methylation domains and of Methylation-of-origin mapping from genomic DNA. Nat Genet 49(4):635-642.) the method of screening MHB (the region with a minimum linkage imbalance of two adjacent CpG sites r2 greater than 0.5) as candidate Methylation markers for FA and FTC samples based on a plurality of DNA Methylation databases (ENCOD, TCGA). The candidate MHB (the region with a minimum linkage imbalance of two adjacent CpG sites r2 greater than 0.5) is selected as a candidate Methylation marker for FA and FTC samples. not less than 25,000 MHB samples are detected for downstream analysis, and candidate MHB (S) are selected from among not less than 90% of the samples for which PDR (Methylation markers) or UTR (Methylation markers) UTR L (MH L) or MH 83 (the Methylation markers are selected as candidate markers of MH 3. M3. M. A. M. A.
TABLE 270 methylation markers and their specific positions
Example 2 construction of an evaluation model of malignant thyroid cancer potential Using 70 differentially methylated markers
After quality control analysis of the sequencing result of each sample in example 1, sequences of 70 marker segments were selected and calculated according to the methylation value calculation method corresponding to each marker (the methylation value calculation method refers to GuoS, et al (2017) Identification of methylation signature blocks available and restriction of methylation groups and tumor tissue-of-alignment from plasmid DNA. nat Genet 49 (635): 635 and 642.) if there is a CpG site depth within a segment below 10 ×, the methylation value of the segment is NA. to form a sample-marker numerical matrix, and some exemplary data are shown in Table 3.
TABLE 310 marker methylation value matrix for thyroid tumor samples
……
Constructing a model:
the methylation level matrices (markers) of 33 FTAs and 33 FTCs used in the model construction above, and the classification information of 66 samples was stored as a first column with sample names and a second column with a matrix (pheno) of classification information (FTC name p.ftc, FTA name n.fta).
Opening the R program package, and importing the classification information matrix and the corresponding methylation level matrix of xx samples
Delay ("storage path of classification information matrix", sep ═ T ", as.is ═ T, head ═ T, check.names ═ FA L SE)
markers ═ ead.delim ("storage path of methylation level matrix", sep ═ T ", as.is ═ T, head ═ T, check.names ═ FA L SE)
Converting the methylation level matrix into a matrix with the row name as the sample name and the column name as the name of the methylation marker
imput＝t(markers)
Complement NA value by proximity value complement method
library(DMwR)
imputed＝knnImputation(imput)
Setting modeling parameters
library(caret,quietly＝T)
ctrl<-trainControl(method＝"repeatedcv",savePredictions＝T,classProbs＝T,number＝3,repeats＝10,allowParallel＝TRUE)
Construction of random forest model
mod_rf<-train(imputed,pheno,method＝'rf',trControl＝ctrl)
Example 3 methylation markers for benign and malignant identification of thyroid tumors
In this example, 26 samples of thyroid follicular tumor were identified as benign or malignant using the model constructed in example 2. The process is as follows:
a sample-marker numerical matrix was constructed similar to that of table 3 for 26 samples, according to the method of example 2.
Opening the R program package, and importing 26 samples to be evaluated-a numerical matrix of markers
Delaim ("storage path of sample-marker matrix", sep ═ T ", as.is ═ T, row.names ═ 1, head ═ T, check.names ═ F)
Converting the matrix into a matrix with the row name as the sample name and the column name as the name of the methylation marker
imput＝t(valdata)
And supplement the NA value by the approach value supplement method
library(DMwR)
imputed＝knnImputation(imput)
Then, the established evaluation model (RFmodelFTC) for the malignant thyroid cancer potential is introduced
model readRDS (RFmodelFTC storage path)
Model evaluation was then initiated to determine whether each sample was more likely to be malignant or benign
library(randomForest)
class＝predict(model,imputed,type＝"response")
And calculating the probability that each sample is malignant tumor
probs＝predict(model,imputed,type＝"prob")
When the probability of malignant tumor predicted by methylation is more than 0.5, the model judges that the malignant tumor is FTC, otherwise, the malignant tumor is FTA. Comparing the final judgment results with the pathological test results (13 cases of FTA and 13 cases of FTC), 26 cases of samples were found, only one of the methylation marker models was inconsistent with the pathological diagnosis (see Table 4), the evaluation sensitivity was 92.3%, the specificity was 100%, the accuracy was 96.2%, and the AUC under the ROC curve was 0.994 (FIG. 1). The result shows that the methylation marker has high consistency on the judgment result of the benign and malignant tumors and the clinical pathological diagnosis result.
