DNA methylation marker for predicting responsiveness to radiotherapy for esophageal cancer and gastric cancer, and uses thereof

Abstract

The present invention relates to DNA methylation markers for predicting responsiveness to radiotherapy for esophageal cancer and gastric cancer, and uses thereof and, more specifically, to a combination of DNA methylation markers capable of predicting the responsiveness to radiotherapy in patients with esophageal and gastric cancer, and uses thereof. The DNA methylation markers for predicting therapeutic responsiveness to radiotherapy for esophageal and gastric cancer according to the present invention can predict the therapeutic responsiveness to radiotherapy in patients with esophageal and gastric cancer with high accuracy using only DNA methylation information from blood samples, without the need for tissue samples from esophageal or gastric cancer, and thus can be advantageously used for establishing a treatment strategy for esophageal cancer and gastric cancer patients.

Description

DNA methylation markers for predicting esophageal and gastric cancer radiotherapy response and their use

[0001] The present invention relates to a DNA methylation marker for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer and its use, and more specifically, to a combination of DNA methylation markers capable of predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer patients and its use.

[0002]

[0003] Gastric cancer is the fourth most common cancer diagnosed worldwide and the third leading cause of cancer-related death. It is a common cancer type in East Asian countries. In Korea, the incidence of gastric cancer is high due to genetics, a diet rich in salty foods, smoking, and a high prevalence of Helicobacter pylori. In Korea, the majority of early-stage gastric cancers are asymptomatic (74.2-78.1%) compared to symptomatic cases (25.9-35.7%). Once gastric cancer progresses and causes serious symptoms and complications, the prognosis is poor, with the survival rate dropping from approximately 65% ​​(if detected early) to less than 20%. Therefore, early diagnosis is crucial.

[0004] Esophageal cancer, which arises from the esophageal cells in the tubular region between the pharynx and the stomach, is the eighth most common cancer. It occurs more frequently in men than in women, and the incidence varies considerably across countries. According to the 2019 Korea Central Cancer Registry, 2,870 new cases of esophageal cancer were diagnosed in 2019, with 2,573 cases (89%) being male. The two most common types of esophageal cancer are esophageal squamous cell carcinoma and esophageal adenocarcinoma. Several rarer subtypes are also known. Squamous cell carcinoma arises from the epithelial cells of the esophagus, while adenocarcinoma arises from the glandular cells present in the lower esophagus.

[0005] A key factor in the high mortality rate of esophageal cancer is the low diagnosis rate in early stages. While the cure rate for early-stage esophageal cancer is much higher than for intermediate and late-stage cancer, the lack of clear and specific symptoms means that most patients are already in the advanced stages by the time they are diagnosed. Clinical studies have shown that the cancer process takes several years, on average, from the time a lesion appears until clinical symptoms appear in the patient. This provides an effective latency period for early detection and improved diagnosis. If this latency period is fully utilized, it is expected to improve the effectiveness of esophageal cancer treatment and reduce mortality.

[0006] Cancer treatments can be broadly categorized into surgery, radiotherapy, and chemotherapy. Surgery is a viable treatment for gastric cancer, making it the most commonly used modality. However, even with surgery, the recurrence rate is very high in advanced cases. Therefore, multidisciplinary approaches, including postoperative chemotherapy or chemoradiotherapy, have been introduced to prevent recurrence and improve the prognosis of gastric cancer patients. While these treatments improve general clinical outcomes, the clinicopathologic heterogeneity of tumors and the varying outcomes among patients at the same stage limit the ability to predict the effectiveness of adjuvant chemotherapy, resulting in a lack of an optimal approach for individual patients.

[0007] A method using copy number variation in cell-free nucleic acids to predict radiotherapy responsiveness of esophageal and gastric cancer has been reported (Cho WK et al., Radiation Oncology Journal, VOl 41(1), pp.32-39, 2023), but has the disadvantage of low accuracy.

[0008] Korean Patent No. 1741898 describes a protein marker for determining the radiation therapy response of gastric cancer patients, and Korean Patent No. 2016796 describes a protein marker for predicting the immunotherapy response of digestive system cancer patients, but these have the disadvantage of requiring the acquisition of tissue cells.

[0009] Accordingly, the present inventors have made great efforts to solve the above problems and develop DNA methylation markers for predicting the radiotherapy response of esophageal cancer and gastric cancer with high sensitivity and accuracy. As a result, they selected regions specifically methylated in each cancer using TCGA methylation data of esophageal cancer and gastric cancer tissue samples, known methylation data related to esophageal cancer and gastric cancer (GEO), and methylated DNA data in the tissues and cfDNA of esophageal cancer or gastric cancer patients, and finally selected esophageal cancer and gastric cancer-specific DNA methylation markers in common regions from the data sets. When the DNA methylation markers are analyzed, it is confirmed that not only can esophageal cancer and gastric cancer be diagnosed at an early stage with high accuracy, but also the radiotherapy response of esophageal cancer and gastric cancer patients can be predicted with the marker set, thereby completing the present invention.

[0010]

[0011] Summary of the invention

[0012] The purpose of the present invention is to provide a combination of DNA methylation markers for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer.

[0013] Another object of the present invention is to provide a method for providing information for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer using the above DNA methylation marker combination.

[0014] Another object of the present invention is to provide a method for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer using the above combination of DNA methylation markers.

[0015] Another object of the present invention is to provide a probe composition capable of detecting the above DNA methylation marker combination, a primer composition, and a kit for predicting esophageal cancer and gastric cancer radiation treatment response comprising the above composition.

[0016] To achieve the above purpose, the present invention provides a combination of DNA methylation markers for predicting esophageal cancer and gastric cancer radiation therapy response, comprising two or more DNA methylation markers selected from the group consisting of DNA methylation markers shown in Table 1.

