DNA methylation markers for the diagnosis of esophageal and gastric cancer and their applications

Abstract

The present invention relates to DNA methylation markers for diagnosing esophageal cancer and gastric cancer and uses thereof, more particularly to a combination of DNA methylation markers capable of determining the presence or absence of esophageal cancer and gastric cancer and uses thereof. The DNA methylation markers for diagnosing esophageal cancer and gastric cancer according to the present invention can diagnose esophageal cancer and gastric cancer with high accuracy using only DNA methylation information from blood samples without using esophageal cancer and gastric cancer tissue samples, and can therefore be useful for the early diagnosis of esophageal cancer and gastric cancer.

Description

[Technical Field]

[0001] The present invention relates to DNA methylation markers for diagnosing esophageal cancer and gastric cancer and uses thereof, and more specifically to a combination of DNA methylation markers that can determine the presence or absence of esophageal cancer and gastric cancer and uses thereof.

[0002] [Background technology]

[0003] Gastric cancer is the fourth most commonly diagnosed cancer worldwide and the third leading cause of cancer-related deaths. It is the most commonly diagnosed cancer type in East Asian countries. In Korea, the incidence of gastric cancer is high due to genetics, a diet containing salted foods, smoking, and a high prevalence of Helicobacter pylori. It has been confirmed that early-stage gastric cancer cases are more common asymptomatic (74.2%-78.1%) than symptomatic (25.9%-35.7%) in Korea. Once gastric cancer progresses to serious symptoms and complications, the prognosis is poor, and the survival rate drops from approximately 65% ​​(at early detection) to less than 20%, making early diagnosis crucial.

[0004] Esophageal cancer originates in esophageal cells in the tube between the throat and stomach. It is listed as the eighth most common cancer, occurring more frequently in men than women, with rates varying significantly by country. According to the Korea Central Cancer Registry in 2019, 2,870 esophageal cancer cases occurred annually, with 2,573 cases, or 89%, being men. The two most common types of esophageal cancer are esophageal squamous cell carcinoma and esophageal adenocarcinoma. Many rarer subtypes are also known. While squamous cell carcinoma originates in the epithelial cells of the esophagus, adenocarcinoma originates in the glandular cells in the lower esophagus.

[0005] A key factor contributing to the high mortality rate from esophageal cancer is the low rate of diagnosis of early-stage esophageal cancer. Although the cure rate for early-stage esophageal cancer is much higher than for mid-stage and late-stage cancer, due to the lack of clear and specific symptoms, most subjects diagnosed with esophageal cancer have already progressed to mid-stage or late-stage disease. Clinical studies have found that the carcinogenesis process from the time of lesion to the time clinical symptoms appear in subjects takes several years on average. This provides an effective latency period for the detection and diagnosis of early-stage esophageal cancer. Making full use of this latency period is expected to improve the effectiveness of esophageal cancer treatment and reduce the mortality rate from esophageal cancer.

[0006] To accurately diagnose cancer, it is important not only to identify mutated genes but also to understand the mechanisms by which those mutations manifest. Previously, research focused on mutations in the coding sequences of genes, i.e., minute changes such as point mutations, deletions, and insertions, as well as macroscopic chromosomal abnormalities. However, recent studies have shown that extragenic changes are just as important, such as promoter CpG island methylation.

[0007] In addition to A, C, G, and T, mammalian genomic DNA contains a fifth base: 5-methylcytosine (5-mC), in which a methyl group is attached to the fifth carbon of the cytosine ring. 5-mC is always attached exclusively to the C of a CG dinucleotide (5'-mCG-3'), and this type of CG is commonly referred to as CpG. Most Cs in CpGs are methylated. This CpG methylation suppresses the expression of repetitive sequences in the genome, such as alu and transposons, and is the most common site of extragenic variation in mammalian cells. The 5-mC in these CpGs naturally deaminates to T, resulting in CpGs occurring at a frequency of only 1% in mammalian genomes, far lower than their normal frequency (1 / 4 x 1 / 4 = 6.25%).

[0008] CpGs are found in exceptionally densely packed regions called CpG islands. CpG islands are 0.2 kb to 3 kb in length, have a distribution percentage of C and G bases exceeding 50%, and are highly concentrated regions with a CpG distribution percentage of 3.75% or higher. Approximately 45,000 CpG islands occur throughout the human genome, and are particularly concentrated in promoter regions that regulate gene expression. In fact, CpG islands appear in the promoters of important genes (housekeeping genes), which account for approximately half of the human genome (Cross, S. et al., Curr. Opin. Gene Develop., 5:309, 1995). Therefore, active efforts have recently been made to investigate promoter methylation of tumor-related genes in blood, sputum, saliva, feces, and urine, with the aim of using this information in the diagnosis and treatment of various cancers.

[0009] Currently, clinical cancer diagnosis involves a medical history, physical examination, and clinical pathology test. If cancer is suspected, radiological and endoscopic examinations are performed, and finally, a tissue examination is performed to confirm the diagnosis. However, current clinical testing methods can only diagnose cancer when the cancer cell count reaches 1 billion and the tumor reaches 1 cm or more in diameter. In this case, the cancer cells already have the ability to metastasize, and in fact, more than half of cancers have already metastasized. Meanwhile, tumor markers, which detect substances produced directly or indirectly by cancer in the blood, are used for cancer screening. However, these tests have limited accuracy and often test positive even when cancer is not present, leading to confusion. Furthermore, anticancer drugs, which are primarily used to treat cancer, have the problem of only being effective when the tumor volume is small.

