Use of plasma-free DNA methylation markers in liver tumor detection

Abstract

This application provides the use of plasma-free DNA methylation markers in liver tumor detection. Specifically, the kit includes a reagent for detecting the methylation level of a combination of DNA methylation markers, the combination of DNA methylation markers being selected from one or more of IKZF1, Septin9, IRF4, DAB2IP, TSPYL5, CHFR, BEND4, GRASP, GPAM, and BDH1, to provide a liver tumor detection kit. The model constructed from the detection results of the methylation of the above marker combinations is used for early screening and recurrence monitoring of HCC.

Description

Technical Field

[0001] This application relates to the field of biotechnology, specifically to the use of plasma-free DNA methylation markers in liver tumor detection, and DNA methylation marker gene combinations, reagents and uses for detecting liver tumor-related diseases.

[0002] This application claims the priority of a Chinese patent application filed with the China National Intellectual Property Administration on March 9, 2023, with the application number 202310226723.2 and the invention title "Use of Plasma-Free DNA Methylation Markers in Liver Tumor Detection", and a Chinese patent application filed with the China National Intellectual Property Administration on September 28, 2023, with the application number 202311274876.0 and the invention title "DNA Methylation Marker Gene Combinations, Reagents and Uses for Detecting Liver Tumor-Related Diseases", the entire contents of which are incorporated herein by reference.

Background Art

[0003] Early detection of liver cancer can increase the probability of surgery and improve the survival rate. Currently, the commonly used early screening techniques include the detection of alpha-fetoprotein (AFP) and ultrasound (US). However, the low sensitivity limits the technical screening and monitoring of the disease state.

[0004] Currently, blood DNA methylation abnormalities have become widely recognized non-invasive cancer detection markers. Among various cancers, changes in DNA methylation have been found to lead to abnormal gene expression, and this phenomenon has been discovered to be an important event in the early stage of cancer development. Liver cancer DNA methylation can reliably distinguish cancer patients from healthy people. However, the release of a very small amount of abnormal methylated DNA (ctDNA) from tumor cells into the peripheral blood in early-stage cancer has posed technical challenges.

[0005] The rapid development of techniques for selectively and specifically enriching and amplifying methylated sequences facilitates highly sensitive detection. High-flux two-generation sequencing (NGS) techniques have been widely applied and have demonstrated feasibility for detecting methylated markers in gastrointestinal and hepatobiliary tumors. However, the complexity and high cost of NGS detection limit its clinical application.

[0006] Therefore, there is an urgent need to develop a highly accurate, widely applicable, low-cost, and simple-to-implement methylation screening technology for liver tumors. [Overview of the project] [Problems that the invention aims to solve]

[0007] The inventors of this application, after numerous experiments and trial and error, unexpectedly discovered that several free DNA methylation markers in plasma can be used for liver tumor testing. Based on this discovery, this application provides a kit and a method for detecting liver tumors. [Means for solving the problem]

[0008] In the first aspect, the present application provides a kit for detecting liver tumors in which a kit comprises a reagent for detecting methylation levels, wherein the combination of DNA methylation markers is selected from one or more of IKZF1, Septin9, IRF4, DAB2IP, TSPYL5, CHFR, BEND4, GRASP, GPAM, and BDH1.

[0009] In some embodiments, the genomic location of the DNA methylation marker is IKZF1, chr7:50343720-50472799, Septin9, chr17:75276651-75496678, IRF4, chr6:391739-411447, DAB2IP, chr9:124329336-124547809, TSPYL5, chr8:98285717-98290176, CHFR, chr12:133398773-133532890, BEND4, chr4:42112955-42154895, GRASP, chr12:52400724-52409673, GPAM, chr10:113909624-113975135, This is the BDH1 region, chr3:197236654-197300194.

[0010] In some embodiments, the combination of methylation markers is IKZF1, chr7:50343867-50343961, Septin9, chr17:75369558-75369622, IRF4, chr6:392282-392377, DAB2IP, chr9:124461322-124461391, TSPYL5, chr8:98290035-98290215, CHFR, chr12:133485000-133485067, BEND4, chr4:42153816-42153921, GRASP, chr12:52401083-52401169, GPAM, chr10:113943540:113943739, BDH1 region 1, chr3:197281790:197281989, Includes BDH1 region 2, chr3:197282545:197282744.

[0011] In some embodiments, the kit is detected using a method selected from PCR amplification, methylation chip method, liquid-phase chip method, bisulfite sequencing, first-generation sequencing, second-generation sequencing and / or third-generation sequencing.