[0017] The present invention also provides a method for providing information for predicting esophageal cancer and gastric cancer radiation therapy response, comprising: (a) a step of isolating DNA from a biological sample; (b) a step of detecting a methylation level of a combination of DNA methylation markers using a primer composition comprising a combination of primers capable of amplifying each of the DNA methylation markers of the combination of DNA methylation markers and / or a probe composition comprising a combination of probes capable of specifically hybridizing with a polynucleotide comprising 10 or more consecutive bases containing a methylated base of the combination of DNA methylation markers or a complementary polynucleotide thereof; and (c) a step of determining that the treatment response of esophageal cancer and gastric cancer is low when the detected level of the DNA methylation marker is equal to or higher than a cut-off value.

[0018] The present invention also provides a method for predicting esophageal cancer and gastric cancer radiotherapy response, comprising: (a) isolating DNA from a biological sample; (b) detecting a methylation level of a combination of DNA methylation markers using a primer composition comprising a combination of primers capable of amplifying each of the DNA methylation markers of the combination of DNA methylation markers and / or a probe composition comprising a combination of probes capable of specifically hybridizing with a polynucleotide comprising 10 or more consecutive bases containing a methylated base of the combination of DNA methylation markers or a complementary polynucleotide thereof; and (c) determining that the treatment response of esophageal cancer and gastric cancer is low when the detected level of the DNA methylation marker is equal to or higher than a cut-off value.

[0019] The present invention also provides a primer composition for predicting esophageal cancer and gastric cancer radiation therapy response, comprising a primer combination capable of amplifying each DNA methylation marker of the above DNA methylation marker combination.

[0020] The present invention also provides a probe composition for predicting esophageal cancer and gastric cancer radiation treatment response, comprising a probe combination capable of specifically hybridizing with a polynucleotide comprising 10 or more consecutive bases containing a methylated base of a DNA methylation marker of the above DNA methylation marker combination or a complementary polynucleotide thereof.

[0021] The present invention also provides a kit for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer, comprising the composition.

[0022]

[0023] Figure 1 is a flowchart showing the process of selecting a DNA methylation marker for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer according to the present invention.

[0024] FIG. 2 is a schematic diagram of a split data set for selecting candidate markers in one embodiment of the present invention.

[0025] FIG. 3 is a schematic diagram of a Leave one out (LOO) method implemented to select candidate markers in one embodiment of the present invention.

[0026] Figure 4 is a flowchart illustrating a process for selecting a minimum combination of DNA methylation markers for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer according to the present invention.

[0027] FIG. 5 is a graph showing the results of determining esophageal cancer radiotherapy responsiveness in clinical samples using a combination of 474 esophageal cancer and gastric cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0028] FIG. 6 is a graph showing the results of determining esophageal cancer radiotherapy responsiveness in clinical samples using a combination of 333 esophageal cancer and gastric cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0029] FIG. 7 is a graph showing the results of determining esophageal cancer radiotherapy responsiveness in clinical samples using a combination of five esophageal cancer and gastric cancer-specific DNA methylation markers selected according to one embodiment of the present invention.

[0030]

[0031] Detailed description of the invention and preferred embodiments

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Generally, the nomenclature used herein and the experimental methods described below are well known and commonly used in the art.

[0033]

[0034] In the present invention, we developed a model capable of predicting the radiation therapy responsiveness of esophageal cancer and gastric cancer patients using methylation information of cell-free nucleic acids in blood, and attempted to confirm its accuracy.

[0035] In the present invention, by combining methylation data of esophageal cancer and gastric cancer tissue samples described in the TCGA database, methylation data of esophageal cancer and gastric cancer tissue samples described in a known GEO database, and methylation data of cell-free nucleic acids extracted from blood samples of esophageal cancer patients, DNA methylation markers capable of determining esophageal cancer and gastric cancer and predicting radiation therapy responsiveness were selected.

[0036] That is, in one embodiment of the present invention, methylation regions specific to esophageal cancer and gastric cancer were selected based on methylation data of esophageal cancer and gastric cancer tissue samples and normal samples described in the TCGA database, methylated DNA extracted from the blood of esophageal cancer patients and normal people was sequenced (cfMeDIP-seq, EM-seq), and then compared to select methylation regions specific to esophageal cancer and gastric cancer tissues, and additional methylation regions specific to esophageal cancer or gastric cancer were selected using known Whole genome bisulfite sequencing (WGBS, GEO) data and TCGA data sets, respectively.

[0037] Afterwards, among the regions selected at each stage, overlapping regions were removed, regions with three or more CpG sites were integrated to select the final marker, and when the marker was used to predict the radiation therapy response of patients with esophageal cancer and gastric cancer, it was confirmed that the radiation therapy response of patients with esophageal cancer and gastric cancer could be predicted with high accuracy (Fig. 1, Fig. 5 to Fig. 7).

[0038] Therefore, the present invention, from a consistent perspective,

[0039] The present invention relates to a combination of DNA methylation markers for predicting responsiveness to radiation therapy of esophageal cancer and gastric cancer, including DNA methylation markers represented in Table 1 below.

[0040]

[0041] In the present invention, the DNA methylation marker combination for predicting esophageal cancer and gastric cancer radiation therapy response may be characterized by additionally including DNA markers shown in Table 2 below, but is not limited thereto.

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] In the present invention, the combination of DNA methylation markers for predicting esophageal cancer and gastric cancer radiation therapy response may be characterized by additionally including two or more DNA methylation markers selected from the group consisting of DNA markers shown in Table 3 below, but is not limited thereto.

[0048]

[0049]

[0050]

[0051] The term "DNA methylation" in the present invention refers to the covalent attachment 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 within, for example, 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 may occur across the entire sequence or a portion of the sequence.

[0052] In the present invention, the esophageal cancer refers to a malignant tumor that has occurred in the esophagus, and more specifically, refers to cervical esophageal cancer, thoracic esophageal cancer, gastroesophageal junction cancer, and metastatic esophageal cancer that has occurred in other organs and has metastasized to the esophagus, but is not limited thereto.