TABLE 426 identification of benign and malignant patients with thyroid tumors
Example 4 methylation markers for the assessment of TT-UMP malignancy potential
The method explores whether the malignant thyroid cancer potential evaluation model constructed based on 70 methylation markers can be used for prediction of TT-UMP malignant potential. A sample-marker numerical matrix was constructed similar to the UMP sample of table 3, according to the method of example 2.
Opening the R-program package, importing the methylation marker numerical matrix of the UMP sample
Delaim ("storage path of sample-marker matrix", sep ═ T ", as.is ═ T, row.names ═ 1, head ═ T, check.names ═ F)
Converting the matrix into a matrix with the row name as the sample name and the column name as the name of the methylation marker
imput＝t(valdata)
And supplement the NA value by the approach value supplement method
library(DMwR)
imputed＝knnImputation(imput)
Then, the established evaluation model (RFmodelFTC) for the malignant thyroid cancer potential is introduced
model readRDS (RFmodelFTC storage path)
Scoring for malignancy potential:
probs＝predict(model,imputed,type＝"prob")
based on methylation scoring results, all UMP samples can be classified into three categories (three risk classes): 1-low risk, methylation score 0-0.4; 2-intermediate risk, methylation score 0.4-0.6; 3-high risk, methylation score 0.6-1. The methylation scores were compared with the results of known detection of genetic mutations associated with thyroid carcinogenesis (Table 5), and no malignant mutation was detected in any of 7 low-risk samples (0%), 5 of 14 at-risk samples (35.7%), and 11 of 15 high-risk samples (73.3%). The high risk group had a significantly higher proportion of mutant samples than the low risk group (p 0.004) and significantly higher than the medium risk group (fig. 2). Indicating that thyroid tumors belonging to the high risk group have a significantly higher malignant potential than the low risk group and the medium risk group, and patients in this group should be closely followed up to avoid developing malignant tumors; and the possibility of carrying gene mutation by patients in a low-risk group is low, and follow-up diagnosis can be carried out to avoid over-treatment.
TABLE 5 results of methylation scoring and Gene mutation detection
Among them, TP53, PIK3CA, TSHR, NRAS, HRAS, GNAS, PPARG-PAX8 and KRAS are mutated genes related to thyroid carcinogenesis in the prior art.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (5)
1. A set of methylation markers for use in differentially diagnosing and/or predicting benign and malignant thyroid follicular tumors, wherein the methylation markers comprise a combination of methylation of genomic regions of TBX intergenic region, HX intron, MYOG exon, MYOG3' UTR, HMX promoter, KRT intergenic region, GPC promoter, FOXA intergenic region, FOXA exon region, SIX promoter, GSE intergenic region, WNK promoter, xb hoa-AS promoter, CB N promoter, POU3F promoter, KIAA2012 intron, 0RRTM promoter, FAM95 intergenic region, SIM intron, PITX intron, CRIPAK intergenic region, RBM promoter, TMEM174 intergenic region, FRMD intergenic region, MY 110 intergenic region, MIR153-2 intron, 2 c 89 promoter, HOXA 0061, RBM promoter, TMEM174 intergenic region, samd promoter, sax-8851 exon region, sax promoter, sax 8851 exon promoter, sax 8853 promoter, sax promoter, sa.
2. The methylation marker of claim 1, wherein the methylation marker comprises 70 regions of methylation haplotypes, and the 70 regions of methylation haplotypes are shown in Table 2.
3. The methylation marker according to claim 1 or 2, wherein the follicular thyroid tumor comprises thyroid adenoma, follicular thyroid carcinoma, and follicular thyroid tumor of indeterminate potential for malignancy.