[0012] In some embodiments, the PCR amplification method is digital PCR or fluorescence quantitative PCR.

[0013] In some embodiments, the liver tumor is HCC.

[0014] In some embodiments, the detection includes early screening and recurrence monitoring of HCC.

[0015] In some embodiments, the detection includes the auxiliary diagnosis of HCC.

[0016] In some embodiments, the reagent for detecting the combined methylation level of the detection DNA methylation markers includes primers and / or probes for DNA methylation specific quantitative detection (qMSP).

[0017] In some embodiments, the primers and / or probes are shown in Table 2 of the specification.

[0018] In a second aspect, the screening method for methylation markers for liver tumor detection provided by the present application is defining the common methylation marker regions that match in HCC tumors using a whole-genome methylation group dataset, further performing optimization and selection by distinguishing the performance between HCC and the control group based on a method based on these region methylation-specific PCR.

[0019] In a third aspect, the present application provides a combination of methylation markers screened by the screening method described in the second aspect of the present application.

[0020] In some embodiments, the marker combination is selected from one or more of IKZF1, Septin9, IRF4, DAB2IP, TSPYL5, CHFR, BEND4, GRASP, GPAM, BDH1.

[0021] In one embodiment, the position information of the DNA methylation marker on the genome is IKZF1, chr7:50343720-50472799, Septin9, chr17:75276651-75496678, IRF4, chr6:391739-411447, DAB2IP, chr9:124329336-124547809, TSPYL5, chr8:98285717-98290176, CHFR, chr12:133398773-133532890, BEND4, chr4:42112955-42154895, GRASP, chr12:52400724-52409673c GPAM, chr10:113909624-113975135, The BDH1 region is chr3:197236654-197300194.

[0022] In one embodiment, the combination of the methylation markers is IKZF1, chr7:50343867-50343961, Septin9, chr17:75369558-75369622, IRF4, chr6:392282-392377, DAB2IP, chr9:124461322-124461391, TSPYL5, chr8:98290035-98290215, CHFR, chr12:133485000-133485067, BEND4, chr4:42153816-42153921, GRASP, chr12:52401083-52401169, GPAM, chr10:113943540:113943739, BDH1 region 1, chr3:197281790:197281989, BDH1 region 2, chr3:197282545:197282744.

[0023] In the fourth aspect, the method for constructing a liver tumor detection model provided in this application is: Nucleic acid extraction from plasma samples: Extraction of free cell DNA (cfDNA), By treating nucleic acids with bisulfite, unmethylated cytosine is converted to uracil, while methylated cytosine maintains its sequence. Pre-amplification and dilution of the target, Perform fluorescence PCR detection, This includes constructing a model using an incremental feature selection (IFS) feature screening method for machine learning models based on the detected Ct value or delta Ct value.

[0024] In some embodiments, a threshold suitable for clinical use is selected based on the numerical values ​​of the model output to perform positive interpretation.

[0025] In some embodiments, the target in step c) is shown in a third aspect of this application.

[0026] In some embodiments, the primers and probes for the fluorescence PCR detection in step d) are shown in Table 2 of the specification.

[0027] In some embodiments, the machine learning model in step e) is selected from an SVR model, a random forest, a logical regression, or an interaction tree.

[0028] To further improve the efficiency, sensitivity, and specificity of isolated DNA methylation tests for liver tumor-related diseases, enhance the efficiency of early screening and diagnosis of liver tumor-related diseases, reduce the burden on clinical practice, and increase the clinical application value, this invention further optimizes the DNA methylation markers obtained from screening through experiments and provides a DNA methylation marker gene combination for detecting liver tumor-related diseases based on the optimization results.

[0029] In the fifth aspect, the present invention provides a DNA methylation marker gene combination for detecting liver tumor-related diseases, wherein the gene combination is selected from a DNA sequence within at least one subregion of at least two of the following genes: DAB2IP gene: Its detection subregion is, chr9:124460896-124462275, chr9:124461322-124461391, chr9:124461043-124461118, chr9:124360386-124362685, chr9:124360868-124360966, CHFR gene: Its detection subregion is, chr12:133483901-133485740, chr12:133485000-133485067, chr12:133484896-133484985, chr12:133463431-133464810, chr12:133464246-133464335, chr12:133404551-133406390, chr12:133405064-133405171, chr12:133532431-133532890, chr12:133532689-133532802, GRASP gene: Its detection subregion is, chr12:52400724-52401698, chr12:52401083-52401169, chr12:52400965-52401055, chr12:52406880-52409127, The address is chr12:52408471-52408566.