[0053] In the present invention, the above gastric cancer refers to a malignant tumor that occurs in the stomach, and more specifically, refers to gastric adenocarcinoma, lymphoma, gastric submucosal tumor, leiomyoma, and metastatic gastric cancer that occurs in other organs and metastasizes to the stomach, but is not limited thereto.

[0054] In the present invention, the term "prediction of treatment responsiveness" refers to predicting whether a patient will respond favorably or unfavorably to radiation therapy, or predicting the patient's prognosis after radiation therapy, such as recurrence, metastasis, survival, or disease-free survival. The methylation marker for predicting treatment responsiveness according to the present invention can provide information for selecting the most appropriate radiation therapy method for patients with esophageal and gastric cancer.

[0055]

[0056] The present invention is from another perspective,

[0057] (a) a step of isolating DNA from a biological sample;

[0058] (b) a step of detecting the methylation level of the DNA methylation marker combination using a primer composition including a primer combination capable of amplifying each DNA methylation marker of the DNA methylation marker combination and / or a probe composition capable of specifically hybridizing with a polynucleotide including 10 or more consecutive bases containing the methylated base of the DNA methylation marker combination or a complementary polynucleotide thereof; and

[0059] (c) a step of determining that the treatment response to esophageal cancer and gastric cancer is low when the detected DNA methylation marker level is higher than the cut-off value;

[0060] The present invention relates to a method for providing information for predicting the response to radiation therapy for esophageal cancer and gastric cancer.

[0061] The present invention is from another perspective,

[0062] (a) a step of isolating DNA from a biological sample;

[0063] (b) a step of detecting the methylation level of the DNA methylation marker combination using a primer composition including a primer combination capable of amplifying each DNA methylation marker of the DNA methylation marker combination and / or a probe composition capable of specifically hybridizing with a polynucleotide including 10 or more consecutive bases containing the methylated base of the DNA methylation marker combination or a complementary polynucleotide thereof; and

[0064] (c) a step of determining that the treatment response to esophageal cancer and gastric cancer is low when the detected DNA methylation marker level is higher than the cut-off value;

[0065] It relates to a method for predicting the response to radiation therapy for esophageal cancer and gastric cancer, including .

[0066]

[0067] In the present invention, the DNA may be used without limitation as long as it is DNA extracted from a biological sample, but is preferably a fragment of cell-free nucleic acid or intracellular nucleic acid, but is not limited thereto.

[0068] In the present invention, the biological sample means any material, biological fluid, tissue or cell obtained from or derived from an individual, for example, 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 fluid, lymph fluid, pleural fluid, nipple It may include, but is not limited to, 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.

[0069]

[0070] In the present invention, the methylation level of step (b) can be detected by various known methods, and is preferably characterized by being obtained by bisulfite conversion or methylated DNA immunoprecipitation (MeDIP), but is not limited thereto.

[0071] In the present invention, a method for detecting DNA methylation additionally includes a restriction enzyme-based detection method, which is a method of cutting unmethylated nucleic acids using a methylation restriction enzyme (MRE) or cutting a specific sequence (recognition site) regardless of methylation and analyzing it in combination with a hybridization method or PCR.

[0072] Methods based on bisulfite substitution in the present invention 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 Methylation-specific PCR (MSP).

[0073] In the present invention, methods for enriching and analyzing methylated DNA include Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq) and Methyl-CpG Binding Domain Protein Capture Sequencing (MBD-seq).

[0074] Another method for analyzing methylated DNA in the present invention is 5-hydroxymethylation profiling, examples of which include 5hmC-Seal (hMe-Seal), hmC-CATCH, Hydroxymethylated DNA Immunoprecipitation Sequencing (hMeDIP-seq), and Oxidative Bisulfite Conversion.

[0075] 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 methylated DNA specific binding protein, quantitative PCR, PCR using methylation specific PNA, melting curve analysis, DNA chip, pyrosequencing, bisulfite sequencing, and methylation next generation base sequence sequencing, but is not limited thereto.

[0076]

[0077] In the present invention, the next-generation sequencer can be used with any sequencing method known in the art. Sequencing of nucleic acids isolated by the selection method is typically performed using next-generation sequencing (NGS). Next-generation sequencing includes any sequencing method that determines the nucleotide sequence of an individual nucleic acid molecule or a clonally expanded proxy for an individual nucleic acid molecule in a highly similar manner (e.g., more than 105 molecules are sequenced simultaneously). In one embodiment, the relative abundance of a nucleic acid species in a library can be estimated by counting the relative occurrence of its cognate sequence in data generated by a sequencing experiment. Next-generation sequencing methods are well known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.

[0078] In one embodiment, next-generation sequencing is used to determine the nucleotide sequence of individual nucleic acid molecules (e.g., the HeliScope Gene Sequencing system from Helicos BioSciences and the PacBio RS system from Pacific BioSciences). In another embodiment, a method of determining the nucleotide sequence of a clonally expanded proxy for an individual nucleic acid molecule is used, for example, by a massively parallel short-read sequencing method (e.g., the Solexa sequencer from Illumina Inc., San Diego, Calif.), which produces more bases of sequence per sequencing unit than other sequencing methods that produce fewer but longer reads (e.g., the Solexa sequencer from Illumina Inc., San Diego, Calif.; 454 Life Sciences, Branford, Conn., and Ion Torrent). Other methods or machines for next-generation sequencing include, but are not limited to, 454 Life Sciences (Branford, CT), Applied Biosystems (Foster City, CA; SOLiD sequencer), Helicos Biosciences Corporation (Cambridge, MA), and emulsion and microfluidic sequencing techniques nanodroplets (e.g., GnuBio drops).

[0079] 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, Helicos BioSciences' HeliScope Gene Sequencing system, the Oxford Nanopore Technologies PromethION, GriION, and MinION systems, and the Pacific Biosciences PacBio RS system.

