CN115491411A

CN115491411A - Methylation marker for identifying pancreatitis and pancreatic cancer and application thereof

Info

Publication number: CN115491411A
Application number: CN202110680924.0A
Authority: CN
Inventors: 马成城; 苏志熙; 何其晔; 徐敏杰; 谢可辉; 杨世方; 马建华; 刘琪; 刘蕊
Original assignee: Shanghai Fuyuan Biotechnology Co ltd
Current assignee: Shanghai Fuyuan Biotechnology Co ltd
Priority date: 2021-06-18
Filing date: 2021-06-18
Publication date: 2022-12-20

Abstract

The invention relates to a methylation marker for distinguishing pancreatitis from pancreatic cancer and application thereof. Specifically, the invention provides a methylation marker for non-invasively distinguishing pancreatic cancer and chronic pancreatitis in an early stage and application of a detection reagent thereof in preparing a kit for distinguishing pancreatic cancer and chronic pancreatitis. The inventor identifies the marker for distinguishing pancreatic cancer and chronic pancreatitis samples based on the analysis of DNA methylation sequencing data of plasma samples of patients with malignant pancreatic cancer and patients with chronic pancreatitis. The detection process is noninvasive and high in safety.

Description

Methylation marker for identifying pancreatitis and pancreatic cancer and application thereof

Technical Field

The invention relates to a related methylation marker for identifying pancreatitis and pancreatic cancer and application thereof, which are used for identifying and distinguishing patients with pancreatic cancer and pancreatitis, and belong to the technical field of molecular biomedicine.

Background

Pancreatic cancer (e.g., pancreatic ductal adenocarcinoma) is one of the most fatal diseases in the world. The 5-year relative survival rate is 9%, and for patients with distant metastases, this rate is further reduced to only 3%. One of the major reasons for the high mortality rate is that the methods for early detection of pancreatic cancer remain limited, which is critical for pancreatic cancer patients undergoing surgical resection. Currently, carbohydrate antigen 19-9 (CA 19-9) is the most common clinical serum biomarker for the adjuvant detection of pancreatic cancer, and can achieve 79-90% sensitivity and 75-90% specificity on patients with pre-resection symptoms. However, several large population studies have demonstrated that CA19-9 is ineffective in detecting pancreatic cancer in the asymptomatic population because of its low positive predictive value, essentially precluding its use for early screening of pancreatic cancer (Kim et al, 2004) (Chang et al, 2006 homma &tsuchiya,1991, kim et al, 2004, satake, takeuchi, homma, & Ozaki, 1994. Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is another common method for obtaining pathological diagnosis without open surgery, but it is invasive and requires clear imaging evidence, which usually means that pancreatic cancer has progressed. During tumorigenesis and progression, the DNA methylation patterns and levels of genomic DNA of malignant cells have profoundly changed. Some tumor-specific DNA methylation has been shown to occur early in tumorigenesis and may be a "driver" of tumorigenesis.

The typical early symptoms of pancreatic cancer, including abdominal and back pain, diarrhea, weight loss, and jaundice, are not specific and may be associated with other gastrointestinal disorders. This complication is particularly common in the diagnosis of chronic pancreatitis, particularly with patients with chronic pancreatitis who are at significantly higher risk of developing pancreatic cancer for a long period of time. Therefore, screening of pancreatic cancer patients among chronic pancreatitis patients requires accurate differential diagnosis of pancreatic cancer from chronic pancreatitis. However, the accuracy of differential diagnosis between chronic pancreatitis and pancreatic cancer is currently 65% or less, and there are many places where improvement is needed.

Circulating tumor DNA (ctDNA) molecules, derived from apoptotic or necrotic tumor cells, carry tumor-specific DNA methylation markers from early malignant tumors, and have recently been investigated as promising new targets for developing noninvasive early screening tools for a variety of cancers. However, most of these studies have not achieved effective results. It has been shown that the ratio of ctDNA in plasma DNA of early-stage tumor patients is very small, and chronic inflammation affects the accuracy of DNA methylation markers (Abbosh et al, 2017), so identifying stable and consistent specific markers for identifying chronic pancreatitis and pancreatic cancer from plasma DNA is very challenging.

Disclosure of Invention

The invention provides a method for detecting DNA methylation of a plasma sample of a patient, and aims to realize noninvasive accurate diagnosis of pancreatic cancer with higher accuracy and lower cost by analyzing and distinguishing pancreatic cancer patients and pancreatitis patients by using differential methylation level data of detection results.

Specifically, the present invention provides in a first aspect an isolated nucleic acid molecule from a mammal, which nucleic acid molecule is a methylation marker associated with the differentiation of pancreatic cancer from pancreatitis, the sequence of which nucleic acid molecule comprises (1) one or more or all of the following sequences or variants thereof having at least 70% identity thereto: 1, 2, 3, wherein the methylation site in the variant is not mutated, (2) the complementary sequence of (1), and (3) the treated sequence of (1) or (2), wherein the treatment converts unmethylated cytosine to a base having a lower binding capacity for guanine than cytosine.

In one or more embodiments, the methylation sites are contiguous cpgs.

In one or more embodiments, the methylation marker can be any one or more CpG sites in the sequence region.

In one or more embodiments, the nucleic acid molecule is used as an internal standard or control for detecting the level of DNA methylation of the corresponding sequence in a sample.

In one or more embodiments, the pancreatic cancer is pancreatic ductal adenocarcinoma.

In one or more embodiments, the pancreatitis is chronic pancreatitis.

In a second aspect, the invention provides a reagent for detecting DNA methylation, the reagent comprising a reagent for detecting the methylation level of a DNA sequence or fragment thereof, or the methylation state or level of one or more CpG dinucleotides in the DNA sequence or fragment thereof, in a sample from a subject, the DNA sequence being selected from one or more (e.g. at least 2) or all of the following gene sequences, or sequences within 20kb upstream or downstream thereof: SIX3, TLX2, CILP2.

In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

In one or more embodiments, the fragment is 1-1000bp in length, preferably 1-700bp in length.

In one or more embodiments, the fragment comprises at least one CpG dinucleotide.

In one or more embodiments, the DNA sequence is selected from one or more (e.g., at least 2) or all of the following or a complement thereof: 1, 2, 3, or a variant having at least 70% identity thereto, wherein the methylation site is not mutated.

In one or more embodiments, the agent is a primer molecule that hybridizes to the DNA sequence or fragment thereof. The primer molecule can amplify the DNA sequence or a fragment thereof. In one or more embodiments, the primer sequence is methylation specific or non-specific. The primer molecule is at least 9bp.

In one or more embodiments, the agent is a probe molecule that hybridizes to the DNA sequence or fragment thereof. In one or more embodiments, the probe further comprises a detectable substance. In one or more embodiments, the detectable species is a 5 'fluorescent reporter and a 3' labeled quencher. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM, and VIC. Preferably, the sequence of the probe comprises MGB (Minor groove binder) or LNA (Locked nucleic acid). The probe molecule is at least 12bp.

In one or more embodiments, the agent comprises a nucleic acid molecule as described herein in the first aspect.

In one or more embodiments, the sample is from a mammal, preferably a human.

In a third aspect, the invention provides a medium bearing DNA sequences or fragments thereof and/or methylation information thereof, wherein the DNA sequences are (i) sequences selected from one or more (e.g. at least 2) or all of the following gene sequences, or up to 20kb upstream or downstream thereof: a treated sequence of SIX3, TLX2, CILP2, or (ii) (i) that converts unmethylated cytosines to bases that have less ability to bind guanine than cytosine.