[0030] In one embodiment, the gene combination is selected from any one of the following gene subregion combinations: chr9:124460896-124462275 and chr12:133483901-133485740, chr9:124460896-124462275 and chr12:52400724-52401698, chr12:133483901-133485740 and chr12:52400724-52401698, chr9:124460896-124462275, chr12:133483901-133485740, The address is chr12:52400724-52401698.

[0031] In one embodiment, the gene combination is Includes DAB2IP:chr9:124460896-124462275, CHFR:chr12:133483901-133485740 and GRASP:chr12:52400724-52401698.

[0032] In one embodiment, the method for detecting the gene combination provided herein includes specifically amplifying the DNA sequence of all genes in the gene combination and determining and / or evaluating liver tumor-related diseases based on their DNA methylation status. Here, the region specifically amplified in the gene subregion may be arbitrary.

[0033] In the sixth aspect, the present application provides a reagent for detecting the presence and / or content of the DNA methylation status of all genes in the gene combination in a sample of organism from which a biological organism has been isolated.

[0034] In one embodiment, the reagent includes a forward primer, a reverse primer, and / or a probe for detecting all genes in the gene combination.

[0035] Furthermore, the forward primers, reverse primers, and / or probes used in the reagents provided in this application to detect all genes in the aforementioned gene combination may be primers and probes that can be used in fluorescence quantitative PCR to amplify the target gene and achieve quantitative detection.

[0036] In one embodiment, the sample to be measured from which the organism has been isolated includes plasma, tissue, and cells, and is either plasma or plasma.

[0037] On the other hand, the present application provides a use for the aforementioned gene combination and / or reagent in manufacturing a liver tumor-related diagnostic product.

[0038] In one embodiment, the liver tumor-related diseases include hepatocellular carcinoma, hepatitis B, cirrhosis, hepatic cysts, and fatty liver.

[0039] In one embodiment, the diagnostic product for liver tumor-related diseases confirms the presence or absence of liver tumor-related diseases, assesses the risk of liver tumor-related disease formation, and / or assesses the progression of liver tumor-related diseases.

[0040] In one embodiment, the diagnostic product includes a kit.

[0041] In actual use, the reagents, kits, or diagnostic products provided in this application are used to detect the presence and / or content of DNA methylation status of all genes in the aforementioned gene combination in the target sample from which the organism has been isolated. Based on the detection results, the presence or absence of liver tumor-related disease is confirmed, the risk of formation of liver tumor-related disease is evaluated, and / or the progression of liver tumor-related disease is evaluated.

[0042] In the seventh aspect, the present application provides a kit containing the reagent.

[0043] In one embodiment, the kit further comprises a reagent for the PCR reaction system and / or a bisulfite conversion reagent.

[0044] In one embodiment, the reagents for the PCR reaction system include reagents necessary for and / or commonly used when performing PCR amplification.

[0045] In one embodiment, the bisulfite conversion reagent can be a commercially available kit. [Effects of the Invention]

[0046] The beneficial effects of this invention include, at a minimum, the following:

[0047] This invention develops a technology for early screening and recurrence monitoring of HCC based on hematological polygene qMSP (DNA methylation-specific quantification).

[0048] The high-performance marker combination determined in this application demonstrated good clinical value in a multi-center study.

[0049] This invention further optimizes gene subregions of liver tumor-related diseases and provides more effective combinations of subregions. When the gene combinations provided are used as biomarkers for plasma-isolated DNA methylation testing, they exhibit better sensitivity and specificity, significantly improving the accuracy of early screening or rescreening of liver tumor-related diseases and contributing to improved screening efficiency. [Brief explanation of the drawing]

[0050] One or more drawings may be referenced to better illustrate and interpret the embodiments and examples of the inventions disclosed herein. Any additional details or examples used to illustrate the drawings should not be considered limitations to the scope of the disclosed inventions, the embodiments and examples described herein, or the best known embodiment of these inventions. [Figure 1] The AUC of a marker combination SVR model for detecting a sample training set and a validation set according to one embodiment of the present invention is shown. [Figure 2]This document shows a comparison of the sensitivity of a marker combination SVR model and AFP according to one embodiment of the present invention to hepatocellular carcinoma. [Figure 3] This demonstrates that a marker combination SVR model according to one embodiment of the present application has a good detection effect on the AFP-negative group. [Modes for carrying out the invention]

[0051] To more clearly illustrate the overall concept of this application, it will be described in detail below with reference to the drawings of the specification.