[0080]

[0081] In the present invention, the methylation level of step (b) may be characterized by being expressed as an alpha value or a beta value, but is not limited thereto.

[0082] 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 contains 10 CpG sites, of which 9 are methylated, the alpha value of the nucleic acid fragment is 90%.

[0083]

[0084] In the present invention, the step (c) may be characterized in that it is performed by a method including the following steps:

[0085] (ci) a step of calculating a methylation score using the following formula 1; and

[0086] Formula 1:

[0087]

[0088] (ci) A step for determining that it is esophageal cancer or stomach cancer when the calculated methylation score exceeds the reference value.

[0089]

[0090] In the present invention, the meaning that the alpha value of the hypermethylation marker is 80% or more means that the nucleic acid fragment includes reads aligned to a region including the hypermethylation marker, and the methylation marker ratio of the CpG site included is 80% or more, and the meaning that the alpha value of the hypomethylation marker is 20% or less means that the nucleic acid fragment includes reads aligned to a region including the hypomethylation marker, and the methylation marker ratio of the CpG site included is 20% or less.

[0091]

[0092] In the present invention, the reference value of step (c) may be used without limitation as long as it is a value that can predict the responsiveness to esophageal cancer and gastric cancer radiation therapy, and is preferably a value between 0.001 and 0.1, more preferably between 0.003 and 0.08, and most preferably between 0.005 and 0.06, but is not limited thereto.

[0093]

[0094] The present invention is from another perspective,

[0095] The present invention relates to a primer composition for predicting esophageal cancer and gastric cancer radiation therapy response, comprising a primer combination capable of amplifying each DNA methylation marker of the above DNA methylation marker combination.

[0096] In the present invention, the appropriate length of the primer may vary depending on the intended use, but generally ranges from 15 to 30 bases. The primer sequence need not be completely complementary to the template, but must be sufficiently complementary to hybridize with the template. The primer can hybridize to a DNA sequence containing a methylation marker, thereby amplifying a DNA fragment containing the methylation marker. The primer of the present invention can be used in diagnostic kits or predictive methods for detecting esophageal cancer and gastric cancer by detecting DNA methylation levels.

[0097] In the present invention, the primer capable of amplifying the DNA methylation marker may be used without limitation as long as it is a base sequence of the same chromosome that does not directly include the marker region, but specifically, it may be 1 to 1000 bp 5' upstream of the marker region and 1 to 1000 bp 3' downstream, and more specifically, it may be 1 to 200 bp 5' upstream of the marker region and 1 to 200 bp 3' downstream, but is not limited thereto.

[0098]

[0099] The present invention is from another perspective,

[0100] The present invention relates to a probe composition for predicting esophageal cancer and gastric cancer radiation therapy response, comprising a probe combination capable of specifically hybridizing with a polynucleotide comprising 10 or more consecutive bases containing a methylated base of a DNA methylation marker of the above DNA methylation marker combination or a complementary polynucleotide thereof.

[0101] 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. Here, hybridization is usually performed under stringent conditions, for example, a salt concentration of 1 M or less and a temperature of 25°C or higher. For example, conditions of 5XSSPE (750 mM NaCl, 50 mM Na Phosphate, 5 mM EDTA, pH 7.4) and 25 to 30°C may be suitable for methylation-specific probe hybridization.

[0102] In the present invention, the probe refers to a hybridization probe, which means an oligonucleotide capable of sequence-specific binding to a complementary strand of a 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, in the presence of methylation among nucleic acid fragments derived from two individuals of the same species. In this case, the hybridization conditions must be sufficiently stringent to allow hybridization depending on the presence or absence of methylation, showing a significant difference in hybridization intensity. It is preferable that the central portion of the probe of the present invention aligns with the region of the methylation marker. The probe of the present invention can be used in a diagnostic kit or prediction method for detecting esophageal cancer or gastric cancer by detecting the level of DNA methylation.

[0103]

[0104] The present invention is from another perspective,

[0105] The present invention relates to a kit for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer, comprising any one of the above compositions.

[0106] 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 analysis method. In one embodiment, the kit of the present invention may be a kit including the essential elements required for performing PCR, and may further include a test tube or other appropriate container, a reaction buffer (with various pH and magnesium concentrations), deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC-water, and sterile water. In another embodiment, the kit of the present invention may be a kit for predicting blood statin concentrations including the essential elements required for performing a DNA chip, and the DNA chip kit may include a substrate to which a specific polynucleotide, primer, or probe for the methylation is attached, and the substrate may include a nucleic acid corresponding to a quantitative control gene or a fragment thereof.

[0107]

[0108] Example

[0109] Hereinafter, the present invention will be described in more detail through examples. These examples are intended solely to illustrate the present invention, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples.

[0110]

[0111] Example 1. Selection of esophageal squamous cell carcinoma (ESCC)-specific methylation regions from TCGA methylation 450K array data.

[0112] The degree of methylation was determined using Infinium Human Methylation 450K BeadChip array data (UCSC Xena, http: / xena.ucsc.edu) from The Cancer Genome Atlas (TCGA). DNA extracted from tissues is converted through bisulfite treatment, and DNA methylation can be confirmed through modification of cytosine bases. The degree of methylation can be determined for each region, and the beta value, which represents the degree of methylation, was used to select differentially methylated regions between ESCC tissues and surrounding normal tissues.

[0113] TCGA methylation 450k array data was divided into Train and Test groups as shown in Table 4, marker selection was performed using the Train group, and it was confirmed whether the discovered markers showed the same pattern in the Test set group.

[0114]

[0115] First, we exclude missing values ​​from an area of ​​about 480K,

[0116] To select ESCC-specific methylation regions, Limma (Linear Models for Microarray Data) software was used to select regions with an FDR value of less than 0.01 and an absolute delta beta of greater than 0.25, excluding sex chromosomes, resulting in 1,020 hypomethylated regions and 3,137 hypermethylated regions specific to ESCC.

