Non-invasive liver cancer occurrence detection method and used reagent set

Abstract

The invention discloses a non-invasive liver cancer occurrence detection method and a reagent set used by the same. The detection method comprises the following steps: performing biomarker identification of a differential methylation gene group in a computing system; performing, in a computing system, a quantitative measurement of the degree of methylation from the selected plurality of biomarkers in the group using a quantitative methylation-specific polymerase chain reaction; executing a formula calculation in the calculation system according to the measured methylation degree of the selected biomarker to obtain an M score of the selected biomarker; and evaluating the grade of the occurrence risk of the liver cancer by using the obtained M score in the computing system.

Description

Technical Field

[0001] The present application belongs to the field of biotechnology and relates to a method for detecting liver cancer occurrence, and more particularly to using differentially methylated genes as biomarkers and utilizing the methylation levels of these biomarkers to detect liver cancer occurrence. Background Art

[0002] Hepatocellular carcinoma (HCC) is considered one of the deadliest cancers worldwide, causing 830,000 deaths each year. Due to the rapid progression of the tumor, the number of deaths from liver cancer each year is almost equal to the total number of cases, highlighting its high malignancy, poor prognosis, and high mortality rate. The incidence of HCC varies by geographic location, which may be due to regional differences in exposure to hepatitis B virus (HBV) and hepatitis C virus (HCV). In addition to hepatitis virus infection, cirrhosis of the liver due to any cause is also a major risk factor for liver cancer.

[0003] HCC is resistant to chemotherapy, and no chemotherapy agent has been shown to improve overall survival. Surgical interventions such as partial hepatectomy and liver transplantation are the only treatments for HCC. However, less than 30% of HCC patients are suitable for surgery due to late diagnosis and the presence of multiple lesions in cirrhotic or fibrotic livers. Therefore, early detection of HCC is crucial to improving overall survival when effective treatment is available.

[0004] Although the sensitivity and specificity of serum alpha-fetoprotein (AFP) are not satisfactory, it is still the most widely used tumor marker for HCC screening and monitoring. Studies have shown that when the cut-off point of AFP is set at 20 ng / mL, the sensitivity of AFP for HCC in patients with cirrhosis ranges from 41% to 65% [1]. However, in the early stages of HCC progression, the detection rate may be as low as one-third, as 80% of small HCC cases do not show elevated AFP levels [2,3]. In contrast, elevated AFP levels can also be observed in other chronic liver diseases such as cirrhosis and liver inflammation, and other types of cancer including non-seminomatous germ cell tumors and gastrointestinal cancers [4]. Therefore, it is necessary to supplement AFP with additional biomarkers to improve the diagnostic accuracy, especially in the early stages of HCC.

[0005] In recent decades, DNA methylation has been increasingly recognized as a valuable biomarker for early detection and diagnosis of cancer. DNA methylation is a key mechanism regulating gene expression in normal cells and plays a role in many physiological events. Abnormal DNA methylation can lead to a variety of human diseases, including cancer. DNA methyltransferases catalyze DNA methylation by adding a methyl group to the carbon 5 position of cytosine residues in CpG dinucleotides. Methylation of the 5' region of a promoter or CpG island can lead to transcriptional repression of downstream genes. Accumulating evidence suggests that DNA hypermethylation can downregulate tumor suppressor genes and DNA repair genes, while DNA hypomethylation can upregulate oncogenes in the early stages of carcinogenesis [5,6]. DNA methylation involves the covalent binding of methyl groups to genomic DNA, making it more stable than protein or RNA marks. In addition, methylation markers can be detected in various types of liquid biopsies such as blood, urine, saliva, and feces, providing a non-invasive method for monitoring cancer progression[7].

[0006] It is crucial to develop new detection methods for liver cancer to improve early detection rates. DNA methylation biomarkers have important clinical value as markers for HCC diagnosis and prognosis. In our previous studies, we used a whole-genome approach to identify significant DNA methylation profiles in HCC cell lines and tissues [8-10].

[0007] The non-patent documents [1] to

[10] cited in the above description are listed below, and the disclosure contents of these non-patent documents are incorporated herein by reference.

[0008] [1]Samir Gupta, Stephen Bent, Jeffrey Kohlwes. Test characteristics of alpha-fetoprotein for detecting hepatocellular carcinoma in patients with hepatitis CA systematic review and critical analysis. Ann Intern Med.2003Jul1;139(1):46-50.doi:10.7326 / 0003-4819-139-1-200307010-00012.