[0010] Recently, various methods for diagnosing cancer through DNA methylation have been proposed. DNA methylation occurs primarily at cytosines in CpG islands in the promoter region of specific genes, preventing transcription factor binding and silencing the gene. This is a major mechanism by which genes lose their function in vivo, even in the absence of mutations in the gene's protein-specifying coding sequence, and is believed to be the cause of the loss of function of many tumor suppressor genes in human cancers. While there is debate as to whether promoter CpG island methylation directly induces carcinogenesis or is a secondary change leading to carcinogenesis, abnormal methylation / demethylation in CpG islands has been reported in various cancer cells, including prostate, colon, uterine, and breast cancers. Therefore, this method can be used in a variety of areas, including early cancer diagnosis, cancer risk prediction, cancer prognosis prediction, post-treatment follow-up, and response prediction to anticancer therapy. Recently, there have been active attempts to test this using methods such as methylation-specific PCR (hereinafter referred to as MSP), automated base analysis, or bisulfite pyrosequencing and use it for cancer diagnosis and screening. However, most of these methods are limited to detecting and analyzing the methylation of a small number of specific genes or promoter sites (e.g., Korean Patent No. 1557183, Korean Patent No. 1191947), and therefore have limitations in the efficiency and accuracy of diagnosis.

[0011] Therefore, the present inventors have made extensive efforts to solve the above problems and develop DNA methylation markers for diagnosing esophageal cancer and gastric cancer with high sensitivity and accuracy. As a result, the present inventors have identified cancer-specific methylated regions using TCGA methylation data of esophageal cancer and gastric cancer tissue samples, publicly known methylation data related to esophageal cancer and gastric cancer (GEO), and DNA data methylated in tissues and cfDNA of esophageal cancer or gastric cancer patients, and finally identified esophageal cancer- and gastric cancer-specific DNA methylation markers in common regions in the datasets. They have confirmed that analysis of the DNA methylation markers enables early diagnosis of the presence or absence of esophageal cancer and gastric cancer with high accuracy, thereby completing the present invention.

[0012]

[0013] Summary of the Invention [Problem to be solved by the invention]

[0014] An object of the present invention is to provide a combination of DNA methylation markers for diagnosing esophageal cancer and gastric cancer.

[0015] Another object of the present invention is to provide a method for providing information for diagnosing esophageal cancer and gastric cancer using the DNA methylation marker combination.

[0016] It is yet another object of the present invention to provide a method for diagnosing esophageal cancer and gastric cancer using the DNA methylation marker combination.

[0017] It is yet another object of the present invention to provide a probe composition, a primer composition, and a diagnostic kit for esophageal cancer and gastric cancer, which can detect the DNA methylation marker combination. [Means for solving the problem]

[0018] To achieve the above object, the present invention provides a DNA methylation marker combination for diagnosing esophageal cancer and gastric cancer, which comprises the DNA methylation markers shown in Table 1.

[0019] The present invention also provides a method for providing information for diagnosing esophageal cancer and gastric cancer, the method comprising: (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination; and (c) determining that the cancer is esophageal cancer or gastric cancer when the detected DNA methylation marker level exceeds a cut-off value.

[0020] The present invention also provides a method for diagnosing esophageal cancer and gastric cancer, comprising: (a) isolating DNA from a biological sample; (b) detecting the methylation levels of the DNA methylation marker combination; and (c) determining that the cancer is esophageal cancer or gastric cancer when the detected DNA methylation marker levels exceed a reference value.

[0021] The present invention also provides a primer composition for diagnosing esophageal cancer and gastric cancer, which can amplify each of the DNA methylation markers in the DNA methylation marker combination.

[0022] The present invention also provides a probe composition for diagnosing esophageal cancer and gastric cancer, which can specifically hybridize with a polynucleotide containing 10 or more consecutive bases containing the methylated bases of the DNA methylation markers of the DNA methylation marker combination, or a complementary polynucleotide thereof.

[0023] The present invention also provides a kit for diagnosing esophageal cancer and gastric cancer, which comprises the composition.

[0024] [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a flowchart showing the process of selecting DNA methylation markers for diagnosing esophageal cancer and gastric cancer according to the present invention.

[0026] [Figure 2] FIG. 1 is a schematic diagram of a split data set for selecting candidate markers in one embodiment of the present invention.

[0027] [Figure 3] FIG. 1 is a schematic diagram of the LOO (Leave one out) method implemented to select candidate markers in one embodiment of the present invention.

[0028] [Figure 4] 1 is a flowchart showing the process of selecting a minimal combination of DNA methylation markers for diagnosing esophageal cancer and gastric cancer according to the present invention.

[0029] [Figure 5] 1 is a graph showing the results of determining the presence or absence of esophageal cancer in clinical samples using a combination of 474 esophageal cancer- and gastric cancer-specific DNA methylation markers selected according to an embodiment of the present invention.

[0030] [Figure 6] 1 is a graph showing the results of determining the presence or absence of esophageal cancer in clinical samples using a combination of 333 esophageal cancer- and gastric cancer-specific DNA methylation markers selected according to an embodiment of the present invention.

[0031] [Figure 7] 1 is a graph showing the results of determining the presence or absence of esophageal cancer in clinical samples using a combination of five esophageal cancer- and gastric cancer-specific DNA methylation markers selected according to an embodiment of the present invention.

[0032]

[0033] DETAILED DESCRIPTION OF THE INVENTION

[0034] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention belongs. Generally, the nomenclature used herein and the experimental procedures described below are those well known and commonly used in the art.

[0035] In the present invention, we have developed a model that can diagnose the presence or absence of esophageal cancer and gastric cancer using methylation information of cell-free nucleic acids in blood, and have attempted to confirm its accuracy.

[0036] In the present invention, we combined the methylation data of esophageal and gastric cancer tissue samples stored in the TCGA database, the publicly known methylation data of esophageal and gastric cancer tissue samples stored in the GEO database, and the methylation data of cell-free nucleic acids extracted from blood samples of esophageal cancer patients to select DNA methylation markers that can distinguish the presence or absence of esophageal and gastric cancer.

[0037] That is, in one embodiment of the present invention, esophageal cancer- and gastric cancer-specific methylated regions were selected based on the methylation data of esophageal cancer and gastric cancer tissue samples and normal samples listed in the TCGA database. Methylated DNA extracted from the blood of esophageal cancer patients and normal individuals was sequenced (cfMeDIP-seq, EM-seq) and compared to select esophageal cancer and gastric cancer tissue-specific methylated regions. After that, esophageal cancer- and gastric cancer-specific methylated regions were further selected using publicly known whole genome bisulfite sequencing (WGBS, GEO) data and the TCGA dataset.