In one or more embodiments, the DNA sequence is (i) a gene sequence selected from any one of the following groups, or a sequence within 20kb upstream or downstream thereof: (1) SIX3, TLX2; (2) SIX3, CILP2; (3) TLX2, CILP2; (4) A treated sequence of SIX3, TLX2, CILP2, or (ii) (i) that converts unmethylated cytosines to bases that have less ability to bind guanine than cytosine.

In one or more embodiments, the medium is used to align with gene methylation sequencing data to determine the presence, amount, and/or level of methylation of a nucleic acid molecule comprising the sequence or fragment.

In one or more embodiments, the methylation information includes information related to cytosines in the sequence of the nucleic acid molecule that are likely to be methylated. Preferably, the cytosine that is likely to be methylated is a C in CpG. In one or more embodiments, the methylation information is the location of a methylation site (e.g., a CpG dinucleotide) of the nucleic acid molecule.

In one or more embodiments, the medium is a support, including cards, such as paper, plastic, metal, glass cards, printed with the DNA sequence or fragment thereof and/or methylation information thereof.

In one or more embodiments, the medium is a computer readable medium storing the sequence and/or its methylation information and a computer program which, when executed by a processor, performs the steps of: comparing methylation sequencing data of the sample to the sequence, thereby obtaining the presence, amount and/or level of methylation of nucleic acid molecules comprising the sequence in the sample. The presence, amount and/or methylation level of nucleic acid molecules comprising said sequences are used to discriminate pancreatic cancer from pancreatitis.

In a further aspect the invention also provides the use of (a) and/or (b) in the preparation of a kit for the differentiation of pancreatic cancer from pancreatitis,

(a) An agent or device for determining the methylation level of a DNA sequence or fragment thereof or the methylation status or level of one or more CpG dinucleotides in said DNA sequence or fragment thereof in a sample of a subject,

(b) A treated nucleic acid molecule of said DNA sequence or fragment thereof, said treatment converting unmethylated cytosine to a base having a lower binding capacity for guanine than cytosine,

wherein the DNA sequence is selected from one or more (e.g., at least 2) or all of the following gene sequences, or sequences within 20kb upstream or downstream thereof: SIX3, TLX2, CILP2.

In one or more embodiments, the DNA sequence comprises a gene sequence selected from any one of the following groups: (1) SIX3, TLX2; (2) SIX3, CILP2; (3) TLX2, CILP2; and (4) SIX3, TLX2 and CILP2.

In one or more embodiments, the DNA sequence comprises a sequence selected from any one of the following: (1) SEQ ID NO 1, SEQ ID NO 2, (2) SEQ ID NO 1, SEQ ID NO 3, (3) SEQ ID NO 2, SEQ ID NO 3, (4) SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 3.

In one or more embodiments, the nucleic acid molecule is a nucleic acid molecule as described in the first aspect herein.

In one or more embodiments, the reagents comprise primer molecules and/or probe molecules.

In one or more embodiments, the agent comprises a primer molecule that hybridizes to the DNA sequence or fragment thereof. The primer molecule can amplify the DNA sequence or a fragment thereof. In one or more embodiments, the primer sequence is methylation specific or non-specific. The primer molecule is at least 9bp.

In one or more embodiments, the reagent comprises a medium as described in any embodiment herein.

In one or more embodiments, the kit is a non-invasive diagnostic kit.

In one or more embodiments, the kit is an auxiliary diagnostic kit.

In one or more embodiments, the subject is a mammal, preferably a human.

In one or more embodiments, the subject is a subject diagnosed with pancreatitis (e.g., chronic pancreatitis).

In one or more embodiments, the sample is from a tissue, cell, or bodily fluid of a mammal, such as pancreatic tissue or blood, preferably a fine needle biopsy or plasma.

In one or more embodiments, the sample comprises genomic DNA or cfDNA.

In one or more embodiments, the DNA sequence is transformed, wherein unmethylated cytosines are converted to bases that have less ability to bind guanine than cytosines. The conversion is carried out using an enzymatic method, preferably a deaminase treatment, or the conversion is carried out using a non-enzymatic method, preferably a treatment with bisulfite, bisulfite or metabisulfite or a combination thereof.

In one or more embodiments, the DNA sequence is treated with a methylation sensitive restriction endonuclease.

In one or more embodiments, the kit further comprises PCR reaction reagents. Preferably, the PCR reaction reagents include DNA polymerase, PCR buffer, dNTP, mg2+.

In one or more embodiments, the kit further comprises additional reagents for detecting DNA methylation, the additional reagents being reagents used in one or more methods selected from the group consisting of: bisulfite conversion based PCR (e.g., methylation specific PCR), DNA sequencing (e.g., bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation sensitive restriction enzyme analysis, fluorometry, methylation sensitive high resolution melting curve, chip-based methylation profile analysis, mass spectrometry (e.g., flight mass spectrometry). Preferably, the additional agent is selected from one or more of: bisulfite, bisulfite or pyrosulfite or their derivatives, restriction endonucleases sensitive or insensitive to methylation, enzyme digestion buffer, fluorescent dyes, fluorescence quenchers, fluorescence reporters, exonucleases, alkaline phosphatase, internal standards, and controls.

In one or more embodiments, the reaction solution for PCR comprises Taq DNA polymerase, PCR buffer (buffer), dNTPs, KCl, mgCl2, and (NH 4) 2SO4. Preferably, the Taq DNA polymerase is a hot start Taq DNA polymerase. Preferably, the final Mg2+ concentration is 1.0-10.0mM.

In one or more embodiments, the diagnosing comprises: a score is derived, either by comparison with a control sample or by calculation, and pancreatic cancer and pancreatitis are identified by the score. In one or more embodiments, the calculation is calculated by constructing a support vector machine model.

In still another aspect, the present invention provides a method for differentiating pancreatic cancer from pancreatitis, comprising:

(1) Detecting the methylation level of a DNA sequence or a fragment thereof or the methylation state or level of one or more CpG dinucleotides in said DNA sequence or fragment thereof in a sample of a subject, said DNA sequence being selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2,

(2) Comparing with a control sample, or calculating to obtain a score,

(3) Pancreatic cancer and pancreatitis were identified by score.

In one or more embodiments, the method further comprises DNA extraction and/or quality control prior to step (1).

In one or more embodiments, the DNA sequence is selected from one or more or all of the following sequences or their complements: 1, 2, 3, or a variant having at least 70% identity thereto, wherein the methylation site is not mutated.

In one or more embodiments, step (1) comprises performing the detecting using a nucleic acid molecule, primer molecule, probe molecule and/or medium as described herein.

In one or more embodiments, the detection includes, but is not limited to: bisulfite conversion based PCR (e.g., methylation specific PCR), DNA sequencing (e.g., bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation sensitive restriction enzyme analysis, fluorometry, methylation sensitive high resolution melting curve, chip-based methylation profile analysis, mass spectrometry (e.g., flight mass spectrometry).

In one or more embodiments, the detecting is DNA sequencing. In one or more embodiments, the DNA sequencing has a sequencing depth greater than or equal to 5M, preferably 7M,11m,13m, or 15M.

In one or more embodiments, the sample is from a tissue, cell, or bodily fluid of a mammal, such as pancreatic tissue or blood. The mammal is preferably a human. In one or more embodiments, the sample is a fine needle biopsy. In one or more embodiments, the sample is plasma.

In one or more embodiments, the sample comprises genomic DNA or cfDNA.

