Composition for predicting risk of developing liver cancer

Abstract

The present application relates to a composition for predicting the risk of developing liver cancer.

Description

[Technical Field]

[0001] The present application relates to a composition for predicting the risk of developing liver cancer. [Background technology]

[0002] Although the numerous cells in the human body possess the same DNA base sequence, the morphology and function of each cell are highly diverse. This is because specific genes are expressed in each cell, and as a result, each cell undergoes a different differentiation process, resulting in each cell possessing an independent phenotype. Various factors, such as DNA methylation, histone modification, and tissue-specific transcription factors, are involved in the differential gene expression in each cell.

[0003] Liquid biopsy is a non-invasive or minimally invasive method of obtaining and testing samples. It places less strain on the subject than conventional tissue biopsies, allows for continuous monitoring through frequent testing, and does not pose the risk of radiation exposure, a side effect of imaging medical methods.

[0004] The survival rate of cancer patients varies significantly depending on the stage of the disease at the time of diagnosis, and treatment based on early detection of cancer can ensure a high survival rate for patients. In the case of liver cancer, the incidence rate is currently increasing worldwide, and most patients are diagnosed with late-stage cancer. Therefore, the development of a method for early diagnosis of liver cancer is considered a technology that can bring social and economic benefits. Summary of the Invention [Problem to be solved by the invention]

[0005] An example of the purpose of the present application is to provide a composition for predicting the risk of developing liver cancer or a method for providing information for predicting the risk of developing liver cancer, which uses liver cancer-specific biomarkers to predict the risk of developing liver cancer and can detect the markers from blood-derived cell-free DNA (cfDNA). [Means for solving the problem]

[0006] One example of the present application relates to a composition for predicting the risk of developing liver cancer, which comprises a preparation capable of measuring the methylation level of at least one of the CpG sites contained in the sequence region from 21810279 to 21810792 of human chromosome 12, the sequence region from 29298021 to 29298631 of human chromosome 17, or the sequence region from 19738547 to 19739846 of human chromosome 19 in a biological sample.

[0007] Another example of the present application relates to a method for providing information for predicting the risk of developing liver cancer, or a method for predicting the risk of liver cancer, which includes measuring the methylation level of at least one CpG site contained in the sequence region of positions 21810279 to 21810792 of human chromosome 12, the sequence region of positions 29298021 to 29298631 of human chromosome 17, or the sequence region of positions 19738547 to 19739846 of human chromosome 19 in a biological sample isolated from a subject.

[0008] The present application will now be described in more detail.

[0009] As used herein, the term "methylation" refers to the attachment of a methyl group to a base that makes up DNA. For example, methylation can occur at a cytosine at a specific CpG site in a particular gene or nucleic acid.

[0010] As used herein, the term "presence or absence of methylation" or "methylation state" refers to the presence or absence of methylation of cytosine at a specific CpG site of a specific gene or nucleic acid. Specifically, it refers to the presence or absence of 5-methyl-cytosine at one or more CpG dinucleotides within a base sequence. For example, it may refer to the presence or absence of methylation of cytosine at one or more CpG sites contained in the sequence represented by SEQ ID NO: 1, the sequence represented by SEQ ID NO: 2, or the sequence represented by SEQ ID NO: 3.

[0011] As used herein, the term "methylation level" or "degree of methylation" refers to the amount of methylation present in a base sequence within the sequence represented by SEQ ID NO: 1, the sequence represented by SEQ ID NO: 2, or the sequence represented by SEQ ID NO: 3.

[0012] As used herein, the term "CpG site" or "CpG sequence" refers to a CpG site present in the base sequence of a specific gene or nucleic acid. The gene is a concept that includes all of the structural units necessary for expression that are operably linked to each other, and may include, for example, a promoter region, a protein-coding region (open reading frame, ORF), and a terminator region. Therefore, the CpG site may be present in the promoter region, protein-coding region (open reading frame, ORF), or terminator region of the gene. For example, it may be a CpG site present in the promoter region of the gene.

[0013] As used herein, the term "nucleic acid" refers to a polymeric form of nucleotide, either ribonucleotides or deoxyribonucleotides, and includes polynucleotides, oligonucleotides, oligomers, oligos, coding sequences, etc. The term "nucleic acid" can be used to refer to single-stranded, double-stranded, or multi-stranded DNA or RNA, genomic DNA, cDNA, DNA-RNA hybrids, or polymers containing purine and pyrimidine bases, or other natural, chemically or biochemically modified, non-natural, or derivatized nucleotide bases. For example, the nucleic acid may contain at least one CpG site contained in a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, and may have a continuous partial sequence of the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. For example, the nucleic acid may be a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3.

[0014] In this specification, when an element is described as "comprising," this means that it may further include other elements, rather than excluding other elements, unless otherwise specified.

[0015] Unless otherwise indicated herein, nucleic acids are written from left to right and 5' to 3', respectively.

[0016] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0017] One example of the present application relates to a composition for predicting the risk of developing liver cancer, comprising a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region from 21810279 to 21810792 of human chromosome 12, the sequence region from 29298021 to 29298631 of human chromosome 17, or the sequence region from 19738547 to 19739846 of human chromosome 19 in a biological sample.

[0018] Specifically, a composition according to one example of the present application may include a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region of positions 21810279 to 21810792 of human chromosome 12 in a biological sample, and may additionally include a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region of positions 29298021 to 29298631 of human chromosome 17; and / or a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region of positions 19738547 to 19739846 of human chromosome 19. According to the examples of the present application, the methylation level of at least one CpG site included in the sequence region of bases 21810279 to 21810792 of human chromosome 12, the methylation level of at least one CpG site included in the sequence region of bases 29298021 to 29298631 of human chromosome 17, and / or the methylation level of at least one CpG site included in the sequence region of bases 19738547 to 19739846 of human chromosome 19 were all measured, resulting in improved tumor coverage and improved accuracy in predicting liver cancer risk.

[0019] The composition may additionally comprise a preparation capable of measuring the concentration of alpha fetoprotein (AFP) in the biological sample.

[0020] The sequence region may be contained in cell-free DNA (cfDNA).

[0021] In one example of the present application, a "biomarker" or a "marker" is a generic term for a nucleic acid containing at least one CpG site contained in the sequence region from 21810279 to 21810792 of human chromosome 12, the sequence region from 29298021 to 29298631 of human chromosome 17, or the sequence region from 19738547 to 19739846 of human chromosome 19.

[0022] The at least one CpG site contained in the sequence region of bases 21810279 to 21810792 of human chromosome 12 may be a CpG site located at bases 21810279 to 21810280 of human chromosome 12, a CpG site located at bases 21810450 to 21810451 of human chromosome 12, a CpG site located at bases 21810458 to 21810459 of human chromosome 12, a CpG site located at bases 21810489 to 21810490 of human chromosome 12, a CpG site located at bases 2181060 The CpG site may be one or more selected from the group consisting of a CpG site located at positions 0 to 21810601 of the sequence, a CpG site located at positions 21810750 to 21810751 of the sequence, a CpG site located at positions 21810759 to 21810760 of the sequence, and a CpG site located at positions 21810791 to 21810792 of the sequence. For example, the CpG site may include a CpG site located at positions 21810750 to 21810751 of human chromosome 12. The sequence region at positions 21810279 to 21810792 of human chromosome 12 may have the sequence represented by SEQ ID NO: 1. Herein, a nucleic acid containing at least one CpG site contained in the sequence region at positions 21810279 to 21810792 of human chromosome 12 is referred to as "marker 1" or "marker 1." The nucleic acid may have a continuous partial sequence of the sequence region.For example, the sequence region from 21810279 to 21810792 of human chromosome 12 specifically includes the sequence region from 21810279 to 21810760 of human chromosome 12, the sequence region from 21810279 to 21810751 of human chromosome 12, the sequence region from 21810450 to 21810792 of human chromosome 12, the sequence region from 21810450 to 21810760 of human chromosome 12, the sequence region from 21810450 to 21810751 of human chromosome 12, the sequence region from 21810458 to 21810792 of human chromosome 12, the sequence region from 21810458 to 21810760 ... The sequence region may be the 751th sequence region, the 21810489 to 21810792nd sequence region, the 21810489 to 21810760th sequence region, the 21810489 to 21810751th sequence region, the 21810600 to 21810792nd sequence region, the 21810600 to 21810760th sequence region, the 21810600 to 21810751th sequence region, the 21810750 to 21810792nd sequence region, the 21810750 to 21810760th sequence region, or the 21810750 to 21810751th sequence region.

[0023] The at least one CpG site included in the sequence region of positions 29298021 to 29298631 of human chromosome 17 may include a CpG site located at positions 29298115 to 29298116, 29298118 to 29298119, 29298124 to 29298125, 29298136 to 29298137, 29298142 to 29298143, or 29298184 to 29298185 of human chromosome 17. The sequence region of positions 29298021 to 29298631 of human chromosome 17 may be represented by SEQ ID NO: 2. Herein, a nucleic acid containing at least one CpG site contained in the sequence region from positions 29298021 to 29298631 of human chromosome 17 is designated as "marker 2." The nucleic acid may have a continuous partial sequence of the sequence region.

[0024] The at least one CpG site contained in the sequence region between positions 19738547 and 19739846 of human chromosome 19 may include a CpG site located in the sequence between positions 19739407 and 19739408 of human chromosome 19. The sequence region between positions 19738547 and 19739846 of human chromosome 19 may be represented by SEQ ID NO: 3. Herein, a nucleic acid containing at least one CpG site contained in the sequence region between positions 19738547 and 19739846 of human chromosome 19 is designated "marker 3" or "marker 3." The nucleic acid may have a continuous partial sequence of the sequence region.