[0038] Then, overlapping regions were removed from the regions selected at each stage, and regions with three or more CpG sites were combined to select the final marker. It was confirmed that the presence or absence of esophageal cancer and gastric cancer could be determined with high accuracy when using this marker (Figure 1, Figures 5 to 7).

[0039] Thus, in one aspect, the present invention provides a method for manufacturing a semiconductor device comprising:

[0040] The present invention relates to a combination of DNA methylation markers for diagnosing esophageal cancer and gastric cancer, which comprises the DNA methylation markers shown in Table 1 below.

[0041] [Table 1]

[0042] In the present invention, the DNA methylation marker combination for diagnosing esophageal cancer and gastric cancer may be characterized by further including the DNA markers shown in Table 2 below, but is not limited thereto.

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] [Table 2] TIFF2025537955000004.tif253133TIFF2025537955000005.tif253133TIFF2025537955000006.tif241133

[0049] In the present invention, the DNA methylation marker combination for diagnosing esophageal cancer and gastric cancer may further include, but is not limited to, two or more DNA methylation markers selected from the group consisting of the DNA markers shown in Table 3 below.

[0050]

[0051]

[0052] [Table 3] TIFF2025537955000008.tif181132

[0053] In the present invention, the term "DNA methylation" refers to the covalent binding of a methyl group to the C5 position of a cytosine base in genomic DNA. The methylation level refers to the amount of methylation present in a DNA base sequence, for example, in all genomic regions and some non-genomic regions, and in the present invention refers to the degree of methylation of the DNA methylation marker. Methylation in the DNA methylation marker can occur over the entire sequence or a portion of the sequence.

[0054] In the present invention, the term "esophageal cancer" refers to a malignant tumor occurring in the esophagus, and more specifically refers to, but is not limited to, cervical esophageal cancer, thoracic esophageal cancer, esophagogastric junction cancer, and metastatic esophageal cancer that has metastasized to the esophagus from another organ.

[0055] In the present invention, the gastric cancer refers to a malignant tumor occurring in the stomach, more specifically, gastric adenocarcinoma, lymphoma, gastric submucosal tumor, leiomyosarcoma, and metastatic gastric cancer that has metastasized to the stomach from another organ, but is not limited thereto.

[0056]

[0057] In another aspect, the present invention provides

[0058] (a) isolating DNA from a biological sample;

[0059] (b) detecting the methylation level of the DNA methylation marker combination; and

[0060] (c) determining that the subject has esophageal cancer and gastric cancer when the detected DNA methylation marker level exceeds the reference value;

[0061] The present invention relates to a method for providing information for diagnosing esophageal cancer and gastric cancer, comprising:

[0062] In still another aspect, the present invention provides

[0063] (a) isolating DNA from a biological sample;

[0064] (b) detecting the methylation level of the DNA methylation marker combination; and

[0065] (c) determining that the subject has esophageal cancer and gastric cancer when the detected DNA methylation marker level exceeds the reference value;

[0066] The present invention relates to a method for diagnosing esophageal cancer and gastric cancer, comprising:

[0067]

[0068] In the present invention, the DNA can be any DNA extracted from a biological sample without limitation, and may be, but is not limited to, cell-free nucleic acid or a fragment of intracellular nucleic acid.

[0069] In the present invention, the biological sample refers to any substance, biological fluid, tissue, or cell obtained from or derived from an individual, such as whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, blood (including plasma and serum), sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, pelvic fluids, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic juice, etc. Examples of fluids that may be used include, but are not limited to, blood, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, semen, hair, saliva, urine, buccal cells, placental cells, cerebrospinal fluid, and mixtures thereof.

[0070]

[0071] In the present invention, the detection of the methylation level in step (b) can be performed by various known methods, preferably, but not limited to, bisulfite conversion or methylated DNA immunoprecipitation (MeDIP).

[0072] In the present invention, another method for detecting DNA methylation is a restriction enzyme-based detection method, which uses a methylation restriction enzyme (MRE) to cleave unmethylated nucleic acids or to cleave a specific sequence (recognition site) regardless of whether it is methylated or not, and then analyzes the cleaved sequence using a hybridization method or PCR.

[0073] In the present invention, methods based on bisulfite substitution include Whole-Genome Bisulfite Sequencing (WGBS), Reduced-Representation Bisulfite Sequencing (RRBS), Methylated CpG Tandems Amplification and Sequencing (MCTA-seq), Targeted Bisulfite Sequencing, Methylation Array, and MSP (Methylation-specific PCR).

[0074] In the present invention, methods for enriching and analyzing methylated DNA include MeDIP-seq (Methylated DNA Immunoprecipitation Sequencing) and MBD-seq (Methyl-CpG Binding Domain Protein Capture Sequencing).

[0075] In the present invention, another method for analyzing methylated DNA is 5-hydroxymethylation profiling, examples of which include 5hmC-Seal (hMe-Seal), hmC-CATCH, hMeDIP-seq (Hydroxymethylated DNA Immunoprecipitation Sequencing), and oxidative bisulfite conversion.

[0076] In the present invention, the detection of the methylation level in step (b) may be characterized by using any one method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, PCR using a methylated DNA-specific binding protein, quantitative PCR, PCR using a methylation-specific PNA, melting curve analysis, DNA chip, pyrosequencing, bisulfite sequencing, and methylation next-generation sequencing, but is not limited thereto.

[0077]

[0078] In the present invention, a next-generation sequencer can be used with any sequencing method known in the art. Sequencing of nucleic acids isolated by a selection method is typically performed using next-generation sequencing (NGS). Next-generation sequencing includes any sequencing method that determines the nucleotide sequence of individual nucleic acid molecules or clonally expanded proxies for individual nucleic acid molecules in a highly similar manner (e.g., 10 or more molecules are sequenced simultaneously). In one embodiment, the relative abundance of nucleic acid species in a library can be estimated by measuring the relative occurrence of their cognate sequences in the data generated by the sequencing experiment. Next-generation sequencing methods are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, incorporated herein by reference.

