A methylation marker, detection reagent and detection model for detecting liver cancer
By detecting methylation markers of liver cancer in the chr1:34792772-34792982 region and combining specific probes and model evaluation, the problems of insufficient sensitivity and specificity in early diagnosis of liver cancer in existing technologies are solved, and efficient and low-cost early liver cancer risk assessment and diagnosis are achieved.
Patent Information
- Application Number
- CN202411259048.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-09
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2044-09-09
AI Technical Summary
Existing technologies have low sensitivity and specificity in the early diagnosis of liver cancer, resulting in many patients being in the late stage when diagnosed, and radical treatments such as surgical resection are no longer feasible. In addition, the accuracy of serum alpha-fetoprotein tests is insufficient, making it difficult to effectively improve the detection rate and diagnostic efficiency of liver cancer.
Methylation markers in the chr1:34792772-34792982 region were used for detection. Combined with methylation fluorescence quantitative PCR and chip detection technology, the CpG island methylation level in the gene promoter region was detected. Specific probes and primers were used for sample analysis, and the HCC_score model was established for risk assessment.
It achieves high-sensitivity and high-specificity early detection of liver cancer, can identify high-risk patients in the early stages, reduce detection costs and time, and improve diagnostic efficiency. The AUC reaches 0.833, and the sensitivity and specificity are both 83.33%.
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Figure CN119220671B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of molecular biology detection, specifically, to the field of liver cancer detection, and more specifically, relates to methylation markers, detection reagents and detection models for detecting liver cancer. Background Art
[0002] According to the World Health Organization, primary liver cancer (PLC) ranks sixth in incidence and third in mortality among all malignant tumors. Hepatocellular carcinoma (HCC) accounts for 85% of all PLC cases, and patients have a low 5-year survival rate (10%-19%). Globally, there are 292 million HCC-related deaths, over half of which are related to HBV infection, making HBV infection a major factor in HCC malignancy. Clinical observations have led to a consensus among clinicians on the pathogenic role of HBV in the progression of liver disease from hepatitis to cirrhosis and further to HCC. At the time of diagnosis, most HCC patients are already in stage C or D (Barcelona Clinic Liver Cancer [BCLC] staging), making radical treatment options such as surgical resection no longer feasible. In contrast, the 5-year survival rate for BCLC stage A HCC is as high as 70%. Serum alpha-fetoprotein (AFP) testing combined with biennial liver ultrasound is currently the mainstay of HCC screening in high-risk patients. However, AFP has a low accuracy, with a specificity of 85%-90% and a sensitivity of 18%-60%, and is particularly inefficient for early-stage liver tumors.
[0003] Progress in epigenetic research has far-reaching significance for the early diagnosis and treatment of liver cancer. Evidence shows that hypermethylation of CpG islands in tumor suppressor gene promoters is an important mechanism of gene inactivation. Transcriptional inactivation of different genes will affect the cell cycle, DNA repair, apoptosis, etc., and is closely related to the occurrence and development of cancer. Gene methylation refers to the process in which cytosine (C) in CpG dinucleosides on DNA molecules selectively adds methyl groups to form 5'-methylcytosine under the action of enzymes. In patients with liver cancer, methylation of CpG islands in gene promoter regions is a relatively common phenomenon. Clinically, it is believed that the detection of methylated gene markers in tissue cells and body fluids of patients with liver cancer is an effective auxiliary detection method for liver cancer, which has the value of improving the detection rate of liver cancer. If it is combined with cytopathological testing, it can greatly improve the efficiency of liver cancer diagnosis, shorten the detection cycle, and improve the accuracy of diagnosis.
[0004] Therefore, there is a need in the art for some markers, in particular, markers with good sensitivity and specificity in early-stage liver cancer. Summary of the Invention
[0005] In view of this, in a first aspect, the present invention provides a methylation marker for assisting in the detection of liver cancer:
[0006] chr1:34792772-34792982.
[0007] The sensitivity, specificity, and AUC for liver cancer detection using the marker of the present invention were 83.33%, 83.33%, and 0.833, respectively. The marker of the present invention requires only a 210-bp probe length (one probe) to detect liver cancer in clinical settings, saving both cost and time. Furthermore, it can detect patients at high risk of liver cancer malignancy at an early stage of the malignancy process with high sensitivity and specificity, resulting in a highly cost-effective test.