[0009] [2]Jingjing Zong, Zhe Fan, Yewei Zhang. Serum Tumor Markers for EarlyDiagnosis of Primary Hepatocellular Carcinoma.J Hepatocell Carcinoma.2020Dec18;7:413-422.doi:10.2147 / JHC.S272762.

[0010] [3]Basim Saleh Samman,Albadr Hussein,Razan Saleh Samman,AbdulazizSaud Alharbi.Common Sensitive Diagnostic and Prognostic Markers inHepatocellular Carcinoma and Their Clinical Significance:A Review.Cureus.2022Apr 8;14(4):e23952.doi:10.7759 / cureus.23952。

[0011] [4]N Zamcheck,G Pusztaszeri.CEA,AFP and other potentialtumormarkers.CA Cancer J Clin.1975 Jul-Aug;25(4):204-14.doi:10.3322 / canjclin.25.4.204。

[0012] [5]Marina Bibikova,Bret Barnes,Chan Tsan,Vincent Ho,BrandyKlotzle,Jennie M Le,David Delano,Lu Zhang,Gary P Schroth,KevinL Gunderson,Jian-BingFan,Richard Shen.High density DNA methylationarray with single CpG siteresolution.Genomics.2011Oct;98(4):288-95.doi:10.1016 / j.ygeno.2011.07.007。

[0013] [6]Martin Widschwendter,Sophia Apostolidou,Elke Raum,DietrichRothenbacher,Heidi Fiegl,Usha Menon,Christa Stegmaier,Ian J Jacobs,Hermann Brenner Epigenotyping in peripheral blood cell DNA and breastcancerrisk:a proof of principle study.PLoS One.2008 Jul 16;3(7):e2656.doi:10.1371 / journal.pone.0002656。

[0014] [7]Huiyan Luo,Wei Wei,Ziyi Ye,Jiabo Zheng,Rui-Hua Xu.LiquidBiopsy ofMethylation Biomarkers in Cell-Free DNA.Trends Mol Med.2021 May;27(5):482-500.doi:10.1016 / j.molmed.2020.12.011。

[0015] [8]Chang-Yi Lu,Sen-Yung Hsieh,Yen-Jung Lu,Chi-Sheng Wu,Lih-ChyangChen,Shao-Jung Lo,Cheng-Tao Wu,Min-Yuan Chou,Tim Hui-Ming Huang,Yu-SunChang.Aberrant DNA methylation profile and frequentmethylation of KLK10 andOXGR1 genes in hepatocellular carcinoma.Genes Chromosomes Cancer.2009 Dec;48(12):1057-68.doi:10.1002 / gcc.20708。

[0016] [9]Chang-Yi Lu, Kai-Yuan Lin, Meng-Tsung Tien, Cheng-Tao Wu, Yih-HueiUen, Tzu-Ling Tseng. Frequent DNA methylation of MiR-129-2 and its potential clinical implication in hepatocellular carcinoma. GenesChromosomes Cancer.2013Jul;52(7):636-43.doi:10.1002 / gcc.22059.

[10] Chang-Yi Lu,Shih-Ya Chen,Hui-LingPeng,Pu-Yeh Kan,Wan-ChiChang,Chia-Jui Yen.Cell-free methylation markers withdiagnosticand prognostic potential in hepatocellularcarcinoma.Oncotarget.2017Jan 24;8(4):6406-6418.doi:10.18632 / oncotarget.14115. Summary of the invention

[0017] In view of the above problems, the present application proposes a non-invasive liver cancer occurrence detection method executed on a computing system. The computing system is used to identify specific differentially methylated genes as biomarkers for liver cancer occurrence detection, detect the methylation levels of these biomarkers, and use the M-scores of these biomarkers to evaluate the level of liver cancer occurrence risk.

