Detection method for early hepatocellular carcinoma and reagent set used in detection method

Abstract

The invention discloses a method for detecting early hepatocellular carcinoma and a reagent set used by the method. The detection method comprises the following steps: performing biomarker identification of a group of methylated genes with difference 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 methylation degree of the selected biomarker to obtain an M score of the selected biomarker; and performing a risk level assessment of hepatocellular carcinoma using the resulting M fraction or a combination of M fraction and serum alpha fetoprotein in a computing system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biotechnology, and relates to a method for detecting early hepatocellular carcinoma (HCC). In particular, the present application proposes a methylation prediction system and a kit for detecting early hepatocellular carcinoma. BACKGROUND

[0002] Hepatocellular carcinoma (HCC) is a major global health issue, ranking as the sixth most commonly diagnosed cancer and the third leading cause of cancer-related deaths. This highly malignant tumor has a poor prognosis and high mortality rate, with almost equal number of deaths and new diagnoses each year. The incidence of HCC varies by geographic location, possibly due to regional differences in exposure to hepatitis B virus (HBV) and hepatitis C virus (HCV). In addition, cirrhosis of the liver and hepatitis virus infection of any cause significantly increase the risk of HCC.

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

[0004] Despite its unsatisfactory sensitivity and specificity, serum alpha-fetoprotein (AFP) remains the most widely used tumor marker for HCC screening and surveillance. Studies have shown that the sensitivity of AFP for HCC in cirrhotic patients ranges from 41% to 65% when the cut-off value is set at 20 ng / mL [1]. However, the detection rate can be as low as one-third in the early stages of HCC progression, as 80% of small HCC cases do not exhibit 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, as well as other types of cancer including nonseminomatous germ cell tumors and gastrointestinal cancers [4]. Therefore, it is necessary to complement AFP with additional biomarkers to improve the accuracy of diagnosis, particularly in the early stages of HCC.

[0005] In recent decades, DNA methylation has increasingly been recognized as a valuable biomarker for early detection and diagnosis of cancer. DNA methylation is a key mechanism that regulates the expression of normal cellular genes and plays a role in many physiological events. Abnormal DNA methylation can lead to various 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. Increasing evidence suggests that DNA hypermethylation can downregulate tumor suppressor genes and DNA repair genes, while DNA hypomethylation can upregulate oncogenes at 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 markers. Furthermore, methylation markers can be detected in various types of liquid biopsies such as blood, urine, saliva, and stool, providing a non-invasive method for cancer progression monitoring [7].

[0006] In our previous study, we used a whole-genome approach to identify significant DNA methylation profiles in HCC cell lines and tissues

[0007] [8-10]. We selected a set of eight genes and miRNAs regulated by DNA methylation and measured their methylation levels in plasma cell-free DNA. The predictive ability of these markers for HCC diagnosis was evaluated independently and in combination with the existing HCC marker AFP to assess their potential for future clinical applications. In addition, we developed a methylation prediction system and reagent kit specifically for the early diagnosis of HCC.

[0008] The non-patent literatures [1] to

[10] cited in the above description are listed as follows, the disclosures of which are incorporated herein by reference.

[0009] [1] Um T-H, Kim H, Oh B-K, Kim MS, Kim KS, Jung G, et al. Aberrant CpG island hypermethylation in dysplastic nodules and early HCC of hepatitis B virus-related human multistep hepatocarcinogenesis. Journal of Hepatology. 2011; 54: 939-47.

[0010] [2] Lu C-Y, Hsieh S-Y, Lu Y-J, Wu C-S, Chen L-C, Lo S-J, et al. Aberrant DNA methylation profile and frequent methylation of KLK10 and OXGR1 genes in hepatocellular carcinoma. Genes Chromosom Cancer. 2009; 48: 1057-68.

[0011] [3] Kitamura Y, Shirahata A, Sakuraba K, Goto T, Mizukami H, Saito M, et al. Aberrant methylation of the Vimentin gene in hepatocellular carcinoma. Anticancer Res. 2011;31 : 1289-91.

[0012] [4] Zamcheck N, Pusztaszeri G. CEA, AFP and Other Potential Tumor Markers. CA: A Cancer Journal for Clinicians. 1975;25:204-14.

[0013] [5] Lu C-Y, Chen S-Y, Peng H-L, Kan P-Y, Chang W-C, Yen C-J. Cell-free methylation markers with diagnostic and prognostic potential in hepatocellular carcinoma. Oncotarget. 2017;8:6406-18.

[0014] [6] Samman BS, Hussein A, Samman RS, Alharbi AS. Common Sensitive Diagnostic and Prognostic Markers in Hepatocellular Carcinoma and Their Clinical Significance: A Review. Cureus [Internet]. 2022 [cited 2023 Sep 27] Available from: https: / / www.cureus.com / articles / 92250-common-sensitive-diagnostic-and-prognostic-markers-in-hepatocellular-carcinoma-and-their-clinical-significance-a-review

[0015] [7] Debruyne EN, Delanghe JR. Diagnosing and monitoring hepatocellular carcinoma with alpha-fetoprotein: New aspects and applications. Clinica Chimica Acta. 2008;395: 19-26.