In one or more embodiments, the DNA sequence is transformed in which unmethylated cytosines are converted to bases that do not bind guanine. The conversion is carried out using an enzymatic method, preferably a deaminase treatment, or the conversion is carried out using a non-enzymatic method, preferably a treatment with bisulfite, bisulfite or metabisulfite or a combination thereof.

In one or more embodiments, the score in step (2) is calculated by constructing a support vector machine model.

In one or more embodiments, step (3) comprises: the methylation level of the subject sample is altered compared to the control sample, and the subject is determined to have pancreatic cancer or pancreatitis based on whether the methylation level reaches a threshold value.

In one or more embodiments, step (3) comprises: determining that the subject has pancreatic cancer or pancreatitis based on whether the score reaches a threshold.

In another aspect, the present invention provides a kit for differentiating pancreatic cancer from pancreatitis, comprising:

(a) Reagents or devices for determining the methylation level of a DNA sequence or fragment thereof or the methylation state or level of one or more CpG dinucleotides in said DNA sequence or fragment thereof in a sample of a subject, and

optionally (b) a treated nucleic acid molecule of said DNA sequence or fragment thereof, said treatment converting unmethylated cytosine to a base having less ability to bind guanine than cytosine,

In one or more embodiments, the kit is suitable for use as described in any of the embodiments herein.

In one or more embodiments, the nucleic acid molecule is a nucleic acid molecule as described herein in the first aspect.

In one or more embodiments, the reagent comprises a primer molecule and/or a probe molecule.

In one or more embodiments, the kit is a non-invasive diagnostic kit.

In one or more embodiments, the subject is a mammal, preferably a human.

In one or more embodiments, the sample is from a tissue, cell, or bodily fluid of a mammal, such as pancreatic tissue or blood. In one or more embodiments, the sample is a fine needle biopsy. In one or more embodiments, the sample is plasma.

In one or more embodiments, the sample comprises genomic DNA or cfDNA.

In one or more embodiments, the DNA sequence is transformed, wherein unmethylated cytosines are converted to bases that have less binding ability to guanine than cytosines. The conversion is carried out using an enzymatic method, preferably a deaminase treatment, or the conversion is carried out using a non-enzymatic method, preferably a treatment with bisulfite, bisulfite or metabisulfite or a combination thereof.

In one or more embodiments, the kit further comprises reagents for detecting DNA methylation, the reagents being used in one or more of the methods selected from the group consisting of: bisulfite conversion based PCR (e.g., methylation specific PCR), DNA sequencing (e.g., bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation sensitive restriction enzyme analysis, fluorometry, methylation sensitive high resolution melting curve, chip-based methylation profile analysis, mass spectrometry (e.g., flight mass spectrometry). Preferably, the agent is selected from one or more of: bisulfite and its derivatives, restriction enzymes sensitive or insensitive to methylation, enzyme digestion buffer, fluorescent dyes, fluorescence quenchers, fluorescent reporters, exonucleases, alkaline phosphatase, internal standards, and controls.

In another aspect, the present invention provides an apparatus for differentiating pancreatic cancer from pancreatitis, the apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of:

(1) Obtaining the methylation level of a DNA sequence or fragment thereof or the methylation status or level of one or more cpgs in said DNA sequence or fragment in a sample from a subject, said DNA sequence being selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2,

(2) Comparing with a control sample, or calculating a score, and

(3) Pancreatic cancer and pancreatitis were identified by score.

In one or more embodiments, step (1) is preceded by a step of obtaining DNA, such as DNA extraction and/or quality control.

In one or more embodiments, step (1) comprises detecting the level of methylation of the sequence in the sample using a nucleic acid molecule, primer molecule, probe molecule and/or medium described herein. In one or more embodiments, the detection includes, but is not limited to: bisulfite conversion based PCR (e.g., methylation specific PCR), DNA sequencing (e.g., bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation sensitive restriction enzyme analysis, fluorometry, methylation sensitive high resolution melting curve, chip-based methylation profile analysis, mass spectrometry (e.g., flight mass spectrometry). In one or more embodiments, the detecting is DNA sequencing. Preferably, the sequencing depth of the DNA sequencing is greater than or equal to 5M, preferably 7M,11m,13m, or 15M.

In one or more embodiments, the sample comprises genomic DNA or cfDNA.

In one or more embodiments, the sequence is transformed, wherein unmethylated cytosines are converted to bases that do not bind guanine. The conversion is carried out using an enzymatic method, preferably a deaminase treatment, or the conversion is carried out using a non-enzymatic method, preferably a treatment with bisulfite, bisulfite or metabisulfite or a combination thereof.

In one or more embodiments, step (3) comprises: the methylation level of the subject sample is altered compared to the control sample, and when the methylation level meets a threshold, the subject is identified as having pancreatic cancer.

In one or more embodiments, step (3) comprises: when the score meets a threshold, the subject is identified as having pancreatic cancer.

Drawings

FIG. 1 is a flow chart of a technical solution according to an embodiment of the present invention.

Fig. 2 is a ROC curve for the pancreatic cancer prediction model to distinguish chronic pancreatitis from pancreatic cancer in the training and test groups.

Fig. 3 shows the distribution of the prediction scores of the pancreatic cancer prediction models in each group.

FIG. 4 is the methylation levels of 3 methylation markers in the training set.

FIG. 5 is the methylation level of 3 methylation markers in the test group.

FIG. 6 is a ROC curve for a pancreatic cancer prediction model diagnosing pancreatic cancer in samples that were judged negative by conventional methods (i.e., CA19-9 measurements were less than 37).

Detailed Description

The invention explores the relationship between DNA methylation and pancreatic cancer and pancreatitis, especially chronic pancreatitis. Aims to improve the accuracy of noninvasive diagnosis of pancreatic cancer by using the DNA methylation level of a marker group as a differential marker of pancreatic cancer and chronic pancreatitis through a noninvasive method.

The inventors found that the differentiation of pancreatic cancer and pancreatitis (e.g., chronic pancreatitis) is associated with methylation levels of 1, 2, 3 genes or sequences within 20kb upstream or downstream thereof selected from the group consisting of: SIX3, TLX2, CILP2. In one or more embodiments, the identification of pancreatic cancer and pancreatitis is associated with a methylation level of a gene selected from any one of the following groups: (1) SIX3, TLX2; (2) SIX3, CILP2; (3) TLX2, CILP2; and (4) SIX3, TLX2 and CILP2. The invention provides a nucleic acid molecule containing one or more CpG of the above gene or its fragment.

Herein, the term "gene" includes both coding and non-coding sequences on the genome of the gene in question. Wherein the non-coding sequence includes introns, promoters and regulatory elements or sequences and the like.

Further, the differentiation of pancreatic cancer and pancreatitis was associated with methylation levels of any 1 segment or random 2 or all 3 segments selected from: SEQ ID NO 1 of the SIX3 gene region, SEQ ID NO 2 of the TLX2 gene region, and SEQ ID NO 3 of the CILP2 gene region.

In certain embodiments, the identification of pancreatic cancer and pancreatitis is associated with a level of methylation of a sequence selected from any one of the following, or a complement thereof: (1) SEQ ID NO:1, SEQ ID NO:2, (2) SEQ ID NO:1, SEQ ID NO:3, (3) SEQ ID NO:2, SEQ ID NO:3, (4) SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3.

The "pancreatic cancer and pancreatitis differentiation-associated sequence" as used herein includes the above-mentioned 3 genes, sequences within 20kb upstream or downstream thereof, the above-mentioned 3 sequences (SEQ ID NOS: 1-3), or their complements.