[0018] In one embodiment, the proposed method includes the following steps: performing biomarker identification of the differentially methylated genes by detecting the methylation levels of all differentially methylated genes in a group consisting of a plurality of differentially methylated genes in a computing system, wherein the group consists of STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, BCAS3 gene, TSPYL5 gene and ZNF497 gene, wherein the computing system integrates a microprocessor and is designed to process, analyze, simulate and model biological data; in the computing system, using quantitative methylation-specific polymerase chain reaction (qMPCR) PCR; qMSP) performs quantitative measurement of the methylation levels of multiple biomarkers selected from the group, and the selected biomarkers are STRIP1 gene, PGAM2 gene, AAMP gene and ZNF497 gene; in the computing system, the formula M score = X1 + X2 × ln (STRIP1) + X3 × ln (PGAM2) + X4 × ln (AAMP) + X5 × ln (ZNF497) is calculated and logistic regression analysis is performed according to the measured methylation levels of the selected biomarkers to obtain the M score of the selected biomarker, wherein X1 is 1.266 to 3.242, X2 is 0.116 to 0.305, X3 is 0.114 to 0.233, X4 is 0.396 to 1.093, X5 is 0.099 to 0.211, ln (STRIP1) is the hyperbolic logarithm of the methylation level of the STRIP1 gene logarithm), ln(PGAM2) is the hyperbolic logarithm of the methylation level of the PGAM2 gene, ln(AAMP) is the hyperbolic logarithm of the methylation level of the AAMP gene, and ln(ZNF497) is the hyperbolic logarithm of the methylation level of the ZNF497 gene; and in the calculation system, the obtained M score is used to evaluate the level of liver cancer risk.

[0019] In one embodiment, the proposed method further comprises the following step: performing a receiver operating characteristic (ROC) analysis of the obtained M-score in the computing system.

[0020] In one embodiment, in the proposed method, the risk of developing liver cancer occurs when the obtained M-score is higher than a pre-confirmed reference value.

[0021] In one embodiment, the method of performing quantitative measurement of the methylation level of the selected biomarkers from the group using quantitative methylation-specific polymerase chain reaction (qMSP) in the computing system comprises the following steps: performing the following steps in the computing system based on the difference in Ct value between each of the selected biomarkers and a housing gene: [Ct(管家基因)-Ct(生物标记的甲基化基因)] ×100, where the Ct value is the value of a single data point in a real-time polymerase chain reaction (PCR) amplification graph. Preferably, the housekeeping gene is a gene selected from the group consisting of β-actin, GAPDH, HPRT, YWHAZ, ARBP, SDHA and UBC.

[0022] In one embodiment, the method of performing quantitative measurement of the methylation level of the selected biomarkers from the group using quantitative methylation-specific polymerase chain reaction (qMSP) in the computing system comprises: using a reagent kit for detection, the reagent kit comprising a plurality of primer and probe sets and a qPCR premix containing Taq DNA polymerase, dNTPs, MgCl2 and a buffer. The primer and probe sets each have a primer pair and a probe and each target gene is STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, BCAS3 gene, TSPYL5 gene and ZNF497 gene. Preferably, each primer pair includes a sense primer and an antisense primer, and the sense primer, antisense primer and probe of each primer and probe set each have a sequence associated with the corresponding target gene.

[0023] In one embodiment, in the proposed method, the methylation level of the STRIP1 gene is calculated by formula 2 [Ct (β-actin)-Ct(STRIP1)] ×100, the methylation degree of PGAM2 gene was calculated by formula 2 [Ct(β-actin)-Ct(PGAM2)] ×100, the methylation degree of AAMP gene was calculated by formula 2 [Ct(β-actin)-Ct(AAMP)] ×100, the methylation degree of ZNF497 gene was calculated by formula 2 [Ct(β-actin)-Ct(ZNF497)] ×100.

[0024] The present application also proposes a reagent kit used in the above-mentioned non-invasive liver cancer occurrence detection method, comprising: a primer pair and a probe for detecting the methylation degree of the STRIP1 gene; a primer pair and a probe for detecting the methylation degree of the ZNF649 gene; a primer pair and a probe for detecting the methylation degree of the CFAP57 gene; a primer pair and a probe for detecting the methylation degree of the GGTA1 gene; a primer pair and a probe for detecting the methylation degree of the PGAM2 gene; a primer pair and a probe for detecting the methylation degree of the FRMD4A gene. a primer pair and a probe to detect the methylation level of the GJB6 gene; a primer pair and a probe to detect the methylation level of the AAMP gene; a primer pair and a probe to detect the methylation level of the miR-203A gene; a primer pair and a probe to detect the methylation level of the SLC22A2 gene; a primer pair and a probe to detect the methylation level of the BCAS3 gene; a primer pair and a probe to detect the methylation level of the TSPYL5 gene; and a primer pair and a probe to detect the methylation level of the ZNF497 gene.