[0016] [8] Widschwendter M, Apostolidou S, Raum E, Rothenbacher D, Fiegl H, Menon U, et al. Epigenotyping in Peripheral Blood Cell DNA and Breast Cancer Risk: A Proof of Principle Study. Goodyear M, editor. PLoS ONE. 2008;3: e2656.

[0017] [9] Lu C-Y, Lin K-Y, Tien M-T, Wu C-T, Uen Y-H, Tseng T-L. Frequent DNA methylation of MiR-129-2 and its potential clinical implication in hepatocellular carcinoma: miR-129-2 Hypermethylation in HCC. Genes Chromosomes Cancer. 2013; n / a-n / a.

[0018]

[10] Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288-95. SUMMARY

[0019] In view of the above, the present application proposes a method for detecting early-stage hepatocellular carcinoma, which is executed on a computing system. The method identifies specific differentially methylated genes as biomarkers for hepatocellular carcinoma occurrence detection on the computing system, and detects the methylation degree of the biomarkers, and performs risk level assessment of hepatocellular carcinoma using the M-score of the biomarkers.

[0020] In one embodiment, the method comprises the following steps: in a computing system, performing biomarker identification of differentially methylated genes by detecting the methylation level of each differentially methylated gene from a group of differentially methylated genes, the group consisting of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene and miR-129-2, the computing system incorporating a microprocessor; in the computing system, performing quantitative measurement of the methylation level of selected biomarkers from the group, the selected biomarkers being APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene and miR-129-2, using quantitative methylation-specific PCR (qMSP); in the computing system, performing calculation of the formula M score = X1+X2Xln(APC)+X3Xln(COX2)+X4Xln(miR-203)+X5Xln(RASSF1A)+X6Xln(VIM)+X7Xln(RGS10)+X8Xln(ST8SIA6)+X9Xln(miR-129-2) and logistic regression analysis according to the measured methylation level of the selected biomarkers to obtain the M score of the selected biomarkers, X1 being 3.701 to 4.914, X2 being 0.117 to 2.229, X3 being 0.104 to 0.176, X4 being 0.114 to 0.254, X5 being 0.125 to 0.237, X6 being 0.213 to 0.317, X7 being 0.075 to 1.087, X8 being 0.085 to 0.108, and X9 being 0.047 to 0.086ln(APC) represents the hyperbolic logarithm of the methylation level of the APC gene, ln(COX2) represents the hyperbolic logarithm of the methylation level of the COX2 gene, ln(miR-203) represents the hyperbolic logarithm of the methylation level of miR-203, ln(RASSF1A) represents the hyperbolic logarithm of the methylation level of the RASSF1A gene, ln(VIM) represents the hyperbolic logarithm of the methylation level of the VIM gene, ln(RGS10) represents the hyperbolic logarithm of the methylation level of the RGS10 gene, ln(ST8SIA6) represents the hyperbolic logarithm of the methylation level of the ST8SIA6 gene, and ln(miR-129-2) represents the hyperbolic logarithm of the methylation level of miR-129-2; and in the computing system, performing risk stratification of hepatocellular carcinoma using the obtained M-score or the combination of the M-score and serum alpha-fetoprotein (AFP).

[0021] In one embodiment, the proposed method further comprises the step of performing receiver operating characteristic (ROC) analysis of the obtained M-score or the combination of the M-score and serum alpha-fetoprotein (AFP) in the computing system.

[0022] In one embodiment, the step of performing quantitative measurement of the methylation level of the selected biomarkers from the group of differentially methylated genes in the computing system in the proposed method comprises the step of performing calculation of Formula 2 [Ct(管家基因)-Ct(生物标记的甲基化基因)] based on the Ct value difference between each of the selected biomarkers and a housing gene in the computing system, where the Ct value is the value of a single data point from real-time Polymerase Chain Reaction (PCR) amplification plots. Preferably, the housing gene is a gene selected from the group consisting of β-actin, GAPDH, HPRT, YWHAZ, ARBP, SDHA and UBC.

[0023] In one embodiment, the step of performing quantitative measurement of the methylation level of the biomarker selected from the group of differentially methylated genes in the computing system in the proposed method comprises performing the detection using a kit comprising primer and probe sets and a qPCR master mix containing Taq DNA polymerase, dNTPs, MgCl2 and buffer. The primer and probe sets each have a primer-pair and a probe and each targets the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2 as the target gene. Preferably, each primer-pair comprises a sense primer and an antisense primer, and the sense primer, the antisense primer and the probe of each primer and probe set each has a sequence associated with the corresponding target gene.