The positions of the above 3 sequences in the human chromosome are as follows: 1, 45028785-45029307 of SEQ ID No. 1, 74742834-74743351 of SEQ ID NO. Herein, the base number of each sequence and methylation site corresponds to the reference genome HG19.

In one or more embodiments, the nucleic acid molecules described herein are fragments of one or more genes selected from SIX3, TLX2, CILP2; the length of the fragment is 1bp-1kb, preferably 1bp-700bp; the fragments comprise one or more methylation sites in the chromosomal region of the corresponding gene. Methylation sites in the genes described herein or fragments thereof include, but are not limited to: 45028802,45028816,45028832,45028839,45028956,45028961,45028965,45028973,45029004,45029017,45029035,45029046,45029057,45029060,45029063,45029065,45029071,45029106,45029112,45029117,45029128,45029146,45029176,45029179,45029184,45029189,45029192,45029195,45029218,45029226,45029228,45029231,45029235,45029263,45029273,45029285,45029288,45029295,74742838,74742840,74742844,74742855,74742879,74742882,74742891,74742913,74742922,74742925,74742942,74742950,74742953,74742967,74742981,74742984,74742996,74743004,74743006,74743009,74743011,74743015,74743021,74743035,74743056,74743059,74743061,74743064,74743068,74743073,74743082,74743084,74743101,74743108,74743111,74743119,74743121,74743127,74743131,74743137,74743139,74743141,74743146,74743172,74743174,74743182,74743186,74743191,74743195,74743198,74743207,74743231,74743234,74743241,74743243,74743268,74743295,74743301,74743306,74743318,74743321,74743325,74743329,74743333,74743336,74743343,74743346 of chr 2; 19650766,19650791,19650796,19650822,19650837,19650839,19650874,19650882,19650887,19650893,19650895,19650899,19650907,19650917,19650955,19650978,19650981,19650995,19650997,19651001,19651008,19651020,19651028,19651041,19651053,19651059,19651062,19651065,19651071,19651090,19651101,19651109,19651111,19651113,19651121,19651123,19651127,19651133,19651142,19651144,19651151,19651166,19651170,19651173,19651176,19651179,19651183,19651185,19651202,19651204,19651206,19651225,19651227,19651235,19651237,19651243,19651246,19651263,19651267 of chr 19. The base numbers of the methylation sites not mutated correspond to the reference genome HG19.

In one or more embodiments, the nucleic acid molecule is 1bp to 1000bp,1bp to 900bp,1bp to 800bp,1bp to 700bp in length. The nucleic acid molecule length can range between any of the above endpoints.

Herein, methods of detecting DNA Methylation are well known in the art, such as bisulfite conversion based PCR (e.g., methylation-specific PCR (MSP), DNA sequencing, whole genome Methylation sequencing, simplified Methylation sequencing, methylation-sensitive restriction enzyme analysis, fluorometry, methylation-sensitive high resolution melting curve, chip-based Methylation profile analysis, mass spectrometry.

Thus, the present invention relates to a reagent for detecting DNA methylation. Reagents used in the above-described methods for detecting DNA methylation are well known in the art. In detection methods involving DNA amplification, the reagents for detecting DNA methylation include primers. The primer sequences are methylation specific or non-specific. Preferably, the sequence of the primer may include a non-methylation specific blocking sequence (Blocker). Blocking sequences may improve the specificity of methylation detection. The reagent for detecting DNA methylation may further comprise a probe. Typically, the sequence of the probe is labeled at the 5 'end with a fluorescent reporter group and at the 3' end with a quencher group. Illustratively, the sequence of the probe comprises MGB (Minor groove binder) or LNA (Locked nucleic acid). MGB and LNA are used to increase Tm, increase specificity of analysis and increase flexibility of probe design.

As used herein, a "primer" refers to a nucleic acid molecule having a specific nucleotide sequence that directs the synthesis at the initiation of nucleotide polymerization. The primers are typically two oligonucleotide sequences synthesized by man, one primer complementary to one DNA template strand at one end of the target region and the other primer complementary to the other DNA template strand at the other end of the target region, which functions as the initiation point for nucleotide polymerization. The primers are usually at least 9bp. Primers designed artificially in vitro are widely used in Polymerase Chain Reaction (PCR), qPCR, sequencing, probe synthesis, and the like. Typically, primers are designed to amplify products of 1-2000bp, 10-1000bp, 30-900bp, 40-800bp, 50-700bp, or at least 150bp, at least 140bp, at least 130bp, at least 120bp in length.

The term "variant" or "mutant" as used herein refers to a polynucleotide that has a nucleic acid sequence altered by insertion, deletion or substitution of one or more nucleotides compared to a reference sequence, while retaining its ability to hybridize to other nucleic acids. A mutant according to any of the embodiments herein comprises a nucleotide sequence having at least 70%, preferably at least 80%, preferably at least 85%, preferably at least 90%, preferably at least 95%, preferably at least 97% sequence identity to a reference sequence and retaining the biological activity of the reference sequence. Sequence identity between two aligned sequences can be calculated using, for example, BLASTn from NCBI. Mutants also include nucleotide sequences that have one or more mutations (insertions, deletions, or substitutions) in the reference sequence and in the nucleotide sequence, while still retaining the biological activity of the reference sequence. The plurality of mutations typically refers to within 1-10, such as 1-8, 1-5, or 1-3. The substitution may be a substitution between purine nucleotides and pyrimidine nucleotides, or a substitution between purine nucleotides or between pyrimidine nucleotides. The substitution is preferably a conservative substitution. For example, conservative substitutions with nucleotides of similar or analogous properties are not typically made in the art to alter the stability and function of the polynucleotide. Conservative substitutions are, for example, exchanges between purine nucleotides (A and G), exchanges between pyrimidine nucleotides (T or U and C). Thus, substitution of one or more sites with residues from the same in the polynucleotides of the invention will not substantially affect their activity. Furthermore, the methylation sites (e.g., contiguous CGs) in the variants of the invention are not mutated. That is, the method of the present invention detects methylation of a methylatable site in the corresponding sequence, and a mutation may occur in the base of a non-methylatable site. Typically, the methylation sites are consecutive CpG dinucleotides.

As described herein, transformation can occur between bases of DNA or RNA. "transformation", "cytosine transformation" or "CT transformation" as used herein is a process of converting an unmodified cytosine base (C) to a base having a lower binding ability to guanine than cytosine, for example, an uracil base (U), by treating DNA using a non-enzymatic or enzymatic method. Non-enzymatic or enzymatic methods of performing cytosine conversion are well known in the art. Exemplary non-enzymatic methods include treatment with a conversion reagent such as bisulfite, or metabisulfite, for example, calcium bisulfite, sodium bisulfite, potassium bisulfite, ammonium bisulfite, and the like. Illustratively, the enzymatic method includes a deaminase treatment. The transformed DNA is optionally purified. DNA purification methods suitable for use herein are well known in the art.

The invention also provides a methylation detection kit for differentiating pancreatic cancer from pancreatitis, the kit comprising the primers and/or probes described herein, for detecting the methylation level of pancreatic cancer and pancreatitis differentiation-associated sequences discovered by the inventors. The kit may further comprise a nucleic acid molecule as described herein, in particular according to the first aspect, as an internal standard or positive control.

As used herein, "hybridization" refers primarily to the pairing of nucleic acid sequences under stringent conditions. Exemplary stringent conditions are hybridization and membrane washing at 65 ℃ in a solution of 0.1 XSSPE (or 0.1 XSSC), 0.1% SDS.