[0052] The following description provides many specific details to fully understand the present application, but the scope of protection is not limited by the specific embodiments disclosed below, as the application can also be carried out in ways other than those described herein.

[0053] In this specification, any reference to terms such as “one embodiment,” “several embodiments,” “example,” “specific example,” or “several examples” means that the specific features, structures, materials, or advantages described in relation to this embodiment or example are included in at least one embodiment or example of this specification. In this specification, the general expressions of the above terms do not necessarily apply to the same embodiment or example. Furthermore, the specific features, structures, materials, or advantages described may be combined in an appropriate manner in any one or more embodiments or examples.

[0054] Unless otherwise defined, all technical terms used herein have the same meaning as those commonly understood by those skilled in the art. Technical terms used herein are for the sole purpose of describing specific embodiments and are not intended to limit the scope of protection of this application.

[0055] Unless otherwise specified, the various raw materials, reagents, equipment, and apparatus used in this application are available through market purchase or can be manufactured by existing methods.

[0056] Example 1 Experimental method: Using RRBS sequencing data of liver cancer, paracancerous tissue, control plasma, and blood cell DNA, as well as methylation chip data obtained from the common database TCGA, three candidate marker sets were generated: a marker set obtained by comparing liver cancer with normal liver tissue in RRBS data, a marker set obtained by comparing liver cancer with healthy plasma in RRBS data, and a marker set obtained by comparing liver cancer with normal liver tissue in methylation chips. The final candidate markers were the crossover of the three candidate methylation markers after median filtration using RRBS blood cell data. After these markers passed fluorescence PCR testing again, a total of 11 methylation candidate markers (Table 1) were determined for the detection of liver cancer.

[0057] Candidate markers were validated in plasma samples, and the detection samples included a control group, a liver cancer group, and a hepatitis B / cirrhosis interference group. Reference levels of the markers and the performance of marker combinations were analyzed in Table 1. Due to the limited amount of free DNA in single-sample plasma, preliminary amplification was performed on the target before fluorescence PCR detection. An SVR model was constructed using the training set, and model stability was validated using the validation set. This included the following steps.

[0058] 1) Nucleic acid extraction from plasma samples: Cell-free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen, 55114) following the manufacturer's instructions.

[0059] 2) The nucleic acid is treated with bisulfite to convert unmethylated cytosine to uracil, while methylated cytosine maintains its sequence. The bisulfite conversion is performed using the EZ DNA Methylation-Lightning Kit (Zymo Research, D5031).

[0060] 3) Preliminary amplification and dilution of the target: Using the primers in Table 2, the target DNA and primer pool were first amplified at 95°C for 3 minutes on a ProFlex™ PCR System (Thermo Fisher) platform. Then, the following procedure was repeated eight times: eight cycles of 30 seconds at 95°C and 60 seconds at 56°C. The amplified product was then diluted 10-fold.

[0061] 4) Fluorescence PCR detection is performed using the primers and probes shown in Table 2. The amplification product is detected by quantifying PCR using the standard procedure (Novo Start Methy Light qPCR Super Mix (Novo Protein)) in an ABI 7500 real-time PCR thermal circulator.

[0062] 5) The Ct values ​​from the training set detection results are used to construct a model using an incremental feature selection (IFS) feature screening method based on an SVR (python v3.9.12, scikit-learn v1.1.2) model. A model for interpreting liver cancer risk is constructed, and the positive interpretation threshold is determined. This detection process was named HepaAiQ.

[0063] 6) The training set data included 293 successful patients from multiple centers with hepatocellular carcinoma (HCC), 96 with chronic hepatitis B (CHB) or cirrhosis, 23 with benign liver lesions (BHL), and 147 healthy controls (HC). More patients with early-stage HCC (77% stage 0 / A), small tumor volume (median 3.5 cm), and compensated liver function (81% Child-Pugh grade A) are planned for inclusion. The validation set will include 523 patients, with 205 hepatocellular carcinomas, 100 with hepatitis B / cirrhosis, 102 with benign liver lesions, and 116 healthy controls.

[0064] [Table 1] [Table 2] Example 2 Experimental results: The results show that the model constructed by combining this marker combination with SVR achieved good detection performance on both the training and validation sets, with a training set AUC of 0.944 and a validation set AUC of 0.940 (Figure 1).