[0024] In one embodiment, the methylation level of the APC gene in the proposed method is calculated by Formula 2 [Ct(β-actin)-Ct(APC)] x 100, the methylation level of the COX2 gene is calculated by Formula 2 [Ct(β-actin)-Ct(COX2)] x 100, the methylation level of miR-203 is calculated by Formula 2 [Ct(β-actin)-Ct(miR-203)] x 100, the methylation level of the RASSF1A gene is calculated by Formula 2 [Ct(β-actin)-Ct(RASSF1A)] x 100, the methylation level of the VIM gene is calculated by Formula 2 [Ct(β-actin)-Ct(VIM)] x 100, the methylation level of the RGS10 gene is calculated by Formula 2 [Ct(β-actin)-Ct(RGS10)] x 100, the methylation level of the ST8SIA6 gene is calculated by Formula 2 [Ct(β-actin)-Ct(ST8SIA6)] x 100, the methylation level of miR-129-2 is calculated by Formula 2 [Ct(β-actin)-Ct(miR-129-2)] x 100, wherein the Ct value in the formula is the value from a single data point in a real-time polymerase chain reaction (PCR) amplification plot.

[0025] The present application also provides a kit for use in the detection method of early hepatocellular carcinoma as described above, comprising: a primer pair and a probe for detecting the methylation level of the APC gene; a primer pair and a probe for detecting the methylation level of the COX2 gene; a primer pair and a probe for detecting the methylation level of miR-203; a primer pair and a probe for detecting the methylation level of the RASSF1A gene; a primer pair and a probe for detecting the methylation level of the VIM gene; a primer pair and a probe for detecting the methylation level of the RGS10 gene; a primer pair and a probe for detecting the methylation level of the ST8SIA6 gene; and a primer pair and a probe for detecting the methylation level of miR-129-2.

[0026] The biomarkers modulated by DNA methylation were identified in the computational system as being involved in the early development of hepatocellular carcinoma and can serve as a powerful predictor for early diagnosis of liver cancer. In the present application, the M-score calculated by the computational system has been proven to be effective in predicting liver cancer. The predictive ability of the M-score exceeds that of the serum alpha-fetoprotein marker.

[0027] The present application discloses the role of DNA methylation in predicting the development of cancer in vivo. The M-score of the biomarkers with differential methylation between hepatocellular carcinoma patients and non-tumor control groups can be used for liver cancer diagnosis. The present application compares the M-score with other clinical factors such as AFP and proves that the M-score performs better in predicting the development of liver cancer.

[0028] In order to make the above features and advantages of the present application more apparent, the following embodiments are described in detail below, together with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1A The methylation level of the selected biomarker APC gene in the biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhosis patients, is shown.

[0030] Figure 1B The methylation level of the selected biomarker COX2 gene in the biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhosis patients, is shown.

[0031] Figure 1C The methylation level of the selected biomarker miR-203 in the biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhosis patients, is shown.

[0032] Figure 1DThe degree of methylation of the selected biomarker RASSFlA gene in biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhotic patients, is shown.

[0033] Figure 1E The degree of methylation of the selected biomarker VIM gene in biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhotic patients, is shown.

[0034] Figure 1F The degree of methylation of the selected biomarker RGS10 gene in biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhotic patients, is shown.

[0035] Figure 1G The degree of methylation of the selected biomarker miR-129-2 in biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhotic patients, is shown.

[0036] Figure 1H The degree of methylation of the selected biomarker ST8SIA6 in biological samples of HCC subjects and normal controls, including healthy donors, hepatitis and cirrhotic patients, is shown.

[0037] Figure 2A The receiver operating characteristic (ROC) curve of the M-score based on the selected biomarkers, including the APC gene, the COX2 gene, miR-203, the RASSFlA gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2, in a receiver operating characteristic (ROC) analysis is shown.

[0038] Figure 2B The receiver operating characteristic (ROC) curve of AFP in a receiver operating characteristic (ROC) analysis with a cutoff of 4.5 ng / mL is shown.

[0039] Figure 2C The receiver operating characteristic (ROC) curve of AFP in a receiver operating characteristic (ROC) analysis with a cutoff of 20 ng / mL is shown.

[0040] Figure 2D The receiver operating characteristic (ROC) curve of the combination of AFP and the M-score based on the previous eight selected biomarkers in a receiver operating characteristic (ROC) analysis is shown.

[0041] Figure 3ADistribution of the M-score calculated based on the eight selected biomarkers in healthy donors, hepatitis patients, cirrhosis patients and HCC patients, with a cutoff of 0.478 ng / mL.

[0042] Figure 3B Distribution of AFP values in healthy donors, hepatitis patients, cirrhosis patients and HCC patients, with cutoffs of 4.5 ng / mL and 20 ng / mL, respectively, as indicated by the lower and upper horizontal lines in the figure.

[0043] Figure 3C Distribution of AFP and the M-score calculated based on the eight selected biomarkers in healthy donors, hepatitis patients, cirrhosis patients and HCC patients, with a cutoff of 0.424 ng / mL.

[0044] Figure 4A Distribution of the M-score calculated based on the eight selected biomarkers in different stages of HCC, with the X-axis representing the TNM staging classification, and the cutoffs indicated by the horizontal lines.

[0045] Figure 4B Distribution of AFP in different stages of HCC, with the AFP values being converted by natural logarithm, and the X-axis representing the TNM staging classification, and the cutoffs indicated by the horizontal lines.

[0046] Figure 4C Distribution of AFP and the M-score calculated based on the eight selected biomarkers in different stages of HCC, with the X-axis representing the TNM staging classification, and the cutoffs indicated by the horizontal lines.