[0117]

[0118] Example 2. Extracting methylated cfDNA from blood and performing next-generation sequencing (cfMeDIP-Seq)

[0119] Blood samples were collected from 68 patients with esophageal cancer and 283 healthy individuals (Table 3). The plasma was centrifuged for 10 minutes at 3000 rpm and 25℃ to separate the plasma. The plasma was then centrifuged for 10 minutes at 16000 g and 25℃ to separate the supernatant. Cell-free DNA was extracted from the separated plasma using the Chemagen DNA kit, and adaptor ligation was performed using the Truseq Nano DNA HT library prep kit (Illumina). 5mC immunoprecipitation was then performed using the antibody in the cfMeDIP kit (diagnode) at 10 rpm and 4℃ for 17 hours. After purification, PCR enrichment was performed using the Truseq Nano DNA HT library prep kit (Illumina) to create the final library. The produced library was sequenced using Novaseq 6000 (Illumina) in 150 paired-end mode, producing approximately 30 million reads per sample.

[0120]

[0121] Example 3. Selection of ESCC-specific methylation regions through cfMeDIP-Seq data analysis.

[0122] Since methylated cell-free nucleic acids were sequenced in Example 2, the obtained nucleic acid fragment data were methylated, and by aligning them to the human reference genome, methylated regions across the entire human genome could be identified. The MeDIP-Seq data represented methylated regions, and using normalized values ​​per 300 bp bin, differentially methylated regions were selected between the esophageal cancer and gastric cancer groups and the normal group.

[0123] The cfMeDIP-Seq data was divided into Train and Test groups as shown in Table 6 below, and the data set was split five times (Fig. 2). Marker selection was performed using the train group, and then markers found in two or more data sets were used.

[0124] That is, the process of isolating the test set into different combinations was repeated five times to create a dataset with five different combinations, and DMRs were discovered (Fig. 2). Additional marker selection work was then performed using markers discovered in two or more data sets among the DMRs.

[0125]

[0126] First, adapter trimming and quality trimming were performed on the fastq file 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, and nucleic acid fragments with a mapping quality of less than 10 were removed using the samtools view (version 1.11) tool. Then, only chr1~22, X, and Y were removed, and the sex chromosomes were excluded, and the read count values ​​were generated per 300 bp bin without overlapping.

[0127] Blacklist regions (Low_mappability_island, centromeric_repeat, etc.) and bins with a total read count of 10 or less in the total samples per bin were excluded.

[0128] Normalized values ​​per 300 bp bin (TMM normalized value) were generated using edgeR (Empirical Analysis of Digital Gene Expression Data in R) software.

[0129] Finally, edgeR software was used to select 544,553 hypermethylated regions and 443,967 hypomethylated regions specific to ESCC with an FDR value less than 0.05.

[0130] Next, 1,497 CpG sites that were identically found in the regions selected in Example 1 and those selected in cfMeDIP-seq and overlapped in at least two of the five cfMeDIP data sets were finally selected as ESCC-specific markers. Defining regions in 300-bp bin units, a total of 1,133 differentially methylated regions (DMRs) were selected.

[0131] Afterwards, an additional selection process was conducted using the group median difference values ​​of AUC and absolute delta beta value that divide tumor and normal to select major markers, and hypermethylated or hypomethylated regions in normal samples. Each criterion was selected as AUC ≥ 0.9, tumor and normal median beta value difference ≥ 0.2, and regions in normal samples with median beta value ≤ 0.2 or ≥ 0.8. Applying these criteria, 28 hypomethylated regions and 145 hypermethylated regions were selected.

[0132] In the case of overlapping regions with previously known methylation regions (WO 2019 / 195268 A2, WO 2019 / 199696 WO 2019 / 195268 A2, WO 2019 / 199696A1), only significant hypermethylated regions were added. The criteria for significance were the AUC that divides tumor and normal samples and the group median difference value of the absolute delta beta value, and the respective criteria were AUC 0.9 or higher and the tumor and normal median beta value difference 0.3 or higher. When only the major regions among the known regions were added by applying the criteria, 19 hypomethylated regions and 29 hypermethylated regions were selected (regions remaining after excluding the known regions: 12 hypermethylated regions, 19 hypomethylated regions, number of major regions among the known regions: 17 hypermethylated regions).

[0133] Finally, only regions with three or more CpG sites in the corresponding DMR were left, and 18 hypomethylated regions and 29 hypermethylated regions were selected. In terms of CpG sites, there were 21 hypomethylated regions and 53 hypermethylated regions.

[0134]

[0135] Example 4. Selection of ESCC-specific methylation regions from whole genome bisulfite sequencing (WGBS) data.

[0136] Whole genome bisulfite sequencing (WGBS) data (GSE149608, GSE186458, source: https: / www.ncbi.nlm.nih.gov / geo / ) were used to determine the degree of methylation. DNA extracted from tissues is converted through bisulfite treatment, and DNA methylation can be confirmed through modification of cytosine bases. The degree of methylation can be determined for each region, and the beta value, which represents the degree of methylation, was used to select differentially methylated regions between ESCC tissues, surrounding normal tissues, and normal blood samples.

[0137] The composition of the above data is as shown in Table 7.

[0138]

[0139] Differentially methylated cytosine analysis was performed using 10,405,333 CpG sites with a median beta value of less than 0.2 or greater than 0.8 in normal esophageal tissue samples and normal blood samples.

[0140] Marker identification was performed using the Leave One Out (LOO) method, which excludes all samples once and uses a T-test for each fold to obtain differentially methylated cytosine (DMC) (Fig. 3). For each fold, regions with a p-value of 0.01 or less and a median bata value difference of 0.05 or greater were selected.

[0141] 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.

[0142] After that, among the regions that were found to be identical in both normal tissue and normal blood samples compared to tumor tissue, 3,593 hypomethylated regions and 341 hypermethylated regions specific to ESCC were selected. After that, CpG sites that were repeated more than 10 times in the entire fold were selected, and 573 hypomethylated regions and 213 hypermethylated regions were selected. Then, the selected regions were extended to ±150 bp based on the CpG site to determine DMR.