[0065] The HepaAiQ model was compared with AFP, a well-known and commonly used HCC marker. In 489 HCC samples (384 early-stage and 105 late-stage) from which two types of detection results were obtained, the detection effect of the HepaAiQ detection model was clearly improved compared to AFP detection, and the detection sensitivity was significantly improved, as shown in Figure 2, for both early-stage and late-stage hepatocellular carcinoma. Good detection performance was also observed in the AFP-negative group (269 AFP-positive patients and 220 AFP-negative patients) (Figure 3). The HepaAiQ detection model of this invention fills the corresponding clinical gap based on AFP.

[0066] Example 3 Experimental method: Using RRBS sequencing data of liver cancer, peri-cancerous tissue, control plasma and blood cell DNA, and methylation chip data obtained from the common database TCGA, candidate marker sets were generated. Specifically, marker sets obtained by comparing liver cancer with normal liver tissue in RRBS data, comparing liver cancer with healthy plasma in RRBS data, and comparing liver cancer with normal liver tissue in methylation chips were generated. After these markers passed fluorescence PCR testing again, Table 3 shows candidate methylation markers for 11 target regions of three representative genes used for liver cancer detection.

[0067] [Table 3] Candidate markers were validated in plasma samples, and the detection samples included a control group, a liver cancer group, and a hepatitis B / cirrhosis prevention group. The reference levels of the markers and the performance of the marker combinations in Table 3 were analyzed. Due to the limited amount of free DNA in single-sample plasma, preliminary amplification was performed on the target region before fluorescence PCR detection. A binary logical regression model was constructed on the training set, and model stability was validated using the validation set. This included the following steps.

[0068] (1) Nucleic acid extraction from plasma samples: Cell-free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen, 55114) following the manufacturer's instructions.

[0069] (2) The nucleic acid is treated with bisulfite to convert unmethylated cytosine to uracil, while methylated cytosine maintains its sequence. The bisulfite conversion is performed using the EZ DNA Methylation-Lightning Kit (Zymo Research, D5031).

[0070] (3) Preliminary amplification and dilution of the target region: Using the primers in Table 4, the target DNA and primer pool were first amplified at 95°C for 3 minutes on a ProFlex™ PCR System (Thermo Fisher) platform, and then the following procedure was repeated eight times: 30 seconds at 95°C and 60 seconds at 56°C eight times. The amplified product was then diluted 10-fold.

[0071] (4) Fluorescence PCR detection is performed using the primers and probes shown in Table 4. The amplification product is detected by quantifying PCR using the standard procedure (Novo Start Methy Light qPCR Super Mix (Novo Protein)) in an ABI 7500 real-time PCR thermal circulator.

[0072] (5) The Ct values ​​detected in the training set are used to construct models using two target region combination models and three target region combination models based on two-way logical regression. The subjects' working curve ROCs are plotted, and the marker regions and marker combinations are determined to distinguish between the abilities of liver cancer patients and non-liver cancer patients.

[0073] (6) The training set included 467 patients, comprising 233 cases of hepatocellular carcinoma (78.5% BCLC stage 0 / A), 93 cases of chronic hepatitis B (CHB) or cirrhosis, 18 cases of hepatic cysts or fatty liver, and 123 healthy controls (HC) from multiple centers. The study set included 466 patients, comprising 209 cases of hepatocellular carcinoma (75.6% BCLC stage 0 / A), 92 cases of hepatitis B / cirrhosis, 25 cases of hepatic cysts or fatty liver, and 140 healthy controls (HC).

[0074] [Table 4] Experimental results: ROC curve analysis was performed on the Ct values ​​of the training and test sets for a total of 11 target regions of the three genes, and the AUC values ​​were determined. All 11 target regions had the ability to distinguish between liver cancer and non-liver cancer in both the training and test sets, and the specific results are shown in Table 5.

[0075] [Table 5] In the training set data, we performed modeling with combinations of two target regions and combinations of three target regions. Table 6 shows the AUC results representing the best representative models for each combination, and Table 7 shows the AUC representing the best representative models for the test set. The fact that the AUC of each combination was greater than that of a single target region indicates that the combination of target regions can improve the ability to distinguish between liver cancer and non-liver cancer. Notably, the AUC of the model with a combination of three target regions reached 0.921 during training and 0.919 during the test set.