[0047] Figure 5 Flow chart of the method for early detection of hepatocellular carcinoma according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The present application discloses a method for detecting early hepatocellular carcinoma. The embodiments of the biomarkers and the corresponding detection / verification / identification / quantification methods described herein are only preferred exemplary. Any modification, including addition and / or replacement, without departing from the scope and spirit of the present application, is still within the scope of the present application. In order not to deviate the focus of the present application, specific details that are obvious to those skilled in the art will not be described in detail. However, the disclosed content still enables those skilled in the art to implement the present application without excessive experiments. In addition, the meanings of the technical terms described herein are subject to the meanings in the text, and the drawings referred to in the text are intended to express the meanings related to the features of the present application, and are not drawn according to the actual size.

[0049] In one embodiment, a method for early hepatocellular carcinoma detection for assessing the risk of a subject to develop liver cancer comprises, but is not limited to, the steps 101-105 as shown below. Figure 5 The subject 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 is a human.

[0050] Step 101: Perform biomarker identification on a group of differentially methylated genes consisting of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene, and miR-129-2 by detecting the methylation level or status of these differentially methylated genes or miRNAs in bio-samples from the subject, respectively. The bio-samples include, but are not limited to, blood, plasma, serum, liver tissue, saliva, sputum, semen, intestinal digestive, respiratory lavage, and feces. In one embodiment, the bio-samples are plasma or serum. The genes or miRNAs mentioned above are potential biomarkers for early hepatocellular carcinoma detection.

[0051] 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 employ specialized algorithms, software, and high-performance hardware to study and solve complex problems in the fields of biology, bioinformatics, computational biology, and other life sciences. Common applications of the computing system include genome sequencing and assembly, molecular modeling and simulation, bioinformatics databases, 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 can include a biological material input device, a biological material processing unit, a biological signal detection unit, and a data processing unit integrated with a microprocessor.

[0052] Step 102: performing quantitative measurement of the methylation level of selected biomarkers from the differential methylation gene panel using quantitative methylation-specific PCR (qMSP) in the computing system. In one embodiment, the selected biomarkers are APC gene, COX2 gene, miR-203, RASSFlA gene, VIM gene, RGS10 gene, ST8SIA6 gene, and miR-129-2.

[0053] Step 103: performing formula calculation and logistic regression analysis based on the measured methylation level or status of the selected biomarkers to obtain M-score of the selected biomarkers in the computing system. In one embodiment, the selected biomarkers are APC gene, COX2 gene, miR-203, RASSFlA gene, VIM gene, RGS10 gene, ST8SIA6 gene, and miR-129-2.

[0054] Step 104: performing risk level assessment of hepatocellular carcinoma in the subject using the obtained M-score or the combination of M-score and AFP in the computing system. In one embodiment, the risk of the subject suffering from liver cancer occurs when the obtained M-score or the combination of M-score and AFP is higher than a pre-confirmed reference value.

[0055] In one embodiment, the early hepatocellular carcinoma detection method for assessing the risk of the subject suffering from liver cancer can further comprise the following steps of Figure 5 .

[0056] Step 105: assessing the effectiveness of the risk level assessment of the selected biomarkers by performing receiver operating characteristic (ROC) curve analysis of the obtained M-score or the combination of M-score and AFP in the computing system.

[0057] In the detection of the methylation level or state of the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2 in step 101, the analysis methods that can be used include, but are not limited to, quantitative methylation-specific polymerase chain reaction (qMSP), combined bisulfite restriction analysis (COBRA), bisulfite sequencing, pyrosequencing, next generation sequencing (NGS) and DNA methylation array chip analysis. These analysis methods are all performed in a computing system.

[0058] In one embodiment, the detection of the methylation level or state of the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2 in step 101 is performed using quantitative methylation-specific polymerase chain reaction (qMSP). In one example, the biological samples are 318 plasma samples from 159 normal controls and 159 HCC patients, as shown in Table 1. The 318 plasma samples include healthy donors (n = 52), chronic hepatitis B patients (n = 61), chronic hepatitis B patients with cirrhosis (n = 46) and HBV-related HCC patients (n = 159). As shown in Table 1, the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2 in the HCC patients exhibit a higher methylation level than in the normal controls, with a statistically significant difference, i.e., a P value less than 0.05. Therefore, the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2 can be used as biomarkers for the diagnosis of HCC. Figures 1A-1H

[0059] Table 1

[0060]

[0061]