[0143] In the case of overlapping regions with previously known methylation regions (WO 2019 / 195268 A2, WO 2019 / 199696 WO 2019 / 195268 A2, WO 2019 / 199696A1), only significant hypermethylated regions were added. The criteria for significance were the AUC that divides tumor and normal samples and the group median difference value of the absolute delta beta value, and the respective criteria were AUC 0.9 or higher and the tumor and normal median beta value difference 0.3 or higher. When only the major regions among the known regions were added by applying the criteria, 36 hypomethylated regions and 119 hypermethylated regions were selected (regions remaining after excluding the known regions: 100 hypermethylated regions, 36 hypomethylated regions, number of major regions among the known regions: 19 hypermethylated regions).

[0144] Finally, only the regions with three or more CpG sites in the corresponding DMR were left, and 17 hypomethylated regions and 118 hypermethylated regions were selected. In terms of CpG sites, there were 19 hypomethylated regions and 134 hypermethylated regions.

[0145]

[0146] Example 5. Selection of methylation regions specific to esophageal adenocarcinoma (EAC) from Infinium Human Methylation 450K BeadChip array data.

[0147] The degree of methylation was determined using Infinium Human Methylation 450K BeadChip array data (GSE72872, source: https: / www.ncbi.nlm.nih.gov / geo / ). DNA extracted from tissues is converted through bisulfite treatment, and DNA methylation can be confirmed through modification of cytosine bases. The degree of methylation can be determined for each region, and the beta value, which represents the degree of methylation, was used to select differentially methylated regions between EAC tissue and surrounding normal tissue, benign disease (gastroesophageal reflux disease), and normal blood samples.

[0148] The composition of the above data is as shown in Table 8.

[0149]

[0150] First, differentially methylated cytosine (DMC) analysis was performed using 225,287 CpG sites with median beta values ​​less than 0.2 or greater than 0.8 in normal esophageal tissue samples and normal blood samples.

[0151] Using Limma (Linear Models for Microarray Data) software, we selected regions with FDR values ​​less than 0.01 and absolute delta beta greater than 0.25, and then selected regions that were found to be identical in both normal tissue and normal blood samples compared to tumor tissue. As a result, excluding sex chromosomes, we selected 41,701 hypomethylated regions and 35,723 hypermethylated regions specific to EAC.

[0152] Next, among the regions with an Adjust P value less than 0.005, the top 500 hypermethylated regions and the top 100 hypomethylated regions were selected based on absolute delta beta. These regions were extended ±150 bp from the CpG site to obtain DMRs.

[0153] After that, for the overlapping regions with the known regions (WO 2019 / 195268 A2, WO 2019 / 199696 WO 2019 / 195268 A2, WO 2019 / 199696A1), only the significant hypermethylated regions were added. The criteria for significance were the AUC that divides the tumor and normal samples and the group median difference value of the absolute delta beta value, and the criteria for each were AUC 0.95 or higher and the tumor and normal median beta value difference 0.47 or higher. When only the major regions among the known regions were added by applying the criteria, 79 hypomethylated regions and 122 hypermethylated regions were selected (regions remaining after excluding the known regions: 102 hypermethylated regions, 79 hypomethylated regions, number of major regions among the known regions: hypermethylated regions 20).

[0154] Only regions with three or more CpG sites in the corresponding DMR were left, resulting in a final selection of 68 hypomethylated regions and 121 hypermethylated regions. In terms of CpG sites, there were 72 hypomethylated regions and 175 hypermethylated regions.

[0155]

[0156] Example 6. Selection of stomach adenocarcinoma-specific methylation regions from known data.

[0157] The degree of methylation was determined using Infinium Human Methylation 450K BeadChip array data from The Cancer Genome Atlas (TCGA) (UCSC Xena, http: / xena.ucsc.edu) and the Infinium Human Methylation 450K BeadChip array data from a published dataset (GSE72872). DNA extracted from tissues is converted through bisulfite treatment, and DNA methylation can be determined through modification of cytosine bases. The degree of methylation can be determined for each region, and the beta value, which represents the degree of methylation, was used to select differentially methylated regions between gastric cancer tissues and surrounding normal tissues and normal blood samples. The composition of the entire dataset is shown in Table 9 below.

[0158]

[0159] DMC analysis was performed using 230,897 CpG sites with a median beta value of less than 0.2 or greater than 0.8 in normal stomach tissue samples and normal blood samples.

[0160] Using Limma (Linear Models for Microarray Data) software, we selected regions with an FDR value of less than 0.01 and an absolute delta beta of more than 0.25. Then, among the regions that differed between normal tissue and normal blood sample compared to tumor tissue, we selected regions that were found to be identical in both cases. After excluding sex chromosomes, we first selected 50,324 hypomethylated regions and 42,214 hypermethylated regions specific to gastric cancer.

[0161] Afterwards, among the regions with an Adjust P value of less than 0.005, the top 500 hypermethylated regions and the top 100 hypomethylated regions were selected based on absolute delta beta, and then the regions were extended by ±150 bp based on the CpG site to obtain DMRs.

[0162] In addition, for regions overlapping with known regions (WO 2019 / 195268 A2, WO 2019 / 199696 WO 2019 / 195268 A2, WO 2019 / 199696A1), only significant hypermethylated regions were added. The criteria for significance were the AUC that divides tumor and normal samples and the group median difference of the absolute delta beta value, and the respective criteria were AUC 95 or higher and the tumor and normal median beta value difference 0.4 or higher. When only the major regions among the known regions were added by applying the criteria, 76 hypomethylated regions and 132 hypermethylated regions were selected (regions remaining after excluding the known regions: 107 hypermethylated regions, 76 hypomethylated regions, number of major regions among the known regions: 25 hypermethylated regions).