[0076] [Table 6] [Table 7] Example 4 Experimental method: Markers were validated in plasma samples, and the detection samples included a control group, a liver cancer group, and a hepatitis B / cirrhosis interference group. Reference levels of the markers and the performance of the marker combinations in Table 8 were analyzed. Because single-sample plasma free DNA is limited, preliminary amplification was performed on the target region before fluorescence PCR detection. A binary logical regression model was constructed on the training set, and model stability was validated using the validation set. This included the following steps.

[0077] (1) Nucleic acid extraction from plasma samples: Cell-free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen, 55114) following the manufacturer's instructions.

[0078] (2) The nucleic acid is treated with bisulfite to convert unmethylated cytosine to uracil, while methylated cytosine maintains its sequence. The bisulfite conversion is performed using the EZ DNA Methylation-Lightning Kit (Zymo Research, D5031).

[0079] (3) Preliminary amplification and dilution of the target region: Using the primers in Table 9, the target DNA and primer pool were first amplified at 95°C for 3 minutes on a ProFlex™ PCR System (Thermo Fisher) platform, followed by the next cycle eight times: 30 seconds at 95°C and 60 seconds at 56°C. The amplified product was then diluted 10-fold.

[0080] (4) The amplification product is detected by quantifying PCR using the standard procedure (NovoStart MethyLight qPCR SuperMix (NovoProtein)) in the cardiovascular system.

[0081] (5) A combinatorial model is constructed using two-way logical regression with the Ct values ​​detected in the training set. The working curve ROC of the subjects is plotted, and the marker regions and marker combinations are determined to distinguish between the abilities of liver cancer patients and non-liver cancer patients.

[0082] (6) The training set included 467 patients, comprising 233 cases of hepatocellular carcinoma (78.5% BCLC stage 0 / A), 93 cases of chronic hepatitis B (CHB) or cirrhosis, 18 cases of hepatic cysts or fatty liver, and 123 healthy controls (HC) from multiple centers. The study set included 466 patients, comprising 209 cases of hepatocellular carcinoma (75.6% BCLC stage 0 / A), 92 cases of hepatitis B / cirrhosis, 25 cases of hepatic cysts or fatty liver, and 140 healthy controls (HC).

[0083] [Table 8] [Table 9] Experimental results: Two-target-region combination modeling and three-target-region combination modeling were performed on the training set data, and the representative combination AUCs of the test set models in the training set are shown in Tables 10 and 11.

[0084] [Table 10] [Table 11] Example 5 Experimental method: In tissue samples, markers will be detected in 24 cases of liver cancer tissue and 24 cases of peri-cancerous tissue. Specifically, This includes extracting tissue DNA using the Qiagen QIAamp DNA FFPE Tissue Kit.

[0085] A 20 ng sample was taken from the DNA obtained in the above steps and treated with a bisulfite reagent (D5031, ZYMO RESEARCH) to obtain the converted DNA.

[0086] Preferably, PCR amplification was performed using the converted DNA as a template with a primer pool containing methylation-specific primers including those listed in Table X, and pairs of forward and reverse primers, with the final concentration of each primer being 100 nM. The PCR reaction system contained 10 μL of converted DNA (including 2.5 μL of a pre-mixture of the above primers) and 12.5 μL of PCR reagent (Luna® Universal Probe qPCR Master Mix (NEB)). The PCR reaction conditions were 95°C for 5 minutes, 95°C for 30 seconds, and 56°C for 60 seconds, for 10 cycles.

[0087] The obtained pre-amplified product was diluted 10-fold, and the detection fluorescence Ct value of the marker was obtained by multiplex fluorescence PCR detection. In the fluorescence PCR reaction system, the final concentration of each primer was 500 nM, and the final concentration of each detection probe was 200 nM. The PCR reaction system contained 10 μL of pre-amplified diluted product, 2.5 μL of a pre-mixture of primers and probes for the detection site, and 12.5 μL of PCR reagent (Luna® Universal Probe qPCR Master Mix (NEB)). The PCR reaction conditions were 95°C for 5 minutes, 95°C for 30 seconds, and 56°C for 60 seconds (fluorescence collection), for 50 cycles. Using the ABI 7500 Real-Time PCR System, different fluorescence was detected in the corresponding fluorescence channels. Sample Ct values ​​obtained from the tunica albuginea, peritumoral tissue, and cancer tissue were calculated and compared, and the target Ct value for which no amplification signal was detected was set to 50.