[0062] ​In one embodiment, the detection of the methylation level or status of the APC gene can use a primer pair comprising a forward primer and a reverse primer, 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 a probe has at least 85% sequence similarity to SEQ ID NO: 3 listed in Table 2. In one embodiment, the detection of the methylation level or status of the COX2 gene can use a primer pair comprising a forward primer and a reverse primer, 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 a probe has at least 85% sequence similarity to SEQ ID NO: 6 listed in Table 2. In one embodiment, the detection of the methylation level or status of the miR-203 can use a primer pair comprising a forward primer and a reverse primer, 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 a probe has at least 85% sequence similarity to SEQ ID NO: 9 listed in Table 2. In one embodiment, the detection of the methylation level or status of the RASSF1A gene can use a primer pair comprising a forward primer and a reverse primer, 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 a probe has at least 85% sequence similarity to SEQ ID NO: 12 listed in Table 2. In one embodiment, the detection of the methylation level or status of the VIM gene can use a primer pair comprising a forward primer and a reverse primer, 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 a probe has at least 85% sequence similarity to SEQ ID NO: 15 listed in Table 2. In one embodiment, the detection of the methylation level or status of the RGS10 gene can use a primer pair comprising a forward primer and a reverse primer, 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 a probe has at least 85% sequence similarity to SEQ ID NO: 18 listed in Table 2.In one embodiment, the detection of the methylation level or status of the ST8SIA6 gene can use a primer pair comprising a forward primer having at least 85% sequence similarity to SEQ ID NO: 19 listed in Table 2, a reverse primer having at least 85% sequence similarity to SEQ ID NO: 20 listed in Table 2, and a probe having at least 85% sequence similarity to SEQ ID NO: 21 listed in Table 2. In one embodiment, the detection of the methylation level or status of miR-129-2 can use a primer pair comprising a forward primer having at least 85% sequence similarity to SEQ ID NO: 22 listed in Table 2, a reverse primer having at least 85% sequence similarity to SEQ ID NO: 23 listed in Table 2, and a probe having at least 85% sequence similarity to SEQ ID NO: 24 listed in Table 2.

[0063] In one embodiment, the selected biomarkers in step 102 are APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene, and miR-129-2.

[0064] The following examples describe the quantitative measurement of the methylation level or status of a methylated gene using quantitative methylation-specific polymerase chain reaction and primer pairs.

[0065] In one example, 400 μΐ of cell-free DNA from the plasma of a subject is bisulfite-converted via sodium bisulfite treatment. The bisulfite-converted cell-free DNA is then amplified using real-time quantitative methylation-specific polymerase chain reaction (qMSP) with fluorescent probes. Each reaction involves lx qPCR Master Mix, 0.5 μΜ of each primer set, and 0.25 μΜ of probe in a total volume of 20 μΐ. Amplification is performed on a StepOnePlus Real-Time PCR System (Thermo Fisher Scientific). The methylation level of the selected methylated gene as a biomarker can be calculated by the following equation: 2 [Ct(管家基因)-Ct(生物标记)] x 100, where Ct is the value of a single data point from real-time PCR amplification, and housekeeping gene is a gene selected from the group consisting of β-actin, GAPDH, HPRT, YWHAZ, ARBP, SDHA, and UBC. In one example, the methylation level is calculated based on the difference in Ct values between β-actin and the selected biomarker using equation 2 [Ct(β-actin)-Ct(生物标记)]Calculated by × 100. In one example, primer pairs and probes for detecting the methylation levels of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene, and miR-129-2 are shown in Table 2.

[0066] Table 2

[0067]

[0068]

[0069]

[0070] In one embodiment, during step 103 , the selected biomarkers are the APC gene, the COX2 gene, the miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene, and the miR-129-2, and the calculation formula utilizes the following formula: M-score = X1 + X2 × ln(APC) + X3 × ln(COX2) + X4 × ln(miR-203) + X5 × ln(RASSF1A) + X6 × ln(VIM) + X7 × ln(RGS10) + X8 × ln(ST8SIA6) + X9 × ln(miR-129-2).

