Materials and methods for tumor assessment

Abstract

The present application relates to materials and methods for assessing tumors. In particular, the present application provides materials, kits, devices, systems and methods for assessing the risk of tumor development and / or tumor progression in a subject. For example, the present application provides a method for assessing the risk of tumor formation and / or tumor progression in a subject based on the methylation status of a target polynucleotide sequence selected from the subject.

Description

[Technical field]

[0001] The present application relates to the field of biomedicine, and in particular to materials and methods for assessing tumors. Background technology Pancreatic cancer, such as pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal diseases worldwide. Its 5-year relative survival rate is 9%, and for patients with distant metastasis, this rate further drops to only 3%. The main reason for the high mortality rate is that methods for early detection of PDAC are still limited, making it important for PDAC patients to undergo surgical resection. Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is another common method to obtain a pathological diagnosis without laparotomy, but it is invasive and requires clear imaging findings that usually mean that PDAC is already advanced. During tumor initiation and development, significant changes occur in DNA methylation patterns and levels of genomic DNA in malignant cells. Some tumor-specific DNA methylation has been shown to occur early in tumor formation and may be a "promoter" of tumor formation. Circulating tumor DNA (ctDNA) molecules are derived from apoptotic or necrotic tumor cells and carry tumor-specific DNA methylation markers from early malignant tumors. In recent years, they have been investigated as a new promising target for the development of non-invasive early screening tools for various cancers. However, most of these studies did not produce meaningful results. Therefore, there is an urgent need in the art for materials and methods capable of identifying pancreatic cancer tumor-specific markers from plasma DNA. The present application aims to non-invasively and accurately diagnose pancreatic cancer with higher accuracy and lower cost by detecting the methylation levels of target genes and / or target sequences in a sample and identifying pancreatic cancer using the differential gene methylation levels resulting from the detection. In one aspect, the present application provides a reagent for detecting DNA methylation, the reagent comprising a reagent for detecting the methylation level of a DNA sequence or a fragment thereof in a sample to be detected, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, the DNA sequence being selected from the following gene sequences: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD The methylation marker is selected from one or more or all of the following: 4, TOPAZ1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX20, IKZF1, INSIG1, SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CLEC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2, or sequences within 20 kb upstream or downstream thereof. The present application further provides a methylation marker having a target sequence selected from the above-mentioned genes as a pancreatic cancer-related gene, including the sequences shown in SEQ ID NOs: 1 to 56. The present application further provides a medium and a device carrying the above-mentioned target gene and / or target sequence DNA sequence or a fragment thereof and / or methylation information thereof. The present application also provides a use of the above-mentioned target gene and / or target sequence DNA sequence or a fragment thereof, and / or methylation information thereof in the preparation of a kit for diagnosing pancreatic cancer in a subject. The present application further provides the above-mentioned kit. In another aspect, the present application provides a reagent for detecting DNA methylation, the reagent comprising a reagent for 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 a DNA sequence or a fragment thereof, in a sample of a subject to be detected, the DNA sequence being selected from one or more (such as at least seven) or all of the following gene sequences, or sequences 20 kb upstream or downstream thereof: SIX3, TLX2, and CILP2. The present application further provides a methylation marker having a target sequence selected from the above-mentioned genes, including sequences shown in SEQ ID NOs: 57 to 59, as a pancreatic cancer-associated gene. The present application further provides a medium and a device carrying the above-mentioned target gene and / or target sequence DNA sequence or a fragment thereof and / or methylation information thereof. The present application also provides the use of the above-mentioned target gene and / or target sequence DNA sequence or a fragment thereof, and / or methylation information thereof, in the preparation of a diagnostic kit for pancreatic cancer in a subject. The present application further provides the above-mentioned kit.

[0002] In another aspect, the present application provides a reagent for detecting DNA methylation, the reagent comprising a reagent for 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 a DNA sequence or a fragment thereof, in a sample to be detected, the DNA sequence being selected from the following gene sequences: ARHGEF16, PRDM16, NFIA, ST6GALNAC5, PRRX1, LHX4, ACBD6, FMN2, CHRM3, FAM150B, TMEM18, SIX3, CAMKMT, OTX1, WDPCP, CY P26B1, DYSF, HOXD1, HOXD4, UBE2F, RAMP1, AMT, PLSCR5, ZIC4, PEX5L, ETV5, DGKG, FGF12, FGFRL1, RNF212, DOK7, HGFAC, EVC, EVC2, HMX1, CPZ, IRX1, GDN F, AGGF1, CRHBP, PITX1, CATSPER3, NEUROG1, NPM1, TLX3, NKX2-5, BNIP1, PROP1, B4GALT7, IRF4, FOXF2, FOXQ1, FOXC1, GMDS, MOCS1, LRFN2, POU3F2, FBXL 4, CCR6, GPR31, TBX20, HERPUD2, VIPR2, LZTS1, NKX2-6, PENK, PRDM14, VPS13B, OSR2, NEK6, LHX2, DDIT4, DNAJB12, CRTAC1, PAX2, HIF1AN, ELOVL3, INA, HMX2, HMX3, MKI67, DPYSL4, STK32C, INS, INS-IGF2, ASCL2, PAX6, RELT, FAM168A, OPCML, ACVR1B, ACVRL1, AVPR1A, LHX5, SDSL, RAB20, COL4A2, CARKD, CA RS2, SOX1, TEX29, SPACA7, SFTA3, SIX6, SIX1, INF2, TMEM179, CRIP2, MTA1, PIAS1, SKOR1, ISL2, SCAPER, POLG, RHCG, NR2F2, RAB40C, PIGQ, CPNE2, NLRC5 , PSKH1, NRN1L, SRR, HIC1, HOXB9, PRAC1, SMIM5, MYO15B, TNRC6C, 9-Sep, TBCD, ZNF750, KCTD1, SALL3, CTDP1, NFATC1, ZNF554, THOP1, CACTIN, PIP5K1C,The methylation marker is selected from one or more (such as at least 7) ​​or all of KDM4B, PLIN3, EPS15L1, KLF2, EPS8L1, PPP1R12C, NKX2-4, NKX2-2, TFAP2C, RAE1, TNFRSF6B, ARFRP1, MYH9, and TXN2, or sequences within 20 kb upstream or downstream thereof. The present application further provides a methylation marker having a target sequence selected from the above-mentioned genes, including sequences shown in SEQ ID NOs: 60 to 160, as a pancreatic cancer-related gene. The present application further provides a medium and a device carrying the above-mentioned target gene and / or target sequence DNA sequence or a fragment thereof and / or methylation information thereof. The present application also provides the use of the above-mentioned target gene and / or target sequence DNA sequence or a fragment thereof and / or methylation information thereof in the preparation of a diagnostic kit for pancreatic cancer in a subject. The present application further provides the above-mentioned kit. In another aspect, the present application provides a method for detecting DNA methylation in a patient's plasma sample, and constructing a machine learning model for diagnosing pancreatic cancer based on the methylation degree data of the target methylation marker and the CA19-9 detection result, in order to achieve the purpose of diagnosing pancreatic cancer non-invasively and accurately with higher accuracy and lower cost.The present application also provides a method for diagnosing pancreatic cancer or constructing a pancreatic cancer diagnostic model, including (1) obtaining a methylation level of a DNA sequence or a fragment thereof in a subject's sample, or a methylation state or level of one or more CpG dinucleotides in the DNA sequence or the fragment thereof, and a CA19-9 level of the subject, (2) using a mathematical model to perform a calculation using the methylation state or level to obtain a methylation score, (3) synthesizing the methylation score and the CA19-9 level into a data matrix, (4) constructing a pancreatic cancer diagnostic model based on the data matrix, and optionally (5) obtaining a pancreatic cancer score and diagnosing pancreatic cancer based on the pancreatic cancer score. In one or more embodiments, the DNA sequence is selected from one or more (e.g., at least two) or all of the following gene sequences: SIX3, TLX2, CILP2, or sequences within 20 kb upstream or downstream thereof. Preferably, the DNA sequence encompasses gene sequences selected from any of the following combinations: (1) SIX3, TLX2; (2) SIX3, CILP2; (3) TLX2, CILP2; (4) SIX3, TLX2, CILP2. The present application also relates to a method for obtaining a methylation score by (1) obtaining a methylation level of a DNA sequence or a fragment thereof in a subject's sample, or a methylation status or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, and a CA19-9 level of the subject; (2) calculating a methylation score using a mathematical model using the methylation status or level; and (3) using the model shown below,

number

number

[0003] In another aspect, the present application provides a method for determining the presence, assessing the onset, and / or assessing the progression of a pancreatic tumor, comprising determining the presence and / or content of modification status of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, and / or TWIST1 or fragments thereof in a sample to be tested.Furthermore, the present application provides a method for determining the presence and / or content of modification status of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, and / or TWIST1 or fragments thereof in a sample to be tested.Furthermore, the present application provides a method for determining the presence and / or content of modification status of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, and / or TWIST1 or fragments thereof in a sample to be tested. derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, 63788812-63788952, from human chr1:248020592-248020779, from human chr2:176945511-176945630, from human chr6:137814700-137814853, from human chr7:155167513-155167628, from human chr19:51228168-51228782, and from human chr7:19156739-19157277, or a complementary region thereof, or a fragment thereof. The present application further provides a combination of probes and / or primers for identifying the modification state of the above fragments.The present application also provides a kit comprising the above material.In another aspect, the present application provides the use of the nucleic acid of the present application, the combination of the nucleic acid of the present application and / or the kit of the present application in the preparation of a disease detection product.In another aspect, the present application provides the use of the nucleic acids of the present application, combinations of nucleic acids of the present application and / or kits of the present application in the preparation of a substance for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease. In another aspect, the present application provides a storage medium having recorded thereon a program capable of carrying out the method of the present application. In another aspect, the present application provides a device comprising the storage medium of the present application. In another aspect, the present application provides a method for determining the presence, assessing the onset of a pancreatic tumor, and / or assessing the progression of a pancreatic tumor comprising determining the presence and / or content of the modification state of a DNA region having genes EBF2 and CCNA1, or KCNA6, TLX2 and EMX1, or TRIM58, TWIST1, FOXD3 and EN2, or TRIM58, TWIST1, CLEC11A, HOXD10 and OLIG3, or fragments thereof in a sample to be tested. Furthermore, the present application relates to a method for detecting the presence or absence of a gene encoding a nucleotide sequence derived from human chr8:25907849-25907950 and derived from human chr13:37005635-37005754, or derived from human chr12:4919142-4919289, or derived from human chr2:74743035-74743151 and derived from human chr2:73147525-73147644, or derived from human chr1:248020592-248020779, or derived from human chr7:19156739-19157277, or derived from human chr1:63788812-63788952, or derived from human chr7:155167513-1551 in a sample to be tested. The present application provides a method for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease, comprising determining the presence and / or content of a modification state of a DNA region selected from the group consisting of DNA regions derived from human chr1:248020592-248020779, human chr7:19156739-19157277, human chr19:51228168-51228782, human chr2:176945511-176945630, and human chr6:137814700-137814853, or complementary regions thereof, or fragments thereof. The present application further provides a combination of probes and / or primers for identifying the modification state of the fragments. The present application also provides a kit comprising the above combination of substances. In another aspect, the present application provides the use of a nucleic acid of the present application, a combination of nucleic acids of the present application, and / or a kit of the present application in the preparation of a disease detection product.In another aspect, the present application provides the use of the nucleic acids of the present application, combinations of nucleic acids of the present application and / or kits of the present application in the preparation of a substance for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease. In another aspect, the present application provides a storage medium having recorded thereon a program capable of carrying out the method of the present application. In another aspect, the present application provides a device comprising the storage medium of the present application.

[0004] Those skilled in the art will readily appreciate other aspects and advantages of the present application from the following detailed description. In the following detailed description, only exemplary embodiments of the present application are shown and described. As those skilled in the art will appreciate, the contents of the present application will enable those skilled in the art to make changes to the specific embodiments disclosed without departing from the spirit and scope of the invention covered by the present application. Therefore, the drawings and descriptions in the specification of the present application are merely illustrative and not restrictive. Particular features of the invention to which this application pertains are set forth in the appended claims. The features and advantages of the invention to which this application pertains can be better understood with reference to the exemplary embodiments and drawings described in detail below, the brief description of which follows. [Brief description of the drawings]

[0005] [Figure 1] 1 is a flowchart of a technical solution according to an embodiment of the present application. [Diagram 2] FIG. 1 shows the ROC curve of the pancreatic cancer prediction model Model CN ​​for diagnosing pancreatic cancer in the test group, with the horizontal axis representing the "false positive rate" and the vertical axis representing the "true positive rate." [Diagram 3] This figure shows the distribution of prediction scores of the pancreatic cancer prediction model ModelCN in each group, with the vertical axis representing "model predicted value." [Figure 4] FIG. 1 is a graph showing the methylation levels of the 56 sequences of SEQ ID NOs: 1 to 56 in the training group as "methylation level" on the vertical axis. [Diagram 5]FIG. 1 is a graph showing the methylation levels of the 56 sequences of SEQ ID NOs: 1 to 56 in a test group as "methylation level" on the vertical axis. [Figure 6] FIG. 1 shows classification ROC curves for SVM model CN ​​constructed using CA19-9 alone, the present application alone, and a model constructed using a combination of the present application and CA19-9, with the horizontal axis representing the "false positive rate" and the vertical axis representing the "true positive rate." [Figure 7] The vertical axis indicates "model predicted value," and the figure shows the distribution of classification prediction scores for CA19-9 alone, the SVM model ModelCN constructed using the present application alone, and a model constructed using a combination of the present application and CA19-9. [Figure 8] FIG. 1 shows the ROC curve of the SVM model CN ​​constructed in the present application for samples determined to be negative for the tumor marker CA19-9 (CA19-9 measurement value less than 37), with the horizontal axis showing the "false positive rate" and the vertical axis showing the "true positive rate." [Figure 9] FIG. 1 shows the ROC curve of a combination model of the seven markers SEQ ID NOs: 9, 14, 13, 26, 40, 43, and 52, with the "false positive rate" on the horizontal axis and the "true positive rate" on the vertical axis. [Figure 10] FIG. 1 shows the ROC curve of a combination model of the seven markers SEQ ID NOs: 5, 18, 34, 40, 43, 45, and 46, with the "false positive rate" on the horizontal axis and the "true positive rate" on the vertical axis. [Figure 11] FIG. 1 shows the ROC curve of a combination model of the seven markers SEQ ID NOs: 11, 8, 20, 44, 48, 51, and 54, with the horizontal axis indicating the "false positive rate" and the vertical axis indicating the "true positive rate." [Figure 12] FIG. 1 shows the ROC curve of a combination model of the seven markers SEQ ID NOs: 14, 8, 26, 24, 31, 40, and 46, with the horizontal axis indicating the "false positive rate" and the vertical axis indicating the "true positive rate." [Figure 13] FIG. 1 shows the ROC curve of a combination model of the seven markers SEQ ID NOs: 3, 9, 8, 29, 42, 40, and 41, with the horizontal axis indicating the "false positive rate" and the vertical axis indicating the "true positive rate." [Figure 14]FIG. 1 shows the ROC curve of a combination model of seven markers, SEQ ID NOs: 5, 8, 19, 7, 44, 47, and 53, with the horizontal axis indicating the "false positive rate" and the vertical axis indicating the "true positive rate." [Figure 15] FIG. 1 shows the ROC curve of a combination model of seven markers, SEQ ID NOs: 12, 17, 24, 28, 40, 42, and 47, with the horizontal axis indicating the "false positive rate" and the vertical axis indicating the "true positive rate." [Figure 16] FIG. 1 shows the ROC curve of a combination model of seven markers, SEQ ID NOs: 5, 18, 14, 10, 8, 19, and 27, with the "false positive rate" on the horizontal axis and the "true positive rate" on the vertical axis. [Figure 17] FIG. 1 shows the ROC curve of a combination model of seven markers, SEQ ID NOs: 6, 12, 20, 26, 24, 47, and 50, with the "false positive rate" on the horizontal axis and the "true positive rate" on the vertical axis. [Figure 18] FIG. 1 shows the ROC curve of a combination model of seven markers, SEQ ID NOs: 1, 19, 27, 34, 37, 46, and 47, with the horizontal axis showing the "false positive rate" and the vertical axis showing the "true positive rate." [Figure 19] This is a graph showing the ROC curve of the pancreatic cancer prediction model that distinguishes between chronic pancreatitis and pancreatic cancer in the training group and the test group, with the horizontal axis representing the "false positive rate" and the vertical axis representing the "true positive rate." [Figure 20] The distribution of prediction scores for the pancreatic cancer prediction model for each group is shown, with the vertical axis representing the "model predicted value." [Figure 21] FIG. 1 shows the methylation degrees of three methylation markers in the training group, with the vertical axis representing "methylation degree." [Figure 22] FIG. 1 shows the methylation levels of three methylation markers in the test group, with the vertical axis representing "methylation level." [Diagram 23] This figure shows the ROC curve of a pancreatic cancer prediction model for diagnosing pancreatic cancer in negative samples measured by conventional methods (i.e., CA19-9 measurement values ​​less than 37), with the horizontal axis showing the "false positive rate" and the vertical axis showing the "true positive rate." [Figure 24] FIG. 2 shows a flow chart for screening methylation markers based on a feature matrix according to the present application. [Diagram 25]FIG. 1 shows the distribution of prediction scores for 101 markers. [Figure 26] FIG. 1 shows the ROC curve of 101 markers. [Figure 27] FIG. 1 shows the distribution of prediction scores for six markers. [Figure 28] FIG. 1 shows ROC curves for six markers. [Figure 29] FIG. 1 shows the distribution of prediction scores for seven markers. [Diagram 30] FIG. 1 shows ROC curves for seven markers. [Diagram 31] FIG. 1 shows the distribution of prediction scores for 10 markers. [Diagram 32] FIG. 1 shows the ROC curves of 10 markers. [Diagram 33] FIG. 13 is a diagram showing the distribution of prediction scores of DUALMODEL markers. [Diagram 34] FIG. 13 is a diagram showing the ROC curve of the DUALMODEL marker. [Diagram 35] FIG. 13 shows the distribution of prediction scores for ALLMODEL markers. [Diagram 36] FIG. 13 shows the ROC curve of ALLMODEL markers. [Figure 37] FIG. 1 shows a flowchart of a technical solution according to an embodiment of the present invention. [Figure 38] FIG. 1 shows the distribution of methylation levels of three methylation markers in the training group. [Figure 39] FIG. 1 shows the distribution of methylation levels of three methylation markers in the study group. [Diagram 40] FIG. 13 shows ROC curves of the differentiation prediction models pp_model and cpp_model for CA19-9, pancreatic cancer, and pancreatitis in the test set. [Diagram 41] FIG. 13 shows the distribution of prediction scores (normalized using the maximum and minimum values) of the differentiation prediction models pp_model and cpp_model for CA19-9, pancreatic cancer, and pancreatitis in the test set samples.

[0006] Detailed Description of the Invention Hereinafter, the embodiments of the present invention will be described with reference to specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the disclosure of this specification. Definition of Terms In this application, the term "sample to be tested" generally refers to a sample that needs to be tested. For example, it can be detected whether one or more gene regions on the sample to be tested are modified. In this application, the term "cell-free nucleic acid" or "cfDNA" generally refers to DNA in a sample that is not contained within a cell when collected. For example, cell-free nucleic acid may not refer to DNA that is made non-cellular by in vitro destruction of cells or tissues. For example, cfDNA may include DNA from both normal cells and cancer cells. For example, cfDNA can be obtained from blood or plasma ("circulatory system"). For example, cfDNA can be released into the circulatory system via secretion or cell death methods such as necrosis or apoptosis. In this application, the term "complementary nucleic acid" generally refers to a nucleotide sequence that is complementary to a reference nucleotide sequence. For example, a complementary nucleic acid can be a nucleic acid molecule that optionally has an opposite orientation. For example, complementarity can refer to having the following complementary associations: guanine and cytosine; adenine and thymine; adenine and uracil. In the present application, the term "DNA region" generally refers to a sequence of two or more covalently linked naturally occurring or modified deoxyribonucleotides. For example, a DNA region of a gene can refer to the location of a particular deoxyribonucleotide sequence where the gene is located, e.g., the deoxyribonucleotide sequence encodes the gene. For example, a DNA region of the present application encompasses the entire length of the DNA region, its complementary region, or a fragment thereof. For example, at least about 20 kb of sequence upstream and downstream of the detection region provided in the present application can be used as a detection site. For example, at least about 20 kb, at least about 15 kb, at least about 10 kb, at least about 5 kb, at least about 3 kb, at least about 2 kb, at least about 1 kb, or at least about 0.5 kb of sequence upstream and downstream of the detection region provided in the present application can be used as a detection site. For example, suitable primers and probes can be designed according to the above using a microcomputer to detect methylation of a sample. In the present application, the term "modification state" generally refers to the modification state of a gene fragment, nucleotide or their base in the present application. For example, the modification state in the present application may refer to the modification state of cytosine. For example, a gene fragment having a modification state in the present application may have an altered gene expression activity. For example, the modification state in the present application may refer to the methylation modification of a base. For example, the modification state in the present application may refer to the covalent attachment of a methyl group at the 5' carbon position of a cytosine in a CpG region of genomic DNA, which may be, for example, 5-methylcytosine (5mC). For example, the modification state may refer to the presence or absence of 5-methylcytosine ("5-mCyt") in a DNA sequence. In the present application, "methylation" refers collectively to the methylation state of a gene fragment, nucleotide, or base thereof in the present application. For example, a DNA segment in which a gene is located in the present application may have methylation at one or more strands. For example, a DNA segment in which a gene is located in the present application may have methylation at one or more sites. In this application, the term "conversion" generally refers to the conversion of one or more structures to another structure. For example, the conversion in this application can be specific. For example, cytosine without methylation modification can be changed to another structure (e.g., uracil) after conversion, and cytosine with methylation modification can remain essentially unchanged after conversion. For example, cytosine without methylation modification can be cleaved after conversion, and cytosine with methylation modification can remain essentially unchanged after conversion. In this application, the term "deamination reagent" generally refers to a substance that has the ability to remove an amino group. For example, a deamination reagent can deaminate an unmodified cytosine. In this application, the term "bisulfite" generally refers to a reagent that can distinguish DNA regions with a modified state from those without a modified state. For example, bisulfite can include bisulfite, or its analogs, or combinations thereof. For example, bisulfite can deaminate the amino group of unmodified cytosine to distinguish it from modified cytosine. In this application, the term "analog" generally refers to a substance with a similar structure and / or function. For example, an analog of bisulfite can have a similar structure to bisulfite. For example, bisulfite analog can refer to a reagent that can also distinguish DNA regions with a modified state from those without a modified state.

[0007] In the present application, "methylation-sensitive restriction enzyme" generally refers to an enzyme that selectively digests nucleic acids depending on the methylation state of its recognition site. For example, in the case of a restriction enzyme that specifically cleaves when its recognition site is unmethylated, cleavage may not occur or may occur with significantly reduced efficiency when the recognition site is methylated. For a restriction enzyme that specifically cleaves when its recognition site is methylated, cleavage may not occur or may occur with significantly reduced efficiency when the recognition site is unmethylated. For example, a methylation-specific restriction enzyme can recognize a sequence that contains CG dinucleotides (e.g., cgcg or cccggg). In the present application, the term "tumor" generally refers to cells and / or tissues that exhibit at least partial loss of control during normal growth and / or development. For example, common tumor or cancer cells often lose contact inhibition and may have the ability to be invasive and / or metastasize. For example, tumors in the present application may be benign or malignant. In this application, the term "progression" generally refers to a change in disease from a less severe to a more severe state. For example, tumor progression can include an increase in the number or severity of tumors, the extent of cancer cell metastasis, or the rate at which the cancer grows or spreads. For example, tumor progression can include the progression of a cancer from a less severe to a more severe state, such as from stage I to stage II, from stage II to stage III, etc. In this application, the term "onset" generally refers to the occurrence of a pathology in an individual. For example, an individual may be diagnosed as a tumor patient if he or she develops a tumor. In this application, the term "fluorescent PCR" generally refers to quantitative or semi-quantitative PCR technology. For example, the PCR technology can be real-time quantitative polymerase chain reaction, quantitative polymerase chain reaction or kinetic polymerase chain reaction. For example, the initial amount of target nucleic acid can be quantitatively detected by using PCR amplification with the help of intercalating fluorescent dyes or sequence-specific probes, and the sequence-specific probes can contain fluorescent reporters that are detectable only when they hybridize to the target nucleic acid. In this application, the term "PCR amplification" generally refers to the polymerase chain reaction. For example, PCR amplification in this application can include any polymerase chain amplification reaction currently known for use in amplifying DNA. In this application, the term "fluorescence Ct value" generally refers to a measurement value for quantitative or semi-quantitative evaluation of a target nucleic acid. For example, it can refer to the number of amplification reaction cycles experienced when the fluorescence signal reaches a set threshold value.

[0008] Detailed Description of the Invention Based on the methylated nucleic acid fragment marker of the present application, pancreatic cancer can be effectively identified, and the present application provides a diagnostic model of the relationship between cfDNA methylation markers and pancreatic cancer based on plasma cfDNA high-throughput methylation sequencing. This model has the advantages of non-invasive, safe and convenient detection, high throughput and high detection specificity. Based on the optimal sequencing obtained in the present application, the detection cost can be effectively controlled while achieving good detection effect. Based on the DNA methylation marker of the present invention, pancreatic cancer patients can be effectively differentiated from chronic pancreatitis patients. The present invention provides a diagnostic model of the relationship between the methylation level of cfDNA methylation markers and pancreatic cancer based on plasma cfDNA high-throughput methylation sequencing. This model has the advantages of non-invasive, safe and convenient detection, high throughput and high detection specificity. Based on the optimal sequencing obtained in the present invention, the detection cost can be effectively controlled while achieving good detection effect.