[0163] Only regions with three or more CpG sites in the corresponding DMR were left, and 60 hypomethylated regions and 132 hypermethylated regions were finally selected. In terms of CpG sites, there are 70 hypomethylated regions and 179 hypermethylated regions.

[0164]

[0165] Example 7. Selection of final marker candidates

[0166] 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 then a total of 646 CpG sites, excluding overlapping regions, were selected as a final candidate group of markers for diagnosing esophageal and gastric cancer and predicting radiotherapy responsiveness.

[0167] The list of 646 selected methylation markers is shown in Table 10 below.

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193] Example 8. Final marker selection and performance verification

[0194] 8-1. A. cfDNA Enzymatic Methylation sequencing (cfEM-seq)

[0195] Blood samples were collected from 48 patients with esophageal cancer and 32 normal individuals, and then the plasma portion was centrifuged for the first time under the conditions of 3000 rpm, 25℃, and 10 minutes. The plasma from the first centrifugation was centrifuged for the second time under the conditions of 16000g, 25℃, and 10 minutes to separate the plasma. After that, cell-free DNA was extracted using the Mag-bind cfDNA kit (Omega).

[0196] The concentration of extracted cfDNA was measured using the Qubit DS DNA HS assay Kit (Thermo Fisher Scientific, USA), and the cfDNA size was confirmed using the Tapestation 4200 (Agilent, USA).

[0197] The maximum amount of extracted cfDNA was used, and a library was prepared by performing methylation conversion through the process of substituting unmethylated cytosine with uracil using ten-eleven translocation dioxygenase 2 (TET2) and APOBEC using enzymatic methyl-seq (NEB Kit). The concentration and size of the prepared DNA library were measured using Qubit DS DNA HS assay Kit (Thermo Fisher Scientific, USA) and Tapestation 4200 (Agilent, USA), respectively.

[0198] 250 ng of the library was pooled into groups of 8 samples, hybridization was performed, and the concentration of the captured sample was measured using the Qubit DS DNA HS assay Kit (Thermo Fisher Scientific, USA). The captured DNA size was confirmed using the High sensitivity D1000 screen tape & Reagent (Agilent, USA) with the Tapestation 4200 (Agilent, USA).

[0199] Sequencing was performed using a Miseq Dx (Illumina) instrument in 150 paired-end mode with a final concentration of 11 pM. A depth of 500X was achieved per sample.

[0200] 8-2. Final marker selection and performance verification

[0201] Forty-eight ESCC patients and 32 normal individuals were used, divided into a discovery set and an external set. Markers were selected from the discovery set, and performance was measured from the external set. The data structure is shown in Table 12 below. Among the external ESCC samples, there were 15 patients in complete remission (GR0) and 17 patients who did not achieve complete remission (non-GR0).

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] Methylation scores were calculated using the methylation values ​​of the selected regions, and performance was measured based on these scores. Methylation scores were defined using the alpha value per DNA fragment for reads present in hypermethylated and hypomethylated regions of esophageal and gastric cancer.

[0219] That is, the alpha value of a fragment unit is calculated as the methylation ratio of the CpG sites present in the fragment. For example, if 9 out of 10 CpG sites present in a fragment are methylated, the alpha value of the fragment is 90%. The score was calculated using fragments present in the entire panel, and the relative frequency of fragments with an alpha value of 80% or more for hypermethylated regions and 20% or less for hypomethylated regions was calculated.

[0220] That is, the methylation score was calculated using Equation 1 below.

[0221] Formula 1:

[0222]

[0223] Changes in methylation scores according to treatment response were confirmed using samples taken before treatment (V1) and one month after treatment (V3).

[0224] As a result, as described in Fig. 5, in the 15 Gr0 and 17 non-Gr0 samples, it was confirmed that 12 Gr0 samples and 7 non-Gr0 samples exceeded the cutoff (methylation score: 0.05) of Normal 90% in V1, which means that in V3, 25% (3 / 12) of the samples fell below the cutoff in the case of Gr0, and 57% (4 / 7) of the samples existed above the cutoff in the case of non-Gr0.

[0225] In addition, based on the normal 95% cutoff (methylation score: 0.054), it was confirmed that 10 samples in Gr0 and 6 samples in non-Gr0 exceeded the cutoff in V1. This means that 60% (6 / 10) of the samples fell below the cutoff in Gr0 in V3, and 67% (4 / 6) of the samples fell above the cutoff in non-Gr0.

[0226] That is, if a patient whose level was above the baseline before radiation therapy achieved complete remission one month after radiation therapy, it means that the patient's level fell below the baseline, indicating a high level of radiation therapy response. In contrast, a patient who did not achieve complete remission was above the baseline, indicating a low level of radiation therapy response.

[0227]

[0228] 8-3. Minimum Marker Selection and Performance Verification

[0229] Among 497 regions, there were 345 hypermethylated regions, and among them, regions with a methylation beta value of 20 or less and an SD of 5 or less in normal and a median coverage of 100 or more in the discovery set were selected (region: 333 / CpG sites: 7,427).

[0230] The list of 333 selected hypermethylated regions is shown in Table 14 below.

[0231]

[0232]

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

[0241]

[0242]

[0243] Alpha values ​​were calculated for fragments present in the hypermethylated region, 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.

[0244] Changes in methylation scores according to treatment response were confirmed using samples taken before treatment (V1) and one month after treatment (V3).

[0245] As a result, as described in Fig. 6, it was confirmed that 15 Gr0, 17 non-Gr0, and 11 non-Gr0 samples exceeded the cutoff in V1 based on the Normal 90% cutoff (methylation score: 0.0073), which means that 72% (8 / 11) of the samples fell below the cutoff in V3 for Gr0, and 75% (6 / 8) of the samples fell above the cutoff in non-Gr0.

