DNA methylation marker gene combination for detecting liver tumor related diseases, reagent and application

Abstract

The invention provides a DNA methylation marker gene combination for detecting liver tumor related diseases, a reagent and application, and belongs to the technical field of biology. Gene subregions of liver tumor related diseases are further optimized, a subregion combination with a better effect is provided, and when the genome provided by the invention cooperates as a biomarker for plasma separation DNA methylation detection, better sensitivity and specificity are achieved; the accuracy of early screening or re-screening of liver tumor related diseases can be remarkably improved, and the screening efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a DNA methylation marker gene combination, reagent and application for detecting liver tumor-related diseases. Background Art

[0002] The incidence and mortality of liver cancer have been on the rise in the past decade, mainly due to the rise of hepatitis virus infection and non-alcoholic fatty liver disease. Hepatocellular carcinoma (HCC) accounts for 80% of liver cancer, and the recurrence rate after surgery is as high as 80%, which is a worrying consequence. Therefore, hepatocellular carcinoma is a major public health burden worldwide.

[0003] DNA methylation is a form of chemical modification of DNA that can change genetic expression without changing the DNA sequence. DNA methylation refers to the covalent bonding of a methyl group to the 5th carbon position of cytosine in the genomic CpG dinucleotide under the action of DNA methyltransferase. DNA methylation can cause changes in chromatin structure, DNA conformation, DNA stability, and the way DNA interacts with proteins, thereby controlling gene expression. Studies have found that changes in DNA methylation may occur earlier than DNA mutations, so it can be used as an effective detection marker for early screening of tumors.

[0004] In the previous work, the applicant found that some target genes can be used as biomarkers related to liver cancer detection. For example, the patents CN116732175A and CN115572764A previously applied by the applicant respectively disclosed multiple target genes that can be used to confirm liver tumors or evaluate their progression. However, it is understandable that although a variety of target genes have been found in the prior art to be used to detect liver tumor-related diseases, the gene domain of each target gene is very long, and the workload is large in the actual DNA methylation status detection, making it difficult to achieve rapid and efficient specific detection. In addition, not all segments in the gene domain are conducive to detection, and it is difficult to predict whether the combination of different segments of different genes is conducive to detection. Therefore, in order to further reduce sequencing costs and improve the sensitivity and specificity of detection, it is urgent to develop a specific combination of DNA methylation biomarkers. Summary of the invention

[0005] In order to solve the above problems, this application aims to improve the efficiency, sensitivity and specificity of isolated DNA methylation detection of liver tumor-related diseases, improve the efficiency of early screening and diagnosis of liver tumor-related diseases, reduce the burden of clinical diagnosis and treatment, and further improve the clinical application value.

[0006] On the one hand, the present application provides a DNA methylation marker gene combination for detecting liver tumor-related diseases, characterized in that the gene combination is selected from DNA sequences in at least any one subregion of at least two of the following genes:

[0007] DAB2IP gene, its detection sub-region is:

[0008] chr9:124460896-124462275;

[0009] chr9:124461322-124461391;

[0010] chr9:124461043-124461118;

[0011] chr9:124360386-124362685;

[0012] chr9:124360868-124360966;

[0013] The CHFR gene, the subregions tested are:

[0014] chr12:133483901-133485740;

[0015] chr12:133485000-133485067;

[0016] chr12:133484896-133484985;

[0017] chr12:133463431-133464810;

[0018] chr12:133464246-133464335;

[0019] chr12:133404551-133406390;

[0020] chr12:133405064-133405171;

[0021] chr12:133532431-133532890;

[0022] chr12:133532689-133532802;

[0023] GRASP gene, its detection sub-regions are:

[0024] chr12:52400724-52401698;

[0025] chr12:52401083-52401169;

[0026] chr12:52400965-52401055;

[0027] chr12:52406880-52409127;

[0028] chr12:52408471-52408566.

[0029] In one embodiment, the gene combination is selected from any one of the following gene subregion combinations:

[0030] chr9:124460896-124462275 and chr12:133483901-133485740;

[0031] chr9:124460896-124462275 and chr12:52400724-52401698;

[0032] chr12:133483901-133485740 and chr12:52400724-52401698;

[0033] chr9:124460896-124462275, chr12:133483901-133485740 and

[0034] chr12:52400724-52401698.

[0035] In a preferred embodiment, the gene combination is DAB2IP: chr9: 124460896-124462275, CHFR: chr12: 133483901-133485740 and GRASP: chr12: 52400724-52401698.

[0036] It is understood that, in one embodiment, the method for detecting the gene combination provided in the present application comprises specifically amplifying the DNA sequences of all genes in the gene combination, and determining and / or evaluating liver tumor-related diseases according to their DNA methylation status. The specific amplified region in the gene subregion may be optional.