[0071] In the above formula, X1 ranges from 3.701 to 4.914, X2 ranges from 0.117 to 2.229, X3 ranges from 0.104 to 0.176, X4 ranges from 0.114 to 0.254, X5 ranges from 0.125 to 0.237, X6 ranges from 0.213 to 0.317, X7 ranges from 0.075 to 1.087, X8 ranges from 0.085 to 0.108, and X9 ranges from 0.047 to 0.086. In the above formula, ln(APC) represents the hyperbolic logarithm of the methylation level of the APC gene, and the methylation level of the APC gene is expressed by Formula 2 [Ct(β-actin)-Ct(APC)] In the above formula, ln(COX2) represents the hyperbolic logarithm of the methylation degree of COX2 gene, and the methylation degree of COX2 gene is calculated by formula 2 [Ct(β-actin)-Ct(COX2)] In the above formula, ln(miR-203) represents the hyperbolic logarithm of the methylation degree of miR-203, and the methylation degree of miR-203 is calculated by formula 2 [Ct(β-actin)-Ct(miR-203)] In the above formula, ln(RASSF1A) represents the hyperbolic logarithm of the methylation degree of the RASSF1A gene, and the methylation degree of the RASSF1A gene is calculated by formula 2 [Ct(β-actin)-Ct(RASSF1A)] The M-score is calculated by the formula: M-score = 4.802 + 0.128 x ln(APC) + 0.154 x ln(COX2) + 0.116 x ln(miR203) + 0.148 x ln(RASSF1A) + 0.257 x ln(VIM) + 0.088 x ln(RGS10) + 0.082 x ln(ST8SIA6) + 0.059 x ln(miR-129-2). In the above formula, ln(VIM) represents the hyperbolic logarithm of the methylation level of the VIM gene, and the methylation level of the VIM gene is calculated by the formula 2: ln(VIM) = log2(VIM+1) - log2(VIM-1). [Ct(β-actin)-Ct(VIM)] The M-score is calculated by the formula: M-score = 4.802 + 0.128 x ln(APC) + 0.154 x ln(COX2) + 0.116 x ln(miR203) + 0.148 x ln(RASSF1A) + 0.257 x ln(VIM) + 0.088 x ln(RGS10) + 0.082 x ln(ST8SIA6) + 0.059 x ln(miR-129-2). In the above formula, ln(RGS10) represents the hyperbolic logarithm of the methylation level of the RGS10 gene, and the methylation level of the RGS10 gene is calculated by the formula 2: ln(RGS10) = log2(RGS10+1) - log2(RGS10-1). [Ct(β-actin)-Ct(RGS10)] The M-score is calculated by the formula: M-score = 4.802 + 0.128 x ln(APC) + 0.154 x ln(COX2) + 0.116 x ln(miR203) + 0.148 x ln(RASSF1A) + 0.257 x ln(VIM) + 0.088 x ln(RGS10) + 0.082 x ln(ST8SIA6) + 0.059 x ln(miR-129-2). In the above formula, ln(ST8SIA6) represents the hyperbolic logarithm of the methylation level of the ST8SIA6 gene, and the methylation level of the ST8SIA6 gene is calculated by the formula 2: ln(ST8SIA6) = log2(ST8SIA6+1) - log2(ST8SIA6-1). [Ct(β-actin)-Ct(ST8SIA6)] The M-score is calculated by the formula: M-score = 4.802 + 0.128 x ln(APC) + 0.154 x ln(COX2) + 0.116 x ln(miR203) + 0.148 x ln(RASSF1A) + 0.257 x ln(VIM) + 0.088 x ln(RGS10) + 0.082 x ln(ST8SIA6) + 0.059 x ln(miR-129-2). In the above formula, ln(miR-129-2) represents the hyperbolic logarithm of the methylation level of the miR-129-2, and the methylation level of the miR-129-2 is calculated by the formula 2: ln(miR-129-2) = log2(miR-129-2+1) - log2(miR-129-2-1). [Ct(β-actin)-Ct(miR-129-2)] The M-score is calculated by the formula: M-score = 4.802 + 0.128 x ln(APC) + 0.154 x ln(COX2) + 0.116 x ln(miR203) + 0.148 x ln(RASSF1A) + 0.257 x ln(VIM) + 0.088 x ln(RGS10) + 0.082 x ln(ST8SIA6) + 0.059 x ln(miR-129-2). In one example, M-score = 4.802 + 0.128 x ln(APC) + 0.154 x ln(COX2) + 0.116 x ln(miR203) + 0.148 x ln(RASSF1A) + 0.257 x ln(VIM) + 0.088 x ln(RGS10) + 0.082 x ln(ST8SIA6) + 0.059 x ln(miR-129-2).

[0072] In one example, step 104 is performed by using the M-score calculated from the methylation levels of the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene, and the miR-129-2. In one example, the subject is at risk of developing liver cancer when the M-score or the combination of the M-score and AFP is higher than a pre-identified reference value. In other words, under this premise, the risk of developing liver cancer increases with the increase of the M-score.

[0073] In one example, step 104 further comprises a step of determining the pre-identified reference value by comparing the methylation levels of the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene, and the miR-129-2 in a group of subjects known not to have liver cancer and another group of subjects known to have liver cancer, respectively, and obtaining a cutoff from a receiver operating characteristic curve of the subjects. In one example, the pre-identified reference value is 0.478, and the subject is at risk of developing liver cancer when the M-score is higher than 0.478.

[0074] In one embodiment, step 104 can further comprise a step of determining the pre-confirmed reference value by comparing the methylation levels of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene, miR-129-2 and AFP in a group of subjects known not to have liver cancer and another group of subjects known to have liver cancer, respectively, and obtaining a cutoff from a receiver operating characteristic analysis of the subjects. In one example, the pre-confirmed reference value is 0.424, and when the M-score is higher than 0.424, the subject is at risk of having liver cancer.

[0075] Based on the above disclosure, in one embodiment, a kit for detecting methylation biomarkers in early diagnosis of liver cancer can be composed of primer and probe sets for each of the methylation biomarkers and a qPCR master mix, wherein each primer and probe set can comprise APC gene (SEQ ID No. 1-3 of Table 2), COX2 gene (SEQ ID No. 4-6 of Table 2), miR-203 (SEQ ID No. 7-9 of Table 2), RASSF1A gene (SEQ ID No. 10-12 of Table 2), VIM gene (SEQ ID No. 13-15 of Table 2), RGS10 gene (SEQ ID No. 16-18 of Table 2), ST8SIA6 gene (SEQ ID No. 19-21 of Table 2), miR-129-2 (SEQ ID No. 22-24 of Table 2) and β-actin gene (SEQ ID No. 25-27 of Table 2), and the qPCR master mix can comprise Taq DNA polymerase, dNTPs, MgCl2 and buffer.