[0009] The present application relates to a method for determining whether pancreatic cancer is characterized by the following genes: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX20, IKZF1, INSIG1, SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CL It was found that the methylation levels of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 genes selected from EC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2, or sequences 20 kb upstream or downstream thereof are associated with the methylation levels of the following genes:In one or more embodiments, the pancreatic cancer signature is a combination of: (1) LBX2, TBR1, EVX2, SFRP2, SYT10, CCNA1, ZFHX3; (2) TRIM58, HOXD4, INSIG1, SYT10, CCNA1, ZIC2, CLEC14A; (3) EMX1, POU3F3, TOPAZ1, ZIC2, OTX2, AHSP, TIMP2; (4) EMX1, EVX2, RPL9, SFRP2, HOXA13, SYT10, CLEC14A; (5) TBX15, EMX1, LBX2, OLIG3, SYT10, AGAP2, TBX3; (6) TR The present invention relates to the methylation level of the sequence of a gene selected from IM58, VAX2, EMX1, HOXD4, ZIC2, CLEC14A, LHX1; (7) POU3F3, HOXD8, RPL9, TBX18, SYT10, TBX3, CLEC14A; (8) TRIM58, EMX1, TLX2, EVX2, HOXD4, HOXD4, IRX4; (9) SIX3, POU3F3, TOPAZ1, RPL9, SFRP2, CLEC14A, BNC1; (10) DMRTA2, HOXD4, IRX4, INSIG1, MOS, CLEC14A, CLEC14A. The present invention provides a nucleic acid molecule comprising one or more CpGs of the above genes or fragments thereof. The present application has found that differentiation between pancreatic cancer and pancreatitis (such as chronic pancreatitis) is associated with the methylation levels of one, two or three genes selected from the following genes: SIX3, TLX2, CILP2 or sequences within 20 kb upstream or downstream thereof. In the present invention, the term "gene" includes both coding and non-coding sequences of a gene of interest on a genome. Non-coding sequences include introns, promoters, regulatory elements or sequences, and the like. Furthermore, the characteristics of pancreatic cancer are SEQ ID NO: 1 in the DMRTA2 gene region, SEQ ID NO: 2 in the FOXD3 gene region, SEQ ID NO: 3 in the TBX15 gene region, SEQ ID NO: 4 in the BCAN gene region, SEQ ID NO: 5 in the TRIM58 gene region, SEQ ID NO: 6 in the SIX3 gene region, SEQ ID NO: 7 in the VAX2 gene region, SEQ ID NO: 8 in the EMX1 gene region, SEQ ID NO: 9 in the LBX2 gene region, SEQ ID NO: 10 in the TLX2 gene region, SEQ ID NO: 11 and SEQ ID NO: 12 in the POU3F3 gene region, SEQ ID NO: 13 in the TBR1 gene region, EV SEQ ID NO:14 and SEQ ID NO:15 in the X2 gene region, SEQ ID NO:16 in the HOXD12 gene region, SEQ ID NO:17 in the HOXD8 gene region, SEQ ID NO:18 and SEQ ID NO:19 in the HOXD4 gene region, SEQ ID NO:20 in the TOPAZ1 gene region, SEQ ID NO:21 in the SHOX2 gene region, SEQ ID NO:22 in the DRD5 gene region, SEQ ID NO:23 and SEQ ID NO:24 in the RPL9 gene region, SEQ ID NO:25 in the HOPX gene region, SEQ ID NO:26 in the SFRP2 gene region, SEQ ID NO:27 in the IRX4 gene region, SEQ ID NO:28 in the TBX18 gene region, SEQ ID NO: 29 in the LIG3 gene region, SEQ ID NO: 30 in the ULBP1 gene region, SEQ ID NO: 31 in the HOXA13 gene region, SEQ ID NO: 32 in the TBX20 gene region, SEQ ID NO: 33 in the IKZF1 gene region, SEQ ID NO: 34 in the INSIG1 gene region, SEQ ID NO: 35 in the SOX7 gene region, SEQ ID NO: 36 in the EBF2 gene region, SEQ ID NO: 37 in the MOS gene region, SEQ ID NO: 38 in the MKX gene region, SEQ ID NO: 39 in the KCNA6 gene region, SEQ ID NO: 40 in the SYT10 gene region, SEQ ID NO: 41 in the AGAP2 gene region, and SEQ ID NO: 42 in the TBX3 gene region. SEQ ID NO: 42 in the CCNA1 gene region, SEQ ID NO: 43 in the CCNA1 gene region, SEQ ID NO: 44 and SEQ ID NO: 45 in the ZIC2 gene region, SEQ ID NO: 46 and SEQ ID NO: 47 in the CLEC14A gene region, SEQ ID NO: 48 in the OTX2 gene region, SEQ ID NO: 49 in the C14orf39 gene region, SEQ ID NO: 50 in the BNC1 gene region, SEQ ID NO: 51 in the AHSP gene region, SEQ ID NO: 52 in the ZFHX3 gene region, SEQ ID NO: 53 in the LHX1 gene region, SEQ ID NO: 54 in the TIMP2 gene region,The methylation level of any one or random 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55 segments or all 56 segments selected from SEQ ID NO: 55 in the ZNF750 gene region and SEQ ID NO: 56 in the SIM2 gene region is related to the methylation level of any one or random 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55 segments or all 56 segments.

[0010] In some embodiments, the characteristic of pancreatic cancer is a combination of: (1) SEQ ID NO:9, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:26, SEQ ID NO:40, SEQ ID NO:43, SEQ ID NO:52; (2) SEQ ID NO:5, SEQ ID NO:18, SEQ ID NO:34, SEQ ID NO:40, SEQ ID NO:43, SEQ ID NO:45, SEQ ID NO:46; (3) SEQ ID NO:8, SEQ ID NO:11, SEQ ID NO:20, SEQ ID NO:44, SEQ ID NO:48, SEQ ID NO:51, SEQ ID NO:54; (4) SEQ ID NO:8, SEQ ID NO:14, SEQ ID NO:24, SEQ ID NO:26, SEQ ID NO:31, SEQ ID NO:40, SEQ ID NO:46; (5) SEQ ID NO:3, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:29, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42; (6) SEQ ID NO: Number 5 is related to the methylation level of a sequence selected from SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:19, SEQ ID NO:44, SEQ ID NO:47, SEQ ID NO:53, (7) SEQ ID NO:12, SEQ ID NO:17, SEQ ID NO:24, SEQ ID NO:28, SEQ ID NO:40, SEQ ID NO:42, SEQ ID NO:47, (8) SEQ ID NO:5, SEQ ID NO:8, SEQ ID NO:10, SEQ ID NO:14, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:27, (9) SEQ ID NO:6, SEQ ID NO:12, SEQ ID NO:20, SEQ ID NO:24, SEQ ID NO:26, SEQ ID NO:47, SEQ ID NO:50, (10) SEQ ID NO:1, SEQ ID NO:19, SEQ ID NO:27, SEQ ID NO:34, SEQ ID NO:37, SEQ ID NO:46, SEQ ID NO:47 or their complementary sequences. The "pancreatic cancer-associated sequences" described herein include the above 50 genes, sequences within 20 kb upstream or downstream thereof, the above 56 sequences (SEQ ID NOs: 1 to 56) or complementary sequences thereof, subregions, and / or processed sequences.

[0011] The locations of the above 56 sequences in human chromosomes are as follows: SEQ ID NO:1: 50884507-50885207bps of chr1, SEQ ID NO:2: 63788611-63789152bps of chr1, SEQ ID NO:3: 119522143-119522719bps of chr1, SEQ ID NO:4: 156611710-156612211bps of chr1, SEQ ID NO:5: 248020391-248020979bps of chr1, SEQ ID NO:6: 45028796-45029378bps of chr2, SEQ ID NO:7: 71 115731-71116272bps, SEQ ID NO:8: 73147334-73147835bps of chr2, SEQ ID NO:9: 74726401-74726922bps of chr2, SEQ ID NO:10: 74742861-74743362bps of chr2, SEQ ID NO:11: 105480130-105480830bps of chr2, SEQ ID NO:12: 105480157-105480659bps of chr2, SEQ ID NO:13: 162280233-162280736bps of chr2, SEQ ID NO:14: 176945095- 176945601bps, SEQ ID NO:15: 176945320-176945821bps of chr2, SEQ ID NO:16: 176964629-176965209bps of chr2, SEQ ID NO:17: 176994514-176995015bps of chr2, SEQ ID NO:18: 177016987-177017501bps of chr2, SEQ ID NO:19: 177024355-177024866bps of chr2, SEQ ID NO:20: 44063336-44063893bps of chr3, SEQ ID NO:21: 157812057 of chr3 -157812604bps, SEQ ID NO:22: 9783025-9783527bps of chr4, SEQ ID NO:23: 39448278-39448779bps of chr4, SEQ ID NO:24: 39448327-39448879bps of chr4, SEQ ID NO:25: 57521127-57521736bps of chr4, SEQ ID NO:26: 154709362-154709867bps of chr4, SEQ ID NO:27: 1876136-1876645bps of chr5, SEQ ID NO:28: 85476916-85477417bps of chr6,SEQ ID NO:29: 137814499-137815053bps of chr6, SEQ ID NO:30: 150285594-150286095bps of chr6, SEQ ID NO:31: 27244522-27245037bps of chr7, SEQ ID NO:32: 35293435-35293950bps of chr7, SEQ ID NO:33: 50343543-50344243bps of chr7, SEQ ID NO:34: 155167312-155167828bps of chr7, SEQ ID NO:35: 10588692-10589253bps of chr8, SEQ ID NO:36: 25907648-25908150bps, SEQ ID NO:37: 57069450-57070150bps of chr8, SEQ ID NO:38: 28034404-28034908bps of chr10, SEQ ID NO:39: 4918941-4919489bps of chr12, SEQ ID NO:40: 33592612-33593117bps of chr12, SEQ ID NO:41: 58131095-58131654bps of chr12, SEQ ID NO:42: 115124763-115125348bps of chr12, SEQ ID NO:43: 37005444-370 05945bps, SEQ ID NO:44: 100649468-100649995bps of chr13, SEQ ID NO:45: 100649513-100650027bps of chr13, SEQ ID NO:46: 38724419-38724935bps of chr14, SEQ ID NO:47: 38724602-38725108bps of chr14, SEQ ID NO:48: 57275646-57276162bps of chr14, SEQ ID NO:49: 60952384-60952933bps of chr14, SEQ ID NO:50: 83952059-83952595bp of chr15 s, SEQ ID NO:51: 31579970-31580561bps of chr16, SEQ ID NO:52: 73096773-73097473bps of chr16, SEQ ID NO:53: 35299694-35300224bps of chr17, SEQ ID NO:54: 76929623-76930176bps of chr17, SEQ ID NO:55: 80846617-80847210bps of chr17, SEQ ID NO:56: 38081247-38081752bps of chr21, where the bases and methylation sites of the sequences are numbered according to the reference genome HG19.

[0012] In one or more embodiments, the nucleic acid molecules described herein are selected from the group consisting of DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX20, IKZF1, INSIG1, A fragment of one or more genes selected from SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CLEC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2, the length of the fragment is 1 bp-1 kb, preferably 1 bp-700 bp, and the fragment comprises one or more methylation sites of the corresponding gene in the chromosomal region.

[0013] Methylation sites in the genes or fragments thereof described herein include, but are not limited to, chr1 chromosome 50884514, 50884531, 50884533, 50884541, 50884544, 50884547, 50884550, 50884552, 50884566, 50884582, 50884586, 50884589, 50884591, 50884598, 50884606, 50884610, 50884612, 50884615, 50884621, 50884633, 50884646, 50884649, 50884658, 50884662, 50884673, 50884682, 50884691, 50884699, 50884702, 50884724, 50884732, 50884735, 50884742, 50884751, 50884754, 5088477 4, 50884777, 50884780, 50884783, 50884786, 50884789, 50884792, 50884795, 50884798, 50884801, 50884804, 50884807, 50884809, 50884820, 508848 22, 50884825, 50884849, 50884852, 50884868, 50884871, 50884885, 50884889, 50884902, 50884924, 50884939, 50884942, 50884945, 50884948, 5088 4975, 50884980, 50884983, 50884999, 50885001, 63788628, 63788660, 63788672, 63788685, 63788689, 63788703, 63788706, 63788709, 63788721, 637 88741, 63788744, 63788747, 63788753, 63788759, 63788768, 63788776, 63788785, 63788789, 63788795, 63788804, 63788816, 63788822, 63788825, 6 3788828, 63788849, 63788852, 63788861, 63788870, 63788872, 63788878, 63788881, 63788889, 63788897, 63788902, 63788906, 63788917, 63788920,63788933、63788947、63788983、63788987、63788993、63788999、63789004、63789011、63789014、63789020、63789022、63789025、63789031、63789035、63789047、63789056、63789059、63789068、63789071、63789073、63789077、63789080、63789083、63789092、63789094、63789101、63789106、63789109、63789124、 119522172、119522188、119522190、119522233、119522239、119522313、119522368、119522386、119522393、119522409、119522425、119522427、119522436、119522440、119522444、119522446、119522449、119522451、119522456、119522459、119522464、119522469、119522474、119522486、119522488、119522500、119522502、119522516、119522529、119522537、119522548、119522550、119522559、119522563、119522566、119522571、119522577、119522579、119522582、119522594、119522599、119522607、119522615、119522621、119522629、119522631、119522637、119522665、119522673、 156611713、156611720、156611733、156611737、156611749、156611752、156611761、156611767、156611784、156611791、156611797、156611802、156611811、156611813、156611819、156611830、156611836、156611842、156611851、156611862、156611890、156611893、156611902、156611905、156611915、156611926、156611945、156611949、156611951、156611960、156611963、156611994、156612002、156612015、156612024、156612034、156612042、156612044、156612079、156612087、156612090、156612094、156612097、156612105、156612140、156612147、156612166、156612188、156612191、156612204、156612209、 248020399、248020410、248020436、248020447、248020450、248020453、248020470、248020495、248020497、248020507、248020512、248020516、248020520、248020526、248020536、248020543、248020559、248020562、248020566、248020573、248020579、248020581、248020589、248020591、248020598、248020625、248020632、248020641、248020671、248020680、248020688、248020692、248020695、248020697、248020704、248020707、248020713、248020721、248020729、248020741、248020748、248020756、248020765、248020775、248020791、248020795、248020798、248020812、248020814、248020821、248020826、248020828、248020831、248020836、248020838、248020840、248020845、248020848、248020861、248020869、248020878、248020883、248020886、248020902、248020905、248020908、248020914、248020925、248020930、248020934、248020937、248020940、248020953、248020956、248020975;

[0014] chr2 chromosome 45028802、45028816、45028832、45028839、45028956、45028961、45028965、45028973、45029004、45029017、45029035、45029046、45029057、45029060、45029063、45029065、45029071、45029106、45029112、45029117、45029128、45029146、45029147 5029176、45029179、45029184、45029189、45029192、45029195、45029218、45029226、45029228、45029231、45029235、45029263、45029273、45029285、45029288、45029295、45029307、45029317、45029353、45029357、71115760、71115787、71115789、7 1115837、71115928、71115936、71115948、71115962、71115968、71115978、71115981、71115983、71115985、71115987、71115994、71116000、71116022、71116024、71116030、71116036、71116047、71116054、71116067、71116096、71116101、71116103、 71116107、71116117、71116119、71116130、71116137、71116141、71116152、71116154、71116158、71116174、71116188、71116190、71116194、71116203、71116215、71116226、71116233、71116242、71116257、71116259、71116261、71116268、71116271、 73147340、73147350、73147364、73147369、73147382、73147405、73147408、73147432、73147438、73147444、73147481、73147491、73147493、73147523、73147529、73147537、73147559、73147571、73147582、73147584、73147592、73147595、73147598、73147607、73147613、73147620、73147623、73147631、73147644、73147668、73147673、73147678、73147687、73147690、73147693、73147695、73147710、73147720、73147738、73147755、73147767、73147771、73147789、73147798、73147803、73147811、73147814、73147816、73147822、73147825、73147827、73147829、 74726438、74726440、74726449、74726478、74726480、74726482、74726484、74726493、74726495、74726524、74726526、74726533、74726536、74726539、74726548、74726554、74726569、74726572、74726585、74726597、74726599、74726616、74726633、74726642、74726649、74726651、74726656、74726668、74726672、74726682、74726687、74726695、74726700、74726710、74726716、74726734、74726746、74726760、74726766、74726772、74726784、74726791、74726809、74726828、74726833、74726835、74726861、74726892、74726894、74726908、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、74743352、74743357、 105480130、105480161、105480179、105480198、105480207、105480210、105480212、105480226、105480254、105480258、105480272、105480291、105480337、105480360、105480377、105480383、105480387、105480390、105480407、105480409、105480412、105480424、105480426、105480429、105480433、105480438、105480461、105480464、105480475、105480481、105480488、105480490、105480503、105480546、105480556、105480571、105480577、105480581、105480604、105480621、105480623、105480630、105480634、105480637、 162280237、162280239、162280242、162280245、162280249、162280257、162280263、162280289、162280293、162280297、162280306、162280309、162280314、162280317、162280327、162280331、162280341、162280351、162280362、162280368、162280393、162280396、162280398、162280402、162280405、162280407、162280409、162280417、162280420、162280438、162280447、162280459、162280462、162280466、162280470、162280473、162280479、162280483、162280486、162280489、162280492、162280498、162280519、162280534、162280539、162280548、162280561、162280570、162280575、162280585、162280598、162280604、162280611、162280614、162280618、162280623、162280627、162280633、162280641、162280647、162280657、162280673、162280681、162280693、162280708、162280728、 176945102、176945119、176945122、176945132、176945134、176945137、176945141、176945144、176945147、176945150、176945159、176945165、176945170、176945177、176945179、176945186、176945188、176945198、176945200、176945213、176945215、176945218、176945222、176945224、176945250、176945270、176945274、176945288、176945296、176945298、176945316、176945329、176945336、176945339、176945345、176945347、176945351、176945354、176945356、176945372、176945374、176945378、176945381、176945384、176945387、176945392、176945398、176945402、176945417、176945422、176945426、176945452、176945458、176945462、176945464、176945468、176945497、176945507、176945526、176945532、176945547、176945550、176945570、176945580、176945582、176945585、176945604、176945609、176945647、176945679、176945695、176945732、176945747、176945750、176945761、176945770、176945789、176945791、176945795、176964640、176964642、176964663、176964665、176964667、176964670、176964672、176964685、176964690、176964694、176964703、176964709、176964711、176964720、176964724、176964736、176964739、176964747、176964769、176964778、176964805、176964811、176964834、176964838、176964843、176964847、176964863、176964865、176964869、176964875、176964879、176964886、176964892、176964930、176964946、176964959、176964966、176964969、176964978、176965003、176965021、176965035、176965062、176965065、176965069、176965085、176965099、176965102、176965109、176965125、176965130、176965140、176965186、176965196、176994516、176994525、176994528、176994531、176994537、176994546、176994557、176994559、176994568、176994570、176994583、176994586、176994623、176994637、176994654、176994661、176994665、176994682、176994688、176994728、176994738、176994747、176994750、176994753、176994764、176994768、176994773、176994778、176994780、176994783、176994793、176994801、176994804、176994807、176994809、176994811、176994822、176994830、176994832、176994837、176994839、176994848、176994851、176994853、176994859、176994864、176994867、176994871、176994880、176994890、176994905、176994909、176994911、176994931、176994934、176994936、176994938、176994942、176994944、176994948、176994952、176994961、176994964、176994971、176994974、176994980、176994983、176994986、176994996、176995011、176995013、 177017050、177017079、177017124、177017173、177017179、177017182、177017193、177017211、177017223、177017225、177017227、177017237、177017239、177017246、177017251、177017253、177017267、177017270、177017276、177017296、177017300、177017331、177017352、177017368、177017374、177017378、177017389、177017446、177017449、177017452、177017463、177017483、177017488、177024359、177024367、177024415、177024502、177024514、177024528、177024531、177024540、177024548、177024550、177024558、177024582、177024605、177024616、177024619、177024634、177024642、177024655、177024698、177024709、177024714、177024723、177024725、177024748、177024756、177024769、177024771、177024776、177024783、177024800、177024836、177024838、177024856、177024861;

[0015] Chr3 chromosome 44063356, 44063391, 44063404, 44063411, 44063417, 44063423, 44063450, 44063516, 44063541, 44063544, 44063559, 44063565, 44063567, 44063574, 44063586, 44063593, 44063602, 44063606, 44063620, 440636 4063633、44063638、44063643、44063649、44063657、44063660、44063662、44063682、44063686、44063719、44063745、44063756、44063768、44063779、44063807、44063821、44063832、44063836、44063858、44063877、 157812071、157812085、157812092、157812117、157812131、157812152、157812170、157812173、157812175、157812184、157812206、157812212、157812226、157812256、157812259、157812275、157812277、157812287、157812294、157812296、157812302、157812305、157812307、157812312、 157812319、157812321、157812329、157812331、157812334、157812354、157812358、157812369、157812380、157812383、157812385、157812404、157812411、157812414、157812420、157812437、157812442、157812457、157812468、157812470、157812475、157812498、157812542、157812548;

[0016] chr4 chromosomes 9783036, 9783050, 9783059, 9783075, 9783080, 9783097, 9783105, 9783112, 9783120, 9783126, 9783142, 9783144, 9783153, 9783160, 9783166, 9783185, 9783192, 9783196, 9783198, 9783206, 9783213, 9783218, 9783220, 9783233, 9783244, 9783246, 9783252, 9783271, 9783275, 9783277, 9783304, 9783322, 9783327, 9783342, 9783348, 9783354, 9783358, 9783361, 9783363, 9783376, 9783398, 9783409, 9783425, 9783427, 9783442, 9783449, 9783467, 9783492, 9783494, 9783496, 9783501, 9783508, 9783511, 39448284, 39448302, 39448320, 39448323, 39448340, 39448343, 39448347, 39448365, 39448422, 39448432, 39448453, 39448464, 39448473, 39448478, 39 448481, 39448503, 39448516, 39448524, 39448528, 39448549, 39448551, 39448557, 39448562, 39448568, 39448575, 39448577, 39448586, 39448593, 3944 8613, 39448625, 39448629, 39448633, 39448647, 39448653, 39448662, 39448665, 39448670, 39448683, 39448695, 39448697, 39448729, 39448732, 394487 48, 39448757, 39448759, 39448767, 39448773, 39448796, 39448800, 39448809, 39448811, 39448836, 39448845, 39448857, 39448864, 39448869, 39448874, 57521138、57521209、57521237、57521297、57521304、57521310、57521336、57521348、57521377、57521397、57521411、57521419、57521426、57521442、57521449、57521486、57521506、57521518、57521537、57521545、57521581、57521603、57521622、57521631、57521652、57521657、57521665、57521680、57521687、57521701、57521716、57521725、57521733、 154709378、154709414、154709425、154709441、154709492、154709513、154709522、154709540、154709557、154709561、154709576、154709591、154709597、154709607、154709612、154709617、154709633、154709640、154709663、154709675、154709684、154709690、154709697、154709721、154709745、154709756、154709759、154709789、154709812、154709828、154709834;

[0017] Chr5 chromosome 1876139, 1876168, 1876200, 1876208, 1876213, 1876215, 1876286, 1876290, 1876298, 1876308, 1876311, 1876337, 1876339, 1876347, 1876354, 1876368, 1876372, 1876374, 1876386, 1876395, 1876397, 1876399, 1876403, 1876420, 1876424, 1876432, 1876436, 1876449, 1876456, 1876459, 1876463, 1876483, 1876498, 1876525, 1876527, 1876557, 1876563, 1876570, 1876576, 1876605, 1876630, 1876634, 1876638;

[0018] chr6 chromosome 85476921, 85476930, 85476974, 85477014, 85477032, 85477035, 85477070, 85477083, 85477106, 85477124, 85477151, 85477153, 85477166, 85477175, 85477186, 85477217, 85477228, 85477230, 85477236, 85477245, 85477249, 85477251, 85477253, 85477261, 85477283, 137814512、137814516、137814523、137814548、137814558、137814561、137814564、137814567、137814620、137814636、137814638、137814642、137814645、137814654、137814666、137814679、137814689、137814695、137814707、137814710、137814717、137814723、137814728、137814744、137814746、137814749、137814768、137814776、137814786、137814788、137814792、137814794、137814803、137814807、137814818、137814824、137814837、137814860、137814920、137814935、137814952、137814957、137814960、137814969、137814971、137814986、137814988、137814995、137815016、137815024、137815030、137815034、137815036、137815040、 150285620、150285634、150285641、150285652、150285659、150285661、150285670、150285677、150285688、150285695、150285697、150285706、150285713、150285715、150285724、150285731、150285733、150285742、150285760、150285767、150285769、150285775、150285778、150285788、150285813、150285815、150285826、150285829、150285844、150285860、150285887、150285890、150285892、150285901、150285908、150285910、150285926、150285928、150285937、150285944、150285956、150285963、150285966、150285974、150285981、150285983、150285992、150285999、150286001、150286010、150286017、150286019、150286028、150286035、150286038、150286046、150286055、150286063、150286073、150286082、150286089、150286091;

[0019] Chr7 chromosome 27244531, 27244533, 27244537, 27244555, 27244564, 27244578, 27244603, 27244609, 27244612, 27244619, 27244621, 27244627, 27244631, 27244657、27244673、27244702、27244704、27244714、27244723、27244755、27244772、27244780、27244787、27244789、27244798、27244800、27244810 、27244833、27244856、27244869、27244874、27244881、27244885、27244887、27244892、27244897、27244907、27244911、27244917、27244920、272449 31, 27244948, 27244951, 27244980, 27244982, 27244986, 27245014, 27245018, 35293441, 35293451, 35293470, 35293479, 35293482, 35293488, 35293 492、35293497、35293502、35293506、35293514、35293531、35293537、35293543、35293588、35293590、35293621、35293652、35293656、35293658、352 93670、35293676、35293685、35293687、35293690、35293692、35293700、35293717、35293721、35293731、35293747、35293750、35293753、35293759、35 293767、35293780、35293783、35293790、35293796、35293809、35293812、35293815、35293821、35293827、35293829、35293834、35293838、35293840、3 5293847、35293849、35293860、35293863、35293867、35293869、35293879、35293884、35293892、35293940、50343545、50343548、50343552、50343555、50343562、50343566、50343572、50343574、50343577、50343579、50343587、50343603、50343605、50343608、50343611、50343624、50343628、50343630、50343635、50343637、50343639、50343648、50343651、50343654、50343656、50343659、50343663、50343669、50343672、50343674、50343678、50343682、50343693、50343696、50343699、50343702、50343714、50343719、50343725、50343728、50343731、50343736、50343739、50343758、50343765、50343768、50343770、50343785、50343789、50343791、50343805、50343813、50343822、50343824、50343826、50343829、50343831、50343833、50343838、50343847、50343850、50343853、50343858、50343864、50343869、50343872、50343883、50343890、50343897、50343907、50343909、50343914、50343926、50343934、50343939、50343946、50343950、50343959、50343961、50343963、50343969、50343974、50343980、50343990、50344001、50344007、50344011、50344028、50344041、155167320、155167333、155167340、155167343、155167345、155167347、155167350、155167357、155167379、155167382、155167394、155167401、155167423、155167430、155167467、155167478、155167480、155167486、155167499、155167505、155167507、155167511、155167513、155167516、155167518、155167528、155167543、155167552、155167555、155167560、155167562、155167568、155167570、155167578、155167602、155167608、155167611、155167617、155167662、155167702、155167707、155167716、155167718、155167739、155167750、155167753、155167757、155167759、155167771、155167773、155167791、155167801、155167803、155167805、155167813、155167819、155167821、155167827;、