[0037] On the other hand, 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 test sample separated from an organism.

[0038] In one embodiment, the reagents include forward primers, reverse primers and / or probes for detecting all genes of the gene panel.

[0039] It is understandable that the forward primers, reverse primers and / or probes for detecting all genes in the gene combination in the reagents provided in the present application can be primers and probes that can be used in fluorescent quantitative PCR, which can amplify the target gene and achieve quantitative detection.

[0040] In one embodiment, the sample to be tested separated from an organism includes plasma, tissue or cells, preferably plasma.

[0041] On the other hand, the present application provides the use of the gene combination and / or the reagent in the preparation of liver tumor related diagnostic products.

[0042] In one embodiment, the liver tumor-related diseases include hepatocellular carcinoma, hepatitis B, cirrhosis, liver cysts, and fatty liver.

[0043] In one embodiment, the liver tumor-related disease diagnostic product is used to confirm the presence or absence of a liver tumor-related disease, assess the risk of developing a liver tumor-related disease, and / or assess the progression of a liver tumor-related disease.

[0044] Preferably, the diagnostic product comprises a kit.

[0045] It is understandable that in actual use, the reagents or kits or diagnostic products provided in the present application can be used to detect the presence and / or content of the DNA methylation status of all genes in the gene combination described in the test sample isolated from the organism, and based on the test results, confirm the presence or absence of liver tumor-related diseases, evaluate the formation or risk of formation of liver tumor-related diseases and / or evaluate the progression of liver tumor-related diseases.

[0046] On the other hand, the present application provides a kit containing the reagent.

[0047] In one embodiment, the kit further comprises reagents for a PCR reaction system and / or a bisulfite conversion reagent.

[0048] Preferably, the reagents used in the PCR reaction system include reagents necessary and / or commonly used in PCR amplification.

[0049] Preferably, the bisulfite conversion reagent can be a commercially available kit.

[0050] The beneficial effects of this application include at least:

[0051] The present application further optimizes the gene subregions of liver tumor-related diseases and provides a subregion combination with better effects. When the gene group combination provided in the present application is used as a biomarker for plasma separation DNA methylation detection, it has better sensitivity and specificity, can significantly improve the accuracy of early screening or rescreening of liver tumor-related diseases, and is conducive to improving screening efficiency. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] Unless otherwise defined, all professional terms used below have the same meanings as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention.

[0054] Unless otherwise specified, various raw materials, reagents, instruments and equipment used in the present invention can be purchased from the market or prepared by existing methods.

[0055] Example 1

[0056] Experimental methods:

[0057] Using RRBS sequencing data of liver cancer, paracancerous tissue, plasma and blood cell DNA of control populations and methylation chip data obtained from the public database TCGA, candidate marker sets were generated: marker sets obtained by comparing liver cancer with normal liver tissue in RRBS data, marker sets obtained by comparing liver cancer with healthy plasma in RRBS data, and marker sets obtained by comparing liver cancer with normal liver tissue in methylation chips. After these markers were tested by fluorescent PCR, the methylation candidate markers of 11 target regions of three representative genes for liver cancer detection are shown in Table 1.

[0058] Table 1 Location information of 11 target regions of three genes

[0059]

[0060] Candidate markers were verified in plasma samples, including control group, liver cancer group, and hepatitis B and liver cirrhosis interference population. Reference levels of markers and marker combination performance in Table 1 were analyzed. Because plasma free DNA in a single sample is limited, the target area was pre-amplified before fluorescent PCR detection. A binary logistic regression model was established in the training set, and the model stability was verified using the test set. The steps include:

[0061] (1) Nucleic acid extraction from plasma samples: Cell-free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen, 55114) according to the manufacturer's instructions.

[0062] (2) Treat the nucleic acid with bisulfite to convert unmethylated cytosine into uracil, while maintaining the sequence of methylated cytosine unchanged. Use the EZ DNA Methylation-Lightning Kit (Zymo research, D5031) for bisulfite conversion.

[0063] (3) Pre-amplification and dilution of the target region. Using the primers in Table 2 on the ProFlexTM PCR System (Thermo Fisher) platform, the target DNA and the primer pool were first amplified at 95°C for 3 minutes, and then the following steps were cycled 8 times: 95°C for 30 seconds and 56°C for 60 seconds. The amplified product was diluted 10 times.

[0064] (4) Fluorescence PCR detection was performed using the primers and probes in Table 2. The amplified products were detected by quantitative PCR using a standard program (Novo Start Method Light qPCR Super Mix (Novo Protein)) in an ABI 7500 real-time PCR thermal cycler.