[0025] The present application also proposes another reagent kit used in the above-mentioned non-invasive liver cancer occurrence detection method, comprising: a primer pair and a probe for detecting the methylation level of the STRIP1 gene; a primer pair and a probe for detecting the methylation level of the PGAM2 gene; a primer pair and a probe for detecting the methylation level of the AAMP gene; and a primer pair and a probe for detecting the methylation level of the ZNF497 gene.

[0026] Biomarkers regulated by DNA methylation have been identified in the computational system as being involved in the development of liver cancer and as powerful predictors of liver cancer. In the present application, the M-score calculated using the computational system has been shown to be effective for the prediction of liver cancer. The predictive power of the M-score exceeds that of serum markers.

[0027] This application discloses the role of DNA methylation in predicting the occurrence of cancer in vivo. The M-score of biomarkers with differential methylation between liver cancer patients and non-tumor controls can be used for liver cancer diagnosis. This application compares the M-score with other clinical factors such as AFP, demonstrating that the M-score performs well in predicting the occurrence of liver cancer.

[0028] In order to make the above features and advantages of the present application more obvious and easy to understand, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1AShown is the methylation level of the STRIP1 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0030] Figure 1B Shown is the methylation level of the ZNF649 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0031] Figure 1C Shown is the methylation level of the CFAP57 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0032] Figure 1D Shown is the methylation level of the GGTA1 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0033] Figure 1E Shown is the methylation level of the PGAM2 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0034] Figure 1F Shown is the methylation level of the FRMD4A gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0035] Figure 1G Shown is the methylation level of the GJB6 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0036] Figure 1H Shown is the methylation level of AAMP gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0037] Fig. 1I Shown is the methylation level of miR-203A in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0038] Figure 1J Shown is the methylation level of the SLC22A2 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0039] Figure 1K Shown is the methylation level of the BCAS3 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0040] Figure 1L Shown is the methylation level of the TSPYL5 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0041] Figure 1M Shown is the methylation level of the ZNF497 gene in biological samples from HCC (liver cancer) subjects and healthy (non-tumor control) donors.

[0042] Figure 2A The receiver operating characteristic (ROC) curve of the M score calculated based on the STRIP1 gene, PGAM2 gene, AAMP gene, and ZNF497 gene in the receiver operating characteristic (ROC) analysis is shown.

[0043] Figure 2B The receiver operating characteristic (ROC) curve of AFP in the receiver operating characteristic (ROC) analysis is shown.

[0044] Figure 3 This is a flow chart of a non-invasive liver cancer occurrence detection method according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] The present application discloses a non-invasive method for detecting the occurrence of liver cancer. The embodiments of the biomarkers and corresponding detection / verification / identification / quantification methods described below are only preferred examples. For ordinary technicians in this field, any modification that does not deviate from the scope and spirit of this application, including additions and / or replacements, still belongs to the scope of this application. In order not to lose the focus of this application, specific details that are already understood by ordinary technicians in this field will not be fully described. However, the disclosed content can still enable ordinary technicians in this field to implement this application without excessive experiments. In addition, if the meaning of the technical terms described below is different from the meaning of the commonly used terms in this field, the meaning in the text shall prevail, and the drawings compared in the text are intended to express the meaning related to the features of this application, and are not fully drawn according to the actual size, which is also stated in advance.

[0046] In one embodiment, a non-invasive method for detecting liver cancer for assessing a subject's risk of developing liver cancer includes but is not limited to: Figure 3 Steps 101 to 104 are described in detail below. The subject referred to includes but is not limited to mammals, such as humans, apes, monkeys, cats, dogs, rabbits, guinea pigs, rats or mice. In one embodiment, the subject referred to is a human.

[0047] Step 101: Perform biomarker identification on a differentially methylated gene group consisting of STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, BCAS3 gene, TSPYL5 gene and ZNF497 gene by detecting the methylation level or status of these differentially methylated genes in bio-samples from subjects. The bio-samples include but are not limited to blood, plasma, serum, liver tissue, saliva, sputum, semen, intestinal digestive fluid, respiratory lavage and feces. In one embodiment, the bio-sample is plasma or serum. The above genes are used as potential biomarkers for liver cancer in this application.