[0020] Chr8 chromosome 10588729, 10588742, 10588820, 10588833, 10588841, 10588851, 10588857, 10588865, 10588867, 10588883, 10588888, 10588895, 10588938, 10588942、10588946、10588948、10588951、10588959、10588992、10589003、10589007、10589009、10589016、10589034、10589060、10589062、10589076 、10589079、10589093、10589152、10589193、10589206、10589241、25907660、25907702、25907709、25907724、25907747、25907752、25907754、259077 57, 25907769, 25907796, 25907800, 25907814, 25907818, 25907821, 25907824, 25907838, 25907848, 25907866, 25907874, 25907880, 25907884, 25907 893、25907898、25907900、25907902、25907906、25907918、25907947、25907976、25908055、25908057、25908064、25908071、25908098、25908101、570 69480、57069544、57069569、57069606、57069631、57069648、57069688、57069698、57069709、57069712、57069722、57069735、57069739、57069755、57 069764、57069773、57069775、57069784、57069786、57069791、57069793、57069800、57069812、57069816、57069823、57069825、57069827、57069839、5 7069842、57069847、57069851、57069853、57069884、57069889、57069894、57069907、57069914、57069919、57069931、57069940、57069948、57069958、57069968、57069973、57069978、57070013、57070035、57070038、57070042、57070046、57070066、57070079、57070087、57070091、57070126、57070143;

[0021] Chr10 chromosome 28034412, 28034415, 28034418, 28034442, 28034444, 28034467, 28034469, 28034494, 28034501, 28034505, 28034545, 28034556, 28034559, 28034568, 28034582、28034591、28034596、28034599、28034605、28034616、28034619、28034622、28034624、28034645、28034651、28034654、28034658、28034669、28034682、 28034687、28034697、28034711、28034714、28034727、28034729、28034739、28034741、28034751、28034757、28034760、28034763、28034768、28034787、28034790、 28034792、28034794、28034797、28034801、28034816、28034843、28034853、28034856、28034867、28034871、28034873、28034882、28034888、28034892、28034907;

[0022] Chr12 chromosome 4918962, 4918966, 4918968, 4918975, 4918982, 4919001, 4919056, 4919065, 4919079, 4919081, 4919086, 4919095, 4919097, 4919118, 4919 124、4919138、4919145、4919147、4919164、4919170、4919173、4919184、4919191、4919199、4919215、4919230、4919236、4919239、4919242、4919253、4 919260、4919281、4919293、4919300、4919303、4919309、4919327、4919331、4919351、4919358、4919376、4919386、4919395、4919401、4919408、491942 1. 4919424, 4919430, 4919438, 4919453, 4919465, 4919469, 4919475, 4919486, 33592615, 33592629, 33592635, 33592642, 33592659, 33592661, 33592 663、33592674、33592681、33592683、33592692、33592704、33592707、33592709、33592711、33592715、33592720、33592725、33592727、33592744、335 92774、33592798、33592803、33592811、33592831、33592848、33592859、33592862、33592865、33592867、33592875、33592882、33592885、33592887、33 592891、33592905、33592908、33592913、33592915、33592923、33592931、33592933、33592953、33592955、33592977、33592981、33592986、33592989、3 3592998、33593004、33593017、33593035、33593049、33593090、33593093、58131100、58131102、58131111、58131133、58131154、58131168、58131175、58131181、58131224、58131242、58131261、58131277、58131300、58131303、58131306、58131309、58131312、58131318、58131321、58131331、58131345、58131348、58131384、58131390、58131404、58131412、58131414、58131426、58131429、58131445、58131453、58131475、58131478、58131487、58131503、58131510、58131523、58131546、58131549、58131553、58131557、58131564、58131571、58131576、58131586、58131605、58131608、58131624、58131642、115124768、115124773、115124782、115124811、115124838、115124853、115124871、115124874、115124894、115124904、115124924、115124930、115124933、115124935、115124946、115124970、115124973、115124981、115124999、115125013、115125034、115125053、115125060、115125098、115125107、115125114、115125121、115125131、115125141、115125151、115125177、115125192、115125225、115125305、115125335;、

[0023] chr13 chromosome 37005452、37005489、37005501、37005520、37005551、37005553、37005557、37005562、37005566、37005570、37005582、37005596、37005608、37005629、37005633、37005635、37005673、37005678、37005686、37005694、37005704、37005706、37005721、37005732、37005738、370057 41、37005745、37005773、37005778、37005794、37005801、37005805、37005814、37005816、37005821、37005833、37005835、37005844、37005855、37005857、37005878、37005881、37005883、37005892、37005899、37005909、37005924、37005929、37005934、37005939、37005941、100649486、1 00649489、100649519、100649538、100649567、100649569、100649577、100649584、100649601、100649603、100649605、100649623、100649625、100649628、100649648、100649671、100649673、100649686、100649689、100649691、100649701、100649705、100649715、100649718、100649721、 649725、100649731、100649734、100649738、100649740、100649745、100649763、100649769、100649777、100649785、100649792、100649800、100649847、100649886、100649912、100649915、100649917、100649941、100649945、100649949、100649965、100649975、100649982、100650005;

[0024] Chr14 chromosome 38724435, 38724459, 38724473, 38724486, 38724507, 38724511, 38724527, 38724531, 38724534, 38724540, 38724544, 38724546, 38724565 、38724578、38724586、38724597、38724624、38724627、38724646、38724648、38724650、38724669、38724675、38724680、38724682、38724685、3872472 6. 38724732, 38724734, 38724746, 38724765, 38724771, 38724780, 38724796, 38724798, 38724806, 38724808, 38724810, 38724821, 38724847, 387248 52, 38724858, 38724864, 38724867, 38724873, 38724896, 38724906, 38724929, 38724935, 38724945, 38724978, 38724995, 38725003, 38725005, 38725 014、38725016、38725023、38725026、38725030、38725034、38725038、38725048、38725058、38725077、38725081、38725088、38725101、57275669、572 75674、57275677、57275681、57275683、57275687、57275690、57275706、57275725、57275749、57275752、57275761、57275768、57275772、57275778、57 275785、57275821、57275823、57275827、57275829、57275831、57275835、57275852、57275874、57275876、57275885、57275896、57275908、57275912、5 7275914、57275924、57275956、57275967、57275969、57275971、57275981、57275988、57275993、57275995、57276000、57276031、57276035、57276039、57276057、57276066、57276073、57276090、60952394、60952398、60952405、60952418、60952421、60952425、60952464、60952468、60952482、60952500、60952503、60952505、60952517、60952522、60952544、60952550、60952554、60952593、60952599、60952615、60952618、60952634、60952658、60952683、60952687、60952730、60952738、60952755、60952762、60952781、60952791、60952799、60952827、60952829、60952836、60952839、60952841、60952848、60952855、60952857、60952870、60952876、60952878、60952887、60952896、60952898、60952908、60952919、60952921、60952931;、

[0025] Chr15 chromosome 83952068, 83952081, 83952084, 83952087, 83952095, 83952105, 83952108, 83952114, 83952125, 83952135, 83952140, 83952156, 83952160, 83952162, 83952175, 8395218 52178、83952181、83952184、83952188、83952200、83952206、83952209、83952214、83952220、83952225、83952229、83952236、83952238、83952242、83952266、83952285、83952 291、83952298、83952309、83952314、83952317、83952345、83952352、83952358、83952360、83952367、83952406、83952411、83952414、83952418、83952420、83952425、8395243 0, 83952453, 83952464, 83952472, 83952486, 83952496, 83952498, 83952500, 83952506, 83952508, 83952527, 83952553, 83952559, 83952566, 83952570, 83952582, 83952592;

[0026] Chr16 chromosome 31579976, 31580071, 31580078, 31580081, 31580089, 31580100, 31580110, 31580117, 31580138, 31580150, 31580153, 31580159, 31580165, 31580220, 31580246, 31580254, 31580269, 3158028 7. 31580296, 31580299, 31580309, 31580311, 31580316, 31580343, 31580424, 31580496, 31580524, 31580560, 73096786, 73096842, 73096889, 73096894, 73096903, 73096914, 73096923, 73096929, 73096 934、73096943、73096948、73096966、73096970、73096979、73097000、73097015、73097017、73097019、73097028、73097037、73097045、73097057、73097060、73097066、73097069、73097078、73097080、73 097082, 73097084, 73097108, 73097114, 73097142, 73097156, 73097183, 73097260, 73097267, 73097284, 73097296, 73097301, 73097329, 73097357, 73097364, 73097377, 73097381, 73097387, 73097470;

[0027] Chr17 chromosome 35299698, 35299703, 35299710, 35299719, 35299729, 35299731, 35299741, 35299746, 35299776, 35299813, 35299816, 35299822, 35299837, 35299850, 35299877, 35299885, 35299913, 35299915, 35299926, 35299928, 35299933, 3529993 5. 35299944, 35299946, 35299963, 35299966, 35299972, 35299974, 35299990, 35299996, 35299999, 35300006, 35300010, 35300020, 35300027, 35300036, 35300039, 35300044, 35300059, 35300068, 35300074, 35300086, 35300097, 35300109, 35300 115、35300146、35300151、35300163、35300167、35300172、35300196、35300202、35300214、35300217、35300221、76929645、76929709、76929713、76929742、76929769、76929829、76929873、76929926、76929982、76930043、76930095、76930148、76 930169、80846623、80846652、80846683、80846709、80846717、80846730、80846745、80846763、80846794、80846860、80846867、80846886、80846960、80846965、80847079、80847092、80847115、80847128、80847137、80847153、80847158、80847209;

[0028] chr21 chromosome 38081248, 38081253, 38081300, 38081303, 38081306, 38081321, 38081327, 38081333, 38081341, 38081344, 38081352, 38 081354, 38081356, 38081363, 38081394, 38081396, 38081407, 38081421, 38081430, 38081443, 38081454, 38081461, 38081478, 380 81480, 38081492, 38081497, 38081499, 38081502, 38081514, 38081517, 38081520, 38081537, 38081557, 38081563, 38081566, 38081577, 38081583, 38081586, 38081606, 38081625, 38081642, 38081665, 38081695, 38081707, 38081719, 38081725, 38081732. The bases of the methylation sites are numbered relative to the reference genome HG19.

[0029] In one or more embodiments, differentiation between pancreatic cancer and pancreatitis correlates with the methylation level of sequences from genes selected from any of the following combinations: (1) SIX3, TLX2; (2) SIX3, CILP2; (3) TLX2, CILP2; (4) SIX3, TLX2, CILP2. The present invention provides nucleic acid molecules comprising one or more CpGs of the above genes or fragments thereof.

[0030] In addition, the distinction between pancreatic cancer and pancreatitis is related to the methylation level of any one segment selected from sequence number 57 in the SIX3 gene region, sequence number 58 in the TLX2 gene region, and sequence number 59 in the CILP2 gene region, or any two or all three random segments. In some embodiments, differentiation between pancreatic cancer and pancreatitis correlates with the methylation level of a sequence selected from any one of the group consisting of: (1) SEQ ID NO:57, SEQ ID NO:58; (2) SEQ ID NO:57, SEQ ID NO:59; (3) SEQ ID NO:58, SEQ ID NO:59; (4) SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59, or complementary sequences thereof. As used herein, the term "sequences relevant to the differentiation between pancreatic cancer and pancreatitis" encompasses the above-mentioned three genes, sequences within 20 kb upstream or downstream thereof, the above-mentioned three sequences (SEQ ID NOs: 57 to 59), or complementary sequences thereof. The locations of the above three sequences in human chromosomes are as follows: SEQ ID NO:57: 45028785-45029307 in chr2, SEQ ID NO:58: 74742834-74743351 in chr2, SEQ ID NO:59: 19650745-19651270 in chr19, where the bases and methylation sites of the sequences are numbered corresponding 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 fragments are 1 bp-1 kb in length, preferably 1 bp-700 bp in length, and the fragments comprise one or more methylation sites of the corresponding genes in the chromosomal region. Methylation sites in the genes or fragments thereof described herein include, but are not limited to, the following: 45028802, 45028816, 45028832, 45028839, 45028956, 45028961, 45028965, 45028973, 45029004, 45029017, 45029035, 45029046, 45029057, 45029060, 45029063, 45029065, 45029071, 450290 106, 45029112, 45029117, 45029128, 45029146, 45029176, 45029179, 45029184, 45029189, 45029192, 45029195, 45029218, 45029226, 45029228, 45029231, 45029235, 45029263, 45029273, 45029285, 45029288, 45029295,74742838, 74742840, 74742844, 747 42855, 74742879, 74742882, 74742891, 74742913, 74742922, 74742925, 74742942, 74742950, ​​74742953, 74742967, 74742981, 74742984, 74742996, 74743004, 74743006, 74743009, 74743011, 74743015, 74743021, 74743035, 74743056, 74743059, 74743061, 7 4743064, 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; 19650766, 19650791, 19650792; 650796, 19650822, 19650837, 19650839, 19650874, 19650882, 19650887, 19650893, 19650895, 19650899, 19650907, 19650917, 19650955, 19650978, 19650981, 19650995, 19650997, 19651001, 19651008, 1 9651020, 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. The non-mutated bases of the above methylation sites are numbered according to the reference genome HG19.

[0031] In one or more embodiments, differentiation between pancreatic cancer and pancreatitis is determined by the expression of ARHGEF16, PRDM16, NFIA, ST6GALNAC5, PRRX1, LHX4, ACBD6, FMN2, CHRM3, FAM150B, TMEM18, SIX3, CAMKMT, OTX1, WDPCP, CYP26B1, DYSF, HOXD1, HOXD4, UBE2F, RAMP1, AMT, PLSCR5, ZIC4, PEX5L, ETV5, DGKG, FGF12, FGFRL1, RNF212, DOK7, HGFAC, EVC, EVC2, HMX1, CPZ, IRX1, GDNF, AGGF1, CRHBP, PITX1, CATSPER3, NEUROG1, NPM1, TLX3, NKX2-5, BNIP1, PROP1, B4GALT7, IRF4, FOXF2, FOXQ1, FOXC1, GMDS, MOCS1, LRFN2, POU3F2, FBXL4, CCR6, GPR31, TBX20, HERPUD2, VIPR2, LZTS1, NKX2-6, PENK, PRDM14, VPS13B, OSR2, NEK6, LHX2, DDIT4, DNAJB12, CRTAC1, PAX 2, HIF1AN, ELOVL3, INA, HMX2, HMX3, MKI67, DPYSL4, STK32C, INS, INS-IGF2, ASCL2, PAX6, RELT, FAM168A, OPCML, ACVR1B, ACVRL1, AVPR1A, LHX5, S DSL, RAB20, COL4A2, CALKD, CARS2, SOX1, TEX29, SPACA7, SFTA3, SIX6, SIX1, INF2, TMEM179, CRIP2, MTA1, PIAS1, SKOR1, ISL2, SCAPER, POLG, RHCG, NR2F2, RAB40C, PIGQ, CPNE2, NLRC5, PSKH1, NRN1L, SRR, HIC1, HOXB9, PRAC1, SMIM5, MYO15B, TNRC6C, 9-Sep, TBCD, ZNF750, KCTD1, SALL3, CTDP1, NF ATC1, ZNF554, THOP1, CACTIN, PIP5K1C, KDM4B, PLIN3, EPS15L1, KLF2, EPS8L1, PPP1R12C, NKX2-4, NKX2-2, TFAP2C, RAE1, TNFRSF6B, ARFRP1, MYH9,The present invention relates to the methylation level of a sequence from a gene selected from any one of: TXN2. The present invention provides a nucleic acid molecule comprising one or more CpGs of the above genes or fragments thereof. In some embodiments, differentiation between pancreatic cancer and pancreatitis correlates with the methylation level of a sequence selected from the group consisting of any of SEQ ID NOs: 60-160 or a complementary sequence thereof. Here, the "sequence associated with differentiation between pancreatic cancer and pancreatitis" includes the above 101 genes, sequences within 20 kb upstream or downstream thereof, the above 101 sequences (SEQ ID NOs: 60 to 160) or complementary sequences thereof. Here, the bases and methylation sites of the sequences are numbered according to the reference genome HG19. In one or more embodiments, the length of the nucleic acid molecule is 1 bp-1000 bp, 1 bp-900 bp, 1 bp-800 bp, 1 bp-700 bp. The length of the nucleic acid molecule can range between any of the above end values.

[0032] As used 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 assays, fluorescence quantitation, methylation-sensitive high-resolution melting curve assays, chip-based methylation atlases, mass spectrometry, etc. In one or more embodiments, detection encompasses detecting any strand at a gene or site. Therefore, the present invention relates to a reagent for detecting DNA methylation. The reagents used in the above-mentioned method for detecting DNA methylation are well known in the art. In a detection method involving DNA amplification, the reagent for detecting DNA methylation includes a primer. The sequence of the primer is methylation-specific or non-specific. The sequence of the primer may include a non-methylation-specific blocker. The blocker can improve the specificity of methylation detection. The reagent for detecting DNA methylation may also include a probe. Typically, the 5' end of the probe sequence is labeled with a fluorescent reporter and the 3' end is labeled with a quencher. The sequence of the probe illustratively includes an MGB (minor groove binder) or an LNA (locked nucleic acid). MGB and LNA are used to increase the Tm value, increase the specificity of the assay, and increase the flexibility of the probe design. As used herein, a "primer" refers to a nucleic acid molecule having a specific nucleotide sequence that guides synthesis when nucleotide polymerization is initiated. A primer is usually two artificially synthesized oligonucleotide sequences. One primer is complementary to a DNA template strand at one end of the target region, and the other primer is complementary to another DNA template strand at the other end of the target region, and they serve as the initiation point for nucleotide polymerization. The primers are usually at least 9 bp. In vitro artificially designed primers are widely used in polymerase chain reaction (PCR), qPCR, sequencing and probe synthesis. Typically, the primers are designed so that the amplification product has a length of 1-2000 bp, 10-1000 bp, 30-900 bp, 40-800 bp, 50-700 bp, or at least 150 bp, at least 140 bp, at least 130 bp, at least 120 bp. The term "mutant" or "variant" herein refers to a polynucleotide whose nucleic acid sequence is altered by the insertion, deletion or substitution of one or more nucleotides compared to a reference sequence while retaining the ability to hybridize with other nucleic acids. A variant according to any of the embodiments herein encompasses 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 while retaining the biological activity of the reference sequence. The sequence identity between two aligned sequences can be calculated, for example, using BLASTn from NCBI. A variant also encompasses a nucleotide sequence having one or more mutations (insertions, deletions or substitutions) in the nucleotide sequence of a reference sequence while still retaining the biological activity of the reference sequence. Multiple mutations usually refer to mutations in the range of 1 to 10, such as 1 to 8, 1 to 5 or 1 to 3. The substitutions can be between purine and pyrimidine nucleotides, or between purine nucleotides or between pyrimidine nucleotides. The substitutions are preferably conservative substitutions. For example, in the art, conservative substitution with nucleotides having similar or similar properties generally does not change the stability and function of a polynucleotide. Conservative substitution includes exchanges between purine nucleotides (A and G) and between pyrimidine nucleotides (T or U and C). Thus, substitution of one or several sites in a polynucleotide of the present invention with residues from the same side chain will not substantially affect its activity. Furthermore, methylation sites (such as consecutive CGs) are not mutated in the mutants of the present invention. That is, the method of the present invention detects the methylation state of methylable sites in the corresponding sequence, and mutations may occur at bases of non-methylable sites. Typically, the methylation sites are consecutive CpG dinucleotides.

[0033] As described herein, conversion can occur between bases of DNA or RNA. As described herein, "conversion", "cytosine conversion" or "CT conversion" refers to a method of converting unmodified cytosine (C) to a base that is less capable of binding to guanine than cytosine (e.g., uracil (U)) by treating DNA with a non-enzymatic or enzymatic method. Non-enzymatic or enzymatic methods for converting cytosine are well known in the art. Exemplary non-enzymatic methods include treatment with a conversion reagent such as bisulfite, acidic sulfite or metabisulfite, such as calcium bisulfite, sodium bisulfite, potassium bisulfite, ammonium bisulfite, sodium bisulfate, potassium bisulfate and ammonium bisulfate. Exemplary enzymatic methods include deaminase treatment. The converted DNA is optionally purified. DNA purification methods suitable for use herein are well known in the art. The present invention further provides a methylation detection kit for diagnosing pancreatic cancer. The kit comprises the primers and / or probes described herein and is used to detect the methylation level of the pancreatic cancer-associated sequences discovered by the present inventors. The kit may also comprise the nucleic acid molecules described herein, particularly those described in the first aspect, as an internal standard or positive control. The term "hybridization" as used herein refers primarily to the pairing of nucleic acid sequences under stringent conditions. Exemplary stringent conditions are hybridization and membrane washing at 65°C in a solution of 0.1xSSPE (or 0.1xSSC) and 0.1% SDS. In addition to the primers, probes and nucleic acid molecules, the kit also includes other reagents required for detecting DNA methylation. Illustratively, the other reagents for detecting DNA methylation may include one or more of the following: bisulfite and its derivatives, PCR buffer, polymerase, dNTPs, primers, probes, methylation-sensitive or insensitive restriction endonucleases, digestion buffers, fluorescent dyes, fluorescent quenchers, fluorescent reporters, exonucleases, alkaline phosphatases, internal standards, and controls. The kit may also include a conversion positive standard in which unmethylated cytosines are converted to bases that do not bind to guanines. The positive standard may be fully methylated. The kit may also include PCR reaction reagents. Preferably, the PCR reaction reagents include Taq DNA polymerase, PCR buffer, dNTPs and Mg. 2+ Includes: The present invention further provides a method for screening for pancreatic cancer, comprising: (1) detecting the methylation level of a pancreatic cancer-associated sequence described herein in a subject's sample; (2) obtaining a score by comparing with a control sample and / or reference level or by calculation; and (3) identifying whether the subject is affected with pancreatic cancer based on the score. Typically, prior to step (1), the method further comprises extracting and quality testing the sample DNA, and / or converting unmethylated cytosines on the DNA to bases that do not bind to guanine. In a specific embodiment, step (1) comprises: treating genomic DNA or cfDNA with a conversion reagent to convert unmethylated cytosines to bases that have a lower binding affinity for guanine than cytosine (e.g., uracil), performing a PCR amplification using primers suitable for amplifying the converted sequences of the pancreatic cancer-associated sequences described herein, and determining the methylation status or level of at least one CpG by the presence or absence of an amplification product or by sequence identification (e.g., probe-based PCR identification or DNA sequencing identification). Alternatively, step (1) comprises treating genomic DNA or cfDNA with a methylation-sensitive restriction endonuclease, performing PCR amplification using primers suitable for amplifying the sequence of at least one CpG of the pancreatic cancer-associated sequence described herein, and determining the methylation status or level of at least one CpG by the presence or absence of an amplification product. As used herein, "methylation status" includes the relationship of the methylation status of any number of CpGs at any position in the sequence of interest. This relationship can be the result of adding or subtracting a methylation status parameter (e.g., 0 or 1) or calculating a mathematical algorithm (e.g., average, percentage, fraction, ratio, degree, or calculation using a mathematical model), which includes, but is not limited to, a methylation level measurement, a methylation haplotype ratio, or a methylation haplotype load. The term "methylation status" refers to the methylation of a particular CpG site, which typically includes methylation or non-methylation (e.g., methylation status parameter 0 or 1).

[0034] In one or more embodiments, the methylation level in the subject sample increases or decreases when compared with the control sample and / or reference level. When the level of the methylation marker meets a certain threshold, pancreatic cancer is identified. Alternatively, the methylation level of the tested gene can be mathematically analyzed to obtain a score. For the tested sample, if the score is greater than the threshold, the determination result is positive, i.e., pancreatic cancer is present, and if not, it is negative, i.e., there is no pancreatic cancer plasma. Conventional mathematical analysis methods and methods for determining thresholds are known in the art. An exemplary method is mathematical modeling. For example, for differential methylation markers, a support vector machine (SVM) model is constructed for two groups of samples, and the accuracy, sensitivity and specificity of the detection result and the area under the predictive characteristic curve (ROC) (AUC) are statistically analyzed using this model, and the prediction score of the test set samples is statistically analyzed. In one or more embodiments, the methylation level in the subject sample is increased or decreased when compared with the control sample and / or reference level. If the level of the methylation marker meets a certain threshold, pancreatic cancer is identified, otherwise it is chronic pancreatitis. Alternatively, the methylation level of the tested gene can be mathematically analyzed to obtain a score. For the tested sample, if the score is greater than the threshold, the discrimination result is positive, i.e., pancreatic cancer is present, otherwise it is negative, i.e., it is pancreatitis. Conventional mathematical analysis methods and methods for determining the threshold are known in the art, and an exemplary method is the support vector machine (SVM) mathematical model. For example, for differential methylation markers, a support vector machine (SVM) is constructed for the above-mentioned learning group samples, and the model is used to statistically analyze the accuracy, sensitivity and specificity of the detection result, as well as the area under the predictive characteristic curve (ROC) (AUC), and the predicted score of the test set samples is statistically analyzed. In one embodiment of the support vector machine, the score threshold is 0.897. If the score is greater than 0.897, the subject is considered to be a pancreatic cancer patient, otherwise the subject is a chronic pancreatitis patient. In a preferred embodiment, the model training method is as follows: firstly, obtain differentially methylated segments according to the methylation level of each site, and construct a differentially methylated region matrix, for example, construct a methylation data matrix from the methylation level data of a single CpG dinucleotide position in the HG19 genome via samtools software, etc.; and then train an SVM model. An exemplary SVM model training method is as follows. a) The training model mode is constructed. Using the sklearn software package (0.23.1) in Python software (v3.6.9), construct the training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. Typically, during model construction, the category of having pancreatic cancer can be coded as 1, and the category of not having pancreatic cancer can be coded as 0. In the present invention, the threshold is set at 0.895 by python software (v3.6.9) and sklearn software package (v0.23.1). The constructed model finally distinguishes samples with and without pancreatic cancer at 0.895. Here, the sample is from a mammal, preferably from a human. The sample can be from any organ (e.g., pancreas), tissue (e.g., epithelial tissue, connective tissue, muscle tissue and nerve tissue), cell (e.g., pancreatic cancer biopsy) or body fluid (e.g., blood, plasma, serum, interstitial fluid, urine). In general, the sample may contain genomic DNA or cfDNA (free-circulating DNA or cell-free DNA). cfDNA, also called free-circulating DNA or cell-free DNA, is degraded DNA fragments released into plasma. Exemplarily, the sample is a pancreatic cancer biopsy, preferably a fine needle aspiration biopsy. Alternatively, the sample is plasma or cfDNA.