[0065] (5) The Ct values ​​of the training set detection results were used to construct a two-target area combination model and a three-target area combination model based on binary logistic regression. The receiver operating characteristic (ROC) curve was drawn to determine the ability of the marker area and marker combination to distinguish between liver cancer patients and non-liver cancer patients.

[0066] (6) The training set included 467 people, including 233 cases of HCC (78.5% BCLC stage 0 / A), 93 cases of chronic hepatitis B (CHB) or cirrhosis, 18 cases of liver cysts or fatty liver, and 123 healthy controls (HC) from multiple centers. The test set included 466 people, including 209 cases of HCC (75.6% BCLC stage 0 / A), 92 cases of CHB / cirrhosis, 25 cases of liver cysts or fatty liver, and 140 healthy controls (HC).

[0067] Table 2 Primers and probes

[0068]

[0069] Experimental results:

[0070] The Ct values ​​of the 11 target areas of the three genes in the training set and the test set were analyzed by ROC curve analysis and the AUC values ​​were obtained. The 11 target areas have the ability to distinguish liver cancer from non-liver cancer in both the training set and the test set. The specific results are shown in Table 3.

[0071] Table 3 AUC values ​​of each target area in the training set and test set

[0072] Amplification region number Training set AUC Test set AUC DAB2IP-1 0.734 0.729 DAB2IP-2 0.724 0.726 DAB2IP-3 0.704 0.701 DAB2IP-4 0.701 0.708 DAB2IP-5 0.706 0.716 CHFR-1 0.734 0.726 CHFR-2 0.727 0.711 CHFR-3 0.705 0.714 CHFR-4 0.701 0.715 GRASP-1 0.731 0.727 GRASP-2 0.70 0.713

[0073] In the training set data, two-target combination modeling and three-target combination modeling were performed. The AUC results of the representative models with excellent performance in the combination are shown in Table 4, and the AUC of the representative models with excellent performance in the test set are shown in Table 5. The AUC of the combination is greater than the AUC of a single target, indicating that the combination of targets can increase the ability to distinguish liver cancer from non-liver cancer. It is worth noting that the AUC of the three-target combination model is as high as 0.921 in training and as high as 0.919 in the test set.

[0074] Table 4 Performance of representative model combinations in the training set

[0075]

[0076] Table 5. Performance of representative model combinations in the test set

[0077]

[0078] Example 2

[0079] Experimental methods:

[0080] The markers were detected in plasma samples, including the control group, liver cancer group, and hepatitis B and liver cirrhosis interference groups. The reference levels of the markers and the performance of the marker combination in Table 6 were analyzed. Because the plasma free DNA of a single sample is limited, the target area was pre-amplified when performing fluorescent PCR detection. A binary logistic regression model was established in the training set, and the model stability was verified using the test set. The steps include:

[0081] (1) Nucleic acid extraction from plasma samples: Cell-free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen, 55114) according to the manufacturer's instructions.

[0082] (2) Treat the nucleic acid with bisulfite to convert unmethylated cytosine into uracil, while maintaining the sequence of methylated cytosine unchanged. Use the EZ DNA Methylation-Lightning Kit (Zymo research, D5031) for bisulfite conversion.

[0083] (3) Perform pre-amplification and dilution of the target region. Use the primers in Table 7 in ProFlex TM PCR System (ThermoFisher) platform, the target DNA and primer pool were first amplified at 95°C for 3 minutes, and then the following steps were cycled 8 times: 95°C for 30 seconds and 56°C for 60 seconds. The amplified product was diluted 10 times.

[0084] (4) The amplified product was detected by quantitative PCR using a standard program (NovoStart MethyLight qPCR SuperMix (NovoProtein)) in a cycler.

[0085] (5) The Ct values ​​of the training set detection results were used to construct a combined model based on binary logistic regression. The receiver operating characteristic (ROC) curve was drawn to determine the ability of the marker region and marker combination to distinguish between liver cancer patients and non-liver cancer patients.

[0086] (6) The training set included 467 people, including 233 cases of HCC (78.5% BCLC stage 0 / A), 93 cases of chronic hepatitis B (CHB) or cirrhosis, 18 cases of liver cysts or fatty liver, and 123 healthy controls (HC) from multiple centers. The test set included 466 people, including 209 cases of HCC (75.6% BCLC stage 0 / A), 92 cases of CHB / cirrhosis, 25 cases of liver cysts or fatty liver, and 140 healthy controls (HC).

[0087] Table 6 Location information of amplified genes and target regions

[0088]

[0089]

[0090] Table 7 Primer probe

[0091]

[0092] Experimental results:

[0093] Two-target area combination modeling and three-target area combination modeling were performed in the training set data. The representative combination AUCs of the training set and test set models are shown in Tables 8 and 9.