In addition to the primers, probes, nucleic acid molecules, the kit contains other reagents required for detecting DNA methylation. Illustratively, other reagents for detecting DNA methylation may comprise one or more of: bisulfite and its derivatives, PCR buffer solution, polymerase, dNTP, primer, probe, restriction enzyme sensitive or insensitive to methylation, enzyme digestion buffer solution, fluorescent dye, fluorescence quencher, fluorescence reporter, exonuclease, alkaline phosphatase, internal standard, and reference substance.

The kit can also include a transformed positive standard in which unmethylated cytosines are converted to bases that do not bind guanine. The positive standard may be fully methylated. The kit may further comprise PCR reaction reagents. Preferably, the PCR reaction reagent comprises Taq DNA polymerase, PCR buffer (buffer), dNTPs, mg2+.

The present invention also provides a method for pancreatic cancer and pancreatitis differentiation comprising: (1) Detecting the level of methylation of pancreatic cancer and pancreatitis identifying related sequences described herein in a sample from a subject; (2) Comparing to a control sample, or calculating a score; (3) differentiating pancreatic cancer and pancreatitis of the subject according to the score. Typically, the method further comprises, prior to step (1): extracting sample DNA, inspecting quality and/or converting unmethylated cytosine in DNA into base not combined with guanine.

The subject is, for example, a patient diagnosed with pancreatitis or once diagnosed with pancreatitis (a priori diagnosis). That is, in one or more embodiments, the methods identify pancreatic cancer in patients diagnosed with chronic pancreatitis (including patients diagnosed as such). Of course, the method of the present invention is not limited to the above-described subjects, and can be used for diagnosing or identifying pancreatitis or pancreatic cancer directly in an undiagnosed subject.

In a specific embodiment, step (1) comprises: treating genomic DNA or cfDNA with a conversion reagent to convert unmethylated cytosine to a base that has less binding capacity to guanine than cytosine (e.g., uracil); performing PCR amplification using primers suitable for amplifying transformed sequences of the pancreatic cancer and pancreatitis discrimination-related sequences described herein; the methylation level of at least one CpG is determined by the presence or absence of an amplification product, or by sequence identification (e.g., probe-based PCR detection identification or DNA sequencing identification).

Or the step (1) may further include: treating genomic DNA or cfDNA with a methylation sensitive restriction enzyme; performing PCR amplification using primers suitable for amplifying sequences having at least one CpG of the pancreatic cancer and pancreatitis discrimination-related sequences described herein; the methylation level of at least one CpG is determined by the presence or absence of amplification products.

As used herein, "methylation level" includes any number and any position of the CpG in the sequence referred to in relation to methylation level. The relationship can be addition or subtraction of a methylation level parameter (e.g., 0 or 1) or a calculation of a mathematical algorithm (e.g., a mean, a percentage, a fraction, a ratio, a degree, or a calculation using a mathematical model), including but not limited to a methylation level metric, a methylation haplotype ratio, or a methylation haplotype burden. The term "methylation state" indicates the methylation of a particular CpG site, and typically includes methylated or unmethylated (e.g., methylation state parameter 0 or 1).

In one or more embodiments, the methylation level of a subject sample is increased or decreased when compared to a control sample. Pancreatic cancer is identified when the methylation marker levels meet a certain threshold, otherwise chronic pancreatitis. Alternatively, the methylation level of the gene being tested can be mathematically analyzed to obtain a score. And for the detected sample, when the score is larger than a threshold value, judging that the result is positive, namely the pancreatic cancer, otherwise, judging that the result is negative, namely pancreatitis. Methods of conventional mathematical analysis and processes of determining thresholds are known in the art, and an exemplary method is a Support Vector Machine (SVM) mathematical model. For example, for differential methylation markers, a Support Vector Machine (SVM) is constructed for training group samples, and the accuracy, sensitivity and specificity of the detection result and the area under the predictive value characteristic curve (ROC) (AUC) are counted by using a model to calculate the prediction scores of the samples in the test set. In an embodiment of the support vector machine, a score threshold of 0.897, greater than 0.897, indicates that the subject is a pancreatic cancer patient, otherwise, a chronic pancreatitis patient.

In a preferred embodiment, the model training process is as follows: firstly, obtaining differential methylation sections according to the methylation level of each site and constructing a differential methylation region matrix, for example, a methylation data matrix can be constructed from the methylation level data of a single CpG dinucleotide position of HG19 genome through samtools software; then SVM model training is carried out.

An exemplary SVM model training process is as follows:

a) The sklern software package (v0.23.1) of python software (v3.6.9) was used to construct a training pattern for training the model cross-validation training model, command line: model = SVR ().

b) Inputting a data matrix by using a sklern software package (v0.23.1), and constructing an SVM model (model. Fit (x _ train, y _ train)), wherein x _ train represents a training set data matrix, and y _ train represents phenotype information of a training set.

Generally, in constructing the model, the pancreatic cancer type may be coded as 1 and the pancreatitis type may be coded as 0. In the present invention, the threshold is set to 0.897 by python software (v3.6.9), the sklern software package (v0.23.1). The constructed model finally distinguishes the samples by taking 0.897 as a threshold value.

Herein, the sample is from a mammalian subject, preferably a human. The sample may be from any organ (e.g., pancreas), tissue (e.g., epithelial tissue, connective tissue, muscle tissue, and neural tissue), cell, or bodily fluid (e.g., blood, plasma, serum, interstitial fluid, urine). In general, as long as the sample contains genomic DNA or cfDNA (Circulating free DNA or Cell free DNA). cfDNA is called circulating free DNA or cell free DNA, and is a degraded DNA fragment that is released into plasma. Illustratively, the sample is a pancreatic cancer biopsy, preferably a fine needle biopsy. Alternatively, the sample is plasma or cfDNA.

Also described herein are methods of obtaining methylation haplotype ratios associated with pancreatic cancer and pancreatitis. Taking methylation data obtained by methylation target sequencing (MethylTitan) as an example, the process of screening and testing marker sites is as follows: original double-end sequencing reading, reading combination to obtain combined single-end reading, removing a connector to obtain reading without a connector, comparing the reading with a human DNA genome to form a BAM file, extracting CpG site methylation level of each reading to form a Haplotype file by samtools, counting C site methylation Haplotype ratio proportion to form a meth file, calculating MHF (Methylated Haplotype framework) methylation value, filtering by using a Coverage 200 filtering site to form a meth.matrix matrix file, filtering by using a filtering site with an NA value larger than 0.1, dividing the sample into a training set and a test set in advance, establishing a logistic regression model for each Haplotype pair type of the training set, selecting a regression P value of each methylation Haplotype ratio, counting the methylation Haplotype ratio value in each methylation Haplotype amplification region to represent the methylation Haplotype ratio level of the region, forming a result (ROC diagram) of the region through a support vector machine, and verifying by using a prediction test set. Specifically, the method for obtaining the methylation haplotype ratio related to the pancreatic cancer comprises the following steps: (1) Obtaining plasma of a patient sample of pancreatic cancer or pancreatitis to be detected, extracting cfDNA, and performing library building and sequencing by adopting a MethTitan method to obtain a sequencing read; (2) Preprocessing sequencing data, including performing joint removal and splicing treatment on the sequencing data generated by a sequencer; (3) And aligning the sequencing data after the pretreatment to an HG19 reference genome sequence of the human genome, and determining the position of each fragment. The data of step (2) can be derived from double-ended 150bp sequencing of an Illumina sequencing platform. The step (2) of removing the joints is to remove the sequencing joints at the 5 'end and the 3' end of the two pieces of double-ended sequencing data respectively, and to remove the low-quality bases after removing the joints. And (3) the splicing treatment in the step (2) is to merge and reduce the double-end sequencing data into the original library fragment. Therefore, the sequencing fragments can be well compared and accurately positioned. Illustratively, the sequencing library is about 180bp in length, and 150bp of each end can completely cover the whole library fragment. The step (3) comprises the following steps: (a) Respectively carrying out CT and GA conversion on HG19 reference genome data, constructing two sets of converted reference genomes, and respectively constructing comparison indexes on the converted reference genomes; (b) The upper combined sequencing sequence data is also subjected to CT and GA transformation; (c) And respectively comparing the converted reference genome sequences, and finally summarizing comparison results to determine the position of the sequencing data in the reference genome.