[0088] [Table 12] [Table 13] Experimental results: Logical regression combinations were performed on the detection results, and the AUC results of the best models, distinguishing between peri-cancer tissue and cancerous tissue, are shown in Table 14.

[0089] [Table 14] The basic principles, main features, and advantages of the present application have been described above. However, it will be clear to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that it can be realized in other specific forms without departing from the spirit or basic features of the present application. Therefore, in all respects, the embodiments are exemplary and non-limiting, and the scope of the present application is limited not by the above description but by the appended claims, and the aim is to cover in the present application all variations that fall within the meaning and scope of the equivalent elements of the claims.

[0090] Furthermore, although this specification is described according to embodiments, each embodiment does not contain only one independent technical proposal. This descriptive form of the specification is simply for clarity, and those skilled in the art should understand that the specification should be considered as a whole, and that the technical proposals of each embodiment can be appropriately combined to form other embodiments that will be understood by those skilled in the art.

[0091] Each embodiment described herein should be described in part by reference to the same similar parts between the embodiments, and each embodiment should focus on the differences from the other embodiments. The above are merely examples of the present application and are not intended to limit the application. The present application can be modified in various ways by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present application should be included within the scope of the claims.

Claims

1. A kit for detecting liver tumors, The kit includes a reagent for detecting methylation levels, which detects combinations of DNA methylation markers. The kit is characterized in that the combination of DNA methylation markers is selected from one or more of IKZF1, Septin9, IRF4, DAB2IP, TSPYL5, CHFR, BEND4, GRASP, GPAM, and BDH1.

2. The genomic location of the aforementioned DNA methylation marker is, IKZF1, chr7:50343720-50472799, Septin9, chr17:75276651-75496678, IRF4, chr6:391739-411447, DAB2IP, chr9:124329336-124547809, TSPYL5, chr8:98285717-98290176, CHFR, chr12:133398773-133532890, BEND4, chr4:42112955-42154895, GRASP, chr12:52400724-52409673, GPAM, chr10:113909624-113975135, The kit according to claim 1, characterized in that the BDH1 region is chr3:197236654-197300194.

3. The combination of methylation markers is IKZF1, chr7:50343867-50343961, Septin9, chr17:75369558-75369622, IRF4, chr6:392282-392377, DAB2IP, chr9:124461322-124461391, TSPYL5, chr8:98290035-98290215, CHFR, chr12:133485000-133485067, BEND4, chr4:42153816-42153921, GRASP, chr12:52401083-52401169, GPAM, chr10:113943540:113943739, BDH1 region 1, chr3:197281790:197281989, The kit according to claim 2, characterized by including BDH1 region 2, chr3:197282545:197282744.

4. The kit according to any one of claims 1 to 3, characterized in that the kit is detected using a method selected from PCR amplification, methylation chip method, liquid-phase chip method, bisulfite sequencing, first-generation sequencing, second-generation sequencing and / or third-generation sequencing.

5. The kit according to claim 4, characterized in that the PCR amplification method is digital PCR or fluorescence quantitative PCR.

6. The kit according to any one of claims 1 to 5, characterized in that the liver tumor is HCC.

7. The kit according to claim 6, characterized in that the detection includes early screening and recurrence monitoring for HCC.

8. The kit according to claim 6, characterized in that the detection includes an auxiliary diagnosis of HCC.

9. The kit according to any one of claims 1 to 8, characterized in that the reagent for detecting the methylation level of the combination of DNA methylation markers comprises primers and / or probes for DNA methylation-specific quantitative detection (qMSP).

10. The kit according to claim 9, characterized in that the primer and / or probe are as shown in Table 2 of the specification.

11. A screening method for methylation markers for detecting liver tumors, To define matching common methylation marker regions in HCC tumors using a whole-genome methylation group dataset, A methylation marker screening method characterized by further optimizing and selecting the performance of HCCs and control groups based on these region methylation-specific PCR-based methods.

12. A combination of methylation markers, characterized in that it is obtained by screening using the screening method described in claim 11.

13. The combination of methylation markers according to claim 12, characterized in that the combination is selected from IKZF1, Septin9, IRF4, DAB2IP, TSPYL5, CHFR, BEND4, GRASP, GPAM, and BDH1.