[0094] Table 8 Performance of representative model combinations of training sets

[0095]

[0096] Table 9 Performance of representative model combinations in the test set

[0097]

[0098]

[0099] Embodiment:3

[0100] Experimental methods:

[0101] Among the tissue samples, 24 liver cancer tissues and 24 adjacent tissues were tested for markers. The specific steps include:

[0102] Tissue DNA was extracted using Qiagen QIAamp DNA FFPE Tissue Kit.

[0103] A 20 ng sample of the DNA obtained in the above step was treated with a bisulfite reagent (D5031, ZYMO RESEARCH) to obtain converted DNA.

[0104] Optionally, PCR amplification is performed using a primer pool containing methylation-specific primers from Table X and a forward primer and a reverse primer pair, with the converted DNA as a template, and the final concentration of each primer is 100 nM. The PCR reaction system comprises: 10 μL of converted DNA (including 2.5 μL of the premix of the above primers) and 12.5 μL of PCR reagent ( Universal Probe qPCR Master Mix (NEB). PCR reaction conditions were: 95°C for 5 minutes, 95°C for 30 seconds, 56°C for 60 seconds, for 10 cycles.

[0105] The obtained pre-amplification product was diluted 10 times and detected by multiplex fluorescence PCR to obtain the detection fluorescence Ct value of the marker. In the fluorescence PCR reaction system, the final concentration of each primer was 500nM, and the final concentration of each detection probe was 200nM. The PCR reaction system contained: 10μL of pre-amplification diluted product, 2.5μL of primer and probe premix containing the detection site; 12.5μL PCR reagent ( Universal Probe qPCR Master Mix (NEB). PCR reaction conditions were as follows: 95°C for 5 min; 95°C for 30 s, 56°C for 60 s (fluorescence collection), for 50 cycles. ABI 7500 Real-Time PCR System was used to detect different fluorescence in the corresponding fluorescence channels. The Ct values ​​of samples obtained from the buffy coat, paracancerous tissue, and cancerous tissue were calculated and compared, and the Ct value of the target point where no amplification signal was detected was set to 50.

[0106] Table 10 Location information of amplified genes and target regions

[0107]

[0108]

[0109] Table 11 Primer probe

[0110]

[0111] Experimental results:

[0112] The detection results were combined by logistic regression, and the AUC results of the models that performed well in distinguishing adjacent cancer tissues from cancer tissues in these combinations are shown in Table 12.

[0113] Table 12 Performance of representative model combinations

[0114]

[0115] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended to include all changes within the meaning and scope of the equivalent elements of the claims. In addition, it should be understood that although this specification is described in accordance with the implementation methods, not each implementation method contains only one independent technical solution. The description of the specification is only for the sake of clarity. Those skilled in the art should take the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0116] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A DNA methylation marker gene combination for detecting liver tumor-related diseases, characterized in that: The gene combination is selected from the DNA sequences in at least one subregion of at least two of the following genes: DAB2IP gene, its detection sub-region is: chr9:124460896-124462275; chr9:124461322-124461391; chr9:124461043-124461118; chr9:124360386-124362685; chr9:124360868-124360966; The CHFR gene, the subregions tested are: 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 sub-regions are: chr12:52400724-52401698; chr12:52401083-52401169; chr12:52400965-52401055; chr12:52406880-52409127; chr12:52408471-52408566.

2. The gene combination according to claim 1, characterized in that: The gene combination is selected from any one of the following gene sub-region 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 and chr12:52400724-52401698.

3. A reagent, characterized in that The reagent is used to detect the presence and / or content of the DNA methylation status of all genes in the gene combination as claimed in claim 1 or 2 in a test sample separated from an organism.

4. The reagent according to claim 3, characterized in that The reagents include forward primers, reverse primers and / or probes for detecting all genes in the gene combination.

5. The reagent according to claim 3, characterized in that The samples to be tested separated from an organism include plasma, tissues, and cells.

6. Use of the gene combination according to claim 1 or 2 and / or the reagent according to any one of claims 3 to 5 in the preparation of a diagnostic product for liver tumor-related diseases.

7. The use according to claim 6, characterized in that: The liver tumor-related diseases include hepatocellular carcinoma, hepatitis B, cirrhosis, liver cysts, and fatty liver.

8. The use according to claim 7, characterized in that: The liver tumor-related disease diagnostic product is used to confirm the presence or absence of a liver tumor-related disease, assess the risk of developing a liver tumor-related disease and / or assess the progression of a liver tumor-related disease.

9. A kit comprising the reagent according to any one of claims 3 to 5.

10. The kit according to claim 9, characterized in that The kit also includes reagents for a PCR reaction system and / or a bisulfite conversion reagent.

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