[0048] Step 101 is preferably performed in a computing system, such as a computer, or a system equipped with a microprocessor capable of computing data and processing biological signals or biological data. The computing system is used to process, analyze, simulate and model biological signals or biological data, and can use specialized algorithms, software and high-performance hardware to study and solve complex problems in biology, bioinformatics, computational biology and other life sciences. Common applications of the computing system include genome sequencing and assembly, molecular modeling and simulation, bioinformatics database, gene expression analysis, protein structure prediction and drug design. Key components that enable the computing system to manage large amounts of biological data include clustering algorithms, artificial intelligence / machine learning models, distributed storage and cloud computing, and advanced visualization capabilities. In one example, the computing system may include a biomaterial input device, a biomaterial processing unit, a biosignal detection unit and a data processing unit integrated with a microprocessor.

[0049] Step 102: Perform quantitative measurement of the methylation levels of multiple biomarkers selected from the differentially methylated gene group using quantitative methylation-specific PCR (qMSP) in a computing system. In one embodiment, the selected biomarkers are STRIP1 gene, PGAM2 gene, AAMP gene and ZNF497 gene.

[0050] Step 103: Perform a formula calculation and logistic regression analysis in the computing system according to the measured methylation level or state of the selected biomarker to obtain the M-score of the selected biomarker. In one embodiment, the selected biomarkers are STRIP1 gene, PGAM2 gene, AAMP gene and ZNF497 gene.

[0051] Step 104: Using the M score in the computing system to evaluate the risk of liver cancer for the subject. In one embodiment, the risk of the subject suffering from liver cancer occurs when the M score is higher than a pre-confirmed reference value.

[0052] In one embodiment, the non-invasive liver cancer occurrence detection method for assessing the risk of a subject suffering from liver cancer may further include the following steps: Figure 3 shown.

[0053] Step 105: Evaluate the effect of risk level assessment of the selected biomarker by performing a receiver operating characteristic (ROC) curve analysis of the M-score in the computing system.

[0054] In the process of detecting the methylation degree or state of STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, BCAS3 gene, TSPYL5 gene and ZNF497 gene in step 101, available analysis methods include but are not limited to quantitative methylation-specific polymerase chain reaction (qMSP), combined bisulfite restriction analysis (Combined Bisulfite Restriction Analysis; COBRA), bisulfite sequencing (Bisulfite Sequencing), pyrosequencing (Pyrosequencing), next generation sequencing (Next Generation sequencing; NGS) and DNA methylation array chip analysis (DNA Methylation Array Chip Analysis). These analysis methods are all executed in a computing system.

[0055] In one embodiment, the methylation level or state of STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, BCAS3 gene, TSPYL5 gene and ZNF497 gene in step 101 is detected by quantitative methylation-specific polymerase chain reaction (qMSP). In one example, the biological sample is 65 plasma samples from 20 healthy donors and 45 HCC patients. Figures 1A to 1M As shown in Figure 1, STRIP1, PGAM2, FRMD4A, AAMP, SLC22A2, and TSPYL5 genes in HCC patients showed higher methylation levels than those in healthy donors, with statistically significant differences, i.e., P values ​​less than 0.05. On the other hand, as shown in Table 1, the methylation status of three potential biomarkers, namely, ZNF649 gene, GGTA1 gene, and GJB6 gene, was found to be statistically significantly associated with the occurrence of HCC, i.e., P values ​​less than 0.05. Therefore, STRIP1, PGAM2, FRMD4A, AAMP, SLC22A2, TSPYL5, ZNF649, GGTA1, and GJB6 genes can be used as biomarkers for the diagnosis of HCC.

[0056] Table 1

[0057]

[0058] In one embodiment, the methylation level or state of the STRIP1 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 1 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 2 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 3 listed in Table 2. In one embodiment, the methylation level or state of the ZNF649 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 4 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 5 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 6 listed in Table 2. In one embodiment, the methylation level or state of the CFAP57 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 7 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 8 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 9 listed in Table 2. In one embodiment, the methylation level or state of the GGTA1 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 10 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 11 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 12 listed in Table 2. In one embodiment, the methylation level or state of the PGAM2 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 13 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 14 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 15 listed in Table 2. In one embodiment, the methylation level or state of the FRMD4A gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 16 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 17 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 18 listed in Table 2.In one embodiment, the methylation level or state of the GJB6 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 19 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 20 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 21 listed in Table 2. In one embodiment, the methylation level or state of the AAMP gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 22 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 23 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 24 listed in Table 2. In one embodiment, the methylation level or state of miR-203A can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 25 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 26 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 27 listed in Table 2. In one embodiment, the methylation level or state of SLC22A2 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO: 28 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO: 29 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO: 30 listed in Table 2. In one embodiment, the methylation level or state of the BCAS3 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO:31 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO:32 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO:33 listed in Table 2. In one embodiment, the methylation level or state of the TSPYL5 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO:34 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO:35 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO:36 listed in Table 2.In one embodiment, the methylation level or status of the ZNF497 gene can be detected using a primer pair including a forward primer and a reverse primer and a probe, wherein the forward primer has at least 85% sequence similarity to SEQ ID NO:37 listed in Table 2, the reverse primer has at least 85% sequence similarity to SEQ ID NO:38 listed in Table 2, and the probe has at least 85% sequence similarity to SEQ ID NO:39 listed in Table 2.