[0079] In one embodiment, next-generation sequencing is performed to determine the nucleotide sequence of individual nucleic acid molecules (e.g., Helicos BioSciences' HeliScope Gene Sequencing system and Pacific Biosciences' PacBio RS system). In other embodiments, sequencing, such as massively parallel short-read sequencing (e.g., Solexa sequencers from Illumina Inc., San Diego, CA), which produces more bases of sequence per sequencing unit than other sequencing methods that produce fewer but longer reads, determines the nucleotide sequence of clonally extended proxies for individual nucleic acid molecules (e.g., Solexa sequencers from Illumina Inc., San Diego, CA; 454 Life Sciences (Branford, Connecticut) and Ion Torrent). Other methods or machines for next-generation sequencing include, but are not limited to, those provided by 454 Life Sciences (Branford, Connecticut), Applied Biosystems (Foster City, California; SOLiD sequencer), Helicos Biosciences Corporation (Cambridge, Massachusetts), and emulsion and microfluidic sequencing techniques such as nano-drip (e.g., GnuBio drip).

[0080] Platforms for next-generation sequencing include, but are not limited to, the Roche / 454 Genome Sequencer (GS) FLX system, the Illumina / Solexa Genome Analyzer (GA), the Life / APG Support Oligonucleotide Ligation Detection (SOLiD) system, the Polonator G.007 system, the Helicos BioSciences HeliScope Gene Sequencing system, the Oxford Nanopore Technologies PromethION, GriION, and MinION systems, and the Pacific Biosciences PacBio RS system.

[0081]

[0082] In the present invention, the methylation level in step (b) may be characterized as being expressed as an alpha value or a beta value, but is not limited thereto.

[0083] In the present invention, the alpha value can be defined as the methylation ratio of CpG sites present in a nucleic acid fragment containing a lead. For example, if a nucleic acid fragment has 10 CpG sites and 9 of these sites are methylated, the alpha value of the nucleic acid fragment is 90%.

[0084]

[0085] In the present invention, the step (c) may be characterized by being performed by a method including the following steps:

[0086] (ci) calculating a methylation score using the following formula 1:

[0087]

[0088]

number

[0089] (ci) determining that the cancer is esophageal cancer or gastric cancer if the calculated methylation score exceeds the reference value.

[0090]

[0091] In the present invention, an alpha value of a hypermethylation marker of 80% or more means that the nucleic acid fragment contains reads aligned to a region containing a hypermethylation marker and the proportion of methylation markers at CpG sites contained therein is 80% or more, and an alpha value of a hypomethylation marker of 20% or less means that the nucleic acid fragment contains reads aligned to a region containing a hypomethylation marker and the proportion of methylation markers at CpG sites contained therein is 20% or less.

[0092]

[0093] In the present invention, the reference value in step (c) can be any value that can determine the presence or absence of esophageal cancer and gastric cancer, and may be, but is not limited to, a value of preferably 0.001 to 0.1, more preferably 0.003 to 0.08, and most preferably 0.005 to 0.05.

[0094]

[0095] In still another aspect, the present invention provides

[0096] The present invention relates to a primer composition for diagnosing esophageal cancer and gastric cancer, which comprises a primer combination capable of amplifying each of the DNA methylation markers in the DNA methylation marker combination.

[0097] In the present invention, the appropriate length of the primer may vary depending on the intended use, but may generally be 15 to 30 bases. The primer sequence does not need to be completely complementary to the template, but must be sufficiently complementary to hybridize with the template. The primer hybridizes to a DNA sequence containing a methylation marker and can amplify a DNA fragment containing the methylation marker. The primer of the present invention can be used in diagnostic kits and prognostic methods for detecting DNA methylation levels and confirming the presence or absence of esophageal cancer and gastric cancer.

[0098] In the present invention, the primers capable of amplifying the DNA methylation marker can be used without limitation as long as they are of the same chromosomal base sequence that does not directly include the marker region. Specifically, the primers may be 1 bp to 1000 bp 5' upstream and 1 bp to 1000 bp 3' downstream of the marker region, more specifically, 1 bp to 200 bp 5' upstream and 1 bp to 200 bp 3' downstream of the marker region, but are not limited thereto.

[0099]

[0100] In still another aspect, the present invention provides

[0101] The present invention relates to a probe composition for diagnosing esophageal cancer and gastric cancer, which comprises a probe combination that can specifically hybridize with a polynucleotide containing 10 or more consecutive bases containing the methylated bases of the DNA methylation markers of the DNA methylation marker combination, or with a complementary polynucleotide thereof.

[0102] In the present invention, the probe may be methylation-specific, meaning that it specifically hybridizes only to methylated nucleic acids in the methylation marker region. Hybridization is typically performed under stringent conditions, such as a salt concentration of 1 M or less and a temperature of 25° C. or higher. For example, conditions such as 5X SSPE (750 mM NaCl, 50 mM Na Phosphate, 5 mM EDTA, pH 7.4) and a temperature of 25° C. to 30° C. may be suitable for methylation-specific probe hybridization.

[0103] In the present invention, the probe refers to a hybridization probe, which is an oligonucleotide capable of sequence-specifically binding to a complementary strand of nucleic acid. The methylation-specific probe of the present invention may hybridize to a DNA fragment derived from one individual but not to a fragment derived from the other individual when methylation is present in nucleic acid fragments derived from two individuals of the same species. In this case, the hybridization conditions must be sufficiently stringent to show a significant difference in hybridization intensity, allowing hybridization depending on the presence or absence of methylation. Preferably, the central region of such a probe of the present invention is aligned with the region of a methylation marker. The probe of the present invention can be used in diagnostic kits and prognostic methods for detecting DNA methylation levels and confirming the presence or absence of esophageal cancer and gastric cancer.

[0104]

[0105] In still another aspect, the present invention provides

[0106] The present invention also relates to a kit for diagnosing esophageal cancer and gastric cancer, which comprises any one of the above compositions.