[0035] The present application further relates to a method for obtaining methylation haplotype fractions associated with pancreatic cancer. Taking the methylation data obtained by methylation targeted sequencing (MethylTitan) as an example, the methods for screening and testing marker sites are as follows: original paired-end sequencing reads - combining reads to obtain combined single-end reads - removing adapters to obtain adapter-free reads - Bismark aligning to human DNA genome to form BAM file - extracting CpG site methylation level of each read by samtools to form haplotype file - statistically analyzing C site methylation haplotype fractions to form metafile - calculating MHF(methylation haplotype fractions - meth.matrix using coverage200 to filter sites Forming a matrix file - Filtering sites based on NA values ​​greater than 0.1 - Pre-splitting samples into training and test sets - Building a logistic regression model of the phenotype for each haplotype in the training set - Selecting the regression P-value for each methylation haplotype fraction - Statistically analyzing each MethylTitan amplified region and selecting the methylation haplotype with the most significant P-value to represent the methylation level of the region and modeling via support vector machine - Forming the results of the training set (ROC plot) and predicting the test set using the model for validation. Specifically, we identified methylation haplotypes associated with pancreatic cancer. The method for obtaining the cfDNA-based haplotypes includes the following steps: (1) obtaining plasma samples from patients with or without pancreatic cancer to be tested, extracting cfDNA, constructing libraries and sequencing using the MethylTitan method, and obtaining sequencing reads; (2) preprocessing the sequencing data (including adapter removal and splicing of the sequencing data generated by the sequencer); and (3) aligning the above preprocessed sequencing data to the HG19 reference genome sequence of the human genome to determine the position of each fragment. The data in step (2) can be obtained from paired-end 150bp sequencing on the Illumina sequencing platform.The adapter removal in step (2) is to respectively remove the sequencing adapters at the 5' and 3' ends of the two paired-end sequencing data, and to remove low-quality bases after removing the adapters. The splicing step in step (2) is to combine the paired-end sequencing data and restore them to the original library fragments. This allows for better alignment and accurate positioning of the sequencing fragments. For example, the length of the sequencing library is about 180 bp, and the paired ends of 150 bp can completely cover the entire library fragment. Step (3) includes the steps of (a) constructing two sets of converted reference genomes and respectively carrying out CT and GA conversion on the HG19 reference genome data to respectively construct the alignment indexes of the converted reference genomes, (b) also carrying out CT and GA conversion on the above combined sequencing sequence data, and (c) respectively aligning the above converted reference genome sequences, and finally summarizing the alignment results to determine the position of the sequencing data in the reference genome. Also, the method for obtaining methylation values ​​for pancreatic cancer includes (4) calculating MHF, (5) constructing a methylation haplotype MHF data matrix, and (6) constructing a logistic regression model for each methylation haplotype by sample grouping. Step (4) includes obtaining methylation haplotype status and sequencing depth information at the position of the HG19 reference genome based on the alignment result obtained in step (3). Step (5) includes combining the methylation haplotype status and sequencing depth information data into a data matrix. Among them, each data point with a depth of less than 200 is treated as a missing value, and a K-nearest neighbor (KNN) method is used to fill the missing value. Step (6) includes screening haplotypes with significant regression coefficients between two groups based on statistical modeling of each position in the matrix using logistic regression.

[0036] The present invention examines the relationship between DNA methylation and CA19-9 levels and pancreatic cancer and pancreatitis. The purpose of the present invention is to improve the accuracy of non-invasive diagnosis of pancreatic cancer by using the marker group DNA methylation level and CA19-9 level as markers for non-invasively distinguishing pancreatic cancer from chronic pancreatitis. The present inventors have found that in the screening and diagnosis of pancreatic cancer markers, the diagnostic accuracy can be significantly improved by combining CA19-9 levels. The present invention first provides a method for screening pancreatic cancer methylation markers, comprising: (1) obtaining methylation haplotype fraction and sequencing depth of DNA segments of a subject's genome (such as cfDNA); optionally (2) preprocessing the methylation haplotype fraction and sequencing depth data; and (3) performing cross-validation incremental feature selection to obtain feature methylation segments. The data acquisition in step (1) may be data analysis after methylation detection or may be read directly from a file. In an embodiment where methylation detection is performed, step (1) includes the steps of: 1.1) detecting DNA methylation of a sample of interest to obtain sequencing read data; 1.3) aligning the sequencing data to a reference genome to obtain methylated segment location and sequencing depth information; 1.4) determining the position of methylated segments based on the following formula:

number

[0037] After obtaining the characteristic methylated segments, they can be combined with the CA19-9 level to construct a more accurate pancreatic cancer diagnostic model. Therefore, in addition to the above steps (1) to (3), the method for constructing a pancreatic cancer diagnostic model also includes a step of (4) constructing a mathematical model for the data of the characteristic methylated segments to obtain a methylation score, and a step of (5) combining the methylation score and the CA19-9 level into a data matrix and constructing a pancreatic cancer diagnostic model based on the data matrix. The "data" in step (4) is the methylation detection result of the characteristic methylated segments, preferably a matrix combining the methylated haplotype fraction and the sequencing depth. The mathematical model in step (4) can be any mathematical model commonly used in diagnostic data analysis, such as a support vector machine (SVM) model, a random forest, and a regression model, where an exemplary mathematical model is a support vector machine (SVM) model. The pancreatic cancer diagnostic model in step (5) may be any mathematical model used in diagnostic data analysis, such as a support vector machine (SVM) model, a random forest, a regression model, etc. Here, an example of the pancreatic cancer diagnostic model is the logistic regression pancreatic cancer model shown below:

number

number

[0038] The present invention further provides a kit for diagnosing pancreatic cancer, comprising a reagent or device for detecting DNA methylation and a reagent or device for detecting CA19-9 levels. The reagent for detecting DNA methylation is used to determine the methylation level of a DNA sequence or a fragment thereof in a sample of a subject, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof. Exemplary reagents for detecting DNA methylation include the primers and / or probes described herein for detecting the methylation level of a sequence associated with the differentiation between pancreatic cancer and pancreatitis found by the present inventors. The CA19-9 level described herein mainly refers to the CA19-9 level in body fluids (such as blood or plasma). The reagent for detecting the CA19-9 level can be any reagent known in the art that can be used in CA19-9 detection methods, such as, but not limited to, an antibody against CA19-9, and a detection reagent based on immune reaction, including any buffer, washing solution, etc. An exemplary detection method used in the present invention detects the content of CA19-9 by chemiluminescence immunoassay. The specific steps are as follows: first, an antibody against CA19-9 is labeled with a chemiluminescence marker (acridinium ester), and the labeled antibody and CA19-9 antigen are immunoreacted to form a CA19-9 antigen-acridinium ester-labeled antibody complex, and then an oxidizing agent (H2O2) and NaOH are added to form an alkaline environment. At this time, the acridinium ester can be decomposed and emit light without a catalyst. The photon energy generated per unit time is received and recorded by a light collector and a photomultiplier tube (chemiluminescence detector). This light integral is proportional to the amount of CA19-9 antigen, and the CA19-9 content can be calculated according to the standard curve. The present invention further includes a method for diagnosing pancreatic cancer, comprising: (1) obtaining a methylation level of a DNA sequence or a fragment thereof in a sample of a subject, or a methylation state or level of one or more CpG dinucleotides in the DNA sequence or a fragment thereof, and a CA19-9 level of the subject; (2) using a mathematical model (e.g., a support vector machine model or a random forest model) to calculate and obtain a methylation score using the methylation state or level; (3) combining the methylation score and the CA19-9 level into a data matrix; (4) constructing a pancreatic cancer diagnostic model (e.g., a logistic regression model) based on the data matrix; and optionally (5) obtaining a pancreatic cancer score and diagnosing pancreatic cancer according to whether the pancreatic cancer score reaches a threshold value. The method may further include DNA extraction and / or quality testing before step (1). The present invention is particularly suitable for identifying pancreatic cancer in patients with pancreatitis, i.e., distinguishing pancreatic cancer from pancreatitis. The subject may be, for example, a patient diagnosed with pancreatitis or a patient diagnosed (previously diagnosed) with pancreatitis. That is, in one or more embodiments, the method identifies pancreatic cancer in a patient diagnosed with chronic pancreatitis, including a patient previously diagnosed with chronic pancreatitis. Of course, the method of the present invention is not limited to the above subjects, and may also be used to directly diagnose and identify pancreatitis or pancreatic cancer in undiagnosed subjects.

[0039] In certain embodiments, step (1) comprises detecting the methylation level of a DNA sequence or a fragment thereof in a sample from a subject, or the methylation status or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, e.g., detecting the methylation status or level using a primer molecule and / or a probe molecule as described herein. Methods for detecting methylation status or level and detecting CA19-9 level are described elsewhere herein.Specific methods for detecting methylation status or level include treating genomic DNA or cfDNA with a conversion reagent to convert unmethylated cytosine to a base that has a lower binding ability to guanine than cytosine (such as uracil), performing PCR amplification using primers suitable for amplifying the converted sequence of the sequence associated with differentiation between pancreatic cancer and pancreatitis described herein, and determining the methylation level of at least one CpG by the presence or absence of the amplification product or by sequence identification (e.g., probe-based PCR identification or DNA sequencing identification). In a preferred embodiment, the model training method is as follows: firstly, obtain differentially methylated segments according to the methylation level of each site, and construct a differentially methylated region matrix, for example, construct a methylation data matrix from the methylation level data of a single CpG dinucleotide position in the HG19 genome via samtools software, etc.; and then train an SVM model. An exemplary SVM model training method is as follows. a) Use the sklearn software package (v0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (v0.23.1) was used to input the data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. According to the findings of the present inventors, the diagnostic accuracy can be significantly improved by combining the methylation score and the CA19-9 level. Specifically, the methylation score and the CA19-9 level are synthesized into a data matrix, and a pancreatic cancer diagnostic model (e.g., a logistic regression model) is constructed based on the data matrix to obtain the pancreatic cancer score. The data matrix of methylation scores and CA19-9 levels is optionally normalized. The normalization can be performed using conventional normalization methods in the art. In an embodiment of the present invention, the robust scalar normalization method is used as an example, and the normalization formula is as follows:

number

[0040] In another aspect, the present application provides a method for determining the presence, assessing the onset, and / or assessing the progression of a pancreatic tumor, comprising determining the presence and / or content of the modification status of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a fragment thereof, in a sample to be tested. For example, the method of the present application may comprise determining whether a pancreatic tumor is present based on the result of determining the presence and / or content of the modification status of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a fragment thereof, in a sample to be tested. For example, the method of the present application may include assessing whether the onset of pancreatic tumor is diagnosed based on the result of determining the presence and / or content of the modification state of the DNA region having the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or a fragment thereof, in the sample to be tested. For example, the method of the present application may include whether there is a risk and / or a level of risk of being diagnosed with the onset of pancreatic tumor based on the result of determining the presence and / or content of the modification state of the DNA region having the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or a fragment thereof, in the sample to be tested. For example, the methods of the present application may include assessing the progression of a pancreatic tumor based on the determination of the presence and / or content of the modification status of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or fragments thereof, in the sample being tested. In another aspect, the present application provides a method for assessing the methylation status of a pancreatic tumor-associated DNA region, which may include determining the presence and / or content of the modification status of a DNA region with genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or fragments thereof, in a sample to be tested. For example, based on the determination result regarding the presence and / or content of the modification status of a DNA region with genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or fragments thereof, evaluating the methylation status of a pancreatic tumor-associated DNA region. For example, the methylation status of a pancreatic tumor-associated DNA region may be associated with the development of a pancreatic tumor and may refer to a confirmed presence or increased content of methylation in that DNA region compared to a reference level. For example, the DNA regions of the present application are derived from human chr2:74740686-74744275, derived from human chr8:25699246-25907950, derived from human chr12:4918342-4960278, derived from human chr13:37005635-37017019, derived from human chr1:63788730-63790797, derived from human chr1:248020501-248043438 The genes may be derived from human chr2:176945511-176984670, from human chr6:137813336-137815531, from human chr7:155167513-155257526, from human chr19:51226605-51228981, from human chr7:19155091-19157295, and from human chr2:73147574-73162020. For example, the genes of the present application may be described by their name and their chromosomal coordinates. For example, the chromosomal coordinates may correspond to the Hg19 version of the human genome database published in February 2009 (or "Hg19 coordinates"). For example, the DNA regions of the present application may be derived from the regions defined by the Hg19 coordinates.

[0041] In another aspect, the present application provides a method for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease, comprising determining the presence and / or content of the modification state of a specific subregion of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or complementary regions or fragments thereof, in a sample to be tested. In another aspect, the present application provides a method for detecting a gene encoding a nucleotide sequence derived from human chr2:74743035-74743151, and derived from human chr2:74743080-74743301, derived from human chr8:25907849-25907950, derived from human chr8:25907698-25907894, derived from human chr12:4919142-4919289, derived from human chr13:11111-1111, derived from human chr14:11111-1111, derived from human chr15:11111-1111, derived from human chr16:11111-1111, derived from human chr17:11111-1111, derived from human chr18:11111-1111, derived from human chr19 ... chr12:4918991-4919187 and human chr12:4919235-4919439, human chr13:37005635-37005754, human chr13:37005458-37005653 and human chr13:37005680-37005904, human chr1:63788812-6378895 2, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137814853, derived from human chr7:155167513-155167628, derived from human chr19:51228168-51228782, and derived from human chr7:19156739-19157277, and derived from human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof. For example, the method of the present application may include identifying whether a disease exists based on the result of determining the presence and / or content of the modification state of the DNA region or its complementary region or fragment thereof in the sample to be tested.For example, the method of the present application may include evaluating whether the onset of a disease is diagnosed based on the result of determining the presence and / or content of the modification state of the DNA region or its complementary region or fragment thereof in the sample to be tested.For example, the method of the present application may include evaluating whether there is a risk and / or the level of risk of being diagnosed with a disease based on the result of determining the presence and / or content of the modification state of the DNA region or its complementary region or fragment thereof in the sample to be tested.For example, the method of the present application may comprise assessing the progression of a disease based on the determination of the presence and / or content of the modification state of a DNA region or its complementary region or a fragment thereof in the sample to be tested.

[0042] In another aspect, the present application provides a method for detecting a nucleotide sequence derived from human chr2:74743035-74743151, and derived from human chr2:74743080-74743301, derived from human chr8:25907849-25907950, and derived from human chr8:25907698-25907894, and derived from human chr12:4919142-491928 in a sample to be tested. 9, human chr12:4918991-4919187, and human chr12:4919235-4919439, human chr13:37005635-37005754, human chr13:37005458-37005653, and human chr13:37005680-37005904, 63788812-63788952, from human chr1:248020592-248020779, from human chr2:176945511-176945630, from human chr6:137814700-137814853, from human chr7:155167513-155167628, from human chr19:51228168-51228782, and from human chr7:19156739-19157277, and from human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof. For example, the presence or increased content identified in the DNA region compared to a reference level of methylation may be associated with the onset of a disease. For example, a DNA region in the present application may refer to a specific segment of genomic DNA. For example, a DNA region in the present application may be designated by a set of gene names and chromosomal coordinates. For example, a gene may be determined in its sequence and chromosomal location by reference to its name, or in its sequence and chromosomal location by reference to its chromosomal coordinates. The present application uses the methylation status of these specific DNA regions as a set of analytical indicators, which can provide significant improvements in sensitivity and / or specificity and simplify screening methods.For example, "sensitivity" can refer to the proportion of correctly identified positive results, i.e., the proportion of individuals correctly identified as having the disease under discussion, and "specificity" can refer to the proportion of correctly identified negative results, i.e., the proportion of individuals correctly identified as not having the disease under discussion.

[0043] For example, variants may contain at least 80%, at least 85%, at least 90%, 95%, 98%, or 99% sequence identity to the DNA regions described herein, and variants may contain one or more deletions, additions, substitutions, inversions, etc. For example, modified states of variants in the present application can achieve the same evaluation results. The DNA regions of the present application may contain any other mutations, polymorphic variations, or allelic variations in any form. For example, the method of the present application may include providing a nucleic acid capable of binding to a DNA region selected from the group consisting of SEQ ID NOs: 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228 and 232, or a complementary region thereof, or a converted region thereof, or a fragment thereof. In another aspect, the present application provides a method of determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease, comprising detecting a gene encoding a chromosome 1111 from human chr2:74743042-74743113, and a gene encoding a chromosome 1111 from human chr2:74743157-74743253, and a gene encoding a chromosome 1111 from human chr2:74743042-74743113, and a gene encoding a chromosome 1111 from human chr2:74743157-74743253, in a sample being tested. 4743253, human chr8:25907865-25907930, and human chr8:25907698-25907814, human chr12:4919188-4919272, human chr12:4919036-4919164, and human chr12:4919341-4919438, and human chr13:37005652-37005721 derived from human chr13:37005458-37005596, derived from human chr13:37005694-37005824, derived from human chr1:63788850-63788913, derived from human chr1:248020635-248020731, derived from human chr2:176945521-176945603, derived from human chr6:137814750-137814815 The method may comprise determining the presence and / or content of the modification state of a selected DNA region from the group consisting of: derived from human chr7:155167531-155167610, derived from human chr19:51228620-51228722, and derived from human chr7:19156779-19157914, and derived from human chr2:73147571-73147626, or a complementary region thereof or a fragment thereof. For example, one or more of the above regions can function as an amplification region and / or a detection region. For example, the method of the present application may include providing a nucleic acid selected from the group consisting of SEQ ID NOs: 165, 169, 173, 177, 181, 185, 189, 193, 197, 201, 205, 209, 213, 217, 221, 225, 229, and 233, or a complementary nucleic acid thereof, or a fragment thereof. For example, the nucleic acid may be used to detect a target region. For example, the nucleic acid may be used as a probe.

[0044] For example, the method of the present application includes providing a combination of nucleic acids selected from the group consisting of SEQ ID NOs: 166 and 167, 170 and 171, 174 and 175, 178 and 179, 182 and 183, 186 and 187, 190 and 191, 194 and 195, 198 and 199, 202 and 203, 206 and 207, 210 and 211, 214 and 215, 218 and 219, 222 and 223, 226 and 227, 230 and 231, and 234 and 235, or a combination of complementary nucleic acids thereof, or a fragment thereof. For example, the combination of nucleic acids can be used to amplify a target region. For example, the combination of nucleic acids can function as a primer combination. For example, the disease may include a tumor. For example, the disease may include a solid tumor. For example, the disease may include any tumor, such as a pancreatic tumor. For example, in some cases, the disease of the present application may include pancreatic cancer. For example, in some cases, the disease of the present application may include pancreatic ductal adenocarcinoma. For example, in some cases, the pancreatic tumor of the present application may include pancreatic ductal adenocarcinoma. For example, "complementary" and "substantially complementary" in the present application can encompass hybridization or base pairing or duplex formation between nucleotides or nucleic acids, such as between the two strands of a double-stranded DNA molecule, or between an oligonucleotide primer and a primer binding site on a single-stranded nucleic acid. Complementary nucleotides can typically be A and T (or A and U) or C and G. For two single-stranded RNA or DNA molecules, the nucleotides of one strand, when optimally aligned and compared, with appropriate nucleotide insertions or deletions, can be considered to be substantially complementary if they pair with at least about 80% (usually at least about 90% to about 95%, or even about 98% to about 100%) of the nucleotides of the other strand. In one embodiment, two complementary nucleotide sequences can hybridize with less than 25% mismatch, more preferably less than 15% mismatch, and less than 5% mismatch, or no mismatch between the inverted nucleotides. For example, the two molecules can hybridize under highly stringent conditions. For example, the modification state in the present application may refer to the presence, absence and / or content of a modification state at a specific nucleotide or nucleotides in a DNA region. For example, the modification state in the present application may refer to the modification state of each base or each specific base (e.g., cytosine) in a specific DNA sequence. For example, the modification state in the present application may refer to the modification state of a combination of base pairs and / or a combination of bases in a specific DNA sequence. For example, the modification state in the present application may refer to information about the density of regional modifications in a specific DNA sequence (including a DNA region in which a gene is located or a specific regional fragment thereof), but may not provide precise location information about where the modifications occur in the sequence. For example, the modification state of the present application may be a methylation state or a situation similar to methylation. For example, a methylated or highly methylated state may be associated with transcriptional silencing of a particular region. For example, a methylated or highly methylated state may be associated with being converted by a methylation-specific conversion reagent (e.g., a deamination reagent and / or a methylation-sensitive restriction enzyme). For example, conversion may refer to being converted to another substance and / or being cut or digested. For example, the method may further include obtaining nucleic acid in the sample to be tested. For example, the nucleic acid may include cell-free nucleic acid. For example, the sample to be tested may include tissue, cells, and / or body fluid. For example, the sample to be tested may include plasma. For example, the detection method of the present application may be performed on any suitable biological sample. For example, the sample to be tested may be any sample of biological material, such as may be derived from an animal, but is not limited to cellular material, biological fluids (such as blood), excreta, tissue biopsy specimens, surgical specimens, or fluids introduced into the body of an animal and subsequently removed. For example, the sample to be tested in the present application may include samples that have been processed in any form after the sample has been isolated. For example, the method may further include converting the DNA region or a fragment thereof. For example, the conversion step of the present application allows modified and unmodified bases to form different entities after conversion. For example, bases with a modified state are substantially unchanged after conversion, and bases without a modified state are changed to other bases different from the converted base (e.g., other bases may include uracil) or are cleaved after conversion. For example, the base may include cytosine. For example, the modification may include a methylation modification. For example, the conversion may include conversion with a deamination reagent and / or a methylation-sensitive restriction enzyme. For example, the deamination reagent may include bisulfite or an analog thereof, such as sodium bisulfite or potassium bisulfite.

[0045] For example, the method may further include amplifying the DNA region or fragment thereof in the sample to be tested before determining the presence and / or content of the modification state of the DNA region or fragment thereof. For example, the amplification may include PCR amplification. For example, the amplification in this application may include any known amplification system. For example, the amplification step in this application may be optional. For example, "amplification" may refer to a method of generating multiple copies of a desired sequence. "Multiple copies" may refer to at least two copies. "Copy" may not mean perfect sequence complementarity or identity to the template sequence. For example, copies may occur during amplification sequence errors, such as nucleotide analogs such as deoxyinosine, intentional sequence alterations (such as those introduced by primers that include sequences that are hybridizable but not complementary to the template), and / or amplification sequence errors. For example, a method for determining the presence and / or content of a modification state may include determining the presence and / or content of a substance formed by a base having a modified state after conversion. For example, a method for determining the presence and / or content of a modification state may include determining the presence and / or content of a DNA region having a modified state or a fragment thereof. For example, the presence and / or content of a DNA region having a modified state or a fragment thereof may be directly detected. For example, a DNA region having a modified state or a fragment thereof may be detected in a manner that may have different characteristics from a DNA region having no modified state or a fragment thereof during a reaction (e.g., an amplification reaction). For example, in a fluorescent PCR method, a DNA region having a modified state or a fragment thereof may be specifically amplified and fluoresce, and a DNA region having no modified state or a fragment thereof may not be substantially amplified and essentially not fluoresce. For example, alternative methods for determining the presence and / or content of a species formed upon conversion of a base having a modified state may be included within the scope of this application. For example, the presence and / or content of a DNA region or a fragment thereof having a modification state is determined by the fluorescence Ct value detected by the fluorescent PCR method. For example, the presence of a pancreatic tumor or the onset or risk of onset of a pancreatic tumor is determined by determining the presence of a modification state of a DNA region or a fragment thereof and / or the content of a modification state of a DNA region or a fragment thereof being higher than a reference level. For example, if the fluorescence Ct value of the sample to be tested is lower than the reference fluorescence Ct value, the presence of a modification state of a DNA region or a fragment thereof can be determined, and / or the content of a modification state of a DNA region or a fragment thereof can be determined to be higher than the content of a modification state in a reference sample. For example, a reference fluorescence Ct value can be determined by detecting a reference sample. For example, if the fluorescence Ct value of the sample to be tested is higher than or substantially equal to the reference fluorescence Ct value, the presence of a modification state of a DNA region or a fragment thereof cannot be excluded, and if the fluorescence Ct value of the sample to be tested is higher than or substantially equal to the reference fluorescence Ct value, the content of a modification state of a DNA region or a fragment thereof can be confirmed to be lower than or substantially equal to the content of a modification state in a reference sample. For example, the present application can express the presence and / or content of the modification state of a particular DNA region or fragment thereof via, for example, a cycle threshold value (i.e., Ct value) encompassing the methylation level of the sample to be tested and the reference level. For example, the Ct value can refer to the cycle number at which the fluorescence of the PCR product can be detected above the background signal. For example, there can be a negative correlation between the Ct value and the starting content of the target marker in the sample, i.e., the lower the Ct value, the greater the content of the modification state of the DNA region or fragment thereof in the sample to be tested. For example, if the Ct value of the sample to be tested is the same as or lower than its corresponding reference Ct value, it can be confirmed as the presence of a particular disease, or can be diagnosed as the onset or risk of onset of a particular disease, or can be evaluated as the specific progression of a particular disease.For example, if the Ct value of the sample to be tested is at least 1 cycle, at least 2 cycles, at least 5 cycles, at least 10 cycles, at least 20 cycles, or at least 50 cycles lower than its corresponding reference Ct value, it can be confirmed as the presence of a particular disease, or can be diagnosed as the onset or risk of onset of a particular disease, or can be evaluated as the specific progression of a particular disease.

[0046] For example, if the Ct value of a cell sample, tissue sample or sample derived from a subject is the same as or higher than its corresponding reference Ct value, it can be confirmed as the absence of a specific disease, or it can not be diagnosed as the onset or risk of onset of a specific disease, or it can not be evaluated as the specific progression of a specific disease. For example, if the Ct value of a cell sample, tissue sample or sample derived from a subject is at least 1 cycle, at least 2 cycles, at least 5 cycles, at least 10 cycles, at least 20 cycles, or at least 50 cycles higher than its corresponding reference Ct value, it can be confirmed as the absence of a specific disease, or it can not be diagnosed as the onset or risk of onset of a specific disease, or it can not be evaluated as the specific progression of a specific disease. For example, if the Ct value of a cell sample, tissue sample or sample derived from a subject is the same or the corresponding reference Ct value, it can be confirmed as the presence or absence of a specific disease, diagnosed as having or not having a specific disease, diagnosed as having or not having a risk of developing a specific disease, evaluated as having or not having a certain degree of progression of a specific disease, and further examination can be suggested. For example, the reference level or control level in this application can refer to a normal level or a healthy level. For example, the normal level can be the modification level of a DNA region of a sample derived from a cell, tissue or individual that does not have a disease. For example, when used to evaluate a tumor, the normal level can be the modification level of a DNA region of a sample derived from a cell, tissue or individual that does not contain a tumor. For example, when used to evaluate a pancreatic tumor, the normal level can be the modification level of a DNA region of a sample derived from a cell, tissue or individual that does not have a pancreatic tumor. For example, the reference level in the present application may refer to a threshold level at which the presence or absence of a particular disease is confirmed in a subject or sample. For example, the reference level in the present application may refer to a threshold level at which a subject is diagnosed as having or at risk of having a particular disease. For example, the reference level in the present application may refer to a threshold level at which a subject is evaluated as having a particular progression of a particular disease. For example, if the modification status of a DNA region in a cell sample, tissue sample, or sample derived from a subject is higher than or substantially equal to the corresponding reference level (e.g., the reference level here may refer to the modification status of a DNA region in a patient who does not have a particular disease), it may be confirmed as the presence of a particular disease, may be diagnosed as having or at risk of having a particular disease, or may be evaluated as a particular progression of a particular disease. For example, in the present application, A and B are "substantially equal" may mean that the difference between A and B is 1% or less, 0.5% or less, 0.1% or less, 0.01% or less, 0.001% or less, or 0.0001% or less. For example, if the modification status of a DNA region in a cell sample, tissue sample or sample from a subject is at least 1%, at least 5%, at least 10%, at least 20%, at least 50%, at least 1-fold, at least 2-fold, at least 5-fold, at least 10-fold or at least 20-fold higher than the corresponding reference level, it can be confirmed as the presence of a particular disease, diagnosed as the onset or risk of onset of a particular disease, or evaluated as the specific progression of a particular disease. For example, if the modification status of a DNA region in a cell sample, tissue sample or sample from a subject is at least 1%, at least 5%, at least 10%, at least 20%, at least 50%, at least 1, at least 2, at least 5, at least 10 or at least 20 times higher than the corresponding reference level in at least one, at least two or at least three detections out of multiple detections, it can be confirmed as the presence of a particular disease, diagnosed as the onset or risk of onset of a particular disease, or evaluated as the specific progression of a particular disease.