[0008] Specifically, the methylation marker of the present invention is located at the sequence shown at position 34792772 to position 34792982 on chromosome 1 of the human genome, specifically, as shown below:
[0009] ACCTGGTGGAGAGAACAGAGGAGAGGAAGGGTAGGTCAGCCCCTCTTGCCCCTCCCCACCGCAAATCACACAGGCCCACCTCCCCGGAGTGGGTTTAGGGAGTCTGCACCTCCCAGTCCCCGCCCCCGCCCTCTCCAGCGCCCGCCGCCCTCCCCGTCGCGTTTCCTGCCCCCACCCCGCCCCTCTGCGCTATTTAAGGCGCCCCC.
[0010] In a second aspect, the present invention provides a composition for assisting in the detection of methylation markers for liver cancer, the composition comprising a detection reagent for detecting methylation levels in the following regions:
[0011] chr1:34792772-34792982.
[0012] Among them, chr1:34792772-34792982 shows a section of the CpG island in the gene promoter region.
[0013] In the present invention, "CpG island" is the abbreviation of cytosine (C)-phosphate (P)-guanine (G), which refers to some regions rich in CpG dinucleotides on the genome, with a length of 300 to 3000 bp.
[0014] In some embodiments, the detection reagent of the present invention can be used to detect the methylation level of a CpG island or a sequence on a CpG island in a corresponding gene region present in a sample.
[0015] In the present invention, a "sample" is a biological sample selected from an individual, specifically, for example, a sample selected from a histological section, a tissue biopsy / paraffin-embedded tissue, and the like.
[0016] In the present invention, "detection reagent" refers to a reagent for detecting the methylation level of a gene in a sample, wherein the methylation level is measured by amplification-sequencing, chip detection, or methylation fluorescence quantitative PCR.
[0017] In some specific embodiments, the methylation level detection reagent can also be a detection reagent for detecting the average methylation level of a gene fragment.
[0018] In some specific embodiments, the methylation level detection reagent can also be a detection reagent for detecting one or more methylation sites within a gene segment.
[0019] In some specific embodiments, the detection reagents include but are not limited to nucleic acid primers and sequencing Tag sequences for measuring methylation levels by amplification-sequencing.
[0020] In some specific embodiments, the detection reagent includes but is not limited to a chip, wherein the chip is a methylation chip having probes that specifically bind to methylated regions, and the chip is used to measure methylation levels.
[0021] In some specific embodiments, the detection reagents include, but are not limited to, nucleic acid primers and nucleic acid probes for measuring methylation levels by methylation fluorescence quantitative PCR.
[0022] Furthermore, the detection reagent also includes an internal standard primer and an internal standard probe.
[0023] In a specific embodiment, the target of the internal standard primer and probe is the ACTB gene.
[0024] When the detection reagent includes nucleic acid primers and nucleic acid probes, the detection reagent detects the methylation level of nucleic acid in the sample through methylation fluorescent quantitative PCR.
[0025] In the present invention, "methylation fluorescent quantitative PCR" refers to converting the region to be detected by sulfite or digesting it with a methylation-sensitive restriction endonuclease, and then performing fluorescent quantitative PCR detection using primers and probes specifically designed for the detection target, thereby obtaining the methylation level of the region to be detected.
[0026] The above reagent combination may also include other reagents, specifically, for example, various reagents required for sample pre-treatment or pre-processing, such as a sample release agent for extracting sample nucleic acid, a purification agent for purifying sample nucleic acid, bisulfite or bisulfite used for conversion, etc.
[0027] In a fourth aspect, the present invention provides a model for assisting in the detection of liver cancer, wherein the model is:
[0028] HCC_score=50+50*[(M-1 / 2*(N_M+C_M)] / [1 / 2*abs(C_M-N_M)]
[0029] Where M is the methylation value of the marker described in the present invention for the subject, N_M is the mean methylation value for healthy individuals in the model (0.37), and C_M is the mean methylation value for liver cancer patients in the model (0.47). When HCC_score >= 50, the subject is predicted to be at high risk for liver cancer; when HCC_score < 50, the subject is predicted to be at low risk for cancer and classified as healthy.