[0107] In the present invention, the kit may include not only the polynucleotide of the present invention but also one or more other component compositions, solutions, or devices suitable for the analytical method. In one embodiment, the kit of the present invention may be a kit containing essential components necessary for PCR, and may further include test tubes or other suitable containers, a reaction buffer (various pH and magnesium concentrations), deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC water (DEPC-water), and sterile water. In another embodiment, the kit of the present invention may be a kit for predicting blood statin concentrations containing essential components necessary for performing a DNA chip assay. The DNA chip kit includes a substrate to which a polynucleotide, primer, or probe specific for the methylation is attached, and the substrate contains a nucleic acid corresponding to a quantitative control gene or a fragment thereof.

[0108]

[0109] [Example]

[0110] The present invention will be described in more detail below through examples. It will be obvious to those skilled in the art that these examples are merely for the purpose of illustrating the present invention and should not be construed as limiting the scope of the present invention.

[0111]

[0112] Example 1. Selection of esophageal squamous cell carcinoma (ESCC)-specific methylated regions using TCGA methylation 450K array data

[0113] Methylation levels were determined using the Infinium Human Methylation 450K BeadChip array data (UCSC Xena, http: / / xena.ucsc.edu) from The Cancer Genome Atlas (TCGA). DNA extracted from tissues was converted through bisulfite treatment, and the presence or absence of DNA methylation was confirmed through the modification of cytosine bases. The methylation level was determined for each region, and differentially methylated regions between ESCC tissues and surrounding normal tissues were identified using beta values, which indicate the degree of methylation.

[0114] The TCGA methylation 450k array data was divided into a train group and a test group as shown in Table 4, and markers were selected using the train group to confirm whether the discovered markers showed the same pattern in the test group.

[0115] [Table 4]

[0116] First, we removed missing values ​​in approximately 480,000 (480K) regions,

[0117] To select ESCC-specific methylated regions, we used Limma (Linear Models for Microarray Data) software to select regions with an FDR value of less than 0.01 and an absolute delta beta of more than 0.25. We then selected 1,020 hypomethylated sites and 3,137 hypermethylated sites specific to ESCC, excluding sex chromosomes.

[0118]

[0119] Example 2. Extraction of methylated cfDNA from blood and next-generation sequencing (cfMeDIP-Seq)

[0120] Blood samples were collected from 68 esophageal cancer patients and 283 normal controls (Table 3). The plasma fraction was first centrifuged at 3,000 rpm and 25°C for 10 minutes. The plasma fraction was then centrifuged again at 16,000 g and 25°C for 10 minutes to remove the precipitate and separate the supernatant. Cell-free DNA was extracted from the separated plasma using the Chemagen DNA kit. Adaptor ligation was then performed using the Truseq Nano DNA HT Library Prep Kit (Illumina). After adapter ligation, 5mC immunoprecipitation was performed using the antibodies in the cfMeDIP Kit (Diagnode) at 10 rpm and 4°C for 17 hours. Purification was then performed, and PCR enrichment was repeated using the Truseq Nano DNA HT Library Prep Kit (Illumina) to create the final library. The constructed libraries were sequenced using a Novaseq 6000 (Illumina) in 150 paired-end mode, producing approximately 30 million reads per sample.

[0121] [Table 5]

[0122]

[0123] Example 3. Identification of ESCC-specific methylated regions through cfMeDIP-Seq data analysis

[0124] Since the methylated cell-free nucleic acids were sequenced in Example 2, the obtained nucleic acid fragment data was methylated, and this data was aligned to the human reference genome to identify each methylated region across the entire human genome. The MeDIP-Seq data indicated methylated regions, and differentially methylated regions between the esophageal cancer and gastric cancer groups and the normal group were selected using normalized values ​​per 300 bp bin.

[0125] The cfMeDIP-Seq data was divided into a train group and a test group as shown in Table 6 below, and the dataset was split five times (Figure 2). Markers were selected using the train group, and markers found in two or more datasets were used.

[0126] Specifically, the process of separating the test set using different combinations was repeated five times to create five different combination datasets, and DMRs were discovered (Figure 2). Additional marker selection was then performed using each marker found in two or more datasets among the DMRs.

[0127] [Table 6]

[0128] First, adapter trimming and quality trimming were performed on the fastq files using Trim Galore (version 0.6.6). Then, the nucleic acid fragment data were aligned to the reference genome (hg19) using the bwa (version 0.7.17-r1188) alignment tool. PCR duplicate nucleic acid fragments were removed using the samtools rmdup (version 1.11) tool. Nucleic acid fragments with a mapping quality of less than 10 were removed using the samtools view (version 1.11) tool. After removing all but chr1-22, X, and Y, the data were binned into 300-bp bins, with no overlapping except for the sex chromosomes, and read counts per 300-bp bin were generated.

[0129] Blacklist regions (Low_mappability_island, centromeric_repeat, etc.) and bins with a sum of read counts of 10 or less across all samples per bin were excluded.

[0130] Normalized values ​​per 300 bp bin (TMM normalized values) were generated using edgeR (Empirical Analysis of Digital Gene Expression Data in R) software.

[0131] Finally, edgeR software was used to select 544,553 hypermethylated regions and 443,967 hypomethylated regions specific to ESCC with FDR values ​​less than 0.05.

[0132] Next, 1,497 CpG sites that were found to overlap between the regions selected in Example 1 and those selected by cfMeDIP-seq and that overlapped in two or more of the five cfMeDIP datasets were finally selected as ESCC-specific markers. A total of 1,133 differentially methylated regions (DMRs) were selected by defining the regions in 300-bp bins.

[0133] Subsequently, to select key markers, an additional selection process was performed using the AUC for distinguishing tumors from normals, the median difference between groups in absolute delta beta values, and hypermethylated or hypomethylated regions in each normal sample. The criteria for each were an AUC of 0.9 or greater, a median beta difference between tumors and normals of 0.2 or greater, and a median beta value of 0.2 or less or 0.8 or greater for each normal sample. Applying these criteria, 28 hypomethylated regions and 145 hypermethylated regions were selected.