[0047] For example, if the modification status of a DNA region in a cell sample, tissue sample or sample derived from a subject is lower than or substantially equal to the corresponding reference level (e.g., the reference level here may refer to the modification status of a DNA region in a patient with a specific disease), it cannot be confirmed as the absence of a specific disease, cannot be diagnosed as having or being at risk of developing a specific disease, or cannot be evaluated as the specific progression of a specific disease. For example, if the modification status of a DNA region in a cell sample, tissue sample or sample derived from a subject is at least 1%, at least 5%, at least 10%, at least 20%, at least 50% and at least 100% lower than the corresponding reference level, it cannot be confirmed as the absence of a specific disease, cannot be diagnosed as having or being at risk of developing a specific disease, or cannot be evaluated as the specific progression of a specific disease. The reference level may be selected by a person skilled in the art based on the desired sensitivity and specificity. For example, the reference level in various situations in the present application may be easily identifiable by a person skilled in the art. For example, the appropriate reference level and / or the appropriate means of obtaining the reference level may be identified based on a limited number of trials. For example, the reference level may be derived from one or more reference samples, the reference level being obtained from an experiment conducted in parallel with the experiment testing the sample of interest. Alternatively, the reference level may be obtained in a database that includes a collection of data, standards or levels from one or more reference samples or disease reference samples. In some embodiments, the set of data, standards or levels may be standardized or normalized so that it can be compared with data from one or more samples and can be used to reduce errors resulting from different detection conditions. For example, the reference levels may be derived from a database, which may be a reference database, including, for example, modification levels of the target markers from one or more reference samples and / or other laboratory and clinical data. For example, the reference database may be established by aggregating reference level data from reference samples obtained from healthy individuals and / or individuals not suffering from the corresponding disease (i.e., individuals known to be free of the disease). For example, the reference database may be established by aggregating reference level data from reference samples obtained from individuals with the corresponding disease undergoing treatment. For example, the reference database may be constructed by aggregating data from reference samples obtained from individuals at different stages of the disease. For example, the different stages may be evidenced by different modification levels of the markers of interest in this application. The skilled artisan may also determine whether an individual suffers from the corresponding disease or is at risk of suffering from the corresponding disease based on various factors such as age, sex, medical history, family history, symptoms, etc. For example, the present application may use cycle threshold (i.e., Ct value) to represent the presence and / or content of modification state in a particular DNA region or fragment thereof. The determination method may calculate a score based on the methylation level of each sequence selected from the gene, and if the score is greater than 0, the result is positive, i.e., the result corresponding to the sample may be a malignant nodule, and in one or more embodiments, if the score is less than 0, the result is negative, i.e., the result corresponding to the pancreatic sample may be a benign nodule. For example, in a PCR embodiment, the methylation level may be calculated as methylation level=2^(-ΔCt sample tested) / 2^(-ΔCt positive standard)×100%, where ΔCt=Ct target gene−Ct internal reference gene. In a sequencing embodiment, the methylation level may be calculated as follows: methylation level=number of methylated bases / number of total bases. For example, the method of the present application may comprise the following steps: obtaining a nucleic acid in a sample to be tested, converting a DNA region or a fragment thereof, and determining the presence and / or content of a substance formed by a base having a modified state after conversion. For example, the method of the present application may comprise the following steps: obtaining a nucleic acid in a sample to be tested, converting a DNA region or a fragment thereof, amplifying the DNA region or a fragment thereof in the sample to be detected, and determining the presence and / or content of a substance formed by a base having a modified state after conversion.

[0048] For example, the method of the present application may include the following steps: obtaining nucleic acid in a sample to be tested, treating the DNA obtained from the sample to be tested with a reagent capable of distinguishing between unmethylated and methylated sites in the DNA to obtain treated DNA, optionally amplifying a DNA region or a fragment thereof in the sample to be tested, quantitatively, semi-quantitatively or qualitatively analyzing the presence and / or content of the methylation status of the treated DNA in the sample to be tested, and comparing the methylation level of the treated DNA in the sample to be tested with a corresponding reference level. If the methylation status of the DNA region in the sample to be tested is equal to or higher than the corresponding reference level, the presence of a specific disease can be confirmed, the onset or risk of onset of a specific disease can be diagnosed, or a certain progression of a specific disease can be evaluated. In another aspect, the present application provides a nucleic acid that may comprise a sequence capable of binding to a DNA region with genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. For example, the nucleic acid may be any probe of the present application. In another aspect, the present application provides a method of preparing a nucleic acid that may comprise designing a nucleic acid capable of binding to a DNA region with genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a complementary region thereof, or a conversion region thereof, or a fragment thereof, based on the modification status of the DNA region, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. For example, the method of preparing a nucleic acid may be any suitable method known in the art. In another aspect, the present application provides a combination of nucleic acids that may include a sequence capable of binding to a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. For example, the combination of nucleic acids may be any combination of primers of the present application. In another aspect, the present application provides a method for preparing a combination of nucleic acids that may include designing a combination of nucleic acids that can amplify a DNA region with genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a complementary region thereof, or a conversion region thereof, or a fragment thereof, based on the modification status of the DNA region, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. For example, the method of preparing the nucleic acids in the combination of nucleic acids can be any suitable method known in the art. For example, the methylation state of the target polynucleotide can be assessed using a single probe or primer configured to hybridize with the target polynucleotide. For example, the methylation state of the target polynucleotide can be assessed using multiple probes or primers configured to hybridize with the target polynucleotide. In another aspect, the present application provides kits that may include the nucleic acids of the present application and / or combinations of the nucleic acids of the present application. For example, the kits of the present application may optionally include reference samples for corresponding applications or provide reference levels for corresponding applications. In another aspect, the probe of the present application may also contain a detectable substance. In one or more embodiments, the detectable substance may be a 5' fluorescent reporter and a 3' labeled quencher. In one or more embodiments, the fluorescent reporter gene may be selected from Cy5, Texas Red, FAM and VIC. In another aspect, the kit of the present application may also include a conversion positive standard in which unmethylated cytosines are converted to bases that are not linked to guanines. In one or more embodiments, the positive standard may be fully methylated. In another aspect, the kit of the present application may also include one or more substances selected from a PCR buffer, a polymerase, a dNTP, a restriction endonuclease, an enzyme digestion buffer, a fluorescent dye, a fluorescent quencher, a fluorescent reporter, an exonuclease, an alkaline phosphatase, an internal standard, a control, KCl, MgCl2, and (NH4)2SO4.

[0049] In another aspect, the reagents used to detect DNA methylation in the present application may be reagents used in one or more of the following methods: 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 endonuclease assays, fluorescence quantification, methylation-sensitive high-resolution melting curve assays, chip-based methylation atlases, and mass spectrometry (e.g., mass spectrometry on flight). For example, the reagents may be selected from one or more of bisulfite and its derivatives, fluorescent dyes, fluorescent quenchers, fluorescent reporters, internal standards, and controls. Diagnostic methods, preparation uses In another aspect, the present application provides the use of a nucleic acid of the present application, a combination of nucleic acids of the present application, and / or a kit of the present application in the preparation of a disease detection product. In another aspect, the present application provides a method for detecting disease, which may include providing a nucleic acid of the present application, a combination of nucleic acids of the present application, and / or a kit of the present application. In another aspect, the present application provides a nucleic acid of the present application, a combination of nucleic acids of the present application, and / or a kit of the present application for use in disease detection. In another aspect, the present application provides the use of a nucleic acid of the present application, a combination of nucleic acids of the present application, and / or a kit of the present application in the preparation of a substance for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease. In another aspect, the present application provides a method of determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease, which may include providing a nucleic acid of the present application, a combination of nucleic acids of the present application, and / or a kit of the present application. In another aspect, the present application provides nucleic acids of the present application, combinations of nucleic acids of the present application, and / or kits of the present application that can be used to determine the presence of a disease, assess the onset or risk of onset of a disease, and / or assess the progression of a disease. In another aspect, the present application provides the use of a nucleic acid of the present application, a combination of nucleic acids of the present application and / or a kit of the present application in the preparation of a substance capable of determining the modification status of a DNA region or a fragment thereof. In another aspect, the present application provides a method for determining the modification status of a DNA region or a fragment thereof, which may comprise providing a nucleic acid of the present application, a combination of nucleic acids of the present application and / or a kit of the present application. In another aspect, the present application provides nucleic acids of the present application, combinations of nucleic acids of the present application and / or kits of the present application that can be used to determine the modification status of a DNA region or fragment thereof. In another aspect, the application provides the use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in the preparation of a substance for determining the presence of a pancreatic tumor, for assessing the onset or risk of onset of a pancreatic tumor and / or for assessing the progression of a pancreatic tumor, wherein the DNA region for determination comprises a DNA region carrying the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or a fragment thereof. In another aspect, the application may include a method for determining the presence of, assessing the onset or risk of onset of, and / or assessing the progression of a pancreatic tumor, providing a kit for determining the modification status of a nucleic acid, combination of nucleic acids and / or DNA regions, the DNA regions for determination including DNA regions having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or fragments thereof. In another aspect, the present application provides nucleic acids, combinations of nucleic acids and / or kits for determining the modification status of DNA regions that can be used to determine the presence of a pancreatic tumor, to assess the onset or risk of onset of a pancreatic tumor, and / or to assess the progression of a pancreatic tumor, wherein the DNA regions for determination include DNA regions carrying genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or fragments thereof.

[0050] In another aspect, the present application provides a method for detecting a nucleotide sequence derived from human chr2:74743035-74743151, and derived from human chr2:74743080-74743301, derived from human chr8:25907849-25907950, and derived from human chr8:25907698-25907894, derived from human chr12:4919142-4919289, in a sample to be tested. derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, and derived from human chr1:63788812-6378895 2, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137814853, derived from human chr7:155167513-155167628, derived from human chr19:51228168-51228782, and derived from human chr7:19156739-19157277, and derived from human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof. In another aspect, the present application provides a method for determining the presence of a pancreatic tumor, assessing the onset or risk of onset of a pancreatic tumor, and / or assessing the progression of a pancreatic tumor, which may include providing a kit for determining the modification status of a nucleic acid, combination of nucleic acids and / or DNA regions, the DNA regions being derived from human chr2:74743035-74743151 and derived from human chr2:74743080-74743301. derived from human chr8:25907849-25907950, and derived from human chr8:25907698-25907894, derived from human chr12:4919142-4919289, derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439, and derived from human chr13:37005635-37005754 derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, derived from human chr1:63788812-63788952, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137 The DNA region may include a DNA region selected from the group consisting of a DNA region derived from human chr7:155167513-155167628, a DNA region derived from human chr19:51228168-51228782, and a DNA region derived from human chr7:19156739-19157277, and a DNA region derived from human chr2:73147525-73147644, or a complementary region thereof or a fragment thereof. In another aspect, the present application provides nucleic acids, combinations of nucleic acids and / or kits for determining the modification status of DNA regions that can be used to determine the presence of a pancreatic tumor, to assess the onset or risk of onset of a pancreatic tumor, and / or to assess the progression of a pancreatic tumor, wherein the DNA regions are derived from human chr2:74743035-74743151 and human chr2:74743080-7474330. 1, human chr8:25907849-25907950, and human chr8:25907698-25907894, human chr12:4919142-4919289, human chr12:4918991-4919187, and human chr12:4919235-4919439, and human chr13:37005635-37005754 derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, derived from human chr1:63788812-63788952, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137 814853, human chr7:155167513-155167628, human chr19:51228168-51228782, and human chr7:19156739-19157277, and human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof.

[0051] In another aspect, the present application provides nucleic acids of DNA regions carrying genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or converted regions thereof, or fragments thereof, and combinations of the above mentioned nucleic acids. In another aspect, the present application provides the use of a nucleic acid of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a converted region thereof, or a fragment thereof, and combinations of said nucleic acids, in the preparation of a substance for determining the presence of a pancreatic tumor, for assessing the onset or risk of onset of a pancreatic tumor, and / or for assessing the progression of a pancreatic tumor. In another aspect, the present application provides a method for determining the presence of, assessing the onset or risk of onset of, and / or assessing the progression of a pancreatic tumor, comprising providing a nucleic acid of a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or conversion regions thereof, or fragments thereof, and combinations of the above-mentioned nucleic acids. In another aspect, the present application provides nucleic acids of DNA regions having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or conversion regions thereof, or fragments thereof, and combinations of the above nucleic acids, that can be used to determine the presence of, assess the development or risk of development of, and / or assess the progression of a pancreatic tumor. In another aspect, the present application relates to human chr12:4919142-4919289 derived from human chr2:74743035-74743151, and derived from human chr2:74743080-74743301, derived from human chr8:25907849-25907950, and derived from human chr8:25907698-25907894. derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439; derived from human chr13:37005635-37005754; derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904; The present invention provides a nucleic acid of a DNA region selected from the group consisting of a DNA region derived from human chr1:63788812-63788952, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137814853, derived from human chr7:155167513-155167628, derived from human chr19:51228168-51228782, and derived from human chr7:19156739-19157277, and derived from human chr2:73147525-73147644, or a complementary region thereof, or a converted region thereof, or a fragment thereof, and a combination of the above nucleic acids. In another aspect, the present application relates to a nucleic acid sequence derived from human chr2:74743035-74743151, and derived from human chr2:74743080-74743301, derived from human chr8:25907849-25907950, and derived from human chr8:25907698-25907894, derived from human chr12:4919142-4919289, 12:4918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, human chr1:63788812-637889 52, human chr1:248020592-248020779, human chr2:176945511-176945630, human chr6:137814700-137814853, human chr7:155167513-155167628, human chr19:51228168-51228782, and human chr7:19156739-19157277, and human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof, and combinations of the above nucleic acids, in the preparation of a substance for determining the onset or risk of a disease and / or assessing the progression of a disease.

[0052] In another aspect, the present application relates to a method for the preparation of a nucleic acid sequence comprising the nucleic acid sequence of human chr2:74743035-74743151, and human chr2:74743080-74743301, human chr8:25907849-25907950, and human chr8:25907698-25907894, human chr12:4919142-4919289, human chr12:4 918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, derived from human chr1:63788812-63788952 and human chr7:19156739-19157277 and human chr2:73147525-73147644, or a complementary region thereof, or a conversion region thereof, or a fragment thereof, and a combination of the above nucleic acids. In another aspect, the present application provides a method for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease, comprising administering to a subject a gene encoding a human chr2:74743035-74743151, and administering to a subject a gene encoding a human chr2:74743080-74743301, and administering to a subject a gene encoding a human chr8:25907849-25907950, and administering to a subject a gene encoding a human chr8:25907698-2590 7894, derived from human chr12:4919142-4919289, derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, and derived from human chr13:3 derived from human chr1:63788812-63788952, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137814853, derived from human chr7:155167513-1551676 28, human chr19:51228168-51228782, and human chr7:19156739-19157277, and human chr2:73147525-73147644, or a complementary region thereof, or a converted region thereof, or a fragment thereof, and a combination of the above nucleic acids.

[0053] For example, the DNA region used for the determination in the present application comprises two genes selected from the group consisting of DNA regions having EBF2 and CCNA1 or fragments thereof. For example, it comprises determining the presence and / or content of the modification state of two DNA regions selected from the group consisting of DNA regions from human chr8:25907849-25907950 and from human chr13:37005635-37005754, or complementary regions thereof, or fragments thereof, in the sample to be tested. For example, in the methods of the present application, the target genes may include two genes selected from the group consisting of KCNA6, TLX2, and EMX1. For example, in the methods of the present application, the target genes may include KCNA6 and TLX2. For example, in the method of the present application, the target genes may include KCNA6 and EMX1. For example, in the method of the present application, the target genes may include TLX2 and EMX1. For example, in the method of the present application, the target genes may include three genes selected from the group consisting of KCNA6, TLX2 and EMX1. For example, in the method of the present application, the target genes may include KCNA6, TLX2 and EMX1. For example, it includes determining the presence and / or content of the modification state of two or more DNA regions selected from the group consisting of DNA regions derived from human chr12:4919142-4919289, derived from human chr2:74743035-74743151, and derived from human chr2:73147525-73147644, or complementary regions thereof, or fragments thereof, in the sample to be tested. For example, in the method of the present application, the target gene may include two genes selected from the group consisting of TRIM58, TWIST1, FOXD3, and EN2. For example, in the method of the present application, the target gene may include TRIM58 and TWIST1. For example, in the method of the present application, the target gene may include TRIM58 and FOXD3. For example, in the method of the present application, the target gene may include TRIM58 and EN2. For example, in the method of the present application, the target gene may include TWIST1 and FOXD3. For example, in the method of the present application, the target gene may include TWIST1 and EN2. For example, in the method of the present application, the target gene may include FOXD3 and EN2. For example, in the method of the present application, the target gene may include three genes selected from the group consisting of TRIM58, TWIST1, FOXD3, and EN2. For example, in the method of the present application, the target genes may include TRIM58, TWIST1 and FOXD3. For example, in the method of the present application, the target genes may include TRIM58, TWIST1 and EN2. For example, in the method of the present application, the target genes may include TRIM58, FOXD3 and EN2. For example, in the method of the present application, the target genes may include TWIST1, FOXD3 and EN2. For example, in the method of the present application, the target genes may include four genes selected from the group consisting of TRIM58, TWIST1, FOXD3 and EN2. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, FOXD3 and EN2. For example, it includes determining the presence and / or content of the modification state of two or more DNA regions selected from the group consisting of the DNA region derived from human chr1:248020592-248020779, the DNA region derived from human chr7:19156739-19157277, the DNA region derived from human chr1:63788812-63788952, and the DNA region derived from human chr7:155167513-155167628, or complementary regions thereof, or fragments thereof, in the sample to be tested. For example, in the method of the present application, the target gene may include two genes selected from the group consisting of TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3. For example, in the method of the present application, the target gene may include TRIM58 and TWIST1. For example, in the method of the present application, the target gene may include TRIM58 and CLEC11A. For example, in the method of the present application, the target gene may include TRIM58 and HOXD10. For example, in the method of the present application, the target gene may include TRIM58 and OLIG3. For example, in the method of the present application, the target gene may include TWIST1 and CLEC11A. For example, in the method of the present application, the target gene may include TWIST1 and HOXD10. For example, in the method of the present application, the target gene may include TWIST1 and OLIG3. For example, in the method of the present application, the target genes may include CLEC11A and HOXD10. For example, in the method of the present application, the target genes may include CLEC11A and OLIG3. For example, in the method of the present application, the target genes may include HOXD10 and OLIG3. For example, in the method of the present application, the target genes may include three genes selected from the group consisting of TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, and CLEC11A. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, and HOXD10. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, and OLIG3. For example, in the method of the present application, the target genes may include TRIM58, CLEC11A, and HOXD10. For example, in the methods of the present application, the target genes may include TRIM58, CLEC11A, and OLIG3. For example, in the methods of the present application, the target genes may include TRIM58, HOXD10, and OLIG3. For example, in the methods of the present application, the target genes may include TWIST1, CLEC11A, and HOXD10. For example, in the methods of the present application, the target genes may include TWIST1, CLEC11A, and OLIG3.For example, in the method of the present application, the target genes may include TWIST1, HOXD10, and OLIG3. For example, in the method of the present application, the target genes may include CLEC11A, HOXD10, and OLIG3. For example, in the method of the present application, the target genes may include four genes selected from the group consisting of TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, CLEC11A, and HOXD10. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, CLEC11A, and OLIG3. For example, in the method of the present application, the target genes may include TRIM58, TWIST1, HOXD10, and OLIG3. For example, in the methods of the present application, the target genes may include TRIM58, CLEC11A, HOXD10, and OLIG3. For example, in the methods of the present application, the target genes may include TWIST1, CLEC11A, HOXD10, and OLIG3. For example, in the methods of the present application, the target genes may include five genes selected from the group consisting of TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3. For example, in the methods of the present application, the target genes may include TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3.

[0054] For example, it includes determining the presence and / or content of the modification state of two or more DNA regions selected from the group consisting of the DNA region derived from human chr1:248020592-248020779, the DNA region derived from human chr7:19156739-19157277, the DNA region derived from human chr19:51228168-51228782, the DNA region derived from human chr2:176945511-176945630, and the DNA region derived from human chr6:137814700-137814853, or complementary regions thereof, or fragments thereof, in the sample to be tested. For example, the nucleic acid of the present application may refer to an isolated nucleic acid. For example, the isolated polynucleotide may be a DNA molecule, an RNA molecule, or a combination thereof. For example, the DNA molecule may be a genomic DNA molecule or a fragment thereof. In another aspect, the present application provides a storage medium having recorded thereon a program capable of carrying out the method of the present application. In another aspect, the present application provides a device that can include the storage medium of the present application. In another aspect, the present application provides a non-volatile computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to perform any one or more of the methods of the present application. For example, the non-volatile computer-readable storage medium can include a floppy disk, a flexible disk, a hard disk, a solid-state storage (SSS) (such as a solid-state drive (SSD)), a solid-state card (SSC), a solid-state module (SSM), an enterprise flash drive, a magnetic tape, or any other non-transitory magnetic medium, etc. The non-volatile computer-readable storage medium can also include a punch card, a paper tape, an optically marked card (or any other physical medium having a hole pattern or other optically identifiable marking), a compact disk read-only memory (CD-ROM), a compact disk rewriteable (CD-RW), a digital versatile disk (DVD), a Blu-ray disk (BD), and / or any other non-transitory optical medium.

[0055] For example, the device of the present application may further include a processor coupled to a storage medium, the processor configured to execute based on a program stored in the storage medium to implement the method of the present application. For example, the device may implement various mechanisms to ensure that the method of the present application produces correct results when executed on a database system. In the present application, the device may use a magnetic disk as permanent data storage. In the present application, the device may provide database storage and processing services for multiple database clients. The device may store database data across multiple shared storage devices and / or may utilize one or multiple execution platforms with multiple execution nodes. The device may be organized to allow for effectively infinite scalability of storage and computing resources. As used herein, "plurality" means any integer. Preferably, "more" in "one or more" can be any integer greater than or equal to 2, including, for example, 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. EMBODIMENT 1 1. A nucleic acid molecule isolated from a mammal, wherein the nucleic acid molecule is a methylation marker of a pancreatic cancer-associated gene, and the sequence of the nucleic acid molecule is one of the following: (1) SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ ID 4, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, or a variant having at least 70% identity thereto, in which the methylation site is not mutated; (2) a complementary sequence of (1); (3) a sequence of (1) or (2) that has been treated to convert unmethylated cytosine to a base that has a lower ability to bind guanine than cytosine; Preferably, the nucleic acid molecule is used as an internal standard or control for detecting the DNA methylation level of the corresponding sequence in the sample. A nucleic acid molecule isolated from a mammal. 2. A reagent for detecting DNA methylation, comprising a reagent for detecting the methylation level of a DNA sequence or a fragment thereof in a sample to be detected, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, wherein the DNA sequence is selected from the following gene sequences: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ 1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX20, IKZF1, INSIG1, SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CLEC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2, or sequences within 20 kb upstream or downstream thereof; Preferably, The DNA sequences are as follows: SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, 34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, or a complementary sequence thereof or a variant having at least 70% identity thereto, wherein the methylation site of the variant is not mutated; and / or the reagent is a primer molecule that hybridizes to a DNA sequence or a fragment thereof, the primer molecule being capable of amplifying the DNA sequence or a fragment thereof after bisulfite treatment; and / or The reagent for detecting DNA methylation is a probe molecule that hybridizes to a DNA sequence or a fragment thereof.

[0056] 3. A medium for recording a DNA sequence or a fragment thereof and / or methylation information thereof, wherein the DNA sequence is selected from the group consisting of (i) the following gene sequences: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX20, IKZF1, IN or (ii) a sequence of (i) that has been treated to convert unmethylated cytosines to bases that have a lower affinity for guanine than for cytosine; Preferably, The medium is used to align with gene methylation sequencing data to determine the presence, content and / or methylation level of a nucleic acid molecule comprising the sequence or a fragment thereof; and / or The DNA sequence may include the sense or antisense strand of DNA, and / or The length of the fragment is between 1 and 1000 bp, and / or The DNA sequences are as follows: SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ ID NO: No. 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, or a complementary sequence thereof, or a variant having at least 70% identity thereto, in which the methylation site in the variant is not mutated; More preferably, The medium is a carrier on which a DNA sequence or a fragment thereof and / or its methylation information is printed, and / or The medium is a computer readable medium storing a sequence or a fragment thereof and / or methylation information thereof and a computer program, which, when executed by a processor, performs a process of comparing methylation sequencing data of a sample with the sequence or the fragment thereof to obtain the presence, content and / or methylation level of a nucleic acid molecule comprising the sequence or the fragment thereof in the sample, wherein the presence, content and / or methylation level is used to diagnose pancreatic cancer. The medium for recording a DNA sequence or a fragment thereof and / or methylation information thereof.

[0057] 4. The following items (a) and / or (b) in the preparation of a kit for diagnosing pancreatic cancer in a subject: (a) a reagent or device for determining the methylation level of a DNA sequence or a fragment thereof in a sample from a subject, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof; (b) a nucleic acid molecule of a DNA sequence or a fragment thereof that has been treated to convert unmethylated cytosines to bases that have a lower binding affinity for guanine than for cytosine; The use of Here, the DNA sequences are the following gene sequences: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA1 3, TBX20, IKZF1, INSIG1, SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CLEC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2, or sequences within 20 kb upstream or downstream thereof; Preferably, the fragment has a length of 1 to 1000 bp. 5. The DNA sequence is selected from the following sequences: SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:62, SEQ ID NO:63, SEQ ID 4. The use according to embodiment 4, wherein the methylation site of the mutant is selected from one or more or all of the mutants that are not mutated, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, or a complementary sequence thereof or a sequence having at least 70% identity thereto.