[0025] In one embodiment of the present application, a "target methylation site" is a site of interest for which the presence or absence of methylation of a biomarker according to one embodiment of the present application is to be confirmed, and can include at least one CpG sequence. The size of the target methylation site can be 5 to 150 bp, 5 to 140 bp, 5 to 130 bp, 5 to 120 bp, 5 to 110 bp, 5 to 100 bp, 5 to 90 bp, 5 to 80 bp, 5 to 70 bp, 5 to 60 bp, 5 to 50 bp, 5 to 40 bp, 5 to 30 bp, 5 to 20 bp, 5 to 15 bp, 5 to 10 bp, 10 to 150 bp, 10 to 140 bp, 10 to 130 bp, 10 to 120 bp, 10 to 110 bp, 10 to 100 bp, 10 ...10 to 150 bp, 10 to 140 bp, 10 to 130 bp, 10 to 120 bp, 10 to 110 bp, 10 0bp, 10~80bp, 10~70bp, 10~60bp, 10~50bp, 10~40bp, 10~30bp, 10~20bp, 10~15bp, 20~150bp, 20~140bp, 20~130bp, 20 ~120bp, 20~110bp, 20~100bp, 20~90bp, 20~80bp, 20~70bp, 20~60bp, 20~50bp, 20~40bp, 20~30bp, 20~25bp, 30~150bp, 30~140bp, 30~130bp, 30~120bp, 30~110bp, 30~100bp, 30~90bp, 30~80bp, 30~70bp, 30~60bp, 30~50bp, 30~40bp, 30~3 5bp, 40~150bp, 40~140bp, 40~130bp, 40~120bp, 40~110bp, 40~100bp, 40~90bp, 40~80bp, 40~70bp, 40~60bp, 40~50bp, It can be 40-45bp, 50-150bp, 50-140bp, 50-130bp, 50-120bp, 50-110bp, 50-100bp, 50-90bp, 50-80bp, 50-70bp, 50-60bp, 50-55bp, 60-150bp, 60-140bp, 60-130bp, 60-120bp, 60-110bp, 60-100bp, 60-90bp, 60-80bp, 60-70bp, or 60-65bp.

[0026] The target methylation site may include some or all of the CpG sites present in the biomarker according to one example of the present application, and may include, for example, CpG sites located at positions 21810750 to 21810751 of human chromosome 12, positions 29298115 to 29298116, 29298118 to 29298119, 29298124 to 29298125, 29298136 to 29298137, 29298142 to 29298143, or 29298184 to 29298185 of human chromosome 17, or positions 19739407 to 19739408 of human chromosome 19.

[0027] The term "methylation marker for liver cancer risk" as used herein refers to a methylation marker that has a specific methylation level only in DNA isolated from a sample derived from a subject at risk of liver cancer, compared to the methylation level of DNA isolated from a sample derived from a subject not at risk of liver cancer. For example, the methylation marker for liver cancer risk may be a marker that has a high methylation level only in DNA isolated from a sample derived from a subject at risk of liver cancer, compared to the methylation level of DNA isolated from a sample derived from a subject not at risk of liver cancer.

[0028] The sequence represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3 may be a methylation marker for liver cancer risk. Specifically, at least one CpG site contained in the liver cancer marker represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3 may have a high methylation level in liver cancer-derived samples and a low methylation level in other samples, including normal liver.

[0029] For example, if the methylation rate of a biomarker according to one example of the present application in the biological sample is 5% or more, 10% or more, 20% or more, 25% or more, or 30% or more, the subject from whom the biological sample was derived can be determined to be at risk of developing liver cancer. The methylation rate may be specifically expressed as a b-value.

[0030] The preparation capable of measuring the methylation level may be a primer for amplifying a target methylation site of a biomarker according to one example of the present application.

[0031] The primers include a forward primer and a reverse primer, and one of the forward primer and the reverse primer can include a methylated primer and an unmethylated primer.

[0032] The methylated primer contains at least one CpG recognition site that recognizes a CpG sequence contained in a sequence of a predetermined length that includes the target methylation site, and the unmethylated primer contains at least one TpG recognition site that recognizes a TpG sequence contained in the sequence of a predetermined length that includes the target methylation site, and the TpG sequence in the sequence of a predetermined length that includes the target methylation site is a converted unmethylated CpG sequence in the sequence of a predetermined length that includes the target methylation site. The predetermined length can be, for example, 5,000 bp or less, 4,500 bp or less, 4,000 bp or less, 3,500 bp or less, 3,000 bp or less, 2,500 bp or less, 2,000 bp or less, 1,500 bp or less, 1,400 bp or less, 1,300 bp or less, less than 1,300 bp, 1,200 bp or less, 1,100 bp or less, 1,000 bp or less, 900 bp or less, 800 bp or less, 700 bp or less, 650 bp or less, 620 bp or less, 611 bp or less, 610 bp or less, 600 bp or less, 550 bp or less, 500 bp or less, 450 bp or less, 400 bp or less, 350 bp or less, 330 bp or less, 329 bp or less, 300 bp or less, 250 bp or less, 200 bp or less, 150 bp or less, 100 bp or less, or 500 bp or less. In this case, even if the lower limit of the predetermined length is not specified, a skilled artisan can clearly set the predetermined length to measure the methylation level of the target methylation site, and it may be, for example, 2 bp or more, 5 bp or more, 10 bp or more, 20 bp or more, 30 bp or more, 40 bp or more, or 50 bp or more, but is not limited thereto.

[0033] The TpG sequence may be converted from an unmethylated CpG sequence by a compound that converts methylated CpG sequences and unmethylated CpG sequences differently. For example, the compound may convert an unmethylated cytosine residue to thymine. For example, the compound may be one or more selected from the group consisting of sulfite, bisulfite, hydrogen sulfite, and disulfite.

[0034] When a template strand, for example, a sequence represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, is treated with a preparation that alters methylated and unmethylated CpG sequences to be different from each other, if the CpG cytosine in the template strand is methylated, the CpG cytosine is not converted to thymine, and if the CpG cytosine is unmethylated, the CpG cytosine is converted to thymine.

[0035] The CpG recognition site of the methylation primer can recognize a CpG sequence of a methylated nucleic acid, for example, the CpG recognition site can include a CG sequence.

[0036] The TpG recognition site of the unmethylated primer can recognize a TpG sequence obtained by converting a CpG sequence of an unmethylated nucleic acid into a TpG sequence by an agent that transforms the methylated nucleic acid and the unmethylated nucleic acid differently. For example, the TpG recognition site can include a TG sequence.

[0037] The CpG recognition site and the TpG recognition site are located near the 3'-end of the methylated primer and the unmethylated primer, respectively, and can specifically recognize methylated nucleic acids and unmethylated nucleic acids converted by a compound that differentially transforms methylated nucleic acids and unmethylated nucleic acids, respectively. For example, the CpG recognition site and the TpG recognition site can be located within 40, 35, 30, 25, 20, 10, 9, 8, 7, 6, 5, 4, 3, or 2 bases from the 3'-end of the methylated primer and the unmethylated primer, or at the 3'-end.

[0038] Because the unmethylated primer has a lower Tm value than the methylated primer, additional nucleotides can be added to the 5' end of the unmethylated primer to compensate for this, thereby designing the methylated and unmethylated primers to have similar Tms. For example, the unmethylated primer may be larger than the methylated primer due to the additional nucleotides at its 5' end. For example, the unmethylated primer may contain 1 to 5, 1 to 4, 1 to 3, 1 to 2, or 1 additional nucleotide at its 5' end compared to the methylated primer. For example, the Tm difference between the methylated primer and the unmethylated primer may be 15°C or less, 14°C or less, 13°C or less, 12°C or less, 11°C or less, 10°C or less, 9°C or less, 8°C or less, 7°C or less, 6°C or less, 5°C or less, 4°C or less, 3°C or less, 2°C or less, or 1.5°C or less.

[0039] The primers include a forward primer and a reverse primer. When the methylated primer and the unmethylated primer are forward primers, the reverse primer is a reverse primer. When the methylated primer and the unmethylated primer are reverse primers, the reverse primer is a forward primer. The reverse primer can bind to both methylated and unmethylated nucleic acids. Therefore, the reverse primer may not contain a CpG binding site. However, if the CpG binding site is unavoidable due to the structure of the nucleic acid, the reverse primer may contain 5 or less, 4 or less, 3 or less, 2 or less, 1 to 5, 1 to 4, 1 to 3, 1 to 2, or for example, 1 CpG binding site at the 5' end. However, even in this case, it is preferable that the reverse primer be designed to have a similar Tm to the methylated primer and the unmethylated primer.

[0040] The methylated primer, the unmethylated primer, and the opposite primer may be sized to contain 15 to 40, 15 to 35, 15 to 30, 15 to 25, 18 to 40, 18 to 35, 18 to 30, 18 to 25, 20 to 40, 20 to 35, 20 to 30, or 20 to 25 bases.

[0041] The methylated primer, the unmethylated primer, and the reverse primer may contain one or more, two or more, or three or more non-CpG cytosines, such that they are capable of binding to nucleic acids converted by an agent that differentially transforms methylated and unmethylated nucleic acids.

[0042] The Tm of the methylated primer, the unmethylated primer, and the opposite primer can be 50 to 80°C, 50 to 75°C, 50 to 70°C, 50 to 65°C, 55 to 80°C, 55 to 75°C, 55 to 70°C, 55 to 65°C, 60 to 80°C, 60 to 75°C, 60 to 70°C, or 60 to 65°C.