[0134] For regions overlapping with previously known methylated regions (WO2019 / 195268A2, WO2019 / 199696A1), only significant hypermethylated regions were added. The criteria for significance were the AUC for separating tumor and normal samples and the difference in median absolute delta beta values ​​by group. The criteria were an AUC of 0.9 or greater and a difference in median beta values ​​between tumor and normal of 0.3 or greater. Applying these criteria and adding only the major regions from the known regions, 19 hypomethylated regions and 29 hypermethylated regions were selected (remaining regions after excluding known regions: 12 hypermethylated regions, 19 hypomethylated regions; number of major regions from known regions: 17 hypermethylated regions).

[0135] Finally, only regions with three or more CpG sites in the DMR were retained, and 18 hypomethylated regions and 29 hypermethylated regions were selected. The CpG site units were 21 hypomethylated sites and 53 hypermethylated sites.

[0136]

[0137] Example 4. Selection of ESCC-specific methylated regions using WGBS (Whole genome bisulfite sequencing) data

[0138] Methylation levels were determined using whole genome bisulfite sequencing (WGBS) data (GSE149608, GSE186458, source: https: / / www.ncbi.nlm.nih.gov / geo / ). DNA extracted from tissues was converted through bisulfite treatment, and the presence or absence of DNA methylation was confirmed through the modification of cytosine bases. The methylation level was determined for each region, and differentially methylated regions between ESCC tissues and surrounding normal tissues and normal blood samples were identified using beta values, which indicate the degree of methylation.

[0139] The data structure is as shown in Table 7.

[0140] [Table 7]

[0141] Normal esophageal tissue samples and normal blood samples were subjected to differentially methylated cytosine (DMC) analysis using 10,405,333 CpG sites with median beta values ​​less than 0.2 or greater than 0.8.

[0142] Markers were analyzed using the leave-one-out (LOO) method, which uses a t-test to determine differentially methylated cytosine (DMC) for each fold, excluding all samples once (Figure 3). For each fold, regions with a p-value of 0.01 or less and a median beta difference of 0.05 or more were selected.

[0143] As a result, 8,989 hypomethylated sites and 499 hypermethylated sites that showed differences between ESCC and normal blood samples were selected, and 10,003 hypomethylated sites and 378 hypermethylated sites that showed differences between ESCC and normal tissue were also selected as candidate markers.

[0144] Next, we selected 3,593 hypomethylated sites and 341 hypermethylated sites specific to ESCC from regions that showed differences between tumor tissue and normal tissue and normal blood samples, and then selected CpG sites that were repeatedly selected 10 or more times across the entire fold, resulting in 573 hypomethylated sites and 213 hypermethylated sites. Each of the selected sites was then extended ±150 bp from the CpG site to determine DMRs.

[0145] For regions overlapping with previously known methylated regions (WO2019 / 195268A2, WO2019 / 199696A1), only significant hypermethylated regions were added. The criteria for significance were the AUC for separating tumor and normal samples and the difference in median absolute delta beta values ​​by group. The criteria were an AUC of 0.9 or greater and a difference in median beta values ​​between tumor and normal of 0.3 or greater. Applying these criteria and adding only the major regions from the known regions, 36 hypomethylated regions and 119 hypermethylated regions were selected (remaining regions after excluding known regions: 100 hypermethylated regions, 36 hypomethylated regions; number of major regions from known regions: 19 hypermethylated regions).

[0146] Finally, only regions with three or more CpG sites in the DMR were retained, and 17 hypomethylated regions and 118 hypermethylated regions were selected. The CpG site units were 19 hypomethylated sites and 134 hypermethylated sites.

[0147]

[0148] Example 5. Selection of esophageal adenocarcinoma (EAC)-specific methylated regions using Infinium Human Methylation 450K BeadChip array data

[0149] Methylation levels were determined using the Infinium Human Methylation 450K BeadChip array data (GSE72872, source: https: / / www.ncbi.nlm.nih.gov / geo / ). DNA extracted from tissues was converted through bisulfite treatment, and the presence or absence of DNA methylation was confirmed through the modification of cytosine bases. The methylation level was determined for each region, and differentially methylated regions were selected between EAC tissues and surrounding normal tissues, benign disease (gastroesophageal reflux disease) samples, and normal blood samples using beta values, which indicate the degree of methylation.

[0150] The data structure is as shown in Table 8.

[0151] [Table 8]

[0152] First, differentially methylated cytosine (DMC) analysis was performed using 225,287 CpG sites with median beta values ​​of less than 0.2 or greater than 0.8 in normal esophageal tissue samples and normal blood samples.

[0153] Using Limma (Linear Models for Microarray Data) software, we selected sites with an FDR value of less than 0.01 and an absolute delta beta of more than 0.25. We then selected sites that were found to match twice between tumor tissue and normal tissue and normal blood samples. As a result, 41,701 hypomethylated sites and 35,723 hypermethylated sites specific to EAC were selected, excluding sex chromosomes.

[0154] Next, we selected the top 500 hypermethylated sites and the top 100 hypomethylated sites based on absolute delta beta from each site with an adjusted P value of less than 0.005. The sites were extended ±150 bp from the CpG site to identify DMRs.

[0155] Next, for regions overlapping with published regions (WO2019 / 195268A2, WO2019 / 199696A1), only significant hypermethylated regions were added. The significance criteria were the AUC for separating tumor and normal samples and the median difference between groups in absolute delta beta values. The criteria were an AUC of 0.95 or greater and a difference in median beta between tumor and normal samples of 0.47 or greater. After applying these criteria and adding only the major regions from the published regions, 79 hypomethylated regions and 122 hypermethylated regions were selected. (After excluding published regions, the remaining regions were 102 hypermethylated regions and 79 hypomethylated regions. The number of major regions from published regions was 20 hypermethylated regions.)

[0156] Only regions with three or more CpG sites in the DMR were retained, and 68 hypomethylated regions and 121 hypermethylated regions were finally selected (72 hypomethylated sites and 175 hypermethylated sites).