[0058] 6. The reagent comprises a primer molecule that hybridizes to a DNA sequence or a fragment thereof; and / or The reagent comprises a probe molecule that hybridizes to the DNA sequence or a fragment thereof; and / or The reagent comprises the medium of embodiment 3; Use according to embodiment 4 or 5. 7. The sample is derived from mammalian tissue, cells or body fluids, such as pancreatic tissue or blood, and / or the sample comprises genomic DNA or cfDNA, and / or and / or the DNA sequence is such that unmethylated cytosine is converted to a base that has a lower binding affinity for guanine than for cytosine; The DNA sequence is treated with a methylation-sensitive restriction enzyme, Use according to embodiment 4 or 5. 8. The use of embodiment 4 or 5, wherein the diagnosis comprises obtaining a score by comparison with a control sample and / or reference level or by calculation, and diagnosing pancreatic cancer based on the score, preferably, the calculation is performed by constructing a support vector machine model. 9. A kit for identifying pancreatic cancer, comprising: (a) a reagent or device for determining the methylation level of a DNA sequence or a fragment thereof, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, in a sample from a subject; and Optionally, (b) a nucleic acid molecule of a DNA sequence or a fragment thereof that has been processed to convert unmethylated cytosines into bases that have a lower binding affinity for guanine than for cytosine. Inclusive of Here, the DNA sequences are the following gene sequences: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ1, SHOX2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX2 0, IKZF1, INSIG1, SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CLEC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2, or sequences within 20 kb upstream or downstream thereof; Preferably, The DNA sequences are as follows: SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, 34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56 or a complementary sequence thereof, or a variant having at least 70% identity thereto, in which the methylation site of the variant is not mutated; and / or The kit is suitable for use according to any one of embodiments 6 to 8, and / or The reagent comprises a primer molecule that hybridizes to the DNA sequence or a fragment thereof; and / or The reagent comprises a probe molecule that hybridizes to the DNA sequence or a fragment thereof; and / or The reagent comprises the medium of embodiment 3, and / or The sample is derived from mammalian tissue, cells or body fluids, such as pancreatic tissue or blood, and / or and / or the DNA sequence is such that unmethylated cytosine is converted to a base that has a lower binding affinity for guanine than for cytosine; A kit for identifying pancreatic cancer, in which a DNA sequence is treated with a methylation-sensitive restriction enzyme.

[0059] 10. A device for diagnosing pancreatic cancer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the device performing the following steps when the processor executes the program: (1) A step of obtaining the methylation level of a DNA sequence or a fragment thereof in a sample to be detected, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, wherein the DNA sequence is one of the following gene sequences: DMRTA2, FOXD3, TBX15, BCAN, TRIM58, SIX3, VAX2, EMX1, LBX2, TLX2, POU3F3, TBR1, EVX2, HOXD12, HOXD8, HOXD4, TOPAZ1, SHO X2, DRD5, RPL9, HOPX, SFRP2, IRX4, TBX18, OLIG3, ULBP1, HOXA13, TBX20, IKZF1, INSIG1, SOX7, EBF2, MOS, MKX, KCNA6, SYT10, AGAP2, TBX3, CCNA1, ZIC2, CLEC14A, OTX2, C14orf39, BNC1, AHSP, ZFHX3, LHX1, TIMP2, ZNF750, SIM2; (2) obtaining a score by comparison with a control sample and / or a reference level or by calculation; (3) diagnosing pancreatic cancer based on the score; and is executed, Preferably, The DNA sequences are as follows: SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, SEQ ID NO:12, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:15, SEQ ID NO:16, SEQ ID NO:17, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, SEQ ID NO:24, SEQ ID NO:25, SEQ ID NO:26, SEQ ID NO:27, SEQ ID NO:28, SEQ ID NO:29, SEQ ID NO:30, SEQ ID NO:31, SEQ ID NO:32, SEQ ID NO:33, 34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40, SEQ ID NO:41, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:46, SEQ ID NO:47, SEQ ID NO:48, SEQ ID NO:49, SEQ ID NO:50, SEQ ID NO:51, SEQ ID NO:52, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:56, or a complementary sequence thereof or a variant having at least 70% identity thereto, wherein the methylation site of the variant is not mutated; and / or Step (1) comprises detecting the methylation level of a sequence in a sample by the nucleic acid molecule of embodiment 1 and / or the reagent of embodiment 2 and / or the medium of embodiment 3; and / or the sample contains genomic DNA or cfDNA; and / or a sequence is altered when an unmethylated cytosine is converted to a base that has a lower binding affinity for guanine than cytosine; and / or The DNA sequence is treated with a methylation-sensitive restriction enzyme, and / or A device for diagnosing pancreatic cancer, wherein the score in step (2) is calculated by constructing a support vector machine model.

[0060] EMBODIMENT 2 1. A nucleic acid molecule isolated from a mammal, the nucleic acid molecule being a methylation marker associated with differentiation between pancreatic cancer and pancreatitis, the sequence of the nucleic acid molecule comprising one or more or all of the sequences selected from the group consisting of SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59, or a variant having at least 70% identity thereto in which the methylation site is not mutated; (2) a complementary sequence of (1); or (3) a sequence of (1) or (2) that has been treated to convert unmethylated cytosine to a base that has a lower binding ability to guanine than cytosine; Preferably, the nucleic acid molecule is used as an internal standard or control for detecting the DNA methylation level of a corresponding sequence in a sample, the nucleic acid molecule being isolated from a mammal. 2. A reagent for detecting DNA methylation, comprising a reagent for 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 a DNA sequence or a fragment thereof, in a sample of a subject to be detected, wherein the DNA sequence is selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2 or sequences 20 kb upstream or downstream thereof; Preferably, the DNA sequence is selected from one or more or all of the following sequences or their complements: SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, or variants having at least 70% identity thereto, in which the methylation site of the variant is not mutated; and / or the reagent is a primer molecule that hybridizes to a DNA sequence or a fragment thereof, the primer molecule being capable of amplifying the DNA sequence or a fragment thereof after bisulfite treatment; and / or The reagent for detecting DNA methylation is a probe molecule that hybridizes to a DNA sequence or a fragment thereof. 3. A medium for recording a DNA sequence or a fragment thereof and / or its methylation information, the DNA sequence being (i) selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2, or sequences within 20 kb upstream or downstream thereof, or (ii) a sequence of (i) that has been treated to convert unmethylated cytosine to a base that has a lower binding ability to guanine than cytosine; Preferably, The medium is used to align with gene methylation sequencing data to determine the presence, content and / or methylation level of a nucleic acid molecule comprising the sequence or a fragment thereof; and / or The DNA sequence may comprise a sense or antisense strand of DNA, and / or The length of the fragment is between 1 and 1000 bp, and / or The DNA sequence is selected from one or more or all of the following sequences or their complements: SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59, or a variant having at least 70% identity thereto, wherein the methylation site of the variant is not mutated; More preferably, The medium is a carrier on which a DNA sequence or a fragment thereof and / or its methylation information is printed, and / or The medium is a computer readable medium storing a sequence or a fragment thereof and / or methylation information thereof, and a computer program, which, when executed by a processor, performs the following steps: comparing methylation sequencing data of the sample with the sequence or the fragment thereof to obtain the presence, content and / or methylation level of a nucleic acid molecule comprising the sequence or the fragment thereof in the sample, wherein the presence, content and / or methylation level is used to differentiate between pancreatic cancer and pancreatitis. The medium for recording a DNA sequence or a fragment thereof and / or methylation information thereof. 4. The following items (a) and / or (b): (a) a reagent or device for determining the methylation level of a DNA sequence or a fragment thereof in a sample from a subject, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof; (b) a nucleic acid molecule of a DNA sequence or a fragment thereof that has been treated to convert unmethylated cytosines to bases that have a lower binding affinity for guanine than for cytosine; in the preparation of a kit for distinguishing between pancreatic cancer and pancreatitis, wherein the DNA sequence is selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2, or sequences within 20 kb upstream or downstream thereof; Preferably, the fragment has a length of 1 to 1000 bp.

[0061] 5. The use according to embodiment 4, wherein the DNA sequence is selected from one or more or all of the following sequences: SEQ ID NO:57, SEQ ID NO:58, SEQ ID NO:59, or a complementary sequence thereof or having at least 70% identity thereto, wherein the methylation site of the mutant is not mutated. 6. The reagent comprises a primer molecule that hybridizes to a DNA sequence or a fragment thereof; and / or The reagent comprises a probe molecule that hybridizes to the DNA sequence or a fragment thereof; and / or The reagent comprises the medium of embodiment 3; Use according to embodiment 4 or 5. 7. The sample is derived from mammalian tissue, cells or body fluids, such as pancreatic tissue or blood, and / or the sample comprises genomic DNA or cfDNA, and / or and / or the DNA sequence is such that unmethylated cytosine is converted to a base that has a lower binding affinity for guanine than for cytosine; The DNA sequence is treated with a methylation-sensitive restriction enzyme, Use according to embodiment 4 or 5. 8. The use of embodiment 4 or 5, wherein the diagnosis comprises obtaining a score by comparison with a control sample and / or a reference level or by calculation, and distinguishing between pancreatic cancer and pancreatitis based on the score, preferably, the calculation is performed by constructing a support vector machine model. 9. A kit for distinguishing between pancreatic cancer and pancreatitis, comprising: (a) a reagent or device for determining the methylation level of a DNA sequence or a fragment thereof, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, in a sample from a subject; and Optionally, (b) a nucleic acid molecule of a DNA sequence or a fragment thereof that has been processed to convert unmethylated cytosines into bases that have a lower binding affinity for guanine than for cytosine. Including, wherein the DNA sequence is selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2, or sequences within 20 kb upstream or downstream thereof; Preferably, and / or the DNA sequence is selected from one or more or all of the following sequences: SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, or a complementary sequence thereof or a variant having at least 70% identity thereto, in which the methylation site of the variant is not mutated; The kit is suitable for use according to any one of embodiments 6 to 8, and / or The reagent comprises a primer molecule that hybridizes to the DNA sequence or a fragment thereof; and / or The reagent comprises a probe molecule that hybridizes to the DNA sequence or a fragment thereof; and / or The reagent comprises the medium of embodiment 3, and / or The sample is derived from mammalian tissue, cells or body fluids, such as pancreatic tissue or blood, and / or and / or the DNA sequence is such that unmethylated cytosine is converted to a base that has a lower binding affinity for guanine than for cytosine; A kit for distinguishing between pancreatic cancer and pancreatitis, in which a DNA sequence is treated with a methylation-sensitive restriction enzyme.

[0062] 10. A device for distinguishing between pancreatic cancer and pancreatitis, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the device performing the following steps when the processor executes the program: (1) obtaining the methylation level of a DNA sequence or a fragment thereof in a sample of a subject to be detected, or the methylation state or level of one or more CpG dinucleotides in the DNA sequence or a fragment thereof, wherein the DNA sequence is selected from one or more or all of the following gene sequences: SIX3, TLX2, CILP2; (2) obtaining a score by comparison with a control sample and / or a reference level or by calculation; (3) differentiating between pancreatic cancer and pancreatitis based on the score; is executed, Preferably, and / or the DNA sequence is selected from one or more or all of the following sequences: SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, or a complementary sequence thereof or a variant having at least 70% identity thereto, in which the methylation site of the variant is not mutated; Step (1) comprises detecting the methylation level of a sequence in a sample by the nucleic acid molecule of embodiment 1 and / or the reagent of embodiment 2 and / or the medium of embodiment 3; and / or the sample contains genomic DNA or cfDNA; and / or sequences in which unmethylated cytosine is converted to a base that has a lower binding affinity for guanine than for cytosine, and / or Treating the DNA sequence with a methylation-sensitive restriction enzyme, and / or A device for distinguishing between pancreatic cancer and pancreatitis, wherein the score in step (2) is calculated by constructing a support vector machine model. EMBODIMENT 3 1. A method for assessing the presence and / or progression of a pancreatic tumor, comprising determining in a sample to be tested the presence and / or content of a modification state of a DNA region selected from the following DNA regions, or their complementary regions, or fragments thereof: [Table 1-1] [Table 1-2] [Table 1-3]

[0063] 2. A method for assessing the presence and / or progression of a pancreatic tumor, comprising determining the presence and / or content of a modification state of a DNA region selected from any one of SEQ ID NOs: 60 to 160, or a complementary region thereof, or a fragment thereof, in a sample to be tested. 10. A method for assessing the presence and / or progression of a pancreatic tumor, comprising the steps of: ARHGEF16, PRDM16, NFIA, ST6GALNAC5, PRRX1, LHX4, ACBD6, FMN2, CHRM3, FAM150B, TMEM18, SIX3, CAMKMT, OTX1, WDPCP, CYP26B1, DYSF, HOXD1, HOXD4, UBE2F, RAMP1, AMT, PLSCR5, ZIC4, PEX5L, ETV5, DGKG, FGF12, FGFRL1, RNF212, DOK7, HGFAC, EVC, EVC2, HGF ... MX1, CPZ, IRX1, GDNF, AGGF1, CRHBP, PITX1, CATSPER3, NEUROG1, NPM1, TLX3, NKX2-5, BNIP1, PROP1, B4GALT7, IRF4, FOXF2, FOXQ1, FOXC1, GMDS, MOC S1, LRFN2, POU3F2, FBXL4, CCR6, GPR31, TBX20, HERPUD2, VIPR2, LZTS1, NKX2-6, PENK, PRDM14, VPS13B, OSR2, NEK6, LHX2, DDIT4, DNAJB12, CRTAC1, P AX2, HIF1AN, ELOVL3, INA, HMX2, HMX3, MKI67, DPYSL4, STK32C, INS, INS-IGF2, ASCL2, PAX6, RELT, FAM168A, OPCML, ACVR1B, ACVRL1, AVPR1A, LHX5, SDSL, RAB20, COL4A2, CALKD, CARS2, SOX1, TEX29, SPACA7, SFTA3, SIX6, SIX1, INF2, TMEM179, CRIP2, MTA1, PIAS1, SKOR1, ISL2, SCAPER, POLG, RHCG , NR2F2, RAB40C, PIGQ, CPNE2, NLRC5, PSKH1, NRN1L, SRR, HIC1, HOXB9, PRAC1, SMIM5, MYO15B, TNRC6C, 9-Sep, TBCD, ZNF750, KCTD1, SALL3, CTDP1, N FATC1, ZNF554, THOP1, CACTIN, PIP5K1C, KDM4B, PLIN3, EPS15L1, KLF2, EPS8L1, PPP1R12C, NKX2-4, NKX2-2, TFAP2C, RAE1, TNFRSF6B, ARFRP1, MYH9,A method for assessing the presence and / or progression of a pancreatic tumor, comprising determining the presence and / or content of a modification state of a DNA region using a gene selected from the group consisting of TXN2 or a fragment thereof.

[0064] 3. The method of any one of the preceding claims, further comprising obtaining nucleic acid in the sample to be tested. 4. The method of embodiment 3, wherein the nucleic acid comprises cell-free nucleic acid. 5. The method of any one of embodiments 1 to 4, wherein the sample to be tested comprises tissue, cells and / or body fluid. 6. The method of any one of embodiments 1 to 5, wherein the sample being tested comprises plasma. 7. The method of any one of the preceding claims, further comprising converting the DNA region or a fragment thereof. 8. The method of embodiment 7, wherein the bases with and without a modified state each form different substances after conversion. 9. The method according to any one of embodiments 7 to 8, wherein the bases having a modified state are not substantially changed after conversion, and the bases not having a modified state are changed to other bases different from the bases after conversion, or are cleaved after conversion. 10. The method of any one of embodiments 8 to 9, wherein the base comprises a cytosine. 11. The method according to any one of the preceding embodiments, wherein the modification state comprises a methylation modification. 12. The method of any one of embodiments 9 to 11, wherein the other bases include cytosine. 13. The method according to any one of embodiments 7 to 12, wherein the conversion comprises conversion with a deamination reagent and / or a methylation-sensitive restriction enzyme. 14. The method of embodiment 13, wherein the deamination reagent comprises bisulfite or an analog thereof. 15. The method according to any one of the preceding claims, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a DNA region or a fragment thereof having a modification state. 16. The method according to any one of the preceding embodiments, wherein the presence and / or content of a DNA region or a fragment thereof having a modification state is detected by sequencing.

[0065] 17. The method according to embodiments 1 to 16, wherein the presence or progression of a pancreatic tumor is determined by determining the presence of a modification state of a DNA region or a fragment thereof and / or a content of a modification state of a DNA region or a fragment thereof that is higher compared to a reference level. 18. A nucleic acid comprising a sequence capable of binding to the DNA region of embodiment 1, or its complementary region, or a converted region thereof, or a fragment thereof. 19. A nucleic acid comprising a sequence capable of binding to a DNA region selected from any of SEQ ID NOs: 60 to 160, or a complementary region thereof, or a converted region thereof, or a fragment thereof. 20. A nucleic acid comprising a sequence capable of binding to a DNA region carrying a gene selected from embodiment 2, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. 21. A kit comprising a nucleic acid according to any one of embodiments 18 to 20. 22. Use of a nucleic acid according to any one of embodiments 18 to 20 and / or a kit according to embodiment 21 in the preparation of a disease detection product. 23. Use of a nucleic acid according to any one of embodiments 18 to 20 and / or a kit according to embodiment 21 in the preparation of a substance for assessing the presence and / or progression of a pancreatic tumor. 24. Use of a nucleic acid according to any one of embodiments 18 to 20 and / or a kit according to embodiment 21 for the preparation of a substance for determining the modification state of a DNA region or a fragment thereof. 25. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region selected from embodiment 1, or its complementary region, or its converted region, or a fragment thereof, based on the modification state of the DNA region, or its complementary region, or its converted region, or a fragment thereof. 26. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region, or a complementary region, or a converted region, or a fragment thereof, selected from any one of SEQ ID NOs: 60 to 160, or a complementary region, or a converted region, or a fragment thereof, based on a modification state of the DNA region, or a complementary region, or a converted region, or a fragment thereof. 27. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region having a gene of embodiment 2, or its complementary region, or its converted region, or its fragment, based on the modification state of the DNA region, or its complementary region, or its converted region, or its fragment. 28. Use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in the preparation of a substance for assessing the presence and / or progression of a pancreatic tumor, wherein the DNA region for determination comprises the sequence of a DNA region selected from embodiment 1, or a complementary region thereof, or a converted region thereof, or a fragment thereof. 29. Use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in a material preparation for assessing the presence and / or progression of a pancreatic tumor, wherein the DNA region for determination comprises the sequence of a DNA region selected from any one of SEQ ID NOs: 60 to 160, or a complementary region thereof, or a converted region thereof, or a fragment thereof. 30. Use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in a material preparation for assessing the presence and / or progression of a pancreatic tumor, wherein the DNA region for determination comprises the sequence of a DNA region carrying a gene selected from embodiment 2, or a complementary region thereof, or a converted region thereof, or a fragment thereof. 31. The use of any one of embodiments 29-30, wherein the modification state comprises a methylation modification. 32. A storage medium having recorded thereon a program capable of executing the method according to any one of embodiments 1 to 17. 33. A device including the storage medium of embodiment 32, and optionally further including a processor coupled to the storage medium, the processor configured to execute based on a program stored in the storage medium to implement the method of any one of embodiments 1 to 17.

[0066] EMBODIMENT 4 1. A method for constructing a pancreatic cancer diagnostic model, comprising: (1) obtaining a methylation level of a DNA sequence or a fragment thereof, or a methylation status or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, in a sample from a subject, and a CA19-9 level in the subject; (2) determining a methylation score by calculation using a mathematical model based on the methylation status or level; (3) combining the methylation scores and CA19-9 levels into a data matrix; and (4) constructing a pancreatic cancer diagnosis model based on the data matrix; and A method for constructing a pancreatic cancer diagnostic model, comprising: 2. The method is as follows: The DNA sequence is selected from one or more of the following gene sequences: SIX3, TLX2, CILP2 or a sequence within 20 kb upstream or downstream thereof; The fragment comprises at least one CpG dinucleotide, Step (1) comprises 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 a DNA sequence or a fragment thereof in a sample from a subject; the sample is from a mammalian tissue, cell or body fluid, such as pancreatic tissue or blood; the CA19-9 level is a blood or plasma CA19-9 level; the mathematical model in step (2) is a support vector machine model; The pancreatic cancer diagnosis model in step (4) is a logistic regression model. 2. The method of embodiment 1, further comprising one or more features selected from: 3. A method for constructing a pancreatic cancer diagnostic model, comprising: (1) obtaining a methylation haplotype fraction and a sequencing depth of a genomic DNA segment of interest; Optionally, (2) preprocessing the methylation haplotype fraction and sequencing depth data; and (3) performing cross-validation incremental feature selection to obtain feature methylation segments; (4) constructing a mathematical model of the methylation detection result of the characteristic methylation segment to obtain a methylation score; (5) constructing a pancreatic cancer diagnostic model based on the methylation score and the corresponding CA19-9 level; and A method for constructing a pancreatic cancer diagnostic model, comprising: 4. Method is as follows Step (1) is 1.1) detecting DNA methylation in a subject sample to obtain sequencing read data; 1.2) optional pre-processing of the sequencing data, such as adapter removal and / or splicing; 1.3) aligning the sequencing data with a reference genome to obtain location and sequencing depth information of methylated segments; 1.4) calculating the methylation haplotype fraction (MHF) of the segment according to the following formula:

number

[0067] 5. The method is as follows: The mathematical model in step (4) is a support vector machine (SVM) model; The methylation detection result in step (4) is a combined matrix of methylation haplotype fraction and sequencing depth; The pancreatic cancer diagnosis model in step (5) is a logistic regression model. The method of embodiment 3 or 4, further comprising one or more features selected from: 6. Use of a reagent or device for detecting DNA methylation and a reagent or device for detecting CA19-9 levels in the preparation of a pancreatic cancer diagnostic kit, wherein the reagent or device for detecting DNA methylation is used to determine the methylation level of a DNA sequence or a fragment thereof in a sample from a subject, or the methylation state or level of one or more CpG dinucleotides in the DNA sequence or a fragment thereof. 7. Use the following: The DNA sequence is selected from one or more of the following gene sequences: SIX3, TLX2, CILP2, or a sequence within 20 kb upstream or downstream thereof; The fragment comprises at least one CpG dinucleotide, The reagent for detecting DNA methylation comprises a primer molecule hybridizing with a DNA sequence or a fragment thereof, the primer molecule being capable of amplifying the DNA sequence or the fragment thereof after sulfite treatment; The reagent for detecting DNA methylation comprises a probe molecule that hybridizes with a DNA sequence or a fragment thereof, The reagent for detecting CA19-9 level is a detection reagent based on immune response, The kit also contains PCR reaction reagents. The kit also includes other reagents for detecting DNA methylation, the reagents being used in one or more methods selected from bisulfite conversion-based PCR, DNA sequencing, methylation-sensitive restriction endonuclease assay, fluorimetric quantification, methylation-sensitive high-resolution melting curve assay, chip-based methylation atlas, and mass spectrometry; The diagnosis further includes constructing a pancreatic cancer diagnostic model described in any one of embodiments 1 to 5, performing a calculation, and diagnosing pancreatic cancer based on the score. 8. A kit for diagnosing pancreatic cancer, comprising: (a) a reagent or device for detecting DNA methylation, the reagent or device being used to determine the methylation level of a DNA sequence or a fragment thereof, or the methylation state or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, in a sample of a subject; (b) a reagent or device for detecting CA19-9 levels A kit for diagnosing pancreatic cancer comprising:

[0068] 9. The kit includes: The DNA sequence is selected from one or more of the following gene sequences: SIX3, TLX2, CILP2 or a sequence within 20 kb upstream or downstream thereof; The fragment comprises at least one CpG dinucleotide, The reagent for detecting DNA methylation comprises a primer molecule hybridizing with a DNA sequence or a fragment thereof, the primer molecule being capable of amplifying the DNA sequence or the fragment thereof after sulfite treatment; The reagent for detecting DNA methylation comprises a probe molecule that hybridizes with a DNA sequence or a fragment thereof, The reagent for detecting CA19-9 level is a detection reagent based on immune response, The kit also contains PCR reaction reagents. The kit of embodiment 8, further comprising one or more features selected from: the kit also comprises other reagents for detecting DNA methylation, which are reagents used in one or more of the following methods: bisulfite conversion-based PCR, DNA sequencing, methylation-sensitive restriction endonuclease assay, fluorimetric quantification, methylation-sensitive high-resolution melting curve assay, chip-based methylation atlas, mass spectrometry. 10. A method for manufacturing a computer system comprising: a memory; a processor; and a computer program stored in the memory and executable on the processor, the computer program performing the following steps when the processor executes the program: (1) obtaining a methylation level of a DNA sequence or a fragment thereof in a sample from a subject, or a methylation status or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, and a CA19-9 level in the subject; (2) determining a methylation score by calculation using a mathematical model based on the methylation status or level; (3) combining the methylation scores and CA19-9 levels into a data matrix; (4) constructing a pancreatic cancer diagnostic model based on the data matrix; optionally (5) obtaining a pancreatic cancer score and diagnosing pancreatic cancer based on the pancreatic cancer score; or (1) obtaining a methylation level of a DNA sequence or a fragment thereof in a sample from a subject, or a methylation status or level of one or more CpG dinucleotides in a DNA sequence or a fragment thereof, and a CA19-9 level in the subject; (2) determining a methylation score by calculation using a mathematical model based on the methylation status or level; (3) obtaining a pancreatic cancer score according to the model shown below, and diagnosing pancreatic cancer based on the pancreatic cancer score,

number

[0069] EMBODIMENT 5 1. A method for determining the presence of a pancreatic tumor, assessing the onset or risk of onset of a pancreatic tumor, and / or assessing the progression of a pancreatic tumor, comprising determining the presence and / or content of the modification state of a DNA region using the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1 or fragments thereof in a sample to be tested. 2. A method for assessing the methylation status of a pancreatic tumor-associated DNA region, comprising determining the presence and / or content of the modification status of the DNA region in the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or fragments thereof, in the sample to be tested. 3. The DNA region is derived from human chr2:74740686-74744275, derived from human chr8:25699246-25907950, derived from human chr12:4918342-4960278, derived from human chr13:37005635-37017019, derived from human chr1:63788730-63790797, derived from human chr1:248020501-248043438, derived from human chr2: 176945511-176984670, derived from human chr6:137813336-137815531, derived from human chr7:155167513-155257526, derived from human chr19:51226605-51228981, derived from human chr7:19155091-19157295, and derived from human chr2:73147574-73162020, the method of any one of embodiments 1-2. 4. The method of any one of embodiments 1 to 3, further comprising obtaining nucleic acid in the sample to be tested. 5. The method of embodiment 4, wherein the nucleic acid comprises cell-free nucleic acid. 6. The method of any one of embodiments 1 to 5, wherein the sample to be tested comprises tissue, cells and / or body fluid. 7. The method of any one of embodiments 1-6, wherein the sample being tested comprises plasma. 8. The method of any one of the preceding claims, further comprising converting the DNA region or a fragment thereof. 9. The method of embodiment 8, wherein the bases with and without the modified state form different substances after conversion. 10. The method according to any one of the preceding claims, wherein the bases having a modified state are not substantially changed after conversion, and the bases not having a modified state are changed to other bases different from the bases after conversion, or are cleaved after conversion. 11. The method of any one of embodiments 9 to 10, wherein the base comprises a cytosine. 12. The method according to any one of the preceding embodiments, wherein the modification state comprises a methylation modification. 13. The method of any one of embodiments 10 to 12, wherein the other bases include cytosine. 14. The method according to any one of embodiments 8 to 13, wherein the conversion comprises conversion with a deamination reagent and / or a methylation-sensitive restriction enzyme. 15. The method of embodiment 14, wherein the deamination reagent comprises bisulfite or an analog thereof.