[0076] In one embodiment, step 105 implements a receiver operating characteristic (ROC) curve analysis to evaluate the performance or diagnostic effectiveness of the methylation biomarkers of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene and miR-129-2. In another embodiment, step 105 implements a receiver operating characteristic (ROC) curve analysis to evaluate the performance or diagnostic effectiveness of the methylation biomarkers of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene, miR-129-2 and AFP.

[0077] Figure 2AThe receiver operating characteristic (ROC) curve of the M-score with the eight selected biomarkers of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene and miR-129-2 is shown. Figure 2B The receiver operating characteristic (ROC) curve of AFP (cut-off at 4.5 ng / mL) is shown. Figure 2C The receiver operating characteristic (ROC) curve of AFP (cut-off at 20 ng / mL) is shown. Figure 2D The receiver operating characteristic (ROC) curve of the M-score with the eight selected biomarkers of APC gene, COX2 gene, miR-203, RASSF1A gene, VIM gene, RGS10 gene, ST8SIA6 gene and miR-129-2 in combination with AFP is shown. From the M-score in Figure 2A The area under the curve (AUC) was 0.875 (P<0.01) from the M-score in Figure 2B The area under the curve (AUC) was 0.635 (P<0.01) from AFP (cut-off at 4.5 ng / mL) in Figure 2C The area under the curve (AUC) was 0.614 (P=0.003) from AFP (cut-off at 20 ng / mL) in Figure 2D The area under the curve (AUC) was 0.905 (P<0.01) from the combination of the M-score and AFP in Figures 2A-2D In the ROC curve, the horizontal axis is characterized by "1 - specificity" and the vertical axis is characterized by "sensitivity".

[0078] As shown in Table 3, the receiver operating characteristic (ROC) analysis determined the sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), false positive rate (FP) and false negative rate (FN) of the M-score, AFP and the combination of the M-score and AFP, respectively.

[0079] AFP demonstrated excellent specificity and positive predictive value at a cutoff of 20 ng / mL, but its sensitivity was only 23.5%. Our previous studies found that the optimal cutoff point for AFP was 4.5 ng / mL, which increased sensitivity to 43.1%, but this was still unacceptable for diagnosis. The M-score showed approximately 80% for Sen, Spe, PPV, NPV, and ACC, and approximately 20% for FP and FN. Combining AFP with the M-score significantly improved diagnostic performance in terms of Sen (86.3%), NPV (87.8%), and FN (12.2%). These results indicate that the M-score is superior to current serum tumor markers and that the integration of the M-score with AFP provides more accurate detection for HCC diagnosis.

[0080] Table 3

[0081]

[0082]

[0083] To further evaluate the specificity of the M score, we examined the M score and AFP levels in HCC and non-tumor controls, including healthy donors, hepatitis patients, and cirrhosis patients. AFP values ​​were converted to natural logarithms before analysis. The median AFP levels in healthy donors, hepatitis patients, cirrhosis patients, and HCC were 0.8961, 0.8755, 1.1663, and 1.2892, respectively. There was no significant difference in AFP levels between tumor and non-tumor controls ( Figure 3B ). Regardless of the cutoff point used, 4.5 or 20 ng / mL, the median AFP level in HCC patients was below the cutoff point, indicating that more than 50% of HCC patients could not be detected using any AFP cutoff point. On the other hand, the M score in HCC patients (median 0.7613) was significantly higher than that in non-tumor controls (medians: healthy donors: 0.2745, hepatitis patients: 0.2173, cirrhosis patients: 0.2833) ( Figure 3A As for the combination of M score and AFP, it showed excellent ability to distinguish between non-tumor patients and HCC patients ( Figure 3C ).

[0084] The following evaluates the detection rates of M-score and AFP for early HCC. Based on the cutoff point of M-score, the detection rates of M-score for stages I, II, and III were 75.0%, 75.0%, and 81.8%, respectively. Figure 4A In contrast, the detection rate of AFP increased from 17.4% in stage I to 36.4% in stage III ( Figure 4B). Although AFP shows period-dependent characteristics, the detection rate for patients with stage I HCC is still very low. In combination with the M-score and AFP, the detection rate can be improved to 91.3% for stage I, 86.2% for stage II, and 90.9% for stage III ( Figure 4C ). These results show that the combination of the M-score and AFP can be a very powerful tool for early diagnosis of HCC.

[0085] The biomarkers modulated by DNA methylation were identified in the computational system as being involved in the early development of hepatocellular carcinoma and can be a powerful predictor of liver cancer. In this application, the M-score calculated using the computational system has been shown to be effective for the prediction of very early liver cancer. The predictive power of the M-score exceeds that of the serum alpha-fetoprotein marker.

[0086] The above detailed description has been described for some possible embodiments of the present application, which are not intended to limit the scope of the patent of the present application, and equivalent implementations or changes made without departing from the spirit of the art of the present application shall be included in the scope of the patent of the present application.