[0157]

[0158] Example 6. Selection of stomach adenocarcinoma-specific methylated regions based on publicly available data

[0159] Methylation levels were determined using the Infinium Human Methylation 450K BeadChip array data (UCSC Xena, http: / / xena.ucsc.edu) from TCGA (The Cancer Genome Atlas) and a publicly available dataset (GSE72872) using the Infinium Human Methylation 450K BeadChip array data. DNA extracted from tissues was converted through bisulfite treatment, and the presence or absence of DNA methylation was confirmed through the modification of cytosine bases. The methylation level was determined for each region, and differentially methylated regions between gastric cancer tissues and surrounding normal tissues and normal blood samples were identified using beta values, which indicate the degree of methylation. The composition of the entire dataset is shown in Table 9 below.

[0160] [Table 9]

[0161] DMC analysis was performed using 230,897 CpG sites with median beta values ​​less than 0.2 or greater than 0.8 in normal stomach tissue samples and normal blood samples.

[0162] Using Limma (Linear Models for Microarray Data) software, we selected sites with an FDR value of less than 0.01 and an absolute delta beta of more than 0.25. We then selected sites that were found to be consistent twice between tumor tissue and normal tissue and normal blood samples. After this, we initially selected 50,324 hypomethylated sites and 42,214 hypermethylated sites specific to gastric cancer, excluding sex chromosomes.

[0163] Next, for each region with an adjusted P value of less than 0.005, the top 500 hypermethylated sites and the top 100 hypomethylated sites were selected based on absolute delta beta, and the DMRs were determined by extending these sites to ±150 bp based on the CpG site.

[0164] Additionally, for regions overlapping with published regions (WO2019 / 195268A2, WO2019 / 199696A1), only significant hypermethylated regions were added. The significance criteria were the AUC for separating tumor and normal samples and the median difference between groups in absolute delta beta values. The criteria were an AUC of 95 or greater and a difference in median beta values ​​between tumor and normal samples of 0.4 or greater. Applying these criteria and adding only the major regions from the published regions, 76 hypomethylated regions and 132 hypermethylated regions were selected. (After excluding published regions, the remaining regions were 107 hypermethylated regions and 76 hypomethylated regions. The number of major regions from published regions was 25 hypermethylated regions.)

[0165] Only regions with three or more CpG sites in the DMR were retained, and 60 hypomethylated regions and 132 hypermethylated regions were finally selected (70 hypomethylated sites and 179 hypermethylated sites).

[0166]

[0167] Example 7. Selection of final marker candidates

[0168] The 18 hypomethylated regions and 29 hypermethylated regions selected in Example 3, the 17 hypomethylated regions and 118 hypermethylated regions selected in Example 4, the 68 hypomethylated regions and 121 hypermethylated regions selected in Example 5, and the 60 hypomethylated regions and 132 hypermethylated regions selected in Example 6 were all integrated, and a total of 646 CpG sites, excluding overlapping regions, were selected as a final group of candidate markers for diagnosing esophageal cancer and gastric cancer.

[0169] The list of 646 selected methylation markers is shown in Table 10 below.

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193]

[0194] [Table 10] TIFF2025537955000017.tif254170TIFF2025537955000018.tif255170TIFF2025537955000019.tif253170TIFF2025537955000020.tif251170 TIFF2025537955000021.tif254170TIFF2025537955000022.tif255170TIFF2025537955000023.tif254170TIFF2025537955000024.tif252170 TIFF2025537955000025.tif255170TIFF2025537955000026.tif253170TIFF2025537955000027.tif255170TIFF2025537955000028.tif254170 TIFF2025537955000029.tif253170TIFF2025537955000030.tif255170TIFF2025537955000031.tif255170TIFF2025537955000032.tif112170

[0195] The 646 selected methylation markers are summarized by region as shown in Table 11 below.

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

Table 11

[0214] 8-1.A.cfEM-seq (cfDNA Enzymatic Methylation sequencing)

[0215] Blood was collected from 48 esophageal cancer patients and 32 normal subjects, and the plasma portion was first centrifuged at 3000 rpm, 25°C, and 10 minutes. The plasma was then centrifuged again at 16000 g, 25°C, and 10 minutes to separate the plasma. Cell-free DNA was then extracted using the Mag-bind cfDNA kit (Omega).

[0216] The concentration of extracted cfDNA was measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA), and the size of cfDNA was confirmed using a Tapestation 4200 (Agilent, USA).

[0217] Using the maximum amount of extracted cfDNA and enzymatic methyl-seq (NEB kit), methylation conversion was performed using TET2 (ten-eleven translocation dioxygenase 2) and APOBEC to convert unmethylated cytosine to uracil to create a library. The concentration and size of the DNA library were then measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA) and Tapestation 4200 (Agilent, USA), respectively.

[0218] Eight samples were pooled and hybridized with 250 ng of the library. The concentration of the captured samples was measured using the Qubit DS DNA HS Assay Kit (Thermo Fisher Scientific, USA). The size of the captured DNA was confirmed using High Sensitivity D1000 Screen Tape & Reagent (Agilent, USA) on a Tapestation 4200 (Agilent, USA).

[0219] Sequencing was performed using a Miseq Dx (Illumina) instrument in 150 paired-end mode with a final concentration of 11 pM, producing 500x depth per sample.

[0220] 8-2. Final marker selection and performance confirmation

[0221] Using 48 ESCC patients and 32 normal individuals, a discovery set and an external set were separated, markers were selected in the discovery set, and performance was measured in the external set. The data structure is shown in Table 12 below.

[0222] [Table 12]

[0223] Of the 497 regions, those with normal methylation beta values ​​below 20 or above 80 with an SD below 5 and median coverage above 100 in the discovery set were selected as the final marker set (regions: 474 / CpG sites: 8423), as shown in Table 13 below.