[0070] 16. The method according to any one of the preceding claims, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a substance formed by a base having a converted modification state. 17. The method according to any one of the preceding claims, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a DNA region or a fragment thereof having a modification state. 18. The method according to any one of the preceding embodiments, wherein the presence and / or content of a DNA region or a fragment thereof having a modification state is determined by the fluorescence Ct value detected by fluorescent PCR. 19. The method according to any one of embodiments 1 to 18, wherein the presence of a pancreatic tumor or the onset or risk of onset of a pancreatic tumor is determined by determining the presence of a modification state of a DNA region or a fragment thereof and / or the content of a modification state of a DNA region or a fragment thereof being higher relative to a reference level. 20. The method according to any one of the preceding embodiments, further comprising amplifying the DNA region or a fragment thereof in the sample to be tested prior to determining the presence and / or content of the modification state of the DNA region or a fragment thereof. 21. The method of embodiment 20, wherein the amplification comprises PCR amplification. 22. In the sample to be tested, human chr2: 74743035-74743151 and human chr2: 74743080-74743301, human chr8: 25907849-25907950 and human chr8: 25907698-25907894, human chr12: 4919142-4919289, human chr1 2:4918991-4919187, and human chr12:4919235-4919439, human chr13:37005635-37005754, human chr13:37005458-37005653, and human chr13:37005680-37005904, human chr1:63788812-6378895 2, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137814853, derived from human chr7:155167513-155167628, derived from human chr19:51228168-51228782, and derived from human chr7:19156739-19157277, and derived from human chr2:73147525-73147644, or complementary regions thereof, or fragments thereof. 23. In the sample to be tested, human chr2: 74743035-74743151 derived, human chr2: 74743080-74743301 derived, human chr8: 25907849-25907950 derived, human chr8: 25907698-25907894 derived, human chr12: 4919142-4919289 derived , derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, and derived from human chr13:37005680-37005904, and derived from human chr1:6 3788812-63788952, from human chr1:248020592-248020779, from human chr2:176945511-176945630, from human chr6:137814700-137814853, from human chr7:155167513-155167628, from human chr19:51228168-51228782, and from human chr7:19156739-19157277, and from human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof.

[0071] 24. The method of any one of embodiments 22-23, comprising providing a nucleic acid capable of binding to a DNA region selected from the group consisting of SEQ ID NOs: 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228 and 232, or a complementary region thereof, or a converted region thereof, or a fragment thereof. 25, derived from human chr2:74743042-74743113 and derived from human chr2:74743157-74743253, derived from human chr2:74743042-74743113 and derived from human chr2:74743157-74743253, derived from human chr8:25907865-25907930 and derived from human chr8:25907698-2590781 4, derived from human chr12:4919188-4919272, derived from human chr12:4919036-4919164 and derived from human chr12:4919341-4919438, derived from human chr13:37005652-37005721, derived from human chr13:37005458-37005596 and derived from human chr13:37005694-3700 5824, derived from human chr1:63788850-63788913, derived from human chr1:248020635-248020731, derived from human chr2:176945521-176945603, derived from human chr6:137814750-137814815, derived from human chr7:155167531-155167610, derived from human chr19:512 25. The method of any one of embodiments 22 to 24, comprising providing a nucleic acid capable of binding to a DNA region selected from the group consisting of: DNA region derived from human chr7:19156779-19157914; and DNA region derived from human chr2:73147571-73147626, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. 26. The method of any one of embodiments 22-25, comprising providing a nucleic acid selected from the group consisting of SEQ ID NOs: 165, 169, 173, 177, 181, 185, 189, 193, 197, 201, 205, 209, 213, 217, 221, 225, 229, and 233, or a complementary nucleic acid thereof, or a fragment thereof. 27. The method of any one of embodiments 22-26, comprising providing a combination of nucleic acids selected from the group consisting of SEQ ID NOs: 166 and 167, 170 and 171, 174 and 175, 178 and 179, 182 and 183, 186 and 187, 190 and 191, 194 and 195, 198 and 199, 202 and 203, 206 and 207, 210 and 211, 214 and 215, 218 and 219, 222 and 223, 226 and 227, 230 and 231, and 234 and 235, or a combination of complementary nucleic acids thereof, or fragments thereof. 28. The method of any one of embodiments 22 to 27, wherein the disease comprises a tumor. 29. The method of any one of embodiments 22 to 28, further comprising obtaining nucleic acid in the sample to be tested. 30. The method of embodiment 29, wherein the nucleic acid comprises cell-free nucleic acid. 31. The method of any one of embodiments 22-30, wherein the sample to be tested comprises tissue, cells and / or body fluid. 32. The method of any one of embodiments 22-31, wherein the sample to be tested comprises plasma. 33. The method of any one of embodiments 22 to 32, further comprising converting the DNA region or a fragment thereof. 34. The method of embodiment 33, wherein the bases with and without the modified state form different substances after conversion.

[0072] 35. The method according to any one of embodiments 22 to 34, wherein the bases having a modified state are not substantially changed after conversion, and the bases not having a modified state are changed to other bases different from the bases after conversion, or are cleaved after conversion. 36. The method of any one of embodiments 34-35, wherein the base comprises a cytosine. 37. The method according to any one of embodiments 22 to 36, wherein the modification state comprises a methylation modification. 38. The method of any one of embodiments 35-37, wherein the other bases include cytosine. 39. The method according to any one of embodiments 33 to 38, wherein the conversion comprises conversion with a deamination reagent and / or a methylation-sensitive restriction enzyme. 40. The method of embodiment 39, wherein the deamination reagent comprises bisulfite or an analogue thereof. 41. The method according to any one of embodiments 22 to 40, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a substance formed by a base having a converted modification state. 42. A method according to any one of embodiments 22 to 41, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a DNA region or a fragment thereof having a modification state. 43. The method according to any one of embodiments 22 to 42, wherein the presence and / or content of a DNA region or a fragment thereof having a modification state is determined by the fluorescence Ct value detected by fluorescent PCR. 44. The method according to any one of embodiments 22 to 43, wherein the presence of a pancreatic tumor or the onset or risk of onset of a pancreatic tumor is determined by determining that the presence of a modification state of a DNA region or a fragment thereof and / or the content of a modification state of a DNA region or a fragment thereof is higher relative to a reference level. 45. The method according to any one of embodiments 22 to 44, further comprising amplifying the DNA region or a fragment thereof in the sample to be tested prior to determining the presence and / or content of the modification state of the DNA region or a fragment thereof. 46. ​​The method of embodiment 45, wherein the amplification comprises PCR amplification. 47. A nucleic acid comprising a sequence capable of binding to a DNA region having the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. 48. A method for preparing a nucleic acid comprising designing a nucleic acid capable of binding to a DNA region in the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or its complementary region, or its converted region, or a fragment thereof, based on the modification status of the DNA region, or its complementary region, or its converted region, or a fragment thereof. 49. A combination of nucleic acids comprising a sequence capable of binding to a DNA region carrying the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. 50. A method for preparing a combination of nucleic acids, comprising designing a combination of nucleic acids capable of amplifying a DNA region having genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1, and / or EMX1, or its complementary region, or its converted region, or a fragment thereof, based on the modification status of the DNA region, or its complementary region, or its converted region, or a fragment thereof.

[0073] 51. A kit comprising a combination of the nucleic acid of embodiment 47 and / or the nucleic acid of embodiment 49. 52. Use of a nucleic acid according to embodiment 47, a combination of nucleic acids according to embodiment 49 and / or a kit according to embodiment 51 in the preparation of a disease detection product. 53. Use of a nucleic acid of embodiment 47, a combination of a nucleic acid of embodiment 49 and / or a kit of embodiment 51 in the preparation of a substance for determining the presence of a disease, assessing the onset or risk of onset of a disease and / or assessing the progression of a disease. 54. Use of a nucleic acid of embodiment 47, a combination of a nucleic acid of embodiment 49 and / or a kit of embodiment 51 in the preparation of a substance for determining the modification state of a DNA region or a fragment thereof. 55. Use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in the preparation of a substance for determining the presence of a pancreatic tumor, for assessing the onset or risk of onset of a pancreatic tumor and / or for assessing the progression of a pancreatic tumor, wherein the DNA region for determination comprises a DNA region carrying the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or a fragment thereof. 56. Use of nucleic acids, combinations of nucleic acids and / or kits for determining the modification status of DNA regions in the preparation of substances for determining the presence of a disease, assessing the onset or risk of onset of a disease and / or assessing the progression of a disease, wherein the DNA regions are derived from human chr2:74743035-74743151 and derived from human chr2:74743080-74743301. derived from human chr8:25907849-25907950, and derived from human chr8:25907698-25907894, derived from human chr12:4919142-4919289, derived from human chr12:4918991-4919187, and derived from human chr12:4919235-4919439, derived from human chr13:37005635-37005754, derived from human chr13:37005458-37005653, derived from human chr13:37005680-37005904, derived from human chr1:63788812-63788952, derived from human chr1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-13781485 3, derived from human chr7:155167513-155167628, derived from human chr19:51228168-51228782, and derived from human chr7:19156739-19157277, and derived from human chr2:73147525-73147644, or a complementary region thereof, or a fragment thereof. 57. Use of nucleic acids of DNA regions carrying the genes TLX2, EBF2, KCNA6, CCNA1, FOXD3, TRIM58, HOXD10, OLIG3, EN2, CLEC11A, TWIST1 and / or EMX1, or converted regions thereof, or fragments thereof, and combinations of the above nucleic acids, in the preparation of substances for determining the presence of a pancreatic tumor, for assessing the onset or risk of onset of a pancreatic tumor and / or for assessing the progression of a pancreatic tumor. 58. Human chr2: 74743035-74743151 and human chr2: 74743080-74743301, human chr8: 25907849-25907950 and human chr8: 25907698-25907894, human chr12: 4919142-4919289, human chr12: 4918991- 4919187, and human chr12:4919235-4919439, human chr13:37005635-37005754, human chr13:37005458-37005653, and human chr13:37005680-37005904, human chr1:63788812-63788952, human ch 2. Use of a nucleic acid of a DNA region selected from the group consisting of DNA regions derived from r1:248020592-248020779, derived from human chr2:176945511-176945630, derived from human chr6:137814700-137814853, derived from human chr7:155167513-155167628, derived from human chr19:51228168-51228782, and derived from human chr7:19156739-19157277, and derived from human chr2:73147525-73147644, or complementary regions thereof, or converted regions thereof, or fragments thereof, and combinations of the above nucleic acids, in the preparation of a substance for determining the presence of a disease, the onset or risk of onset of a disease, and / or assessing the progression of a disease.

[0074] 59. A storage medium having recorded thereon a program capable of executing the method according to any one of embodiments 1 to 46. 60. A device including a storage medium according to embodiment 59. 61. The device of embodiment 60, further comprising a processor coupled to a storage medium, the processor configured to execute based on a program stored in the storage medium to implement the method of any one of embodiments 1 to 46. EMBODIMENT 6 1. A method for determining the presence of a pancreatic tumor, assessing the onset or risk of onset of a pancreatic tumor, and / or assessing the progression of a pancreatic tumor, comprising determining in a sample to be tested the presence and / or content of the modification state of a DNA region by two genes selected from the group consisting of EBF2, CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, or fragments thereof. 2. A method for assessing the methylation status of a pancreatic tumor-associated DNA region, comprising determining the presence and / or content of the modification status of a DNA region carrying two genes, or fragments thereof, selected from the group consisting of EBF2, CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, in a sample to be tested. 3. The DNA region is derived from human chr8:25699246-25907950 and derived from human chr13:37005635-37017019, derived from human chr12:4918342-4960278, derived from human chr2:74740686-74744275 and derived from human chr2:73147574-73162020, derived from human chr1:248020501-248043438, derived from human chr7:19155091-19157295, derived from human chr1:63788730-63790 3. The method of any one of the preceding claims, wherein the DNA region is selected from two of the group consisting of: human chr7:155167513-155257526, human chr1:248020501-248043438, human chr7:19155091-19157295, human chr19:51226605-51228981, human chr2:176945511-176984670, and human chr6:137813336-137815531. 4. The method of any one of embodiments 1 to 3, further comprising obtaining nucleic acid in the sample to be tested. 5. The method of embodiment 4, wherein the nucleic acid comprises cell-free nucleic acid. 6. The method of any one of embodiments 1 to 5, wherein the sample to be tested comprises tissue, cells and / or body fluid. 7. The method of any one of embodiments 1-6, wherein the sample being tested comprises plasma. 8. The method of any one of the preceding claims, further comprising converting the DNA region or a fragment thereof.

[0075] 9. The method of embodiment 8, wherein the bases with and without the modified state form different substances after conversion. 10. The method according to any one of the preceding claims, wherein the bases having a modified state are not substantially changed after conversion, and the bases not having a modified state are changed to other bases different from the bases after conversion, or are cleaved after conversion. 11. The method of any one of embodiments 9 to 10, wherein the base comprises a cytosine. 12. The method according to any one of the preceding embodiments, wherein the modification state comprises a methylation modification. 13. The method of any one of embodiments 10 to 12, wherein the other bases include cytosine. 14. The method according to any one of embodiments 8 to 13, wherein the conversion comprises conversion with a deamination reagent and / or a methylation-sensitive restriction enzyme. 15. The method of embodiment 14, wherein the deamination reagent comprises bisulfite or an analog thereof. 16. The method according to any one of the preceding claims, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a substance formed by a base having a converted modification state. 17. The method according to any one of the preceding claims, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a DNA region or a fragment thereof having a modification state. 18. The method according to any one of the preceding embodiments, wherein the presence and / or content of a DNA region or a fragment thereof having a modification state is determined by the fluorescence Ct value detected by fluorescent PCR. 19. The method according to any one of embodiments 1 to 18, wherein the presence of a pancreatic tumor or the onset or risk of onset of a pancreatic tumor is determined by determining the presence of a modification state of a DNA region or a fragment thereof and / or the content of a modification state of a DNA region or a fragment thereof being higher relative to a reference level. 20. The method according to any one of the preceding embodiments, further comprising amplifying the DNA region or a fragment thereof in the sample to be tested prior to determining the presence and / or content of the modification state of the DNA region or a fragment thereof. 21. The method of embodiment 20, wherein the amplification comprises PCR amplification. 22. In the sample to be tested, human chr8:25907849-25907950 derived from and human chr13:37005635-37005754 derived from, human chr12:4919142-4919289 derived from, human chr2:74743035-74743151 derived from and human chr2:73147525-73147644 derived from, human chr1:248020592-248020779 derived from, human chr7:19156739-19157277 derived from, human chr1:63788812-63788952 derived from and human chr7:155167513-1551676 derived from 28, derived from human chr1:248020592-248020779, derived from human chr7:19156739-19157277, derived from human chr19:51228168-51228782, derived from human chr2:176945511-176945630, and derived from human chr6:137814700-137814853, or complementary regions thereof, or fragments thereof. 23. A method for determining the methylation status of a DNA region, comprising: determining the methylation status of a DNA region derived from human chr8:25907849-25907950 and derived from human chr13:37005635-37005754, or derived from human chr12:4919142-4919289, or derived from human chr2:74743035-74743151 and derived from human chr2:73147525-73147644, or derived from human chr1:248020592-248020779, or derived from human chr7:19156739-19157277, or derived from human chr1:63788812-63788952 in a sample to be tested; and determining the presence and / or content of the modification status of two DNA regions selected from the group consisting of DNA regions derived from human chr7:155167513-155167628, or derived from human chr1:248020592-248020779, derived from human chr7:19156739-19157277, derived from human chr19:51228168-51228782, derived from human chr2:176945511-176945630, and derived from human chr6:137814700-137814853, or complementary regions thereof, or fragments thereof.

[0076] 24. The method according to any one of embodiments 22 to 23, comprising providing a nucleic acid capable of binding to two DNA regions selected from the group consisting of SEQ ID NOs: 1 and 5, or complementary regions thereof, or converted regions thereof, or fragments thereof. 25. Human chr8:25907865-25907930 and human chr13:37005652-37005721, human chr12:4919188-4919272, human chr2:74743042-74743113 and human chr2:73147571-73147626, human chr1:248020635-248020731, human chr7:19156779-19157914, human chr1:63788850-63788913 and human chr7:155167531-1 25. The method of any one of embodiments 22 to 24, comprising providing a nucleic acid capable of binding to two DNA regions selected from the group consisting of DNA regions derived from human chr1:248020635-248020731, derived from human chr7:19156779-19157914, derived from human chr19:51228620-51228722, derived from human chr2:176945521-176945603, and derived from human chr6:137814750-137814815, or complementary regions thereof, or conversion regions thereof, or fragments thereof. 26. The method of any one of embodiments 22 to 25, comprising providing two nucleic acids selected from the group consisting of SEQ ID NOs: 173 and 193, 181, 165 and 233, 209, 229, 205 and 221, 209, 229, 225, 213 and 217, or complementary nucleic acids thereof, or fragments thereof. 27. The method of any one of embodiments 22-26, comprising providing two nucleic acids selected from the group consisting of SEQ ID NOs: 174 and 175, and 194 and 195, 182 and 183, 166 and 167, and 234 and 235, 210 and 211, 230 and 231, 206 and 207, and 222 and 223, 210 and 211, 230 and 231, 226 and 227, 214 and 215, and 218 and 219, or a combination of complementary nucleic acids thereof, or a fragment thereof. 28. The method of any one of embodiments 22 to 27, wherein the disease comprises a tumor. 29. The method of any one of embodiments 22 to 28, further comprising obtaining nucleic acid in the sample to be tested. 30. The method of embodiment 29, wherein the nucleic acid comprises cell-free nucleic acid. 31. The method of any one of embodiments 22-30, wherein the sample to be tested comprises tissue, cells and / or body fluid. 32. The method of any one of embodiments 22-31, wherein the sample to be tested comprises plasma. 33. The method of any one of embodiments 22 to 32, further comprising converting the DNA region or a fragment thereof.

[0077] 34. The method of embodiment 33, wherein the bases with and without the modified state form different substances after conversion. 35. The method according to any one of embodiments 22 to 34, wherein the bases having a modified state are not substantially changed after conversion, and the bases not having a modified state are changed to other bases different from the bases after conversion, or are cleaved after conversion. 36. The method of any one of embodiments 34-35, wherein the base comprises a cytosine. 37. The method according to any one of embodiments 22 to 36, wherein the modification state comprises a methylation modification. 38. The method of any one of embodiments 35-37, wherein the other bases include cytosine. 39. The method according to any one of embodiments 33 to 38, wherein the conversion comprises conversion with a deamination reagent and / or a methylation-sensitive restriction enzyme. 40. The method of embodiment 39, wherein the deamination reagent comprises bisulfite or an analogue thereof. 41. The method according to any one of embodiments 22 to 40, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a substance formed by a base having a converted modification state. 42. A method according to any one of embodiments 22 to 41, wherein the method for determining the presence and / or content of a modification state comprises determining the presence and / or content of a DNA region or a fragment thereof having a modification state. 43. The method according to any one of embodiments 22 to 42, wherein the presence and / or content of a DNA region or a fragment thereof having a modification state is determined by the fluorescence Ct value detected by fluorescent PCR. 44. The method according to any one of embodiments 22 to 43, wherein the presence of a pancreatic tumor or the onset or risk of onset of a pancreatic tumor is determined by determining that the presence of a modification state of a DNA region or a fragment thereof and / or the content of a modification state of a DNA region or a fragment thereof is higher relative to a reference level. 45. The method according to any one of embodiments 22 to 44, further comprising amplifying the DNA region or a fragment thereof in the sample to be tested prior to determining the presence and / or content of the modification state of the DNA region or a fragment thereof. 46. ​​The method of embodiment 45, wherein the amplification comprises PCR amplification. 47. A nucleic acid comprising a sequence capable of binding to a DNA region having two genes selected from the group consisting of EBF2, and CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. 48. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region having two genes selected from the group consisting of EBF2, CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, or a complementary region thereof, or a converted region thereof, or a fragment thereof, based on the modification state of the DNA region, or a complementary region thereof, or a converted region thereof, or a fragment thereof. 49. A combination of nucleic acids comprising a sequence capable of binding to a DNA region having two genes selected from the group consisting of EBF2, and CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, or a complementary region thereof, or a conversion region thereof, or a fragment thereof. 50. A method for preparing a combination of nucleic acids, comprising designing a combination of nucleic acids capable of amplifying a DNA region having two genes selected from the group consisting of EBF2, CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, or a complementary region thereof, or a converted region thereof, or a fragment thereof, based on the modification state of the DNA region, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0078] 51. A kit comprising a combination of the nucleic acid of embodiment 47 and / or the nucleic acid of embodiment 49. 52. Use of a nucleic acid according to embodiment 47, a combination of nucleic acids according to embodiment 49 and / or a kit according to embodiment 51 in the preparation of a disease detection product. 53. Use of a nucleic acid of embodiment 47, a combination of a nucleic acid of embodiment 49 and / or a kit of embodiment 51 in the preparation of a substance for determining the presence of a disease, assessing the onset or risk of onset of a disease and / or assessing the progression of a disease. 54. Use of a nucleic acid of embodiment 47, a combination of a nucleic acid of embodiment 49 and / or a kit of embodiment 51 in the preparation of a substance for determining the modification state of a DNA region or a fragment thereof. 55. Use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in the preparation of a substance for determining the presence of a pancreatic tumor, for assessing the onset or risk of onset of a pancreatic tumor and / or for assessing the progression of a pancreatic tumor, wherein the DNA region for determination comprises a DNA region carrying two genes selected from the group consisting of EBF2 and CCNA1, KCNA6, TLX2 and EMX1, TRIM58, TWIST1, FOXD3 and EN2, TRIM58, TWIST1, CLEC11A, HOXD10 and OLIG3, or a fragment thereof. 56. Use of a nucleic acid, a combination of nucleic acids and / or a kit for determining the modification status of a DNA region in the preparation of a substance for determining the presence of a disease, for assessing the onset or risk of onset of a disease and / or for assessing the progression of a disease, wherein the DNA region is derived from human chr8:25907849-25907950 and derived from human chr13:37005635-37005754, derived from human chr12:4919142-4919289, derived from human chr2:74743035-74743151 and derived from human chr2:73147525-73147644, derived from human chr1:248020592-248020779. 23. Use of a nucleic acid sequence comprising two DNA regions selected from the group consisting of DNA regions derived from human chr7:19156739-19157277, derived from human chr1:63788812-63788952, and derived from human chr7:155167513-155167628, derived from human chr1:248020592-248020779, derived from human chr7:19156739-19157277, derived from human chr19:51228168-51228782, derived from human chr2:176945511-176945630, and derived from human chr6:137814700-137814853, or complementary regions thereof, or fragments thereof. 57. Use of a nucleic acid of a DNA region having two genes selected from the group consisting of EBF2 and CCNA1, KCNA6, TLX2, and EMX1, TRIM58, TWIST1, FOXD3, and EN2, TRIM58, TWIST1, CLEC11A, HOXD10, and OLIG3, or a conversion region thereof, or a fragment thereof, and combinations of the above nucleic acids, in the preparation of a substance for determining the presence of a pancreatic tumor, for assessing the onset of a pancreatic tumor, and / or for assessing the progression of a pancreatic tumor. 58. Human chr8:25907849-25907950 and human chr13:37005635-37005754, human chr12:4919142-4919289, human chr2:74743035-74743151 and human chr2:73147525-73147644, human chr1:248020592-248020779, human chr7:19156739-19157277, human chr1:63788812-63788952 and human chr7:155167513-155167628, human chr1 23. Use of a nucleic acid of two DNA regions selected from the group consisting of DNA regions derived from human chr7:19156739-19157277, derived from human chr19:51228168-51228782, derived from human chr2:176945511-176945630, and derived from human chr6:137814700-137814853, or complementary regions thereof, or converted regions thereof, or fragments thereof, and combinations of the above nucleic acids, in the preparation of a substance for determining the presence of a disease, assessing the onset or risk of onset of a disease, and / or assessing the progression of a disease.

[0079] 59. A storage medium having recorded thereon a program capable of executing the method according to any one of embodiments 1 to 46. 60. A device including a storage medium according to embodiment 59. 61. The device of embodiment 60, further comprising a processor coupled to a storage medium, the processor configured to execute based on a program stored in the storage medium to implement the method of any one of embodiments 1 to 46. Without intending to be limited by any theory, the following examples are merely intended to illustrate the methods and uses of the present application and are not intended to limit the scope of the invention of the present application. example Example 1 1-1: Screening of differentially methylated sites in pancreatic cancer by targeted methylation sequencing We collected a total of 94 pancreatic cancer blood samples and 80 pancreatic cancer-free blood samples, and all enrolled patients signed informed consent forms. See the table below for sample information. [Table 2] Methylation sequencing data of plasma DNA was obtained by the MethylTitan assay to identify methylation classification markers therein. The method is as follows. 1. Extraction of Plasma cfDNA Samples A 2 ml whole blood sample was collected from the patient using a Streck blood collection tube, plasma was separated by centrifugation in due time (within 3 days) and transported to the laboratory, and then cfDNA was extracted using the QIAGEN QIAamp Circulating Nucleic Acid Kit according to the instructions.