[0043] The composition according to one example of the present application may comprise a ratio of the methylated primer and the unmethylated primer of 100:1 to 1:100, 100:1 to 1:50, 100:1 to 1:20, 100:1 to 1:10, 100:1 to 1:5, 100:1 to 1:1, 100:1 to less than 1:1, 100:1 to 1.3:1, 100:1 to 1.5:1, 100:1 to 2:1, 100:1 to 2.5:1, 100:1 to 3:1, 100:1 to 3.5:1, 100:1 to 4: 1, 50:1 to 1:100, 50:1 to 1:50, 50:1 to 1:20, 50:1 to 1:10, 50:1 to 1:5, 50:1 to 1:1, 50:1 to less than 1:1, 50:1 to 1.3:1, 50:1 to 1.5:1, 50:1 to 2:1, 50:1 to 2.5:1, 50:1 to 3:1, 50:1 to 3.5:1, 50:1 to 4:1, 10:1 to 1:100, 10:1 to 1:50, 10:1 to 1:20, 10:1 to 1:10, 10:1 to 1:5, 1 0:1 to 1:1, 10:1 to less than 1:1, 10:1 to 1.3:1, 10:1 to 1.5:1, 10:1 to 2:1, 10:1 to 2.5:1, 10:1 to 3:1, 10:1 to 3.5:1, 10:1 to 4:1, 5:1 to 1:100, 5:1 to 1:50, 5:1 to 1:20, 5:1 to 1:10, 5:1 to 1:5, 5:1 to 1:1, 5:1 to less than 1:1, 5:1 to 1.3:1, 5:1 to 1.5:1, 5:1 to 2:1, 5:1 to 2.5:1, 5:1 The concentration ratio may be less than 3:1, 5:1 to 3.5:1, 5:1 to 4:1, 4.5:1 to 1:100, 4.5:1 to 1:50, 4.5:1 to 1:20, 4.5:1 to 1:10, 4.5:1 to 1:5, 4.5:1 to 1:1, 4.5:1 to less than 1:1, 4.5:1 to 1.3:1, 4.5:1 to 1.5:1, 4.5:1 to 2:1, 4.5:1 to 2.5:1, 4.5:1 to 3:1, 4.5:1 to 3.5:1, or 4.5:1 to 4:1.

[0044] For example, the concentration of the unmethylated primer may be 100% or less, less than 100%, 99.99% or less, 99.95% or less, 99.9% or less, 99.5% or less, 99% or less, 98% or less, 97% or less, 96% or less, 95% or less, 90% or less, 85% or less, 80% or less, 75% or less, 70% or less, 65% or less, 60% or less, 55% or less, 50% or less, 40% or less, 30% or less, or 25% or less of the concentration of the methylated primer, and as an example, 50% or less.

[0045] Another example of the present application relates to a kit for predicting the risk of developing liver cancer, comprising a composition for predicting the risk of developing liver cancer according to an example of the present application.

[0046] Another example of the present application relates to a method for predicting the risk of developing liver cancer or a method for providing information for predicting the risk of developing liver cancer, comprising measuring the methylation level of at least one CpG site contained in a sequence region of positions 21810279 to 21810792 of human chromosome 12, a sequence region of positions 29298021 to 29298631 of human chromosome 17, or a sequence region of positions 19738547 to 19739846 of human chromosome 19 in a biological sample isolated from a subject.

[0047] Specifically, a method according to one example of the present application includes a step of measuring the methylation level of at least one CpG site included in a sequence region of bases 21810279 to 21810792 of human chromosome 12 in a biological sample, and may additionally include a step of measuring the methylation level of at least one CpG site included in a sequence region of bases 29298021 to 29298631 of human chromosome 17 in the biological sample; and / or a step of measuring the methylation level of at least one CpG site included in a sequence region of bases 19738547 to 19739846 of human chromosome 19 in the biological sample.

[0048] The step of measuring the methylation level can be performed by a method selected from the group consisting of PCR, methylation-specific PCR, real-time methylation-specific PCR, MethyLight PCR, MethyLight digital PCR, EpiTYPER, PCR using a methylated DNA-specific binding protein, quantitative PCR, MS-HRM (Methylation-sensitive high-resolution melting), DNA chip, molecular beacon, next-generation sequencing (NGS) panel, pyrosequencing, and bisulfite sequencing. For example, the step of measuring the methylation level can be performed using PCR and / or MS-HRM analysis using a combination of methylated and unmethylated primers established in the present application.

[0049] For example, the methylation level can be identified by a microarray, which can utilize probes immobilized on a solid surface, and the probes can include a sequence complementary to a sequence of 10 to 100 consecutive nucleotides containing the CpG site.

[0050] The method according to an example of the present application may further include a step of quantifying the degree of methylation. The quantifying step may involve quantifying the degree of methylation of the biomarker contained in the biological sample by comparing the area under the melting curve (AUMC) of the melting curves of the biological sample, a sample according to an example of the present application in which the biomarker is 100% methylated, and a sample according to an example of the present application in which the biomarker is 100% unmethylated. The melting curves may be normalized.

[0051] Specifically, the quantifying step may include obtaining a melt curve of the biological sample; obtaining a normalized melt curve of the biological sample having a known ratio of methylated nucleic acid to unmethylated nucleic acid; and quantifying the degree of methylation of the biological sample by comparing the melt curve of the biological sample with the normalized melt curve. The melt curve of the biological sample may be obtained by HRM analysis. For example, the nucleic acid may be a nucleic acid that contains at least one CpG site contained in a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3 and has a continuous partial sequence of the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. For example, the nucleic acid may be a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. The melt curve of the biological sample may be normalized before being compared with the normalized melt curve.

[0052] According to one embodiment of the present invention, the method may further include, after measuring the methylation level, comparing the methylation level with that of a control group. The control group refers to DNA isolated from a biological sample whose tissue of origin is already known. This control group can be any sample derived from a subject with or without liver cancer. For example, when a sample derived from a subject with liver cancer is used as the control group, if the methylation level of DNA isolated from the biological sample is similar to that of DNA isolated from the control group, the subject from which the biological sample was derived can be identified as being at risk for liver cancer. When a sample derived from a subject without liver cancer is used as the control group, if the methylation level of DNA isolated from the biological sample is higher than that of the control group, the subject from which the biological sample was derived can be identified as being at risk for liver cancer. The subject may be, for example, a vertebrate, mammal, rodent, goat, deer, pig, bird, chicken, turkey, cow, horse, sheep, fish, or primate, such as a human.

[0053] The method according to one example of the present application may further include a step of measuring the concentration of alpha fetoprotein (AFP) in the biological sample, and / or a step of collecting gender and / or age information of the subject.

[0054] The composition and method according to one example of the present application can determine the presence or absence of methylation of a biomarker with high sensitivity, even when the concentration of the methylated biomarker in a biological sample is very low.

[0055] To overcome the shortcomings of conventional experimental verification methods, we designed MS-HRM primers using methylated primers containing a CpG recognition site that recognizes CpG sequences, and mixed them with unmethylated primers containing a TpG recognition site that recognizes TpG sequences, which are used to amplify unmethylated template strands, to prevent amplification of only the methylated template strand. To address the relatively low sensitivity of MS-HRM and to prevent biased amplification of primers containing TpG, we developed a method to increase the binding opportunity of primers containing CpG and decrease the binding opportunity of primers containing TpG by adjusting the temperature during the primer annealing step of PCR.

[0056] Therefore, more sensitive PCR can be performed for targets present in very small amounts, such as circulating tumor DNA (ctDNA) present in blood, such as the nucleic acids represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. Experiments can be designed by adjusting the degree of binding of methylated and unmethylated primers to the template strand according to the application, depending on the annealing temperature and primer concentration ratio. This method overcomes the shortcomings of existing qMSP, MethyLight, and MS-HRM analytical methods, allowing the design of new experimental validation methods. Methylation markers represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3 can be detected through liquid biopsies, enabling quantification of methylation levels with higher sensitivity than existing MS-HRM analytical methods.

[0057] Furthermore, the relative methylation ratio can be measured in the same test tube without the need for a control marker analysis, and the detection sensitivity is high because no probes that interfere with amplification efficiency are used. Furthermore, the amplification efficiency of methylated and unmethylated DNA can be adjusted to measure very low methylation levels in biological samples such as blood.

[0058] The composition and method according to one example of the present application uses a combination of methylated and unmethylated primers to prevent the generation of non-specific amplification products, and can measure the methylation level of a sample more sensitively than existing PCR-based analytical methods, even in conditions where the desired target nucleic acid is present in extremely small amounts.

[0059] The composition and method according to one example of the present application can determine the DNA methylation level of circulating tumor DNA (ctDNA), which is present in small amounts in cancer patients among cell-free DNA (cfDNA) present in a biological sample, for example, blood, by an in-sample semi-quantitative method, for example, the methylation level of at least one CpG site contained in a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, and thereby achieve high sensitivity and specificity.

[0060] For example, the concentration of methylated nucleic acid in the biological sample is 100ng / ul or less, 90ng / ul or less, 80ng / ul or less, 70ng / ul or less, 60ng / ul or less, 50ng / ul or less, 40ng / ul or less, 30ng / ul or less, 20ng / ul or less, 15ng / ul or less, 10ng / ul or less, 9ng / ul or less, 8ng / ul or less, 7ng / ul or less, 6ng / ul or less, 5ng / ul or less, 4ng / ul or less, 3ng / ul or less, 2ng / ul or less, 1.5ng / ul or less The nucleic acid may be 0.009 ng / ul or less, 0.008 ng / ul or less, 0.007 ng / ul or less, 0.006 ng / ul or less, 0.005 ng / ul or less, 0.006 ng / ul or less, 0.005 ng / ul or less, or 0.003 ng / ul or less. The nucleic acid may be, for example, a nucleic acid comprising at least one CpG site contained in a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, and having a continuous partial sequence of the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. For example, the nucleic acid may be a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3.