[0224]

[0225]

[0226]

[0227]

[0228]

[0229]

[0230]

[0231]

[0232]

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

Table 13

[0242] For example, if 9 out of 10 CpG sites in a fragment are methylated, the alpha score for that fragment is 90%. The score was calculated for each fragment in the entire panel, and the relative frequency of each fragment was calculated: hypermethylated regions had an alpha score of 80% or more, and hypomethylated regions had an alpha score of 20% or less.

[0243] That is, the methylation score was calculated using the following formula 1.

[0244]

[0245]

number

[0246] After determining the reference value for the discovery set as 0.044 and the reference value for the external set as 0.041, we confirmed the methylation score distribution that could distinguish cancer from normal. As shown in Figure 5, we confirmed the performance of AUC 0.82 for the discovery set and AUC 0.715 for the external set.

[0247]

[0248] 8-3. Minimum marker selection and performance confirmation

[0249] Of the 497 regions, 345 were hypermethylated regions. Of these, regions with a methylation beta value of 20 or less and an SD of 5 or less in the normal range and median coverage of 100 or more in the discovery set were selected (regions: 333 / CpG sites: 7,427).

[0250] The list of 333 selected hypermethylated regions is shown in Table 14 below.

[0251]

[0252]

[0253]

[0254]

[0255]

[0256]

[0257]

[0258]

[0259]

[0260]

[0261]

[0262] [Table 14] TIFF2025537955000061.tif252170TIFF2025537955000062.tif253170TIFF2025537955000063.tif252170TIFF2025537955000064.tif25217 0TIFF2025537955000065.tif255170TIFF2025537955000066.tif252170TIFF2025537955000067.tif255170TIFF2025537955000068.tif61170

[0263] Alpha scores were calculated for each fragment present in the hypermethylated region, and the relative frequency of fragments with an alpha score of 80% or higher was calculated: number of fragments with an alpha score of 80% or higher / total number of fragments. Area under the curve (AUC) values ​​were calculated to determine how well the calculated scores distinguished between ESCC patients and normal subjects. As shown in Figure 6, the AUC was 0.85 for the discovery set and 0.77 for the external set. The reference value for the discovery set was 0.01, and the reference value for the external set was 0.005.

[0264] Additionally, we used the discovery set to select the CpG sites with the greatest difference between cancer and normal from the cancer hypermethylation CpG sites present in the panel. Specifically, using the beta value of each CpG site, we selected five regions with an AUC greater than 0.8 for separating cancer from normal and a median difference greater than 0.05 as minimum markers.

[0265] The list is shown in Table 15 below.

[0266] [Table 15]

[0267] Methylation scores were calculated for each fragment present in the five hypermethylated regions. Specifically, alpha values ​​were calculated for each fragment present in the hypermethylated regions, and the relative frequency of fragments with an alpha value of 80% or higher was calculated: number of fragments with an alpha value of 80% or higher / total number of fragments. Area under the curve (AUC) values ​​were calculated to determine how well the score distinguished between ESCC patients and normal subjects. As shown in Figure 7, the AUC was 0.91 for the discovery set and 0.87 for the external set. The reference value for the discovery set was 0.009, and the reference value for the external set was 0.007.

[0268]

[0269] Although certain parts of the present invention have been described in detail above, it will be apparent to those skilled in the art that such specific descriptions are merely preferred embodiments and do not limit the scope of the present invention. Therefore, the true scope of the present invention is to be defined by the appended claims and their equivalents.

[0270] [Industrial Applicability]

[0271] The DNA methylation marker for diagnosing esophageal cancer and gastric cancer according to the present invention can diagnose esophageal cancer and gastric cancer with high accuracy using only DNA methylation information from blood samples, without using esophageal cancer and gastric cancer tissue samples, and therefore can be useful for the early diagnosis of esophageal cancer and gastric cancer.

Claims

1. A combination of DNA methylation markers for diagnosing esophageal cancer and gastric cancer, comprising the DNA methylation markers shown in Table 1 below. Table 1

2. The DNA methylation marker combination for diagnosing esophageal cancer and gastric cancer according to claim 1, further comprising the DNA markers shown in Table 2 below. Table 2

3. The combination of DNA methylation markers for diagnosing esophageal cancer and gastric cancer according to claim 2, further comprising two or more DNA methylation markers selected from the group consisting of DNA markers shown in Table 3 below. Table 3

4. (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination of claim 1; and (c) determining that the patient has esophageal cancer and gastric cancer when the detected DNA methylation marker level exceeds a reference value; A method for providing information for diagnosing esophageal cancer and gastric cancer, comprising:

5. (a) isolating DNA from a biological sample; (b) detecting the methylation level of the DNA methylation marker combination of claim 1; and (c) determining that the patient has esophageal cancer and gastric cancer when the detected DNA methylation marker level exceeds a reference value; A method for diagnosing esophageal cancer and gastric cancer, comprising:

6. 6. The method of claim 4 or 5, wherein the detection of the methylation level in step (b) is performed using any one method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, PCR using a methylated DNA-specific binding protein, quantitative PCR, PCR using a methylation-specific PNA, melting curve analysis, DNA chip, pyrosequencing, bisulfite sequencing, and methylation next-generation sequencing.

7. The method according to claim 4 or 5, wherein the methylation level in step (b) is expressed as an alpha value.

8. 6. The method according to claim 4 or 5, wherein step (c) is carried out by a method comprising the steps of: (ci) calculating a methylation score using the following Equation 1; and [Equation 1] (ci) determining that the cancer is esophageal cancer or gastric cancer if the calculated methylation score exceeds the reference value.

9. A primer composition for diagnosing esophageal cancer and gastric cancer, comprising a primer combination capable of amplifying each of the DNA methylation markers in the DNA methylation marker combination according to any one of claims 1 to 3.

10. A probe composition for diagnosing esophageal cancer and gastric cancer, comprising a probe combination capable of specifically hybridizing to a polynucleotide comprising 10 or more consecutive bases containing a methylated base of a DNA methylation marker of the DNA methylation marker combination of any one of claims 1 to 3, or a complementary polynucleotide thereof.

11. A kit for diagnosing esophageal cancer and gastric cancer, comprising the composition of claim 9 or 10.

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