[0030] In a fifth aspect, the present invention provides a device comprising:
[0031] at least one processor; and
[0032] a memory communicatively connected to at least one of the processors; wherein,
[0033] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement any of the above-mentioned models for assisting in detecting liver cancer.
[0034] In some embodiments, the device further includes at least one input device and at least one output device; in the device, the processor, memory, input device, and output device are connected via a bus.
[0035] In a sixth aspect, a storage medium is provided, wherein the storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement any of the above-mentioned models for assisting in the detection of liver cancer.
[0036] In some embodiments, the storage medium is a computer-readable storage medium.
[0037] In a seventh aspect, the present invention provides a device comprising
[0038] A model module, configured to execute any one of the aforementioned models for assisting in the detection of liver cancer; and
[0039] Detection module, detects the methylation level of chr1:34792772-34792982 region.
[0040] In an eighth aspect, the present invention provides use of the above-mentioned markers or models in preparing a kit or device for assisting in the detection of liver cancer.
[0041] Furthermore, the kit or device for assisting in the detection of liver cancer is a kit or device for assisting in the detection of liver cancer using a blood sample.
[0042] In a ninth aspect, the present invention provides a kit for detecting methylation genes for liver cancer, the kit comprising the composition or model as described above.
[0043] Furthermore, the kit also includes, but is not limited to, at least one of a reagent for extracting nucleic acid, a reagent for purifying nucleic acid, and bisulfite.
[0044] Furthermore, the kit also includes a negative sample.
[0045] Specifically, the negative sample is human genomic DNA that has been sequenced to verify that there is no target gene methylation.
[0046] Furthermore, the kit also includes dNTPs, Mg 2+ , at least one of a methylation-sensitive restriction endonuclease, a PCR buffer, and a hot-start enzyme.
[0047] Furthermore, the methylation-sensitive restriction endonuclease includes at least one of HpaII, HinP1I and HhaI.
[0048] Furthermore, the range of the final concentration of each component is as follows: Mg 2+ 1~6mM, dNTPs 1~80mM, methylation-sensitive restriction endonuclease 0.01~30U, primer 0.1~40μM, probe 0.1~20μM. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] Figure 1 This is the AUC result graph of the marker of the present invention;
[0050] Figure 2 This is the AUC result graph of the comparative marker. DETAILED DESCRIPTION
[0051] The present invention will be described in detail below in conjunction with specific embodiments and examples, and the advantages and various effects of the present invention will be more clearly presented. It should be understood by those skilled in the art that these specific embodiments and examples are for illustrating the present invention, rather than for limiting the present invention.
[0052] Example 1: Screening of methylation genes
[0053] Methylation gene sequences were determined using data from healthy people, hepatitis B patients, cirrhosis patients, and HCC patients. The DMRs with significant differences (methylation value difference between groups > 0.1 and p-value < 0.05) between the three comparisons (healthy individuals vs. hepatitis B patients, hepatitis B patients vs. cirrhosis patients, and cirrhosis patients vs. liver cancer patients) were calculated using a bioinformatics algorithm. It was found that there was only one identical DMR (chr1: 34792772-34792982) in the three groups, thus screening out the DMR marker chr1: 34792772-34792982 for HCC.
[0054] Example 2: Detection of marker methylation levels
[0055] Targeted bisulfite sequencing (TBS) was performed using 10–40 ng of cfDNA input from each participant. The targeted panel covers 461 kb of the human genome, consisting of 827 CpG islands. Bisulfite conversion is a routine and prerequisite step for TBS, chemically converting unmethylated cytosines into uracils, which will be interpreted as thymines during sequencing. Therefore, bisulfite conversion efficiency directly affects the accuracy of detecting cytosine methylation levels, and evaluating conversion efficiency is an essential QC step for bisulfite sequencing. To quantitatively assess bisulfite conversion efficiency, each DNA sample was bisulfite converted using the EpiArt DNA Methylation Bisulfite Kit (Vazyme Biotechnology, Nanjing, China). WGBS libraries were constructed for each cfDNA sample using the Rainbow Sugar cfDNA Methyl-Seq Library Preparation Kit (Rapha Biotechnology, Inc., Shenzhen, China). Sequencing was performed using Qsep100 (Bio-Optics, Inc., Taiwan, China) and Qubit 4.0 (Thermo Fisher Scientific, Inc., MA, USA). Eight libraries were sent to a separate channel of the MGI-2000 sequencer for sequencing using DNBSEQ technology and PE100 sequencing mode.