[0061] For example, the biological sample comprises methylated nucleic acids and unmethylated nucleic acids, and the concentration of the methylated nucleic acids can be 75% or less, 70% or less, 60% or less, 50% or less, 40% or less, 30% or less, 25% or less, 20% or less, less than 19%, 18% or less, 15% or less, 10% or less, 5%, 4.5% or less, 4% or less, 3.5% or less, 3%, 2.5% or less, 2%, 1.5%, 1.4%, 1.3%, 1.2%, 1.1%, 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, or 0.45% or less of the concentration of the unmethylated nucleic acids. For example, the nucleic acid may be a nucleic acid that contains at least one CpG site contained in the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, and has a continuous partial sequence of the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. As an example, the nucleic acid may be a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3.

[0062] For example, the biological sample contains methylated nucleic acids and unmethylated nucleic acids, and the methylated nucleic acids are expressed as 100% or less, less than 100%, 99% or less, 95% or less, 90% or less, 85% or less, 80% or less, 75% or less, 70% or less, 65% or less, 60% or less, 55% or less, 50% or less, 45% or less, 40% or less, based on the total strands of the methylated nucleic acids and the unmethylated nucleic acids being 100% or less. The nucleic acid may contain at least 35% or less, 30% or less, 25% or less, 20% or less, 15% or less, 10% or less, less than 10%, 5% or less, less than 5%, 4.5% or less, 4% or less, 3.5% or less, 3% or less, 2.5% or less, 2% or less, 1.5% or less, 1.4% or less, 1.3% or less, 1.2% or less, 1.1% or less, 1% or less, 0.9% or less, 0.8% or less, 0.7% or less, 0.6% or less, 0.5% or less, or 0.4% or less. For example, the nucleic acid may contain at least one CpG site contained in a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, and may have a continuous partial sequence of the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. For example, the nucleic acid may be a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3.

[0063] For example, the biological sample can contain up to 20,000 strands, up to 15,000 strands, up to 10,000 strands, up to 5,000 strands, up to 4,000 strands, up to 3,000 strands, up to 2,000 strands, up to 1,000 strands, up to 500 strands, up to 400 strands, up to 300 strands, up to 200 strands, up to 150 strands, up to 100 strands, up to 90 strands, up to 80 strands, up to 70 strands, up to 60 strands, up to 50 strands, up to 40 strands, up to 30 strands, up to 20 strands, up to 10 strands, up to 9 strands, up to 8 strands, up to 7 strands, up to 6 strands, up to 5 ... strands, up to 3 strands, up to 2 strands, or up to 1 strand of methylated nucleic acid. For example, the nucleic acid may be a nucleic acid that contains at least one CpG site contained in the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3, and has a continuous partial sequence of the nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3. As an example, the nucleic acid may be a nucleic acid represented by SEQ ID NO: 1, SEQ ID NO: 2, or SEQ ID NO: 3.

[0064] In one example of the present application, the biological sample may comprise one or more selected from the group consisting of blood, plasma, serum, platelets, tissue, cells, stool, and urine.

[0065] In one example of the present application, the liver cancer may be hepatocellular carcinoma.

[0066] The method according to an example of the present application may further include treating the subject. The treating step may include administering an effective amount of a therapeutic agent, chemotherapy, hormone therapy, radiation therapy, surgery, or a combination thereof to the subject. The therapeutic agent may be, for example, a therapeutic agent for liver cancer.

[0067] Examples of the therapeutic agent include afatinib, AK105, anlotinib, apatinib, atezolizumab, avelumab, axitinib, bevacizumab, bosutinib, BSC, carbozantinib, carbozantinib-S-malate, camrelizumab, canertinib, carboplatin, capecitabine, and the like. Citabine, celecoxib, CC-122, CF102, crizotinib, dasatinib, docetaxel, donafenib, dovitinib, doxorubicin, durvalumab, EKB-569, entrectinib, epirubicin, erlotinib, etoposide, everolimus, FGF401, FOLFOX 4, fostamatinib, galunisertib, gefitinib, gemcitabine, IBI305, ibrutinib, imatinib, INC280, infigratinib, ipilimumab, irinotecan, lapatinib, leflunomide, lenvatinib, LY2875358, mesylate, mitomycin Cc), MSC2156119J, neratinib, nilotinib, nintedanib, nivolumab, oxaliplatin, palbociclib, panobinostat, pazopanib, PDR001, pembrolizumab, pemigatinib, pexavec, phosphate, ramucirumab, regorafenib, ruxolitinib, semaxinib, selumetinib, SGO-110, SHR-12 10, sintilimab, sorafenib, SU6656, sunitinib, sintilimab, spartalizumab, sutent, TACE, tasquinimod, temozolomide, temsirolimus, tislelizumab, tivantinib, tosylate, toripalimab, tremelimumab, vandetanib, vatalanib, XL888, Y90, pharmaceutically acceptable salts thereof, or combinations thereof.

[0068] Another example of the present application relates to a method for treating liver cancer, including treating a subject identified as being at risk for liver cancer according to an example of the present application. The step of treating the subject is as described above. [Effects of the Invention]

[0069] The biomarker according to one example of the present application can achieve higher sensitivity through liquid biopsy diagnosis than the currently used liquid biopsy diagnostic method, thereby significantly increasing the efficiency of liver cancer testing and significantly improving the survival rate of liver cancer patients.

[0070] Furthermore, one example of the present application provides a biomarker that enables early diagnosis of liver cancer through a blood-based liquid biopsy method and a molecular testing method utilizing the biomarker. In particular, the presence or absence of liver cancer cell-derived cfDNA in a sample can be detected by checking the DNA methylation level of blood-derived cell-free DNA (cfDNA). [Brief explanation of the drawings]

[0071] [Figure 1] FIG. 1 is a diagram showing the methylation levels of markers in each tissue according to an example of the present application, where the horizontal axis represents CGRC_N: Cancer Genome Research Center normal; LIHC_N: Liver Hepatocellular carcinoma normal; CGRC_T: Cancer Genome Research Center tumor; and LIHC_T: Liver Hepatocellular carcinoma tumor. [Figure 2] Figure 2 is a diagram showing the methylation distribution of markers according to an example of the present application in normal liver and liver disease, where the horizontal axis represents Liver_N: normal liver; NAFLD: non-alcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; LGDN: low-grade dysplastic nodule; HGDN: high-grade dysplastic nodule; HCC: hepatocellular carcinoma; and HepG2: hepatocellular carcinoma (HCC) cell line. [Figure 3a]3a to 3c are diagrams showing the results of verifying the performance of a biomarker according to one example of the present application using general methylation data in an independent cohort. [Figure 3b] 3a to 3c are diagrams showing the results of verifying the performance of a biomarker according to one example of the present application using general methylation data in an independent cohort. [Figure 3c] 3a to 3c are diagrams showing the results of verifying the performance of a biomarker according to one example of the present application using general methylation data in an independent cohort. [Figure 4] FIG. 4 is a diagram showing the liver cancer specificity of biomarkers according to one example of the present application, where the X-axis indicates normal and cancer tissue samples from a dataset of 23 cancer types including liver cancer, the Y-axis indicates biomarkers, and the color of the matrix indicates methylation levels. [Figure 5a] Figures 5a to 5c show the results of confirming the ability of a method for measuring biomarker methylation levels according to one example of the present application to distinguish between methylated and unmethylated strands, using control DNA (EpiTect PCR Control DNA Set, Qiagen, 59695), gDNA from a liver cancer cell line, and sulfite-converted gDNA derived from human Buffy Coat. [Figure 5b] Figures 5a to 5c show the results of confirming the ability of a method for measuring biomarker methylation levels according to one example of the present application to distinguish between methylated and unmethylated strands, using control DNA (EpiTect PCR Control DNA Set, Qiagen, 59695), gDNA from a liver cancer cell line, and sulfite-converted gDNA derived from human Buffy Coat. [Figure 5c] Figures 5a to 5c show the results of confirming the ability of a method for measuring biomarker methylation levels according to one example of the present application to distinguish between methylated and unmethylated strands, using control DNA (EpiTect PCR Control DNA Set, Qiagen, 59695), gDNA from a liver cancer cell line, and sulfite-converted gDNA derived from human Buffy Coat. [Figure 6a] 6a to 6c show the results of an analysis of the minimum detection limit for methylated and unmethylated DNA strands of Marker 1, a biomarker according to an example of the present application, using a methylation level measurement method according to an example of the present application. [Figure 6b] 6a to 6c show the results of an analysis of the minimum detection limit for methylated and unmethylated DNA strands of Marker 1, a biomarker according to an example of the present application, using a methylation level measurement method according to an example of the present application. [Figure 6c] 6a to 6c show the results of an analysis of the minimum detection limit for methylated and unmethylated DNA strands of Marker 1, a biomarker according to an example of the present application, using a methylation level measurement method according to an example of the present application. [Figure 7] FIG. 7 is a boxplot showing the methylation levels of biomarkers according to one example of the present application for normal and tumor tissue samples of various carcinomas. [Figure 8a] Figures 8a to 8c show the results of MS-HRM analysis based on sulfite-converted gDNA from Buffy Coat collected from 55 healthy individuals, and Figures 8a and 8b show the normalized melting point curves of all samples. [Figure 8b] Figures 8a to 8c show the results of MS-HRM analysis based on sulfite-converted gDNA from Buffy Coat collected from 55 healthy individuals, and Figures 8a and 8b show the normalized melting point curves of all samples. [Figure 8c] Figures 8a to 8c show the results of MS-HRM analysis based on sulfite-converted gDNA from Buffy Coat collected from 55 healthy individuals, and Figure 8c is a boxplot showing the distribution of the integrated values ​​(AUMC) of each sample based on the melting point curve. [Figure 9a]Figure 9a is a boxplot showing the AUMC of 81 healthy individuals and 319 HCC patients, with the dotted line indicating the cutoff, which was set based on the 97th percentile of the AUMC values ​​of the 81 healthy individuals. [Figure 9b] FIG. 9b shows the results of analyzing the accuracy of liver cancer diagnosis of Marker 1, a biomarker according to one example of the present application, for clinical samples from a total of 420 people. [Figure 10] FIG. 10 is a graph showing the results of increasing the accuracy of liver cancer diagnosis when Marker 1, a biomarker according to an example of the present application, is combined with various biomarkers. [Figure 11] FIG. 11 is a diagram confirming the liver cancer specificity of a diagnostic marker according to one example of the present application. [Figure 12] FIG. 12 is a diagram confirming that the risk of liver cancer can be accurately predicted based on the methylation level of a diagnostic marker according to one example of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0072] The present application will be described in more detail below with reference to the following examples, but these examples are for illustrative purposes only and do not limit the scope of the present application.