In addition, the method for obtaining methylation haplotype ratio associated with pancreatic cancer and pancreatitis further comprises (4) calculation of MHF; (5) constructing a methylation haplotype ratio MHF data matrix; and (6) constructing a logistic regression model for each methylation haplotype ratio based on the sample groupings. And (4) acquiring the methylation haplotype ratio state and sequencing depth at the position of the HG19 reference genome according to the comparison result obtained in the step (3). Step (5) comprises merging the methylation haplotype ratio status and sequencing depth information data into a data matrix. Wherein, each data point with the depth less than 200 is treated as a missing value, and the missing value is filled by using a K Nearest Neighbor (KNN) method. Step (6) comprises screening haplotypes with significant regression coefficients between the two groups according to statistical modeling of each position in the above matrix using logistic regression.

As used herein, "plurality" refers to any integer. Preferably, a "plurality" of "one or more" can be any integer, e.g., greater than or equal to 2, including 2, 3,4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, or more.

The beneficial effects of the invention are:

based on the DNA methylation marker, pancreatic cancer and chronic pancreatitis patients can be effectively distinguished; the invention provides a diagnosis model of the relation between the methylation level of a cfDNA methylation marker and pancreatic cancer based on plasma cfDNA high-throughput methylation sequencing, and the model has the advantages of non-invasive detection, safe and convenient detection, high throughput and high detection specificity; based on the optimal sequencing quantity obtained by the invention, the detection cost can be effectively controlled while better detection performance is effectively obtained.

Examples

The present invention will be described in further detail with reference to the following drawings and specific examples. In the following examples, the experimental methods without specifying specific conditions were generally carried out in the same manner as described in the conventional conditions.

Example 1: methylation targeted sequencing screening of methylation sites of pancreatic cancer differences

The inventors collected a total of 94 blood samples from pancreatic cancer patients, 25 blood samples from chronic pancreatitis patients, and all enrolled patients signed informed consent. Pancreatic cancer patients have a past history of pancreatitis diagnosis. Sample information is shown in the table below.

Methylation sequencing data of plasma DNA are obtained by a method of MethylTitan, and DNA methylation classification markers in the data are identified. The process is as follows:

1. extraction of plasma cfDNA samples

2ml of whole blood samples of patients are collected by a streck blood collection tube, plasma is timely centrifugally separated (within 3 days), and after the plasma is transported to a laboratory, cfDNA is extracted by a QIAGEN QIAamp Circulating Nucleic Acid Kit according to instructions.

2. Sequencing and data preprocessing

1) The library was paired-end sequenced using an Illumina Nextseq 500 sequencer.

2) Pear (v0.6.0) software combines double-end sequencing data of identical fragments sequenced by 150bp under an Illumina Hiseq X10/Nextseq 500/Nova seq sequencer into a sequence, the shortest overlapping length is 20bp, and the shortest length is 30bp after combination.

3) And performing de-splicing treatment on the combined sequencing data by using Trim _ galore v0.6.0 and cutatapt v1.8.1 software. The linker sequence was removed at the 5' end of the sequence as "AGATCGGAAGAGCAC" and bases with sequencing quality values below 20 at both ends were removed.

3. Sequencing data alignment

The reference genomic data used herein was from the UCSC database (UCSC: HG19, http:// hgdownload. Soe. UCSC. Edu/goldenPath/HG19/big Zips/HG19.Fa. Gz).

1) HG19 was first transformed with cytosine to thymine (CT) and adenine to Guanine (GA) using Bismark software, respectively, and the transformed genomes were separately indexed using Bowtie2 software.

2) The pre-processed data were also subjected to CT and GA transformation.

3) The transformed sequences were aligned to the transformed HG19 reference genome using Bowtie2 software, respectively, with a minimum seed sequence length of 20, the seed sequences not allowing for mismatches.

4. Calculation of MHF

And obtaining the methylation state corresponding to each site according to the comparison result for the CpG sites of each target area HG19. The nucleotide numbering of the sites herein corresponds to the nucleotide position numbering of HG19. A target methylated region may have multiple methylated haplotypes, and the calculation of this value is required for each methylated haplotype in the target region, and the calculation formula of MHF is exemplified as follows:

wherein i represents the methylation interval of the target, h represents the methylation haplotype of the target, N _i Denotes the number of reads located in the target methylation interval, N _i,h Representing the number of reads containing the target methylated haplotype

5. Matrix of methylated data

1) And combining the methylation sequencing data of each sample in the training set and the test set into a data matrix respectively, and performing deletion value processing on each site with the depth of less than 200.

2) Sites with a deletion value ratio higher than 10% were removed.

3) And performing missing data interpolation on the missing values of the data matrix by using a KNN algorithm.

6. Discovery of characteristic methylated segments from training set sample groupings

1) And (3) constructing a logistic regression model for the phenotype by each methylation section, and screening out the methylation section with the most significant regression coefficient for each amplified target region to form a candidate methylation section.

2) And dividing the training set into ten times randomly and screening the ten times of cross validation increment characteristics.

3) And (3) sorting the candidate methylation sections of each region from large to small according to the significance of the regression coefficient, adding methylation section data each time, and predicting the test data.

4) Step 3) 10 calculations were performed each time using 10 data generated in 2), and the final AUC was averaged over 10. The candidate methylated segment is retained as the characteristic methylated segment if the AUC of the training data increases, otherwise discarded.

5) And taking the feature combination corresponding to the median of the average AUC under the condition of different feature quantities in the training set as the finally determined feature methylation section combination.

The distribution of the characteristic methylation markers in HG19 is specifically as follows: 1 located in the SIX3 gene region, 2 located in the TLX2 gene region, and 3 located in the CILP2 gene region. The levels of the above methylation markers increased or decreased in cfDNA of pancreatic cancer patients (table 1). The sequences of the 3 marker regions are shown in SEQ ID NO 1-3. The methylation level of all CpG sites in each marker region can be obtained by the method of MethylTitan sequencing. The mean of the methylation levels of all CpG sites in each region, as well as the methylation status of individual CpG sites, can be used as markers for the diagnosis of pancreatic cancer.

Table 1: methylation level of DNA methylation markers in training set

Sequence of	Marker substance	Pancreatic cancer	Chronic pancreatitis
				SEQ ID NO:1	chr2:45028785-45029307	0.843731054	0.909570522
SEQ ID NO:2	chr2:74742834-74743351	0.953274962	0.978544302
				SEQ ID NO:3	chr19:19650745-19651270	0.408843665	0.514101315

Methylation levels of methylation markers for populations with concentrated pancreatic cancer and chronic pancreatitis tested are shown in table 2. As can be seen from the table, the distribution difference of the methylation level of the methylation marker in the pancreatic cancer population and the chronic pancreatitis population is obvious, and the methylation marker has a good distinguishing effect.