[0059] In one embodiment, the selected biomarkers in step 102 are the STRIP1 gene, the PGAM2 gene, the AAMP gene and the ZNF497 gene.

[0060] The following examples describe the use of quantitative methylation-specific polymerase chain reaction and primer pairs to quantitatively measure the methylation level or status of methylated genes.

[0061] In one example, cell-free DNA from 400 μl of plasma from a subject was bisulfite-converted via sodium bisulfite treatment. The bisulfite-converted cell-free DNA was then amplified using a fluorescent probe for real-time quantitative methylation-specific polymerase chain reaction (qMSP). Each reaction involved 1x qPCR Master Mix, 0.5 μM of each primer set, and 0.25 μM of probe in a total volume of 20 μl. Amplification was performed on a StepOnePlus real-time polymerase chain reaction (PCR) system (Thermo Fisher Scientific). The methylation level of the methylated gene selected as a biomarker can be calculated using the following formula: [Ct(管家基因)-Ct(生物标记)] ×100, where Ct is the value of a single data point from real-time PCR amplification and the housekeeping gene is a gene selected from β-actin, GAPDH, HPRT, YWHAZ, ARBP, SDHA, and UBC. In one example, the degree of methylation is based on the difference in Ct values ​​between β-actin and the selected biomarker using Formula 2 [Ct(β-actin)-Ct(生物标记)] × 100 calculation. In one example, primer pairs and probes for detecting the methylation levels of STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, and BCAS3 gene are shown in Table 2.

[0062] Table 2

[0063]

[0064]

[0065]

[0066] In one embodiment, during the execution of step 103, the selected biomarkers are the STRIP1 gene, the PGAM2 gene, the AAMP gene and the ZNF497 gene, and the formula calculation utilizes the following formula: M score = X1 + X2 × ln (STRIP1) + X3 × ln (PGAM2) + X4 × ln (AAMP) + X5 × ln (ZNF497).

[0067] In the above formula, X1 ranges from 1.266 to 3.242, X2 ranges from 0.116 to 0.305, X3 ranges from 0.114 to 0.233, X4 ranges from 0.396 to 1.093, and X5 ranges from 0.099 to 0.211. In the above formula, ln(STRIP1) represents the hyperbolic logarithm of the methylation level of the STRIP1 gene, and the methylation level of the STRIP1 gene is expressed by formula 2 [Ct(β-actin)-Ct(STRIP1)] ×100. In the above formula, ln(PGAM2) represents the hyperbolic logarithm of the methylation level of the PGAM2 gene, and the methylation level of the PGAM2 gene is calculated through formula 2 [Ct(β-actin)-Ct(PGAM2)] ×100. In the above formula, ln(AAMP) represents the hyperbolic logarithm of the methylation level of the AAMP gene, and the methylation level of the AAMP gene is calculated through formula 2 [Ct(β-actin)-Ct(AAMP)] × 100. In the above formula, ln(ZNF497) represents the hyperbolic logarithm of the methylation level of the ZNF497 gene, and the methylation level of the ZNF497 gene is calculated through formula 2 [Ct(β-actin)-Ct(ZNF497)] ×100.

[0068] In one embodiment, step 104 is performed using an M score calculated based on the methylation levels of STRIP1, PGAM2, AAMP and ZNF497 genes. In one example, when the M score is higher than a pre-determined reference value, the subject has a risk of developing liver cancer.

[0069] In one embodiment, step 104 may further include the step of determining a pre-confirmed reference value by comparing the methylation levels of the STRIP1 gene, PGAM2 gene, AAMP gene, and ZNF497 gene in a group of subjects known not to have liver cancer and another group of subjects known to have liver cancer, and obtaining a cutoff point from a receiver operating characteristic curve of a receiver operating characteristic analysis. In one example, the pre-confirmed reference value is 0.54, and when the M score is higher than 0.54, the subject has a risk of developing liver cancer.