14. The genomic location information of the aforementioned DNA methylation marker is, IKZF1, chr7:50343720-50472799, Septin9, chr17:75276651-75496678, IRF4, chr6:391739-411447, DAB2IP, chr9:124329336-124547809, TSPYL5, chr8:98285717-98290176, CHFR, chr12:133398773-133532890, BEND4, chr4:42112955-42154895, GRASP, chr12:52400724-52409673, The combination of methylation markers according to claim 13, characterized in that the BDH1 region is chr3:197236654-197300194.

15. The combination of methylation markers is IKZF1, chr7:50343867-50343961, Septin9, chr17:75369558-75369622, IRF4, chr6:392282-392377, DAB2IP, chr9:124461322-124461391, TSPYL5, chr8:98290035-98290215, CHFR, chr12:133485000-133485067, BEND4, chr4:42153816-42153921, GRASP, chr12:52401083-52401169, GPAM, chr10:113943540:113943739, BDH1 region 1, chr3:197281790:197281989, The combination of methylation markers according to claim 14, characterized by including BDH1 region 2, chr3:197282545:197282744.

16. A method for constructing a liver tumor detection model, a) Nucleic acid extraction from plasma samples: Extraction of cell-free DNA (cfDNA), b) Treating nucleic acids with bisulfite to convert unmethylated cytosine to uracil, while maintaining the sequence of methylated cytosine, c) Pre-amplification and dilution of the target, d) Perform fluorescence PCR detection, e) A construction method characterized by comprising: constructing a model using an incremental feature selection (IFS) feature screening method of a machine learning model based on the detected Ct value or delta Ct value; selecting a threshold suitable for clinical use based on the numerical value of the model output and performing positive interpretation.

17. The construction method according to claim 16, characterized in that the target in step c) is as shown in any one of claims 12 to 15.

18. The construction method according to 16, characterized in that the primers and probes for the fluorescence PCR detection in step d) are as shown in Table 2 of the specification.

19. The construction method according to 16, characterized in that the machine learning model in step e) is selected from an SVR model, a random forest, logical regression, and an interaction tree.

20. A DNA methylation marker gene combination for detecting liver tumor-related diseases, wherein the gene combination is selected from a DNA sequence within at least one subregion of at least two of the following genes: DAB2IP gene: Its detection subregion is, chr9:124460896-124462275, chr9:124461322-124461391, chr9:124461043-124461118, chr9:124360386-124362685, chr9:124360868-124360966, CHFR gene: Its detection subregion is, chr12:133483901-133485740, chr12:133485000-133485067, chr12:133484896-133484985, chr12:133463431-133464810, chr12:133464246-133464335, chr12:133404551-133406390, chr12:133405064-133405171, chr12:133532431-133532890, chr12:133532689-133532802, GRASP gene: Its detection subregion is, chr12:52400724-52401698, chr12:52401083-52401169, chr12:52400965-52401055, chr12:52406880-52409127, A DNA methylation marker gene combination characterized by being chr12:52408471-52408566.

21. The aforementioned gene combination is selected from any one of the following gene subregion combinations: chr9:124460896-124462275 and chr12:133483901-133485740, chr9:124460896-124462275 and chr12:52400724-52401698, chr12:133483901-133485740 and chr12:52400724-52401698, chr9:124460896-124462275, chr12:133483901-133485740, The gene combination according to claim 20, characterized in that chr12: 52400724-52401698.

22. It is a reagent, The reagent is characterized by detecting the presence and / or content of all genes of the gene combination described in claim 20 or 21 in a sample of organisms from which a biological organism has been isolated.

23. The reagent according to claim 22, characterized in that the reagent comprises a forward primer, a reverse primer, and / or a probe for detecting all genes in the gene combination.

24. The reagent according to claim 23, characterized in that the sample to be measured from which the organism is isolated includes plasma, tissue, and cells.

25. The use is characterized in that the gene combination according to claim 20 or 21 and / or the reagent according to any one of claims 22 to 24 are used to manufacture a diagnostic product for liver tumor-related diseases.

26. The use according to claim 25, characterized in that the liver tumor-related disease includes hepatocellular carcinoma, hepatitis B, cirrhosis, hepatic cysts, and fatty liver.

27. The use of the aforementioned diagnostic product for liver tumor-related diseases according to claim 25, characterized in that it confirms the presence or absence of liver tumor-related diseases, assesses the risk of development of liver tumor-related diseases, and / or assesses the progression of liver tumor-related diseases.

28. It's a kit, A kit characterized by comprising the reagent described in any one of claims 22 to 24.

29. The kit according to claim 28, further comprising a reagent for the PCR reaction system and / or a bisulfite conversion reagent.

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