[0080] 2. Sequencing and Data Preprocessing 1) The libraries were paired-end sequenced using an Illumina Nextseq 500 sequencer. 2) Pear (v0.6.0) software combined paired-end sequencing data of the same paired-end 150bp sequencing fragments from Illumina Hiseq X10 / Nextseq 500 / Nova seq sequencers into one sequence with a minimum overlap length of 20bp and a minimum length of 30bp after combination. 3) Adapter removal was performed on the combined sequencing data using Trim_galore v 0.6.0 and cutadapt v1.8.1 software. The adapter sequence "AGATCGGAAGAGCAC" was removed from the 5' end of the sequence, and bases with sequencing quality values ​​below 20 on both ends were removed. 3. Sequencing Data Alignment The reference genome data used herein was from the UCSC database (UCSC:HG19, hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / hg19.fa.gz). 1) First, HG19 was converted from cytosine to thymine (CT) and adenine to guanine (GA) using Bismark software, and an index of the converted genome was constructed using Bowtie2 software. 2) The preprocessed data was also subjected to CT and GA transformations. 3) The converted sequences were aligned to the converted HG19 reference genome using Bowtie2 software. The minimum seed sequence length was 20, and no mismatches were allowed in the seed sequence. 4. Calculation of MHF For each CpG site in the target region HG19, the methylation level corresponding to each site was determined based on the above alignment results. The nucleotide numbering of the site in this specification corresponds to the nucleotide position numbering of HG19. One target methylation region may have multiple methylation haplotypes. This value needs to be calculated for each methylation haplotype in the target region. An example of the calculation formula for MHF is as follows:

number

[0081] The distribution of the selected characteristic methylated nucleic acid sequences is as follows: SEQ ID NO: 1 in the DMRTA2 gene region, SEQ ID NO: 2 in the FOXD3 gene region, SEQ ID NO: 3 in the TBX15 gene region, SEQ ID NO: 4 in the BCAN gene region, SEQ ID NO: 5 in the TRIM58 gene region, SEQ ID NO: 6 in the SIX3 gene region, SEQ ID NO: 7 in the VAX2 gene region, SEQ ID NO: 8 in the EMX1 gene region, SEQ ID NO: 9 in the LBX2 gene region, SEQ ID NO: 10 in the TLX2 gene region, SEQ ID NO: 11 and SEQ ID NO: 12 in the POU3F3 gene region, SEQ ID NO: 13 in the TBR1 gene region, SEQ ID NO: 14 in the TBR2 gene region, SEQ ID NO: 15 in the TBR1 gene region, SEQ ID NO: 16 in the TBR1 gene region, SEQ ID NO: 17 in the TBR1 gene region, SEQ ID NO: 18 in the TBR1 gene region, SEQ ID NO: 19 in the TBR1 gene region, SEQ ID NO: 20 in the TBR1 gene region, SEQ ID NO: 21 in the TBR1 gene region, SEQ ID NO: 22 in the TBR1 gene region, SEQ ID NO: 23 in the TBR1 gene region, SEQ ID NO: 24 in the TBR1 gene region, SEQ ID NO: 25 in the TBR1 gene region, SEQ ID NO: 26 in the TBR1 gene region, SEQ ID NO: 27 in the TBR1 gene region, SEQ ID NO: 28 in the TBR1 gene region, SEQ ID NO: 29 in the TBR1 gene region, SEQ ID NO: 30 in the TBR1 gene region, SEQ ID NO: 31 in the TBR1 gene region, SEQ ID NO: 3 SEQ ID NO:13 in the gene region, SEQ ID NO:14 and SEQ ID NO:15 in the EVX2 gene region, SEQ ID NO:16 in the HOXD12 gene region, SEQ ID NO:17 in the HOXD8 gene region, SEQ ID NO:18 and SEQ ID NO:19 in the HOXD4 gene region, SEQ ID NO:20 in the TOPAZ1 gene region, SEQ ID NO:21 in the SHOX2 gene region, SEQ ID NO:22 in the DRD5 gene region, SEQ ID NO:23 and SEQ ID NO:24 in the RPL9 gene region, SEQ ID NO:25 in the HOPX gene region, SEQ ID NO:26 in the SFRP2 gene region, SEQ ID NO:27 in the IRX4 gene region, SEQ ID NO:28 in the TBX18 gene region SEQ ID NO: 28 in the OLIG3 gene region, SEQ ID NO: 29 in the ULBP1 gene region, SEQ ID NO: 30 in the HOXA13 gene region, SEQ ID NO: 31 in the HOXA13 gene region, SEQ ID NO: 32 in the TBX20 gene region, SEQ ID NO: 33 in the IKZF1 gene region, SEQ ID NO: 34 in the INSIG1 gene region, SEQ ID NO: 35 in the SOX7 gene region, SEQ ID NO: 36 in the EBF2 gene region, SEQ ID NO: 37 in the MOS gene region, SEQ ID NO: 38 in the MKX gene region, SEQ ID NO: 39 in the KCNA6 gene region, SEQ ID NO: 40 in the SYT10 gene region, SEQ ID NO: 41 in the AGAP2 gene region, and SEQ ID NO: 42 in the TBX20 gene region. SEQ ID NO: 42 in the X3 gene region, SEQ ID NO: 43 in the CCNA1 gene region, SEQ ID NO: 44 and SEQ ID NO: 45 in the ZIC2 gene region, SEQ ID NO: 46 and SEQ ID NO: 47 in the CLEC14A gene region, SEQ ID NO: 48 in the OTX2 gene region, SEQ ID NO: 49 in the C14orf39 gene region, SEQ ID NO: 50 in the BNC1 gene region, SEQ ID NO: 51 in the AHSP gene region, SEQ ID NO: 52 in the ZFHX3 gene region, SEQ ID NO: 53 in the LHX1 gene region, SEQ ID NO: 54 in the TIMP2 gene region,SEQ ID NO: 55 in the ZNF750 gene region and SEQ ID NO: 56 in the SIM2 gene region. The levels of the above methylation markers were increased or decreased in the cfDNA of pancreatic cancer patients (Table 1-1, Table 1 with corner brackets). The sequences of the above 56 marker regions are shown in SEQ ID NO: 1 to 56. The methylation levels of all CpG sites in each marker region can be obtained by MethylTitan sequencing. The average methylation level of all CpG sites in each region, as well as the methylation level of a single CpG site, can both be used as markers for the diagnosis of pancreatic cancer. [Table 3-1] [Table 3-2]

[0082] The methylation levels of the methylation markers in the pancreatic cancer patients and non-pancreatic cancer patients in the test set are shown in Table 1-2 (Table 4 with corner brackets). As can be seen from the table, the distribution of the selected methylation markers was significantly different between the pancreatic cancer patients and the non-pancreatic cancer patients, and a good differentiation effect was obtained. [Table 4-1] [Table 4-2]

[0083] Tables 1-3 (Table 5 in brackets) show the correlation (Pearson correlation coefficient) between the methylation degree of 10 random CpG sites or their combinations in each selected marker and the methylation degree of the whole marker, and the corresponding significance p-value. It can be seen that the methylation level of a single CpG site or a combination of multiple CpG sites in a marker is significantly correlated with the methylation level of the whole region (p<0.05), and the correlation coefficients are all greater than 0.8. This strong or very strong correlation indicates that a single CpG site or a combination of multiple CpG sites in a marker has the same good differentiation effect as the whole marker.

Table 5-1

Table 5-2

Table 5-3

Table 5-4

Table 5-5

Table 5-6

Table 5-7

Table 5-8

Table 5-9

Table 5-10

Table 5-11

Table 5-12

Table 5-13

Table 5-14

[0084] 1-2: Predictive performance of single methylation markers To verify the discriminatory ability of a single methylation marker in pancreatic cancer patients and non-cancer patients, the predictive ability of the single marker was verified using the methylation level value of the single methylation marker. First, the methylation level values ​​of 56 methylation markers are divided into training set samples for training to determine the threshold, sensitivity and specificity for distinguishing the presence or absence of pancreatic cancer, and then the threshold is used to statistically analyze the sensitivity and specificity of test set samples.The results are shown in Tables 1-4 below (Table 6 with corner brackets).It can be seen that a single marker can also achieve good differentiation performance. [Table 6-1] [Table 6-2] [Table 6-3]

[0085] 1-3: Prediction model for all marker combinations In order to verify the potential ability of using methylated nucleic acid fragment markers to distinguish pancreatic cancer, a support vector machine disease classification model was constructed based on 56 methylated nucleic acid fragment markers in the training group to verify the classification prediction effect of this methylated nucleic acid fragment marker group in the test group. The training group and the test group were divided according to the proportion of inclusion: 117 samples (samples 1-117) in the training group and 57 samples (samples 118-174) in the test group. The discovered methylation markers were used to build support vector machine models on the training sets of both sample groups. 1) The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2) The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows. a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. In the process of building the model, pancreatic cancer sample types were coded as 1, and non-pancreatic cancer sample types were coded as 0. In the method of building the model by sklearn software package (0.23.1), the threshold was set to 0.895 by default. The built model finally distinguished samples with and without pancreatic cancer by 0.895. The prediction scores of the two models for the training set samples are shown in Tables 1-5 (Table 7 with corner brackets). [Table 7-1] [Table 7-2]

[0086] Based on the methylated nucleic acid fragment marker group of the present application, prediction was made in the test set according to the model established by SVM in this example. The prediction function was used to predict the test set and output the prediction result (disease probability: the default score threshold is 0.895, and if the score is greater than 0.895, the subject is considered malignant). The test set includes 57 samples (samples 118-174), and the calculation method is as follows: Command line: test_pred=model.predict(test_df) Here, test_pred represents the predicted scores of samples in the test set obtained using the SVM predictive model constructed in this example, model represents the SVM predictive model constructed in this example, and test_df represents the test set data.

[0087] The predicted scores of the test group are shown in Tables 1-6. The ROC curve is shown in Figure 2. The predicted score distribution is shown in Figure 3. The area under the total AUC of the test group was 0.911. In the training set, the sensitivity of the model could reach 71.4% when the specificity was 90.7%, and in the test set, the sensitivity of the model could reach 83.9% when the specificity was 88.5%. It can be seen that the differentiation effect of the SVM model established by the selected variables is good. Figures 4 and 5 show the distribution of 56 methylated nucleic acid fragment markers in the training group and the test group, respectively. It can be seen that the difference in the methylation marker group between the plasma of non-pancreatic cancer patients and the plasma of pancreatic cancer patients is relatively stable. [Table 8] 1-4: Tumor marker prediction comparison Based on the methylation marker group of this application, prediction was made in the test set according to the model established by SVM in Example 1-3. Pancreatic cancer was predicted based on CA19-9 marker. There were 130 samples (Table 1-7). The calculation method is as follows: Command line: Combine_scalar=RobustScaler().fit(combine_train_df) scaled_combine_train_df=combine_scalar.transform(combine_train_df) scaled_combine_test_df=combine_scalar.transform(combine_test_df) combine_model=LogisticRegression().fit(scaled_combine_train_df,train_ca19_pheno) Here, combine_train_df represents the training set data matrix obtained by combining the prediction scores obtained by the SVM prediction model constructed in Examples 1-3 for the test set samples with CA19-9, scaled_combine_train_df represents the training set data matrix after standardization, scaled_combine_test_df represents the standardized test set data matrix, and combine_model represents the logistic regression model fitted using the standardized training set data matrix.

[0088] The prediction scores of each sample are shown in Tables 1-7. The ROC curve is shown in Figure 6. The prediction score distribution is shown in Figure 7. The overall AUC of the test group is 0.935. From this figure, it can be seen that the differential effect of the established logistic regression model is good. Figure 7 shows the distribution of classification prediction scores for the SVM model constructed using only CA19-9, the SVM model constructed using only Example 3, and the model constructed using a combination of Example 3 and CA19-9. It can be seen that this method is more stable in identifying pancreatic cancer. [Table 9-1] [Table 9-2] [Table 9-3] [Table 9-4]

[0089] 1-5: Performance of classification and prediction models in samples negative for conventional markers Based on the methylation marker group of the present application, the model established by SVM in Examples 1-3 was tested on samples that were negative for the traditional tumor marker CA19-9 (CA19-9 measurement value <37). The CA19-9 measured values ​​and model predicted values ​​of the relevant samples are shown in Tables 1-8, and the ROC curve is shown in Figure 8. The AUC value of the test set reached 0.885, using 0.895 as the scoring threshold. It can be seen that for patients who cannot be distinguished using CA19-9, the SVM model constructed in Example 3 also gives relatively good results. [Table 10-1] [Table 10-2]

[0090] 1-6: Model construction and performance evaluation of the combination of seven markers: SEQ ID NO: 9, SEQ ID NO: 14, SEQ ID NO: 13, SEQ ID NO: 26, SEQ ID NO: 40, SEQ ID NO: 43, and SEQ ID NO: 52 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NOs: 9, 14, 13, 26, 40, 43, and 52, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing.

[0091] 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 9. The AUC of the constructed model was 0.881. In the test set, when the specificity was 0.846, the sensitivity reached 0.774 (Tables 1-9) (Table 11 with corner brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 11] 1-7: Model construction and performance evaluation of the combination of seven markers: SEQ ID NO:5, SEQ ID NO:18, SEQ ID NO:34, SEQ ID NO:40, SEQ ID NO:43, SEQ ID NO:45, and SEQ ID NO:46 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NO:5, SEQ ID NO:18, SEQ ID NO:34, SEQ ID NO:40, SEQ ID NO:43, SEQ ID NO:45, SEQ ID NO:46, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 10. The AUC of the constructed model was 0.881. In the test set, when the specificity was 0.692, the sensitivity reached 0.839 (Tables 1-10, Table 12 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 12]

[0092] 1-8: Model construction and performance evaluation of the combination of seven markers: SEQ ID NO: 8, SEQ ID NO: 11, SEQ ID NO: 20, SEQ ID NO: 44, SEQ ID NO: 48, SEQ ID NO: 51, and SEQ ID NO: 54 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NO:8, SEQ ID NO:11, SEQ ID NO:20, SEQ ID NO:44, SEQ ID NO:48, SEQ ID NO:51, SEQ ID NO:54, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 11. The AUC of the constructed model was 0.880. In the test set, when the specificity was 0.769, the sensitivity reached 0.839 (Tables 1-11, Table 13 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 13] 1-9: Model construction and performance evaluation of the combination of seven markers: SEQ ID NO: 8, SEQ ID NO: 14, SEQ ID NO: 26, SEQ ID NO: 24, SEQ ID NO: 31, SEQ ID NO: 40, and SEQ ID NO: 46 To verify the predictive performance of different marker combinations, based on the cluster of 56 methylation markers in this application, seven markers, SEQ ID NO:8, SEQ ID NO:14, SEQ ID NO:26, SEQ ID NO:24, SEQ ID NO:31, SEQ ID NO:40, SEQ ID NO:46, were selected for model building and performance testing. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups.

[0093] 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 12. The AUC of the constructed model was 0.871. In the test set, when the specificity was 0.885, the sensitivity reached 0.710 (Tables 1-12, Table 14 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 14] 1-10: Model construction and performance evaluation of the combination of seven markers of SEQ ID NOs: 3, 9, 8, 29, 42, 40, and 41 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NOs: 3, 9, 8, 29, 42, 40, and 41, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data.

[0094] The ROC curve of this 7-marker combination model is shown in Figure 13. The AUC of the constructed model was 0.866. In the test set, when the specificity was 0.538, the sensitivity reached 0.903 (Tables 1-13, Table 15 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 15] 1-11: Model construction and performance evaluation of the combination of seven markers of SEQ ID NOs: 5, 8, 19, 7, 44, 47, and 53 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NOs: 5, 8, 19, 7, 44, 47, and 53, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 14. The AUC of the constructed model was 0.864. In the test set, when the specificity was 0.577, the sensitivity reached 0.774 (Tables 1-14, Table 16 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 16] 1-12: Model construction and performance evaluation of the combination of seven markers of SEQ ID NOs: 12, 17, 24, 28, 40, 42, and 47 To verify the predictive performance of different marker combinations, based on the cluster of 56 methylation markers in this application, 7 markers SEQ ID NO: 12, 17, 24, 28, 40, 42, 47 were selected for model building and performance testing. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups.

[0095] 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 15. The AUC of the constructed model was 0.862. In the test set, when the specificity was 0.731, the sensitivity reached 0.871 (Tables 1-15, Table 17 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 17] 1-13: Model construction and performance evaluation of the combination of seven markers: SEQ ID NO:5, SEQ ID NO:18, SEQ ID NO:14, SEQ ID NO:10, SEQ ID NO:8, SEQ ID NO:19, and SEQ ID NO:27 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NO:5, SEQ ID NO:18, SEQ ID NO:14, SEQ ID NO:10, SEQ ID NO:8, SEQ ID NO:19, SEQ ID NO:27, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data.

[0096] The ROC curve of this 7-marker combination model is shown in Figure 16. The AUC of the constructed model was 0.859. In the test set, when the specificity was 0.615, the sensitivity reached 0.839 (Tables 1-16, Table 18 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 18] 1-14: Model construction and performance evaluation of the combination of seven markers: SEQ ID NO:6, SEQ ID NO:12, SEQ ID NO:20, SEQ ID NO:26, SEQ ID NO:24, SEQ ID NO:47, and SEQ ID NO:50 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NO:6, SEQ ID NO:12, SEQ ID NO:20, SEQ ID NO:26, SEQ ID NO:24, SEQ ID NO:47, SEQ ID NO:50, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174). Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 17. The AUC of the constructed model was 0.857. In the test set, when the specificity was 0.846, the sensitivity reached 0.774 (Table 1-17, Table 19 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 19] 1-15: Model construction and performance evaluation of the combination of seven markers of SEQ ID NOs: 1, 19, 27, 34, 37, 46, and 47 To verify the predictive performance of different marker combinations, seven markers, SEQ ID NO: 1, 19, 27, 34, 37, 46, and 47, were selected for model building and performance testing based on the cluster of 56 methylation markers in this application. The training and testing groups were divided, including 117 samples in the training group (samples 1-117) and 57 samples in the testing group (samples 118-174).

[0097] Seven methylation markers were used to build support vector machine models on the training sets of both sample groups. 1. The samples were pre-split into two parts, one part was used for training the model and one part was used for model testing. 2. The SVM model was trained using the methylation marker levels in the training set. The specific training method is as follows: a) Use the sklearn software package (0.23.1) in Python software (v3.6.9) to build a training model and cross-validate the training mode of the training model. Command line: model=SVR(). b) The sklearn software package (0.23.1) was used to input the methylation value data matrix to construct the SVM model model.fit(x_train, y_train), where x_train represents the training set data matrix and y_train represents the phenotypic information of the training set. 3. Testing was performed using the test set data: The above model was put into the test set for testing. Command line: test_pred=model.predict(test_df), where test_pred represents the prediction scores obtained by the SVM prediction model built in this example for the test set samples, model represents the SVM prediction model built in this example, and test_df represents the test set data. The ROC curve of this 7-marker combination model is shown in Figure 18. The AUC of the constructed model was 0.856. In the test set, when the specificity was 0.808, the sensitivity reached 0.742 (Table 1-18, Table 20 with square brackets), and the discrimination effect between pancreatic cancer patients and healthy people was good. [Table 20] In this study, the methylation levels of relevant genes in plasma cfDNA were used to test the difference between the plasma of subjects without pancreatic cancer and the plasma of subjects with pancreatic cancer, and 56 methylated nucleic acid fragments with significant differences were screened. Based on the above methylated nucleic acid fragment marker group, a pancreatic cancer risk prediction model was constructed using the support vector machine method, which can effectively identify pancreatic cancer with high sensitivity and specificity, and is suitable for the screening and diagnosis of pancreatic cancer. Example 2 2-1: Screening of differentially methylated sites in pancreatic cancer by targeted methylation sequencing The inventors collected blood samples from a total of 94 pancreatic cancer patients and 25 chronic pancreatitis patients, all of whom signed informed consent forms. Patients with pancreatic cancer had a previous diagnosis of pancreatitis. See the table below for sample information. [Table 21]

[0098] Methylation sequencing data of plasma DNA was obtained by the MethylTitan assay to identify DNA methylation typing markers therein. The method is as follows. 1. Extraction of Plasma cfDNA Samples A 2 ml whole blood sample was collected from the patient using a Streck blood collection tube, plasma was separated by centrifugation in due time (within 3 days) and transported to the laboratory, and then cfDNA was extracted using the QIAGEN QIAamp Circulating Nucleic Acid Kit according to the instructions. 2. Sequencing and Data Preprocessing 1) The libraries were paired-end sequenced using an Illumina Nextseq 500 sequencer. 2) Pear (v0.6.0) software combined paired-end sequencing data of the same paired-end 150bp sequencing fragments from Illumina Hiseq X10 / Nextseq 500 / Nova seq sequencers into one sequence with a minimum overlap length of 20bp and a minimum length of 30bp after combination. 3) Adapter removal was performed on the combined sequencing data using Trim_galore v 0.6.0 and cutadapt v1.8.1 software. The adapter sequence "AGATCGGAAGAGCAC" was removed from the 5' end of the sequence, and bases with sequencing quality values ​​below 20 on both ends were removed. 3. Sequencing Data Alignment The reference genome data used herein was from the UCSC database (UCSC:HG19, hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / hg19.fa.gz). 1) First, HG19 was converted from cytosine to thymine (CT) and adenine to guanine (GA) using Bismark software, and an index of the converted genome was constructed using Bowtie2 software. 2) The preprocessed data was also subjected to CT and GA transformations. 3) The converted sequences were aligned to the converted HG19 reference genome using Bowtie2 software. The minimum seed sequence length was 20, and no mismatches were allowed in the seed sequence. 4. Calculation of MHF For each CpG site in the target region HG19, the methylation state corresponding to each site was determined based on the above alignment results. The nucleotide numbering of the sites in this specification corresponds to the nucleotide position numbering of HG19. One target methylation region may have multiple methylation haplotypes. This value needs to be calculated for each methylation haplotype in the target region. An example of the calculation formula for MHF is as follows:

number

[0099] 5. Methylation Data Matrix 1) The methylation sequencing data for each sample in the training and test sets were combined into a data matrix, with each site at a depth below 200 being a missing value. 2) Sites with a missing value rate of more than 10% were removed. 3) For missing values ​​in the data matrix, the KNN algorithm was used to interpolate the missing data. 6. Discovery of characteristic methylation segments based on the training set samples 1) A logistic regression model was constructed for each methylation segment with respect to the phenotype, and the methylation segments with the most significant regression coefficient were screened for each amplified target region to form candidate methylation segments. 2) The training set was randomly divided into 10 parts for 10-fold cross-validation incremental feature selection. 3) Candidate methylation segments in each region were ranked in descending order according to the significance of the regression coefficients, and the data of one methylation segment was added each time to predict the test data. 4) In step 3), 10 copies of the data generated in step 2) were used. For each copy of the data, 10 calculations were performed, and the final AUC was the average of the 10 calculations. If the AUC of the training data increased, the candidate methylation segment was retained as a feature methylation segment, otherwise it was discarded. 5) The feature combination corresponding to the average median AUC under different numbers of features in the training set was taken as the final combination of feature methylation segments. The distribution of selected characteristic methylation markers in HG19 is as follows: SEQ ID NO: 57 in the SIX3 gene region, SEQ ID NO: 58 in the TLX2 gene region, and SEQ ID NO: 59 in the CILP2 gene region. The levels of the above methylation markers were increased or decreased in the cfDNA of pancreatic cancer patients (Table 2-1). The sequences of the above three marker regions are shown in SEQ ID NOs: 57 to 59. The methylation levels of all CpG sites in each marker region can be obtained by MethylTitan sequencing. The average methylation level of all CpG sites in each region, as well as the methylation status of a single CpG site, can both be used as markers for the diagnosis of pancreatic cancer. [Table 22] The methylation levels of the methylation markers in the pancreatic cancer patients and chronic pancreatitis patients in the test set are shown in Table 2-2. As can be seen from the table, the distribution of the methylation levels of the methylation markers was significantly different between the pancreatic cancer patients and the chronic pancreatitis patients, and a good discrimination effect was obtained. [Table 23] Table 2-3 shows the correlation (Pearson correlation coefficient) between the methylation degree of 10 random CpG sites or their combinations in each selected marker and the methylation degree of the whole marker, and the corresponding significance p-value. It can be seen that the methylation level of a single CpG site or a combination of multiple CpG sites in a marker has a significant correlation with the methylation status or level of the whole region (p<0.05), and the correlation coefficients are al...

Claims

1. For determining the presence of a pancreatic tumor, for assessing the development or risk of development of a pancreatic tumor, and / or for assessing the progression of a pancreatic tumor, determining the presence and / or content of a methylation modification state of a DNA region having the gene EBF2 or a fragment thereof in a sample to be tested, A method.

2. For assessing the methylation state of a DNA region associated with a pancreatic tumor, determining the presence and / or content of a modification state of a DNA region having the gene EBF2 or a fragment thereof in a sample to be tested, A method.

3. The method according to any one of claims 1 to 2, wherein the DNA region is derived from human chr8: 25699246-25907950.

4. The method according to any one of claims 1 to 2, further comprising obtaining a nucleic acid in a sample to be tested.

5. The method according to any one of claims 1 to 2, wherein the sample to be tested includes a tissue, a cell, and / or a body fluid.

6. The method according to any one of claims 1 to 2, further comprising converting the DNA region or a fragment thereof.

7. The method according to claim 6, wherein the base having the modification state is substantially unchanged after conversion, and the base not having the modification state is changed to another base different from the base after conversion or is cleaved after conversion.

8. The method according to claim 6, wherein the conversion includes conversion by a deaminating reagent and / or a methylation-sensitive restriction enzyme.

9. The method according to any one of claims 6, wherein the method for determining the presence and / or content of the modification state includes determining the presence and / or content of a substance formed after conversion of the base by the modification state.

10. The method for determining the presence and / or content of a methylation modification state includes determining the presence and / or content of a DNA region having the modification state or a fragment thereof. The method according to any one of claims 1.

11. The method according to claim 1, wherein the presence and / or content of a DNA region having a methylation modification state or a fragment thereof is determined by a fluorescence Ct value detected by a fluorescence PCR method.

12. The presence of a pancreatic tumor, or the development or risk of development of a pancreatic tumor, is determined by determining the presence and / or a higher content of the modification state of the DNA region or a fragment thereof relative to a reference level of the methylation modification state of the DNA region or a fragment thereof. The method according to claim 1.

13. The method according to claim 1, further comprising amplifying the DNA region or a fragment thereof in a sample to be tested before determining the methylation modification state and / or content of the DNA region or a fragment thereof.

14. For determining the presence of a disease, for evaluating the onset or risk of onset of a disease, and / or for evaluating the progression of a disease, determining the presence and / or content of the methylation modification state for the DNA region selected from the group consisting of the DNA region derived from human chr8:25907849-25907950 and the DNA region derived from human chr8:25907698-25907894 in a sample to be tested, or its complementary region, or a fragment thereof, Method.

15. The method according to claim 14, comprising providing a nucleic acid capable of binding to a DNA region selected from the group consisting of SEQ ID NO: 172 and SEQ ID NO: 176, or its complementary region, or its converted region, or a fragment thereof.

16. The method according to any one of claims 14 to 15, comprising providing a nucleic acid capable of binding to a DNA region selected from the group consisting of the DNA region derived from human chr8:25907865-25907930 and the DNA region derived from human chr8:25907698-25907814, or its complementary region, or its converted region, or a fragment thereof.

17. The method according to any one of claims 14 to 15, comprising providing a nucleic acid selected from the group consisting of SEQ ID NO: 173 and SEQ ID NO: 177, or its complementary nucleic acid, or a fragment thereof.

18. The method according to any one of claims 14 to 15, comprising providing a combination of nucleic acids selected from the group consisting of SEQ ID NO: 174 and 175, and SEQ ID NO: 178 and 179, or a combination of their complementary nucleic acids, or a fragment thereof.

19. A kit for determining the presence of a pancreatic tumor, evaluating the onset or risk of onset of a pancreatic tumor, and / or evaluating the progression of a pancreatic tumor, the kit comprising a nucleic acid capable of determining the modification state of a DNA region having the gene EBF2, or its complementary region, or its converted region, or a fragment thereof.

20. For determining the modification state of a DNA region in the preparation of a substance, of a nucleic acid, a combination of nucleic acids and / or a kit, For determining the presence of a pancreatic tumor, for assessing the onset or risk of onset of a pancreatic tumor, and / or for assessing the progression of a pancreatic tumor, Use, wherein The DNA region for determination includes a DNA region having the gene EBF2 or a fragment thereof.

21. For determining the modified state of a DNA region in the preparation of a substance, of a nucleic acid, a combination of nucleic acids and / or a kit, For determining the presence of a disease, for assessing the onset or risk of onset of a disease, and / or for assessing the progression of a disease, Use, wherein The DNA region includes a DNA region selected from the group consisting of a DNA region derived from human chr8:25907849-25907950 and a DNA region derived from human chr8:25907698-25907894, or its complementary region, or a fragment thereof.