[0070] Based on the above disclosure, in one embodiment, a reagent kit for detecting methylation biomarkers in the detection of liver cancer occurrence may be composed of a plurality of primers and probe sets of these methylation biomarkers and a qPCR premix, wherein each primer and probe set may respectively include the STRIPE1 gene (SEQ ID No. 1-3 in Table 2), the PGAM2 gene (SEQ ID No. 13-15 in Table 2), the AAMP gene (SEQ ID No. 22-24 in Table 2), the ZNF497 gene (SEQ ID No. 37-39 in Table 2) and the β-actin gene (SEQ ID No. 40-42 in Table 2), and the qPCR premix may include TaqDNA polymerase, dNTPs, MgCl2 and a buffer.

[0071] In one embodiment, step 105 implements receiver operating characteristic (ROC) curve analysis to evaluate the performance or diagnostic effect of methylation biomarkers of STRIP1 gene, PGAM2 gene, AAMP gene and ZNF497 gene.

[0072] Figure 2A Receiver operating characteristic (ROC) curves of the M-score using the STRIP1 gene, PGAM2 gene, AAMP gene, and ZNF497 gene as biomarkers are shown. Figure 2B The receiver operating characteristic (ROC) curve for AFP (cutoff at 20 ng / mL) is shown. The receiver operating characteristic (ROC) curve analysis estimates the area under the receiver operating characteristic curve (AUC), cutoff value, sensitivity, and specificity. Figure 2A From the M score, the AUC (area under the curve) was 0.936 (95% CI: 0.869-1, P < 0.001), while from Figure 2BBased on the AFP in the , the AUC was 0.634 (95% CI: 0.495-0.773, P = 0.091). These P values ​​show that the M score is statistically significant. Figure 2A and Figure 2B In the figure, the horizontal axis is "1-specificity" and the vertical axis is "sensitivity".

[0073] As shown in Table 3, the receiver operating characteristic (ROC) analysis determined the sensitivity, specificity, precision, false positive rate, false negative rate, and accuracy of the M score and AFP, respectively. For the M score, these values ​​were 91.11%, 80.00%, 91.11%, 20.00%, 8.89%, and 87.69%, respectively. For AFP, these values ​​were 26.83%, 100.00%, 100.00%, 0.00%, 73.17%, and 50.82%, respectively. These results indicate that the M score performs better than AFP in detecting liver cancer.

[0074] Table 3

[0075]

[0076]

[0077] Biomarkers regulated by DNA methylation have been identified in the computational system as being involved in the development of liver cancer and as powerful predictors of liver cancer. In the present application, the M-score calculated using the computational system has been shown to be effective for the prediction of liver cancer. The predictive power of the M-score exceeds that of serum markers.

[0078] The above detailed description has provided specific descriptions for some feasible embodiments of the present application. These embodiments are not intended to limit the patent scope of the present application. Any equivalent implementation or modification that does not deviate from the technical spirit of the present application should be included in the patent scope of the present application.

Claims

1. A non-invasive method for detecting liver cancer, characterized in that: include: In a computing system, biomarker identification of differentially methylated genes is performed by detecting the methylation levels of all differentially methylated genes in a group consisting of a plurality of differentially methylated genes, the group consisting of STRIP1 gene, ZNF649 gene, CFAP57 gene, GGTA1 gene, PGAM2 gene, FRMD4A gene, GJB6 gene, AAMP gene, miR-203A, SLC22A2 gene, BCAS3 gene, TSPYL5 gene and ZNF497 gene, the computing system integrating a microprocessor; In the computing system, quantitative measurement of the methylation levels of a plurality of biomarkers selected from the group is performed using quantitative methylation-specific polymerase chain reaction (qMSP), wherein the selected biomarkers are the STRIP1 gene, the PGAM2 gene, the AAMP gene, and the ZNF497 gene; In the calculation system, the formula M score = X1 + X2 × ln (STRIP1) + X3 × ln (PGAM2) + X4 × ln (AAMP) + X5 × ln (ZNF497) is calculated and the logistic regression analysis is performed according to the measured methylation degree of the selected biomarker to obtain the M score of the selected biomarker, wherein X1 is 1.266 to 3.242, X2 is 0.116 to 0.305, and X3 is 0.114 to 0. 233, X4 is 0.396 to 1.093, X5 is 0.099 to 0.211, ln(STRIP1) is the hyperbolic logarithm of the methylation degree of the STRIP1 gene, ln(PGAM2) is the hyperbolic logarithm of the methylation degree of the PGAM2 gene, ln(AAMP) is the hyperbolic logarithm of the methylation degree of the AAMP gene, ln(ZNF497) is the hyperbolic logarithm of the methylation degree of the ZNF497 gene; and In the calculation system, the obtained M score is used to evaluate the level of liver cancer risk.