[0073] Example 1. Discovery of markers for predicting the risk of developing liver cancer To identify markers for predicting the risk of developing liver cancer that can be detected by blood biopsy, we analyzed DNA methylation data from the TCGA (The Cancer Genome Atlas) database and the CGRC (Cancer Genome Research Center). Methylation data from normal liver tissues and liver cancer tissues in these databases were analyzed to identify CpG sites that were hypomethylated to 15% or less in 95% or more of normal liver samples, hypomethylated to 15% or less in 95% or more of blood samples, and hypermethylated to 30% or more in liver cancer samples.

[0074] In order to discover general biomarkers, machine learning was performed for each dataset of the database, and CpG sites with a common ranking of importance in machine learning were selected.

[0075] To identify markers that can be biopsied using blood samples, we also identified a group of marker candidates that were hypomethylated to 15% or less in normal tissues of organs other than the liver.

[0076] The information on the markers selected to satisfy all of the above conditions is shown in Table 1. The selected biomarker is herein designated "marker 1."

[0077] In addition, two additional markers that were shown to be specific to liver cancer were selected and named "marker 2" and "marker 3," respectively, and their information is shown in Table 1.

[0078] [Table 1] TIFF2026500811000003.tif203161

[0079] In the following examples, a CpG site located at positions 21810750-21810751 on chromosome 12 was used as an example of marker 1, a CpG site located at positions 29298124-29298125 on chromosome 17 was used as an example of marker 2, and a CpG site located at positions 19739407-19739408 on chromosome 19 was used as an example of marker 3. In the sequences in Table 1, the CpG sites located at positions 21810750-21810751 on chromosome 12, the CpG sites located at positions 29298124-29298125 on chromosome 17, and the CpG sites located at positions 19739407-19739408 on chromosome 19 are shown in bold and underlined.

[0080] The selected liver cancer markers were visualized in a box plot using the Graph pad prism tool and shown in Figure 1 (X-axis: sample type; CGRC_N: Cancer Genome Research Center normal; LIHC_N: Liver Hepatocellular carcinoma normal; CGRC_T: Cancer Genome Research Center tumor; LIHC_T: Liver Hepatocellular carcinoma tumor, Y-axis: methylation B-value).

[0081] As shown in Figure 1, the marker according to one example of the present application was hypomethylated in normal blood, normal liver tissue, normal tissues of organs other than the liver, and cancer tissues of organs other than the liver, and showed a characteristic of being specifically hypermethylated in liver cancer samples. Specifically, the marker according to one example of the present application showed an average methylation value of 30% or more in liver cancer samples, but the methylation level was significantly lower in other samples, making it possible to specifically distinguish liver cancer samples based on the methylation level of the marker.

[0082] Example 2. Methylation changes according to the stage of liver cancer development To confirm methylation mutations according to the stage of liver cancer development for the biomarkers of one example of the present application, a dataset of cancer development stages was downloaded from the GEO (Gene Expression Omnibus) database. The methylation data for each stage was combined and visualized in a box plot using the Graph Pad Prism tool, as shown in Figure 2 (X-axis: sample type; Liver_N: normal liver; NAFLD: non-alcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; LGDN: low-grade dysplastic nodule; HGDN: high-grade dysplastic nodule; HCC: hepatocellular carcinoma; HepG2: hepatocellular carcinoma-derived (HCC) cell line; Y-axis: methylation B-value).

[0083] As shown in Figure 2, the biomarker according to one example of the present application showed hypomethylation in normal blood, normal liver tissue, fatty liver, and cirrhosis, and methylation gradually increased from the stage of dysplastic liver and dysplastic nodule, a precancerous lesion of liver cancer, until the methylation level was significantly high in hepatocellular carcinoma (HCC). In Figure 2, the X axis represents the sample and the Y axis represents the methylation value.

[0084] Example 3. Validation of the clinical performance of biomarkers in an independent cohort To validate the biomarkers according to one example of the present application in multiple independent cohorts, four liver cancer methylation datasets, GSE54503, GSE56588, GSE60753, and GSE89852, were downloaded from the GEO (Gene Expression Omnibus) database and used in the experiment. The two datasets used for marker mining in Example 1 (CGRC HCC, TCGA HCC) and these four datasets were combined, and hierarchical clustering was performed using the biomarkers according to one example of the present application. The results are visualized and shown in Figures 3a to 3c (X axis: sample, Y axis: marker probe ID, green box: normal sample, red box: cancer sample).

[0085] Figure 3a shows the clustering results for a single marker, Marker 1, according to an example of the present application. Figure 3b shows the clustering results for a combination of Markers 1 and 2. Figure 3c shows the clustering results for a combination of Markers 1 and 3. In Figures 3a-3c, the color of the heatmap indicates the degree of methylation, with higher methylation indicated from white to red. As shown in Figures 3a-3b, clustering of all samples from the six datasets on the heatmap confirmed that normal samples, shown in green, and liver cancer samples, shown in pink, were distinct. Furthermore, hierarchical clustering using Marker 1 and Marker 2 or Marker 3 according to an example of the present application together resulted in further improvement in tumor coverage.

[0086] Example 4. Confirmation of biomarker specificity for liver cancer To confirm the liver cancer specificity of the biomarker according to one example of the present application, the average methylation level was analyzed by cancer type using the methylation data of the TCGA and GEO databases used in Example 1 or Example 2.

[0087] Specifically, the methylation levels of markers 1 and 2 for each dataset were confirmed using a heatmap using the ggplot2 R package in R software, as shown in Figure 4 (blue: hypomethylated, red: hypermethylated, X axis: sample, Y axis: marker probe ID, green box: normal sample, red box: cancer sample).

[0088] As shown in Figure 4, the average methylation levels of the biomarkers according to one example of the present application for each cancer type were confirmed. Markers 1 and 2 appeared in red only in liver cancer samples, confirming hypermethylation, while they appeared in blue in normal liver and other cancer types, confirming hypomethylation. Therefore, both of the two biomarkers showed high methylation levels only in liver cancer and low methylation levels in other cancer types, confirming their liver cancer specificity.

[0089] Example 5. Establishment of a method for measuring biomarker methylation levels A method for measuring the methylation levels of biomarkers according to one example of the present application was established as follows.

[0090] Various samples containing Marker 1 according to an example of the present application and control samples were prepared and used in the experiment as shown in Table 2. Samples 2, 3, and 5 were purchased from Qiagen. Sample 3 was obtained by converting all cytosine residues to thymines using the EpiTect Bisulfite Kit after sulfite treatment of gDNA from Sample 2. Sample 5 was obtained by 100% methylation of gDNA from Sample 2 using SssI methylase, followed by sulfite conversion using the EpiTect Bisulfite Kit to convert cytosines other than CpG cytosines to thymine. For Sample 4, gDNA was extracted from peripheral blood mononuclear cells isolated from healthy individuals using a Buffy Coat using a QIAmp DNA Blood Kit (Qiagen, Cat. No. 51104) according to the manufacturer's instructions. The extracted gDNA was then sulfite converted using the EZ DNA Methylation-Lightning Kit and used in the experiment. For sample 6, gDNA from the hepatoma cell line Huh-1 was prepared in the same way as PBMC using the QIAmp DNA Blood Kit and EZ DNA Methylation-Lightning Kit for use in experiments. The concentration of each sample was measured using Qubit and then diluted to 0.33ng / μL.

[0091] [Table 2]

[0092] The sulfite converted sequences of biomarkers according to one example of the present application are shown in Table 3. In Table 3, C, which has undergone CT conversion by sulfite treatment, is indicated by a lowercase t, and methylated CpG sites are indicated by uppercase CG. In Table 3, CpG sites typically used in experiments in the examples of the present application are indicated in bold and underlined.

[0093] [Table 3] TIFF2026500811000006.tif54162

[0094] The size of the PCR amplification product should be 50-100 bp. The PCR annealing temperature is approximately 60°C. m The temperature was set to 60–65°C. One of the forward or reverse primers contained a CpG or TpG sequence near its 3'-end to specifically bind to either the methylated or unmethylated strand. Because the CpG cytosine in the methylated template strand remains unconverted to thymine after sulfite treatment, the methylated primer was designed to contain the CpG sequence, while the unmethylated primer was designed to contain TpG. Because the primer specifically binding to the unmethylated template strand has a lower Tm than the primer specifically binding to the methylated template strand, additional nucleotides can be added to the 5'-end to compensate for this, making the Tm of the two primers similar. The primers opposite the methylated and unmethylated primers were designed to bind regardless of the methylation status of the methylated or unmethylated strand. All primers contained two or more non-CpG cytosines to enable specific binding to the sulfite-converted DNA strand. Exemplary sequences of primer sets prepared for Marker 1, a biomarker according to one example of the present application, are shown in Table 4. In each primer sequence, the sites corresponding to CpG or non-CpG cytosine in Marker 1 are written in uppercase letters, and other bases are written in lowercase letters.