Claims

1. A method for detecting early stage hepatocellular carcinoma, characterized by, Comprising: In a computing system, biomarker identification of differentially methylated genes is performed by detecting the methylation level of each of a plurality of differentially methylated genes consisting of a panel of the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2, the computing system incorporating a microprocessor; In the computing system, quantitative measurement of the methylation level of a plurality of biomarkers selected from the panel is performed using quantitative methylation-specific polymerase chain reaction (qMSP), the biomarkers selected being the APC gene, the COX2 gene, miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene and miR-129-2; In the computing system, a formula calculation and logistic regression analysis are performed according to the measured methylation level of the biomarkers selected to obtain an M-score of the biomarkers selected; And In the computing system, risk level assessment of hepatocellular carcinoma is performed using the M-score obtained or the M-score in combination with serum alpha-fetoprotein (AFP).

2. The method of claim 1, wherein, The formula is: M-score = X1 + X2*ln(APC) + X3*ln(COX2) + X4*ln(miR-203) + X5*ln(RASSF1A) + X6*ln(VIM) + X7*ln(RGS10) + X8*ln(ST8SIA6) + X9*ln(miR-129-2), wherein X1 is 3.701 to 4.914, X2 is 0.117 to 2.229, X3 is 0.104 to 0.176, X4 is 0.114 to 0.254, X5 is 0.125 to 0.237, X6 is 0.213 to 0.317, X7 is 0.075 to 1.087, X8 is 0.085 to 0.108, X9 is 0.047 to 0.086, ln(APC) represents the hyperbolic logarithm of the methylation level of the APC gene, ln(COX2) represents the hyperbolic logarithm of the methylation level of the COX2 gene, ln(miR-203) represents the hyperbolic logarithm of the methylation level of miR-203, ln(RASSF1A) represents the hyperbolic logarithm of the methylation level of the RASSF1A gene, ln(VIM) represents the hyperbolic logarithm of the methylation level of the VIM gene, ln(RGS10) represents the hyperbolic logarithm of the methylation level of the RGS10 gene, ln(ST8SIA6) represents the hyperbolic logarithm of the methylation level of the ST8SIA6 gene, and ln(miR-129-2) represents the hyperbolic logarithm of the methylation level of miR-129-2. Further comprising:

3. The method of claim 1, wherein, ​ In the computing system, a receiver operating characteristic curve analysis of the M-score or the M-score in combination with serum alpha-fetoprotein (AFP) is performed.

4. The method of claim 1, wherein, The step of performing the quantitative measurement of the degree of methylation of the biomarker selected from the group 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, a calculation of Formula 2 is performed [Ct(管家基因)-Ct(生物标记的甲基化基因)] x 100, the Ct value being a value from a single data point in a real-time polymerase chain reaction (PCR) amplification plot.

5. The method of claim 4, wherein, The housekeeping gene is a gene selected from the group consisting of beta-actin, GAPDH, HPRT, YWHAZ, ARBP, SDHA, and UBC.

6. The method of claim 1, wherein, The step of performing the quantitative measurement of the degree of methylation of the biomarker selected from the group in the computing system comprises: The detection is performed using a reagent kit comprising: a plurality of primer and probe sets, each comprising a primer pair and a probe and each targeting the APC gene, the COX2 gene, the miR-203, the RASSF1A gene, the VIM gene, the RGS10 gene, the ST8SIA6 gene, and the miR-129-2; a qPCR premix containing Taq DNA polymerase, dNTPs, MgCl2, and buffer.

7. The method of claim 6, wherein, The primer pair comprises a forward primer and a reverse primer, and the forward primer, the reverse primer, and the probe each have a sequence associated with the corresponding target gene.

8. The method of claim 1, wherein, The methylation degree of the APC gene is calculated by Formula 2 [Ct (β-actin)-Ct(APC)] The methylation degree of the COX2 gene is calculated by Formula 2 [Ct(β-actin)-Ct(COX2)] The methylation degree of the miR-203 is calculated by Formula 2 [Ct(β-actin)-Ct(miR-203)] The methylation degree of the RASSF1A gene is calculated by Formula 2 [Ct(β-actin)-Ct(RASSF1A)] The methylation degree of the VIM gene is calculated by Formula 2 [Ct(β-actin)-Ct(VIM)] The methylation degree of the RGS10 gene is calculated by Formula 2 [Ct(β-actin)-Ct(RGS10)] The methylation degree of the ST8SIA6 gene is calculated by Formula 2 [Ct(β-actin)-Ct(ST8SIA6)] The methylation degree of the miR-129-2 is calculated by Formula 2 [Ct (β-actin)-Ct(miR-129-2)] The Ct value in the formula is a value from a single data point in a real-time polymerase chain reaction (PCR) amplification graph.

9. A reagent set for use in the method according to claim 1, characterized in that comprises: a primer pair and a probe for detecting the degree of methylation of the APC gene; a primer pair and a probe for detecting the degree of methylation of the COX2 gene; a primer pair and a probe for detecting the degree of methylation of the miR-203; a primer pair and a probe for detecting the degree of methylation of the RASSF1A gene; a primer pair and a probe for detecting the degree of methylation of the VIM gene; a primer pair and a probe for detecting the degree of methylation of the RGS10 gene; a primer pair and a probe for detecting the degree of methylation of the ST8SIA6 gene; and a primer pair and a probe for detecting the degree of methylation of the miR-129-2. ​

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