2. The method according to claim 1, characterized in that Also includes: In the computing system, a receiver operating characteristic curve analysis of the obtained M-score is performed.

3. The method according to claim 1, characterized in that The risk of developing liver cancer occurs when the obtained M-score is higher than a pre-determined reference value.

4. The method according to claim 1, characterized in that: The step of performing the quantitative measurement of the methylation level of the biomarkers selected from the group using the quantitative methylation-specific polymerase chain reaction (qMSP) in the computing system comprises: In the computing system, based on the Ct value difference between each of the selected biomarkers and a housekeeping gene, formula 2 is executed. [Ct(管家基因)-Ct(生物标记的甲基化基因)] ×100, the Ct value is the value of a single data point in a real-time polymerase chain reaction (PCR) amplification graph.

5. The method according to claim 4, characterized in that The housekeeping gene is a gene selected from the group consisting of β-actin, GAPDH, HPRT, YWHAZ, ARBP, SDHA and UBC.

6. The method according to claim 1, characterized in that The step of performing the quantitative measurement of the methylation level of the biomarkers selected from the group using the quantitative methylation-specific polymerase chain reaction (qMSP) in the computing system comprises: The detection is performed using a reagent kit, the reagent kit comprising: A plurality of primer and probe sets, each of which comprises a primer pair and a probe and each of which targets the STRIP1 gene, the ZNF649 gene, the CFAP57 gene, the GGTA1 gene, the PGAM2 gene, the FRMD4A gene, the GJB6 gene, the AAMP gene, the miR-203A, the SLC22A2 gene, the BCAS3 gene, the TSPYL5 gene and the ZNF497 gene; and A qPCR master mix containing Taq DNA polymerase, dNTPs, MgCl2 and buffer.

7. The method according to claim 6, characterized in that The primer pair includes a positive strand primer and a reverse strand primer, and the positive strand primer, the reverse strand primer and the probe each have a sequence associated with the corresponding target gene.

8. The method according to claim 1, characterized in that The methylation level of the STRIP1 gene was calculated using formula 2 [Ct(β-actin)-Ct(STRIP1)] × 100, the methylation degree of the PGAM2 gene was calculated by formula 2 [Ct (β-actin)-Ct(PGAM2)] × 100, the methylation degree of the AAMP gene is calculated by formula 2 [Ct(β-actin)-Ct(AAMP)] × 100, the methylation degree of the ZNF497 gene was calculated by formula 2 [Ct(β-actin)-Ct(ZNF497)] ×100.

9. A reagent kit for use in the method according to claim 1, characterized in that: include: A primer pair and a probe for detecting the methylation degree of the STRIP1 gene; A primer pair and a probe for detecting the methylation degree of the ZNF649 gene; A primer pair and a probe for detecting the methylation degree of the CFAP57 gene; A primer pair and a probe for detecting the methylation degree of the GGTA1 gene; A primer pair and a probe for detecting the methylation degree of the PGAM2 gene; A primer pair and a probe for detecting the methylation degree of the FRMD4A gene; A primer pair and a probe for detecting the methylation degree of the GJB6 gene; A primer pair and a probe for detecting the methylation degree of the AAMP gene; A primer pair and a probe for detecting the methylation degree of miR-203A; A primer pair and a probe for detecting the methylation degree of the SLC22A2 gene; A primer pair and a probe for detecting the methylation degree of the BCAS3 gene; A primer pair and a probe for detecting the methylation degree of the TSPYL5 gene; and A primer pair and a probe are used to detect the methylation degree of the ZNF497 gene.

10. A reagent kit for use in the method according to claim 1, characterized in that: include: A primer pair and a probe for detecting the methylation degree of the STRIP1 gene; A primer pair and a probe for detecting the methylation degree of the PGAM2 gene; A primer pair and a probe for detecting the methylation degree of the AAMP gene; and A primer pair and a probe are used to detect the methylation degree of the ZNF497 gene.

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