[0056] The raw sequencing data of the TBS samples were filtered using fastp. The bisulfite conversion rate was calculated as the percentage of cytosine converted to thymine in lambda DNA, as both are unmethylated. Next, after removing PCR duplicates using SAMtools, qualified reads were mapped to the human reference genome (GRCh38.p14) using the sequence alignment software BWA. Methylation levels of all CpG sites were then detected using MethDackel, and finally, methylation levels within the sample range of chr1:34792772-34792982 were calculated using Python. The clinical status of the data cohort and the methylation levels within the marker region were combined into a two-dimensional expression profile, and the XGBoost machine learning algorithm was used for model training and testing. After the optimal model was established, the model was used to score the risk of liver cancer (HCC_score) based on the methylation levels of the subject's markers. For example, the marker methylation level of subject test1 is 0.45, and the HCC_score calculated by the model is 65. Clinical testing verifies that test1 is a liver cancer patient, which is consistent with the model prediction result.
[0057] Example 3: Effect of the markers of the present invention on the detection of liver cancer
[0058] To demonstrate the detection effect of the GJA4 marker of the present invention in a specific region, the detection performance of the markers of different regions of GJA4 in clinical samples was compared. Blood samples collected from 45 HCC patients and 58 healthy subjects were used to detect the markers of the present invention and their comparative markers. The detection was performed according to the method described in Example 2. The results are shown in FIG. Figures 1-2 and as shown in Table 1.
[0059] Table 1
[0060] markers Sensitivity Specificity AUC chr1:34792772-3479298 83.33％ 83.33％ 0.833 chr1:34792944-34793200 21.42％ 70.58％ 0.563
Claims
1. Use of a composition for assisting the detection of methylation markers for liver cancer in the preparation of a kit for assisting the detection of liver cancer, the composition comprising a detection reagent for detecting the methylation level in the following region: chr1: 34792772-34792982; in, The specific sequence of the region is: ACCTGGTGGAGAGAACAGAGGAGAGGAAGGGTAGGTCAGCCCCTCTTGCCCCTCCCCACCGCAAATCACACAGGCCCACCTCCCCGGAGTGGGTTTAGGGAGTCTGCACCTCCCAGTCCCCGCCCCCGCCCTCTCCAGCGCCCGCCGCCCTCCCCGTCGCGTTTCCTGCCCCCACCCCGCCCCTCTGCGCTATTTAAGGCGCCCCC.
2. The use according to claim 1, characterized in that The detection reagent is a detection reagent used in amplification-sequencing, chip detection, or methylation fluorescence quantitative PCR detection of methylation levels.
3. The use according to claim 2, characterized in that The detection reagent is any one or more of a nucleic acid primer, a methylation chip, and a nucleic acid probe.
4. Use of a model for assisting the detection of liver cancer in preparing a device for assisting the detection of liver cancer, wherein the model is: HCC_score = 50-50*[M-1 / 2*(N_M+C_M)] / [1 / 2*abs(N_M-C_M)], in, M is the methylation value of GRCh38 chr1: 34792772-34792982 of the subject, N_M is the mean methylation value of the healthy population in the model, which is 0.37, and C_M is the mean methylation value of the liver cancer patients in the model, which is 0.
47. When HCC_score>=50, the subject is predicted to have a high risk of liver cancer; HCC_score<50, the subject is predicted to have a low risk of cancer and belongs to the healthy population; The specific sequence of GRCh38 chr1:34792772-34792982 is: ACCTGGTGGAGAGAACAGAGGAGAGGAAGGGTAGGTCAGCCCCTCTTGCCCCTCCCCACCGCAAATCACACAGGCCCACCTCCCCGGAGTGGGTTTAGGGAGTCTGCACCTCCCAGTCCCCGCCCCCGCCCTCTCCAGCGCCCGCCGCCCTCCCCGTCGCGTTTCCTGCCCCCACCCCGCCCCTCTGCGCTATTTAAGGCGCCCCC.
5. The use according to any one of claims 1 to 3, characterized in that The kit further comprises at least one of a reagent for extracting nucleic acid, a reagent for purifying nucleic acid, and bisulfite.