Table 2: methylation level of DNA methylation markers in test set

Sequence of	Marker substance	Pancreatic cancer	Chronic pancreatitis
				SEQ ID NO:1	chr2:45028785-45029307	0.843896661	0.86791556
SEQ ID NO:2	chr2:74742834-74743351	0.926459851	0.954493044
				SEQ ID NO:3	chr19:19650745-19651270	0.399831579	0.44918572

Table 3 lists the correlation (Pearson correlation coefficient) between the methylation level of 10 random CpG sites or combinations in each selected marker and the methylation level of the whole marker and the corresponding significance p value, and it can be seen that the methylation state or level of a single CpG site or a combination of multiple CpG sites in the marker has significant correlation (p < 0.05) with the methylation level of the whole region, and the correlation coefficients are all above 0.8, which have strong correlation or strong correlation, indicating that the single CpG site or the combination of multiple CpG sites in the marker has good differentiation effect as the whole marker.

Table 3: correlation of methylation levels of random CpG sites or combinations of sites in 3 markers with methylation levels of the entire marker

Example 2: predicted performance of Individual methylation markers

To verify the differential performance of a single methylation marker on pancreatitis and pancreatic cancer, the values of the methylation levels of a single methylation marker were used to verify the predicted performance of a single marker.

Firstly, the methylation level values of 3 methylation markers are respectively and independently used for training in a training set sample, the threshold value for distinguishing pancreatic cancer from pancreatitis, the sensitivity and the specificity are determined, then the threshold value is used for counting the sensitivity and the specificity of the test set sample, the result is shown in the following table 4, and the result shows that a single marker can also achieve better distinguishing performance.

Table 4 predicted performance of 56 methylation markers

Marker substance	Grouping	AUC value	Sensitivity of the composition	Specificity of the drug	Threshold value
						SEQ ID NO:1	Training set	0.8870	0.7937	0.8824	0.8850
SEQ ID NO:1	Test set	0.6532	0.7742	0.3750	0.8850
						SEQ ID NO:2	Training set	0.8497	0.6508	0.8824	0.9653
SEQ ID NO:2	Test set	0.6210	0.8065	0.5000	0.9653
						SEQ ID NO:3	Training set	0.8301	0.4286	0.8824	0.3984
SEQ ID NO:3	Test set	0.6694	0.5806	0.6250	0.3984

Example 3: construction of a Classification prediction model

To verify the potential capability of the classifier for pancreatic cancer-chronic pancreatitis patients using marker DNA methylation levels (e.g., methylation haplotype ratios), a support vector machine disease classification model was constructed based on a combination of 3 DNA methylation markers in the training set to verify the classification predictive effect of the set of DNA methylation markers in the test set. The training set and the test set were divided by scale, with 80 training sets (samples 1-80) and 39 test sets (samples 80-119).

Support vector machine models were constructed in the training set for both sets of samples using the found DNA methylation markers.

1) The samples were pre-divided into 2 parts, with 1 part for training the model and 1 part for model testing.

2) To exploit the potential of using methylation markers for pancreatic cancer identification, disease classification systems have been developed based on genetic markers. SVM model training was performed using methylation marker levels in the training set. The specific training process is as follows:

b) And inputting a methylation numerical matrix by using a sklern software package (v0.23.1), and constructing a SVM model (x _ train, y _ train), wherein x _ train represents the methylation numerical matrix of the training set, and y _ train represents the phenotype information of the training set.

In constructing the model, pancreatic cancer type was coded as 1, chronic pancreatitis type was coded as 0, and by sklern software package (v0.23.1) type, the threshold was set to 0.897 by default. The constructed model finally distinguishes the pancreatic cancer from pancreatitis by taking 0.897 as a scoring threshold. The prediction scores of the two models for the training set samples are shown in table 5.

Table 5: predictive scoring of models in a training set

Example 4: classified predictive model testing

And (3) carrying out MethTitan sequencing by using the blood samples of the pancreatic cancer and pancreatitis subjects, and carrying out classification analysis such as PCA (principal component analysis) and clustering according to characteristic methylation marker signals in sequencing results.

Based on the methylation marker population of the present invention, the model established by SVM according to example 3 was predicted in the test set. The test set was predicted using a prediction function, and the outcome was the predicted outcome (disease probability: default score threshold of 0.897, greater than 0.897 then the subject was considered a pancreatic cancer patient, otherwise chronic pancreatitis patient). 57 samples (samples 118-174) of the test set were calculated as follows:

command line:

test_pred＝model.predict(test_df)

wherein test _ pred represents a prediction score of a test set sample obtained by the SVM prediction model constructed in the embodiment 3, model represents the SVM prediction model constructed in the embodiment 3, and test _ df represents test set data.

The prediction scores of the test groups are shown in Table 6, the ROC curve is shown in FIG. 2, the prediction score distribution is shown in FIG. 3, and the area of the test group under the total AUC is 0.847. The model is in a training set, and when the specificity is 88.2%, the sensitivity can reach 88.9%; in the test set, when the specificity is 87.5%, the sensitivity can reach 74.2%. Therefore, the discrimination of the SVM models established by the selected variables is good.

FIGS. 4 and 5 show the distribution of the 3 methylation markers in the training group and the test group, respectively, and it can be found that the difference between the methylation markers in the group in the plasma of patients with pancreatitis and the plasma of patients with pancreatic cancer is stable.

Table 6: prediction scores for test set sample models

Sample ID	Type (B)	Score value	Sample ID	Type (B)	Score value
						Sample 81	Chronic pancreatitis	0.610488911	Sample 101	Pancreatic cancer	15.62766141
Sample 82	Pancreatic cancer	0.912018264	Sample 102	Pancreatic cancer	0.909976179
						Sample 83	Pancreatic cancer	0.870225426	Sample 103	Pancreatic cancer	0.92289051
Sample 84	Pancreatic cancer	0.897368929	Sample 104	Pancreatic cancer	1.823319531
						Sample 85	Pancreatic cancer	1.491556374	Sample 105	Pancreatic cancer	0.913625979
Sample 86	Pancreatic cancer	0.99785215	Sample 106	Pancreatic cancer	0.730447081
						Sample 87	Pancreatic cancer	0.909901733	Sample 107	Pancreatic cancer	0.900701224
Sample 88	Pancreatic cancer	0.955726751	Sample 108	Chronic pancreatitis	0.893221308
						Sample 89	Pancreatic cancer	0.96582068	Sample 109	Chronic pancreatitis	0.899073184
Sample 90	Pancreatic cancer	0.910414113	Sample 110	Chronic pancreatitis	0.783284566
						Sample 91	Pancreatic cancer	0.850903621	Sample 111	Chronic pancreatitis	0.725251615
Sample 92	Pancreatic cancer	0.916651697	Sample 112	Pancreatic cancer	0.893141436
						Sample 93	Chronic pancreatitis	0.904231501	Sample 113	Pancreatic cancer	1.354991317
Sample 94	Pancreatic cancer	0.764872522	Sample 114	Pancreatic cancer	0.817727331
						Sample 95	Pancreatic cancer	1.241367038	Sample 115	Pancreatic cancer	1.079401681
Sample 96	Chronic pancreatitis	0.897789105	Sample 116	Pancreatic cancer	0.969607597
						Sample 97	Chronic pancreatitis	0.852404121	Sample 117	Pancreatic cancer	0.878877727
Sample 98	Pancreatic cancer	1.068601129	Sample 118	Pancreatic cancer	0.911801452
						Sample 99	Pancreatic cancer	3.715591125	Sample 119	Pancreatic cancer	0.934497862
Sample 100	Pancreatic cancer	0.920532374

Example 5: predictive effect on tumor marker negative patients

Based on the methylation marker population of the present invention, discrimination was performed on patients who were negative (< 37) in the tumor marker CA19-9 discrimination according to the model established by SVM in example 3.