[0246] In addition, based on the normal 95% cutoff (methylation score: 0.0075), it was confirmed that 11 samples in Gr0 and 8 samples in non-Gr0 exceeded the cutoff in V1. This means that 72% (8 / 11) of the samples fell below the cutoff in Gr0 in V3, and 75% (6 / 8) of the samples fell above the cutoff in non-Gr0.

[0247] In addition, among the cancer hypermethylation CpG sites present in the panel, the discovery set was used to select the CpG sites with the greatest differences between cancer and normal. That is, using the beta value of each CpG site, five regions with an AUC higher than 0.8 for dividing cancer and normal and a median difference value higher than 0.05 between the cancer and normal groups were selected as minimum markers.

[0248] The list is shown in Table 15 below.

[0249]

[0250] The methylation score was calculated using fragments present in five hypermethylated regions. That is, the alpha value was calculated using fragments present in the hypermethylated regions, and the relative frequency of fragments with an alpha value of 80% or higher was calculated: number of fragments 80% or higher / total number of fragments.

[0251] Changes in methylation scores according to treatment response were confirmed using samples taken before treatment (V1) and one month after treatment (V3).

[0252] As a result, as described in Fig. 7, it was confirmed that 15 Gr0, 17 non-Gr0, and 10 non-Gr0 samples exceeded the cutoff in V1 based on the Normal 90% cutoff (methylation score: 0.0076), which means that 80% (8 / 10) of the samples fell below the cutoff in V3 for Gr0, and 70% (7 / 10) of the samples fell above the cutoff in V3 for non-Gr0.

[0253] In addition, based on the normal 95% cutoff (methylation score: 0.0085), it was confirmed that 10 samples in Gr0 and 10 samples in non-Gr0 exceeded the cutoff in V1, which means that 77% (7 / 9) of the samples fell below the cutoff in Gr0 in V3, and 66% (6 / 9) of the samples fell above the cutoff in non-Gr0.

[0254]

[0255] While specific aspects of the present invention have been described in detail above, it will be apparent to those skilled in the art that these specific descriptions merely represent preferred embodiments and are not intended to limit the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the appended claims and their equivalents.

[0256]

[0257] The DNA methylation marker for predicting esophageal cancer and gastric cancer radiation therapy response according to the present invention can predict the radiation therapy response of esophageal cancer and gastric cancer patients with high accuracy using only DNA methylation information of blood samples without using esophageal cancer and gastric cancer tissue samples, and thus can be usefully utilized in establishing treatment strategies for esophageal cancer and gastric cancer patients.

Claims

1. A combination of DNA methylation markers for predicting esophageal cancer and gastric cancer radiotherapy response, including DNA methylation markers shown in Table 1 below. [Table 1] 2. A combination of DNA methylation markers for diagnosing lung cancer, characterized in that in the first paragraph, the combination of DNA methylation markers for predicting esophageal cancer and gastric cancer radiation therapy response additionally includes DNA markers shown in Table 2 below. [Table 2] 3. In the second paragraph, the combination of DNA methylation markers for predicting esophageal cancer and gastric cancer radiotherapy response is characterized in that it additionally includes two or more DNA methylation markers selected from the group consisting of DNA markers shown in Table 3 below. [Table 3] 4.(a) A step of isolating DNA from a biological sample; (b) a step of detecting the methylation level of the DNA methylation marker combination of any one of claims 1 to 3 using a primer composition comprising a combination of primers capable of amplifying each of the DNA methylation markers of the DNA methylation marker combination of any one of claims 1 to 3 and / or a probe composition comprising a combination of probes capable of specifically hybridizing with a polynucleotide comprising 10 or more consecutive bases containing the methylated base of the DNA methylation marker combination of any one of claims 1 to 3 or a complementary polynucleotide thereof; and (c) a step of determining that the treatment response to esophageal cancer and gastric cancer is low when the detected DNA methylation marker level is higher than the cut-off value; A method for providing information for predicting responsiveness to radiation therapy for esophageal cancer and gastric cancer. 5.(a) A step of isolating DNA from a biological sample; (b) a step of detecting the methylation level of the DNA methylation marker combination of any one of claims 1 to 3 using a primer composition comprising a combination of primers capable of amplifying each of the DNA methylation markers of the DNA methylation marker combination of any one of claims 1 to 3 and / or a probe composition comprising a combination of probes capable of specifically hybridizing with a polynucleotide comprising 10 or more consecutive bases containing the methylated base of the DNA methylation marker combination of any one of claims 1 to 3 or a complementary polynucleotide thereof; and (c) a step of determining that the treatment response to esophageal cancer and gastric cancer is low when the detected DNA methylation marker level is higher than the cut-off value; A method for predicting esophageal cancer and gastric cancer radiation therapy response, including:

6. A method according to claim 4 or 5, characterized in that the detection of the methylation level in step (b) uses any one method selected from the group consisting of PCR, methylation specific PCR, real time methylation specific PCR, PCR using methylation DNA-specific binding protein, quantitative PCR, PCR using methylation-specific PNA, melting curve analysis, DNA chip, pyrosequencing, bisulfite sequencing, and methylation next-generation base sequence sequencing.

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

8. A method according to claim 4 or 5, characterized in that step (c) is performed by a method including the following steps: (ci) a step of calculating a methylation score using the following formula 1; and Equation 1: (ci) A step for determining whether it is esophageal cancer or gastric cancer when the calculated methylation score exceeds the reference value.

9. A primer composition for predicting esophageal cancer and gastric cancer radiotherapy response, comprising a combination of primers capable of amplifying each of the DNA methylation markers of any one of claims 1 to 3.

10. A probe composition for predicting esophageal cancer and gastric cancer radiotherapy response, comprising a combination of probes that can specifically hybridize with a polynucleotide comprising 10 or more consecutive bases containing a methylated base of a combination of DNA methylation markers according to any one of claims 1 to 3, or a complementary polynucleotide thereof.

11. A kit for predicting esophageal cancer and gastric cancer radiotherapy response comprising the composition of claim 9 or 10.

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