4. Use of the methylation marker of claim 1 or 2 for the preparation of a tool for differential diagnosis of benign and malignant thyroid follicular tumors.
5. Use of the methylation marker of claim 1 or 2 for the preparation of a tool for assessing the malignant potential of a follicular thyroid tumor of indefinite malignant potential.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010311494.0A CN111424091B (en) | 2020-04-20 | 2020-04-20 | Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010311494.0A CN111424091B (en) | 2020-04-20 | 2020-04-20 | Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111424091A true CN111424091A (en) | 2020-07-17 |
| CN111424091B CN111424091B (en) | 2022-02-08 |
Family
ID=71554846
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010311494.0A Active CN111424091B (en) | 2020-04-20 | 2020-04-20 | Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111424091B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113178264A (en) * | 2021-05-04 | 2021-07-27 | 温州医科大学附属第一医院 | Deep muscle layer infiltration data prediction method and system |
| CN113322320A (en) * | 2021-05-27 | 2021-08-31 | 杭州医学院 | Long non-coding RNA and application thereof in diagnosis and treatment of glioma |
| CN115112745A (en) * | 2022-07-19 | 2022-09-27 | 中国医学科学院北京协和医院 | Marker for identifying and diagnosing thyroid follicular tumor and application thereof |
| CN115128285A (en) * | 2022-08-30 | 2022-09-30 | 西湖大学 | Kit and system for identifying and evaluating thyroid follicular tumor by protein combination |
| CN117487914A (en) * | 2023-10-27 | 2024-02-02 | 广东药科大学 | Application of targeting ZC3H18/PD-L1 signal axis in tumor immune escape detection, treatment and prognosis |
| WO2024074110A1 (en) * | 2022-10-04 | 2024-04-11 | 中国医学科学院药物研究所 | Keratin yk93-5, preparation method, and pharmaceutical composition and use thereof |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180305689A1 (en) * | 2015-04-22 | 2018-10-25 | Mina Therapeutics Limited | Sarna compositions and methods of use |
| WO2019140380A1 (en) * | 2018-01-12 | 2019-07-18 | Kymera Therapeutics, Inc. | Protein degraders and uses thereof |
| CN110257525A (en) * | 2019-08-05 | 2019-09-20 | 上海奕谱生物科技有限公司 | There is the marker and application thereof of conspicuousness to diagnosing tumor |
| CN110283907A (en) * | 2019-05-31 | 2019-09-27 | 江苏大学 | The specific gene marker object of thyroid malignancy and its application |
| CN110499364A (en) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | A kind of probe groups and its kit and application for detecting the full exon of extended pattern hereditary disease |
| US20200010468A1 (en) * | 2018-07-06 | 2020-01-09 | Kymera Therapeutics, Inc. | Tricyclic crbn ligands and uses thereof |
| CN110982907A (en) * | 2020-02-27 | 2020-04-10 | 上海鹍远生物技术有限公司 | Thyroid nodule-related rDNA methylation marker and application thereof |
-
2020
- 2020-04-20 CN CN202010311494.0A patent/CN111424091B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180305689A1 (en) * | 2015-04-22 | 2018-10-25 | Mina Therapeutics Limited | Sarna compositions and methods of use |
| WO2019140380A1 (en) * | 2018-01-12 | 2019-07-18 | Kymera Therapeutics, Inc. | Protein degraders and uses thereof |
| US20200010468A1 (en) * | 2018-07-06 | 2020-01-09 | Kymera Therapeutics, Inc. | Tricyclic crbn ligands and uses thereof |
| CN110283907A (en) * | 2019-05-31 | 2019-09-27 | 江苏大学 | The specific gene marker object of thyroid malignancy and its application |
| CN110499364A (en) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | A kind of probe groups and its kit and application for detecting the full exon of extended pattern hereditary disease |
| CN110257525A (en) * | 2019-08-05 | 2019-09-20 | 上海奕谱生物科技有限公司 | There is the marker and application thereof of conspicuousness to diagnosing tumor |
| CN110982907A (en) * | 2020-02-27 | 2020-04-10 | 上海鹍远生物技术有限公司 | Thyroid nodule-related rDNA methylation marker and application thereof |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113178264A (en) * | 2021-05-04 | 2021-07-27 | 温州医科大学附属第一医院 | Deep muscle layer infiltration data prediction method and system |