The predicted scores for the test groups are shown in Table 7 and the ROC curves are shown in FIG. 6. It can be seen that the established SVM model can also achieve better effect on patients which cannot be distinguished by the traditional tumor marker CA 19-9.

Table 7: CA19-9 measurements and predictive scores for SVM models

In the research, the difference between the plasma of a chronic pancreatitis object and the plasma of a pancreatic cancer population is researched through the methylation level of methylation markers in plasma cfDNA, and 3 DNA methylation markers with obvious difference are screened out. Based on the DNA methylation marker group, a malignant pancreatic cancer risk prediction model is established by a method of a support vector machine, pancreatic cancer and chronic pancreatitis patients can be effectively distinguished, and the kit has high sensitivity and specificity and is suitable for screening and diagnosing pancreatic cancer in chronic pancreatitis patients.

Sequence listing

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Claims

1. An isolated mammalian-derived nucleic acid molecule that is a methylation marker differentially associated with pancreatic cancer and pancreatitis, the sequence of the nucleic acid molecule comprising (1) one or more or all of the following sequences, or variants thereof having at least 70% identity thereto: 1, 2, 3, wherein the methylation site in said variant is not mutated, (2) the complementary sequence of (1), and (3) the treated sequence of (1) or (2), wherein said treatment converts unmethylated cytosine to a base having a lower binding capacity for guanine than cytosine,

preferably, the nucleic acid molecule is used as an internal standard or control for detecting the level of DNA methylation of the corresponding sequence in a sample.

2. A reagent for detecting methylation of DNA, said reagent comprising a reagent for detecting the level of methylation of a DNA sequence or fragment thereof, or the methylation status or level of one or more CpG dinucleotides in said DNA sequence or fragment thereof, in a sample from a subject, said DNA sequence being selected from one or more or all of the following gene sequences, or sequences within 20kb upstream or downstream thereof: SIX3, TLX2, CILP2,

preferably, the first and second electrodes are formed of a metal,

the DNA sequence is selected from one or more or all of the following sequences or the complementary sequences thereof: 1, 2, 3, or a variant thereof having at least 70% identity thereto, wherein the methylation site is not mutated, and/or

Said agent is a primer molecule which hybridizes to said DNA sequence or a fragment thereof and which is capable of amplifying said DNA sequence or a fragment thereof, and/or

The agent is a probe molecule that hybridizes to the DNA sequence or a fragment thereof.

3. A medium carrying a DNA sequence or a fragment thereof and/or methylation information thereof, wherein the DNA sequence is (i) one or more or all of the following gene sequences, or (ii) a sequence within 20kb upstream or downstream thereof: a treated sequence of SIX3, TLX2, CILP2, or (ii) (i) that converts unmethylated cytosine to a base that has less binding capacity to guanine than cytosine,

preferably, the first and second electrodes are formed of a metal,

the medium is used for alignment with gene methylation sequencing data to determine the presence, amount and/or methylation level of a nucleic acid molecule comprising the sequence or fragment thereof, and/or

The DNA sequence comprises a sense strand or an antisense strand of DNA, and/or

The length of the fragment is 1-1000bp, and/or

The DNA sequence is selected from one or more or all of the following sequences or the complementary sequences thereof: 1, 2, 3, or a variant thereof having at least 70% identity thereto, wherein the methylation site is not mutated,

more preferably, the amount of the organic solvent is,

the medium is a carrier printed with the DNA sequence or its fragment and/or its methylation information, and/or

The medium is a computer-readable medium having stored thereon the sequence or a fragment thereof and/or methylation information thereof and a computer program which, when executed by a processor, performs the steps of: comparing the methylation sequencing data of the sample with said sequence or fragment thereof, thereby obtaining the presence, amount and/or methylation level of a nucleic acid molecule comprising said sequence or fragment thereof in said sample, wherein said presence, amount and/or methylation level is used to discriminate pancreatic cancer from pancreatitis.

4. Use of the following items (a) and/or (b) for the preparation of a kit for differentiating pancreatic cancer from pancreatitis,

(a) Reagents or devices for determining the methylation level of a DNA sequence or fragment thereof or the methylation state or level of one or more CpG dinucleotides in said DNA sequence or fragment thereof in a sample of a subject,

wherein the DNA sequence is selected from one or more or all of the following gene sequences, or sequences within 20kb upstream or downstream of the gene sequences: SIX3, TLX2, CILP2,

preferably, the fragment is 1-1000bp in length.

5. Use according to claim 4, wherein the DNA sequence is selected from one or more or all of the following sequences or their complements: 1, 2, 3, or a variant having at least 70% identity thereto, wherein the methylation site is not mutated.

6. The use according to claim 4 or 5,

said reagent comprising a primer molecule which hybridizes to said DNA sequence or fragment thereof, and/or

Said agent comprising a probe molecule which hybridizes to said DNA sequence or a fragment thereof, and/or

The reagent comprises the medium of claim 3.

7. Use according to claim 4 or 5,

the sample is derived from a tissue, cell or body fluid of a mammal, e.g. from pancreatic tissue or blood, and/or

The sample comprises genomic DNA or cfDNA, and/or

The DNA sequence is transformed, wherein unmethylated cytosine is converted to a base having a lower binding capacity to guanine than cytosine, and/or

The DNA sequence is treated with a methylation sensitive restriction endonuclease.

8. The use of claim 4 or 5, wherein said diagnosis comprises: comparing with a control sample or calculating to obtain a score, and identifying pancreatic cancer and pancreatitis according to the score; preferably, the calculation is performed by constructing a support vector machine model.

9. A kit for differentiating pancreatic cancer from pancreatitis, comprising:

wherein the DNA sequence is selected from one or more or all of the following gene sequences, or sequences within 20kb of the upstream or downstream of the gene sequences: SIX3, TLX2, CILP2,

preferably, the first and second electrodes are formed of a metal,

The kit is suitable for use according to any one of claims 6 to 8, and/or

Said agent comprising a primer molecule hybridizing to said DNA sequence or fragment thereof, and/or

Said reagent comprising a probe molecule which hybridizes to said DNA sequence or fragment thereof, and/or

Said reagent comprising the medium of claim 3, and/or

10. An apparatus for differentiating pancreatic cancer from pancreatitis, the apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of:

(1) Obtaining the methylation level of a DNA sequence or fragment thereof or the methylation state or level of one or more CpG dinucleotides in said DNA sequence or fragment thereof in a sample from a subject, said DNA sequence being selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2,

(2) Comparing with a control sample, or calculating a score, and

(3) Pancreatic cancer and pancreatitis were identified by score,

preferably, the first and second electrodes are formed of a metal,

Step (1) comprises detecting the methylation level of said sequence in a sample by means of a nucleic acid molecule according to claim 1 and/or a reagent according to claim 2 and/or a medium according to claim 3, and/or

The sample comprises genomic DNA or cfDNA, and/or

The sequence is transformed, wherein unmethylated cytosine is converted to a base having a lower binding capacity to guanine than cytosine, and/or

The DNA sequence is treated by a methylation sensitive restriction endonuclease, and/or

And (3) calculating the score in the step (2) by constructing a support vector machine model.