[0095] [Table 4]

[0096] A PCR mixture was prepared using the primers in Table 4 as follows (1 rxn basis): Master Mix (2X): 12.5μL (Final Conc. 1X) EvaGreen(20X):1.25μL(Final Conc. 1X) Methylated Forward Primer (10uM): 1μL (Final Conc. 0.4uM) Methylated Reverse Primer (10uM): 1μL (Final Conc. 0.4uM) Unmethylated Forward Primer (10uM): 0.5μL (Final Conc. 0.2uM) NFW (nuclease-free water): 5.75μL Total: 22 μL

[0097] 22 μL of the prepared PCR mixture was dispensed into each well of a 96-well plate, and 3 μL of a control nucleic acid sample at a concentration of 0.33 ng / μL was dispensed into each well. PCR reaction and HRM analysis were performed under the following conditions: PCR reaction: 95°C for 5 minutes, 95°C for 20 seconds, 69°C for 40 seconds (20 cycles, starting at 69°C and decreasing by 0.3°C per cycle, ending at 63°C). 95℃ 20 seconds - 63℃ 40 seconds (40 cycles, proceeding at a constant temperature of 63℃): PCR reaction 63℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0098] The fluorescence values ​​for PCR and HRM analysis were confirmed, and the melting curve or peak was confirmed to confirm the characteristics of the methylated and unmethylated control groups. The PCR and HRM analysis results for Marker 1 according to one example of the present application are shown in Figures 5a to 5c.

[0099] Figure 5a shows the fluorescence signal observed during the PCR step during MS-HRM analysis. During MS-HRM analysis, only signals were observed for samples that had undergone methylsulfite conversion. Specifically, the NTC and EpiTect gDNA results showed that the primers for the biomarkers according to an example of the present application did not undergo dimer formation through primer-primer reactions, did not react with non-methylsulfite converted gDNA, and did not produce PCR amplification products. Therefore, it was confirmed that the designed primers react specifically only with methylsulfite converted samples.

[0100] Figure 5b is a melt curve showing the degradation of double strands of PCR amplification products with increasing temperature after PCR reaction, and Figure 5c is a melt peak graph showing the differential value of the melt curve of Figure 5b. In the case of a methylated template strand, denaturation of the PCR amplification product occurred at a relatively higher temperature than in the case of an unmethylated template strand in which CpG was maintained and modified to TpG. In other words, it can be seen that the PCR amplification product generated from unmethylated DNA is degraded from double strands to single strands at a lower temperature than the PCR amplification product generated from methylated DNA. Therefore, it can be seen that primers designed to target biomarkers according to an example of the present application can reflect the characteristics of amplification products depending on the methylation level of the sample.

[0101] Example 6. Minimum detection limit (LoD) (1) Analysis of minimum detection limit (1) The control nucleic acid samples (Buffy Coat and Huh-1 sulfite-converted gDNA) were measured for concentration using Qubit and then diluted to 1 ng / μL. The 1 ng / μL control nucleic acid was serially diluted two-fold with water to a concentration of 0.016 ng / μL (1 ng / μL, 0.5 ng / μL, 0.25 ng / μL, 0.13 ng / μL, 0.06 ng / μL, 0.03 ng / μL, and 0.016 ng / μL, for a total of seven concentrations of control nucleic acid).

[0102] A PCR mixture was prepared using the primers in Table 4 as follows (1 rxn basis): Master Mix (2X): 12.5μL (Final Conc. 1X) EvaGreen(20X):1.25μL(Final Conc. 1X) Methylated Forward Primer (10uM): 1μL (Final Conc. 0.4uM) Methylated Reverse Primer (10uM): 1μL (Final Conc. 0.4uM) Unmethylated Forward Primer (10uM): 0.5μL (Final Conc. 0.2uM) NFW (nuclease-free water): 5.75μL Total: 22 μL

[0103] 22 μL of the prepared PCR mixture was dispensed into each well of a 96-well plate, and 3 μL of a control nucleic acid sample at a concentration of 0.33 ng / μL was dispensed into each well. PCR reaction and HRM analysis were performed under the following conditions: PCR reaction: 95°C for 5 minutes, 95°C for 20 seconds, 69°C for 40 seconds (20 cycles, starting at 69°C and decreasing by 0.3°C per cycle, ending at 63°C). 95℃ 20 seconds - 63℃ 40 seconds (40 cycles, proceeding at a constant temperature of 63℃): PCR reaction 63℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0104] The correlation between PCR Cq values ​​and input concentrations was confirmed to determine the minimum limit of detection (LoD). The area under the melting curve (AUMC) based on the normalized melting curves at all concentrations was used to confirm whether PCR amplification products of methylated and unmethylated template strands were correctly formed.

[0105] As shown in Figure 6a, the Cq values ​​decreased with increasing concentration for sulfite-converted methylated (Huh-1 gDNA) and unmethylated (Buffy Coat gDNA) gDNA, confirming that the correlation between PCR amplification and concentration was statistically significant (the higher the concentration of the sample used, the faster the PCR amplification, resulting in a lower Cq value). Theoretically, 1 ng of DNA has 304 strands, and the lowest amount of DNA used in this experiment, 0.0625 ng, had 19 strands, indicating that up to 19 strands could be stably detected.

[0106] As shown in Figure 6b, the area under the melting curve (AUMC) values ​​were calculated based on the normalized melting curves of the PCR amplification products of the samples used. We confirmed that the AUMCs matching each methylation feature appeared similarly across all concentration ranges of methylated and unmethylated gDNA.

[0107] (2) Analysis of minimum detection limit (2) Zymo Research's Human Methylated & Non-methylated DNA Set (Cat.# D5014) was subjected to sulfite conversion using their EZ DNA Methylation-Lightning™ Kit (Cat.# D5030) and then quantified using the Qubit™ ssDNA Assay Kit (Cat.# Q10212). The sulfite-converted methylated and non-methylated DNA were then diluted to 0.1ng / uL in Nuclease-Free Water (Cat.# AM9937) and serially diluted 4-fold by diluting the methylated DNA with the non-methylated DNA to prepare samples containing 100%, 25%, 6.25%, 1.56%, 0.39%, and 0% methylated DNA.

[0108] Exemplary sequences of primer sets prepared for Marker 1, a biomarker according to one example of the present application, are shown in Table 5. In each primer sequence, the sites corresponding to CpG or non-CpG cytosine in Marker 1 are written in uppercase letters, and other bases are written in lowercase letters.

[0109] [Table 5]

[0110] The PCR mixture used in the limit of detection (LoD) experiment was prepared as follows using the primers in Table 5 (1rxn basis): Master Mix (2X): 12.5μL (Final Conc. 1X) LightCycler (20X): 1.25μL (Final Conc. 1X) Primer Mixture (each primer concentration is 12 μM): 1.25 μL (Final Conc. 0.6 μM) Total: 15 μL

[0111] 15 μL of the prepared PCR mixture was dispensed into each well of a 96-well plate, and 10 μL of methylated DNA samples (100%, 25%, 6.25%, 1.56%, 0.39%, and 0%) at a concentration of 0.1 ng / μL was dispensed into each well. PCR reactions and HRM analysis were performed under the following conditions: 95℃ 5 min / 95℃ 10 sec - 63℃ 30 sec - 72℃ 15 sec (39 cycles): PCR reaction 72℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0112] The temperature-shifted area under the melting curve (TS-AUMC) was determined based on the ratio of methylated DNA used, and the minimum limit of detection (LoD) for measuring detectable methylated DNA in the presence of background non-methylated DNA was determined.

[0113] As shown in Figure 6c, the resolution of the temperature-shifted AUMC value, which is the methylation signal value, was confirmed to be 0% even in an environment where 99.6% unmethylated strands were present. Specifically, when the 0.4% level for 1 ng of input DNA is converted to copy number, it is calculated as a 1.2 copy level for the target strand. Therefore, the method for measuring the methylation level of biomarkers established in Example 5 demonstrated the effectiveness of being able to detect trace amounts of methylated strands even in the presence of a large amount of unmethylated strands in blood.

[0114] Example 7. Diagnosis of liver cancer using tissue samples gDNA extraction and sulfite conversion were performed on normal and tumor tissues collected from patients with liver cancer (LIHC) (normal; 120, tumor; 115), lung cancer (LUAD) (normal; 8, tumor; 9), colorectal cancer (COAD) (normal; 10, tumor; 10), kidney cancer (KIRP and KIRC) (normal; 16, tumor; 15), bladder cancer (BLCA) (normal; 10, tumor; 9), and thyroid cancer (THYM) (normal; 6, tumor; 6). For kidney cancer, patients were classified as kidney renal papillary cell carcinoma (KIRP) (normal; 9, tumor; 5) or kidney renal clear cell carcinoma (KIRC) (normal; 7, tumor; 10).

[0115] Specifically, a PCR mixture was prepared using the primers in Table 4 as follows (1rxn basis): Master Mix (2X): 12.5μL (Final Conc. 1X) EvaGreen(20X):1.25μL(Final Conc. 1X) Methylated Forward Primer (10uM): 1μL (Final Conc. 0.4uM) Methylated Reverse Primer (10uM): 1μL (Final Conc. 0.4uM) Unmethylated Forward Primer (10uM): 0.5μL (Final Conc. 0.2uM) NFW (nuclease-free water): 5.75μL Total: 22 μL

[0116] The prepared sample was quantified and diluted to 0.33 ng / μL, and 3 μL of the sample was added. PCR reaction and HRM analysis were performed under the following conditions: PCR reaction: 95°C for 5 minutes, 95°C for 20 seconds, 69°C for 40 seconds (20 cycles, starting at 69°C and decreasing by 0.3°C per cycle, ending at 63°C). 95℃ 20 seconds - 63℃ 40 seconds (40 cycles, proceeding at a constant temperature of 63℃): PCR reaction 63℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0117] The MS-HRM analytical method established in Example 5 was applied to each sample, and the AUMC (Area under the melting curve) was calculated using the normalized melting curve.

[0118] As shown in FIG. 7, the biomarker according to one example of the present application exhibits a significantly high methylation level in liver cancer tissues, and therefore can be utilized for the diagnosis of liver cancer.

[0119] Example 8. Analysis of blood samples from healthy individuals For sample preparation, gDNA extraction and sulfite conversion were performed from Buffy Coat samples collected from 55 healthy individuals. To measure the methylation level of Marker 1 according to an example of the present application, a PCR mixture was prepared as follows (1rxn standard) using the primers in Table 4: Master Mix (2X): 12.5μL (Final Conc. 1X) EvaGreen(20X):1.25μL(Final Conc. 1X) Methylated Forward Primer (10uM): 1μL (Final Conc. 0.4uM) Methylated Reverse Primer (10uM): 1μL (Final Conc. 0.4uM) Unmethylated Forward Primer (10uM): 0.5μL (Final Conc. 0.2uM) NFW (nuclease-free water): 5.75μL Total: 22 μL

[0120] 3 μL of Buffy Coat sample (concentration: 0.33 ng / μL) was added to each well containing PCR mixture, and 3 μL of EpiTect Unmethylated DNA and Methylated DNA were also added to each well. PCR reaction and HRM analysis were performed under the following conditions: PCR reaction: 95°C for 5 minutes, 95°C for 20 seconds, 69°C for 40 seconds (20 cycles, starting at 69°C and decreasing by 0.3°C per cycle, ending at 63°C). 95℃ 20 seconds - 63℃ 40 seconds (40 cycles, proceeding at a constant temperature of 63℃): PCR reaction 63℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0121] The MS-HRM analytical method established in Example 5 was applied to each sample to derive the normalized melting curve, the melting peak (Peak) differentiated by temperature and taken as the negative number, and the AUMC.

[0122] As shown in Figure 8, all of the Buffy Coat gDNA from 55 healthy individuals showed the same melting curves and melting peak results as unmethylated DNA, indicating that the blood cells of healthy individuals did not show methylation levels for the biomarkers according to one example of the present application.

[0123] Example 9. Diagnosis of liver cancer using blood samples (1) Analysis of the accuracy of liver cancer diagnosis (1) cfDNA extracted from the plasma of a total of 81 healthy individuals and 319 HCC patients was subjected to sulfite conversion to prepare samples for MS-HRM analysis. To measure the methylation level of Marker 1 in each sample, a PCR mixture was prepared as follows (1rxn basis) using the primers in Table 4: Master Mix (2X): 12.5μL (Final Conc. 1X) EvaGreen(20X):1.25μL(Final Conc. 1X) Methylated Forward Primer (10uM): 1μL (Final Conc. 0.4uM) Methylated Reverse Primer (10uM): 1μL (Final Conc. 0.4uM) Unmethylated Forward Primer (10uM): 0.5μL (Final Conc. 0.2uM) NFW (nuclease-free water): 5.75μL Total: 22 μL

[0124] 3 μL of a 0.33 ng / μL control nucleic acid sample was dispensed into each well, and PCR reaction and HRM analysis were carried out under the following conditions: PCR reaction: 95°C for 5 minutes, 95°C for 20 seconds, 69°C for 40 seconds (20 cycles, starting at 69°C and decreasing by 0.3°C per cycle, ending at 63°C). 95℃ 20 seconds - 63℃ 40 seconds (40 cycles, proceeding at a constant temperature of 63℃): PCR reaction 63℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0125] The MS-HRM analysis method established in Example 5 was applied to each sample, and the AUMC was derived from the normalized melting point curve. th The percentile cutoff was set, and the AUMC distribution of healthy individuals and early-, intermediate-, and late-stage liver cancer patients is shown in a boxplot in Figure 9a (A). Furthermore, the methylation level was quantified using the AUMC of the 100% methylated control sample and the 100% unmethylated control sample as the reference, as shown in Figure 9a (B), using Equation 1 below. A methylation level below 0 was calculated as 0, and a methylation level above 100 was calculated as 100. The cutoff value set in Figure 9a is indicated by a dotted line.

[0126]

number

[0127] As shown in Figure 9a, the distribution of AUMC between the healthy group and the HCC patient group showed a statistically significant difference based on the t-test, confirming that the methylation level of Marker 1 according to one example of the present application accurately predicts the risk of developing liver cancer.

[0128] (2) Analysis of the accuracy of liver cancer diagnosis (2) A total of 420 clinical samples (89 healthy controls, 196 high-risk patients, and 135 liver cancer patients) were prepared using the same method as in Example 9(1), and the results of AUMC analysis using the primers in Table 5 are shown in Figure 9b. As shown in Figure 9b, a statistically significant increase in AUMC values ​​(the degree of methylation in the samples) was confirmed in the early-stage liver cancer group (BCLC 0-A) and the late-stage liver cancer group (BCLC BD) compared to the control group.

[0129] In addition, a cut-off value of 95% specificity was set based on the TS-AUMC value observed in a healthy control group in the clinical data based on the produced blood samples, and the sensitivity and specificity were calculated based on this cut-off value by calculating the sensitivity for each BCLC stage of liver cancer patients, and the results are shown in Table 6. As shown in Table 6, Marker 1, a biomarker according to an example of the present application, had a high specificity level, showing a sensitivity of 50% or more and 70% or more in early-stage and late-stage liver cancer patients, respectively, and was therefore able to diagnose liver cancer with excellent accuracy.

[0130] [Table 6]

[0131] Example 10. Comparison with conventional liver cancer diagnostic markers HCC patients were divided into early and late stages according to BCLC stage (Early: BCLC 0 & A, Late: BCLC B, C & D). The diagnostic ability of the analytical method developed in this application was compared with that of the conventional liver cancer testing method, AFP value confirmation, according to the stage of HCC patients. 97% of AUMC values ​​confirmed from 81 healthy subjects were th The sensitivity and specificity for determining whether HCC was positive or negative based on percentile and AFP ≥ 20 are shown in Table 7.

[0132] [Table 7]

[0133] As shown in Table 7, the diagnostic sensitivity of the liver cancer testing method based on the biomarker according to an example of the present application was confirmed to be about 23.6% higher in all liver cancer patients than the conventional liver cancer testing method, the AFP test, and in particular, the sensitivity was confirmed to be about 30.1% higher than the AFP test in patients with early-stage liver cancer. Furthermore, when the liver cancer testing method using the biomarker according to an example of the present application was performed in parallel with the AFP test, the overall diagnostic sensitivity was confirmed to be significantly increased, which can aid in the diagnosis of liver cancer in parallel with conventional testing methods in terms of blood-based liquid biopsy.

[0134] Example 11. Improving diagnostic performance by combining biomarkers (1) Diagnosis of liver cancer by combining markers 1 and 2 HCC patients were classified according to BCLC stage into early and late stages (Early: BCLC 0 & A, Late: BCLC B, C & D). In the case of a combination of Marker 1 and Marker 2 according to an example of the present application, the diagnostic ability for liver cancer was compared according to the stage of HCC patients, as shown in Table 8.

[0135] [Table 8]

[0136] As shown in Table 8, when marker 1 according to one example of the present application is used, superior sensitivity is confirmed in all liver cancer stages compared to marker 2, and when the two markers are used in combination, the sensitivity increases by more than 10% while maintaining the same specificity, thereby indicating that the performance of liver cancer diagnosis can be improved.

[0137] (2) Diagnosis of liver cancer using marker 1 and a combination of various biomarkers We investigated whether the accuracy of liver cancer diagnosis could be improved by combining Marker 1, a biomarker according to one example of the present application, with various other biomarkers, such as AFP, age, and gender. A conventional MS-HRM method was used as a control. Specifically, logistic regression analysis was performed using the glm() function in R on samples with all age, gender, and AFP test values ​​among the generated data. A diagnostic prediction model was constructed in combination with the TS-AUMC value, and its clinical performance was confirmed using the roc() function.

[0138] As shown in Figure 10, Marker 1, a biomarker according to one example of the present application, showed excellent diagnostic ability for liver cancer even when used alone, and when combined with AFP test method, age information, and gender information to predict liver cancer groups based on a logistic regression model, it showed an improving effect, with the maximum AUC increasing to 0.9524 (0.9309-0.9740).

[0139] Example 12. Liver cancer specificity of target methylation sites To confirm the liver cancer specificity of the target site of Marker 1 according to one example of the present application, the average methylation level was analyzed by cancer type using the methylation data of the TCGA and GEO databases used in Example 4.

[0140] Specifically, the methylation level for each dataset was confirmed using a heatmap with the ggplot2 R package in R software, and the results are shown in Figure 11 (X axis: sample, Y axis: probe ID in the LDHB gene). In Figure 11, the X axis represents normal and cancer tissue samples from 23 cancer types, including liver cancer, the Y axis represents biomarkers, and the color of the matrix represents the degree of methylation. The boxed areas in the regions indicated by the IDs indicate CpG sites located at positions 21810279-21810280, 21810450-21810451, 21810458-21810459, 21810489-21810490, 21810600-21810601, 21810750-21810751, 21810759-21810760, and 21810791-21810792 of human chromosome 12 in the CGI region contained in Marker 1 according to one example of the present application, and the shaded areas indicate the target sites of the CGI at positions 21810750-21810751.

[0141] As shown in Figure 11, the CGI (CpG island) region contained in the sequence region from 21810279 to 21810792 of chromosome 12 was hypermethylated only in liver cancer samples, and showed a hypomethylated pattern in normal liver and other cancer types. Therefore, high liver cancer specificity can be ensured through the degree of methylation of the target site. On the other hand, other regions, such as cg26362257 (sequence of chromosome 12, positions 21788425-21788426), were hypermethylated in most samples, while cg03243946 (sequence of chromosome 12, positions 21811034-21811035) and cg10195295 (sequence of chromosome 12, positions 21809973-21809974), were hypermethylated in cholangiocarcinoma (CHOL), pancreatic adenocarcinoma (PAAD), and prostate adenocarcinoma (PRAD), and did not show a liver cancer-specific methylation status.

[0142] Therefore, as a result of checking the average methylation value for each cancer type, it was confirmed that only the marker 1 region according to one example of the present application has liver cancer specificity.

[0143] Example 13. Confirmation of the diagnostic ability of biomarkers for liver cancer A total of 210 healthy individuals, 176 high-risk patients, and 133 liver cancer patients were subjected to sulfite conversion of cfDNA extracted from their plasma to prepare samples for MS-HRM analysis. Exemplary sequences of primer sets prepared for Marker 2 are shown in Table 9, and exemplary sequences of primer sets prepared for Marker 3 are shown in Table 10. In each primer sequence, the sites corresponding to CpG or non-CpG cytosine of Marker 3 are written in uppercase letters, and other bases are written in lowercase letters.

[0144] [Table 9]

[0145] [Table 10]

[0146] To measure the methylation level of Marker 2 according to one example of the present application in each sample, a PCR mixture was prepared as in Example 5 using the primers in Table 7, and PCR reaction and HRM analysis were performed under the same conditions.

[0147] In addition, to measure the methylation level of Marker 3 according to one example of the present application in each sample, a PCR mixture was prepared using the primers in Table 8 as follows (1rxn basis): Master Mix (2X): 12.5μL (Final Conc. 1X) EvaGreen(20X):1.25μL(Final Conc. 1X) Methylated Forward Primer (10uM): 1μL (Final Conc. 0.4uM) Methylated Reverse Primer (10uM): 1μL (Final Conc. 0.4uM) Unmethylated Reverse Primer (10uM): 0.5μL (Final Conc. 0.2uM) NFW (nuclease-free water): 5.75μL Total: 22 μL

[0148] 3 μL of a 0.33 ng / μL control nucleic acid sample was dispensed into each well, and PCR reaction and HRM analysis were carried out under the following conditions: 95℃ 5 min / 95℃ 20 sec - 61℃ 30 sec - 72℃ 30 sec (50 cycles): PCR reaction 72℃ 5 min / 60℃-95℃ (increase in 0.2℃ increments and measure fluorescence after 10 seconds of incubation): HRM analysis

[0149] The MS-HRM analytical method established in Example 5 was applied to each sample to derive the AUMC from the normalized melting point curve. The 95th percentile cutoff was set based on the AUMC of 210 healthy subjects, and the AUMC distributions for healthy subjects, high-risk patients, and liver cancer patients are shown in a boxplot in Figure 12. The cutoff values ​​set in Figure 12 are indicated by dotted lines.

[0150] As shown in Figure 12, based on the t-test between the healthy group, high-risk patients, and liver cancer patient group, the liver cancer patient group showed a statistically significantly higher AUMC distribution, confirming that the methylation levels of markers 2 and 3 according to one example of the present application accurately predict the risk of developing liver cancer.

Claims

1. A composition for predicting the risk of developing liver cancer, comprising a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region of human chromosome 12 from bases 21810279 to 21810792 in a biological sample.

2. The composition according to claim 1, wherein the CpG site comprises a CpG site located in the sequence of positions 21810750 to 21810751 of human chromosome 12.

3. The composition of claim 1 , wherein the region has a sequence represented by SEQ ID NO:

1.

4. The composition of claim 1, further comprising: a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region of human chromosome 17 at positions 29298021 to 29298631 in the biological sample; and / or a preparation capable of measuring the methylation level of at least one CpG site contained in the sequence region of human chromosome 19 at positions 19738547 to 19739846 in the biological sample.

5. the at least one CpG site included in the sequence region of bases 29298021 to 29298631 of human chromosome 17 includes a CpG site located at bases 29298115 to 29298116, 29298118 to 29298119, 29298124 to 29298125, 29298136 to 29298137, 29298142 to 29298143, or 29298184 to 29298185 of human chromosome 17; The composition according to claim 4, wherein the at least one CpG site contained in the sequence region from bases 19738547 to 19739846 of human chromosome 19 includes a CpG site located in the sequence from bases 19739407 to 19739408 of human chromosome 19.

6. The composition according to claim 4, wherein the sequence region from bases 29298021 to 29298631 of human chromosome 17 has the sequence represented by SEQ ID NO: 2, and the sequence region from bases 19738547 to 19739846 of human chromosome 19 has the sequence represented by SEQ ID NO:

3.

7. 10. The composition of claim 1, further comprising a preparation capable of measuring the concentration of alpha fetoprotein (AFP) in the biological sample.

8. The composition of claim 1, wherein the agent is a primer for amplifying a target methylation site in the sequence region.

9. the primers include a forward primer and a reverse primer; one of the forward primer and the reverse primer comprises a methylated primer and an unmethylated primer; the methylation primer includes at least one CpG recognition site that recognizes a CpG sequence included in a sequence of a predetermined length including the target methylation site; the unmethylated primer includes at least one TpG recognition site that recognizes a TpG sequence included in a sequence of a predetermined length including the target methylation site; The composition of claim 8, wherein the TpG sequence of the predetermined length sequence containing the target methylation site is a conversion of an unmethylated CpG sequence of the predetermined length sequence containing the target methylation site.

10. The composition according to claim 9 , wherein the TpG sequence is modified by a preparation that modifies methylated and unmethylated CpG sequences differently.

11. 11. The composition of claim 10, wherein the modifying agent converts unmethylated cytosine residues to thymine.

12. 2. The composition of claim 1, wherein the biological sample is one or more selected from the group consisting of blood, plasma, serum, platelets, tissue, cells, feces, and urine.

13. The composition according to claim 1 , wherein the sequence region is contained in cell-free DNA (cfDNA).

14. The composition of claim 1, wherein the liver cancer is hepatocellular carcinoma (HCC).

15. A kit for predicting the risk of developing liver cancer, comprising the composition according to any one of claims 1 to 14.

16. A method for providing information for predicting the risk of developing liver cancer, comprising the step of measuring the methylation level of at least one CpG site contained in the sequence region of bases 21810279 to 21810792 of human chromosome 12 in a biological sample isolated from a subject.

17. The method according to claim 16, wherein the CpG site comprises the CpG site at positions 21810750 to 21810751 of human chromosome 12.

18. The method of claim 16, wherein the region has the sequence represented by SEQ ID NO:

1.

19. The method of claim 16, further comprising the steps of: measuring the methylation level of at least one CpG site contained in the sequence region of human chromosome 17 at positions 29298021 to 29298631 in the biological sample; and / or measuring the methylation level of at least one CpG site contained in the sequence region of human chromosome 19 at positions 19738547 to 19739846 in the biological sample.

20. the at least one CpG site included in the sequence region of bases 29298021 to 29298631 of human chromosome 17 includes a CpG site located at bases 29298115 to 29298116, 29298118 to 29298119, 29298124 to 29298125, 29298136 to 29298137, 29298142 to 29298143, or 29298184 to 29298185 of human chromosome 17; The method according to claim 19, wherein the at least one CpG site contained in the sequence region of 19738547 to 19739846 of human chromosome 19 includes a CpG site located in the sequence of 19739407 to 19739408 of human chromosome 19.

21. The method according to claim 19, wherein the sequence region from 29298021 to 29298631 of human chromosome 17 has the sequence represented by SEQ ID NO: 2, and the sequence region from 19738547 to 19739846 of human chromosome 19 has the sequence represented by SEQ ID NO:

3.

22. 17. The method of claim 16, further comprising measuring the concentration of alpha fetoprotein (AFP) in the biological sample, collecting gender information of the subject, and / or collecting age information of the subject.

23. 17. The method of claim 16, wherein the methylation level is measured using primers to amplify target methylation sites of the sequence.

24. the primers include a forward primer and a reverse primer; one of the forward primer and the reverse primer comprises a methylated primer and an unmethylated primer; the methylation primer includes at least one CpG recognition site that recognizes a CpG sequence included in a sequence of a predetermined length including the target methylation site; the unmethylated primer includes at least one TpG recognition site that recognizes a TpG sequence included in a sequence of a predetermined length including the target methylation site; The method of claim 23, wherein the TpG sequence of the predetermined length sequence containing the target methylation site is a conversion of an unmethylated CpG sequence contained in the predetermined length sequence containing the target methylation site.

25. 17. The method of claim 16, further comprising the step of quantifying the methylation level of the CpG site.

26. 26. The method of claim 25, wherein the quantifying step comprises comparing the AUC (area under the curve) of normalized melting curves of the biological sample; a sample in which the CpG sites are 100% methylated; and a sample in which the CpG sites are 100% unmethylated, to quantify the degree of methylation of the CpG sites.

27. obtaining a melt curve for said biological sample; Obtaining a normalized melt curve of a biological sample having a known content ratio of the CpG site methylated sequence and the CpG site unmethylated sequence; and 17. The method of claim 16, further comprising the step of comparing the melting curve of the biological sample and the normalized melting curve to quantify the degree of methylation of the CpG sites.

28. 17. The method of claim 16, further comprising the step of comparing the methylation level with the methylation level of a control group.

29. The method of claim 16, further comprising a step of comparing the methylation level with the methylation level of a normal control group, and providing information that the subject is at risk of developing liver cancer if the methylation level is higher than the methylation level of the normal control group.

30. 17. The method of claim 16, wherein the biological sample is one or more selected from the group consisting of blood, plasma, serum, platelets, tissue, cells, stool, and urine.

31. 17. The method of claim 16, wherein the sequence is contained in cell-free DNA (cfDNA).

32. The method according to claim 16, wherein the liver cancer is hepatocellular carcinoma (HCC).

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