| CN113322320A (en) * | 2021-05-27 | 2021-08-31 | 杭州医学院 | Long non-coding RNA and application thereof in diagnosis and treatment of glioma |
| CN115112745A (en) * | 2022-07-19 | 2022-09-27 | 中国医学科学院北京协和医院 | Marker for identifying and diagnosing thyroid follicular tumor and application thereof |
| CN115128285A (en) * | 2022-08-30 | 2022-09-30 | 西湖大学 | Kit and system for identifying and evaluating thyroid follicular tumor by protein combination |
| CN115128285B (en) * | 2022-08-30 | 2023-01-06 | 西湖大学 | Kit and system for identifying and evaluating thyroid follicular tumor by protein combination |
| WO2024074110A1 (en) * | 2022-10-04 | 2024-04-11 | 中国医学科学院药物研究所 | Keratin yk93-5, preparation method, and pharmaceutical composition and use thereof |
| CN117487914A (en) * | 2023-10-27 | 2024-02-02 | 广东药科大学 | Application of targeting ZC3H18/PD-L1 signal axis in tumor immune escape detection, treatment and prognosis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111424091B (en) | 2022-02-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111424091B (en) | Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof | |
| CN110536967B (en) | Reagents and methods for analyzing associated nucleic acids | |
| US11549148B2 (en) | Neuroendocrine tumors | |
| Polzer et al. | Molecular profiling of single circulating tumor cells with diagnostic intention | |
| JP6161607B2 (en) | How to determine the presence or absence of different aneuploidies in a sample | |
| JP2020174678A (en) | Processes and compositions for methylation-based enrichment of fetal nucleic acid from maternal sample useful for non invasive prenatal diagnoses | |
| US11396678B2 (en) | Breast and ovarian cancer methylation markers and uses thereof | |
| Bhagwate et al. | Bioinformatics and DNA-extraction strategies to reliably detect genetic variants from FFPE breast tissue samples | |
| US20190300965A1 (en) | Liver cancer methylation markers and uses thereof | |
| US20210238695A1 (en) | Methods of mast cell tumor prognosis and uses thereof | |
| JP2015503923A (en) | Methods and biomarkers for the analysis of colorectal cancer | |
| Lee et al. | Clinical utility of a fully automated microsatellite instability test with minimal hands-on time | |
| JP2024020392A (en) | Composition for diagnosing liver cancer by using cpg methylation changes in specific genes, and use thereof | |
| CN103403187A (en) | Prognostic signature for colorectal cancer recurrence | |
| Talebi et al. | Fusion transcript discovery using RNA sequencing in formalin-fixed paraffin-embedded specimen | |
| EP1650311A1 (en) | Compounds and methods for assessment of Microsatellite Instability (MSI) status | |
| US20220162710A1 (en) | Composition for diagnosis or prognosis prediction of glioma, and method for providing information related thereto | |
| CN110656168A (en) | COPD early diagnosis marker and application thereof | |
| CN116121390A (en) | Marker for prognosis of cancer and suitability for immunotherapy and application thereof | |
| Wilmott et al. | Tumour procurement, DNA extraction, coverage analysis and optimisation of mutation-detection algorithms for human melanoma genomes | |
| Rasmussen et al. | Mismatch repair deficiency testing in Lynch syndrome-associated urothelial tumors | |
| Turner et al. | The basics of commonly used molecular techniques for diagnosis, and application of molecular testing in cytology | |
| Kašubová et al. | Next Generation Sequencing in Molecular Diagnosis of Lynch Syndrome–a Pilot Study Using New Stratification Criteria | |
| KR102036222B1 (en) | Markers for prognosing a patient with chronic kidney disease patients caused by diabetic nephropathy | |
| Yang et al. | A new strategy for the discrimination of mitochondrial DNA haplogroups in Han population |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |