System for early warning of coronary heart disease

Abstract

A system for early warning of coronary heart disease. A computer device comprises a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the following steps: receiving methylation level data of a DNA methylation marker combination of a potential coronary heart disease patient and a healthy control sample, establishing a mathematical model, and determining a threshold; and inputting methylation level data of the DNA methylation marker combination of a subject to be tested and substituting same into the mathematical model to obtain a detection index, and comparing the detection index with the threshold to obtain a conclusion of whether a sample to be tested belongs to the potential coronary heart disease patient or the healthy control. The present invention has important scientific significance and clinical application value in early warning of coronary heart disease and diagnosis and treatment of coronary heart disease.

Description

A system for early warning of coronary heart disease TECHNICAL FIELD

[0001] The present application relates to the field of medical informatics, and particularly to a system for early warning of coronary heart disease. BACKGROUND

[0002] Coronary heart disease refers to heart disease caused by coronary atherosclerosis, which causes stenosis, spasm or obstruction of the lumen, leading to myocardial ischemia, hypoxia or necrosis. According to the clinical characteristics of the lesion site, range and degree, coronary heart disease is divided into five types: (1) occult or asymptomatic myocardial ischemia: asymptomatic, but shows myocardial ischemia changes in resting, dynamic or stress electrocardiogram, or radionuclide myocardial imaging suggests myocardial perfusion deficiency without tissue morphology change; (2) angina pectoris: paroxysmal retrosternal pain caused by myocardial ischemia; (3) myocardial infarction: severe ischemic symptoms due to coronary artery occlusion leading to acute myocardial ischemic necrosis; (4) ischemic cardiomyopathy: long-term chronic myocardial ischemia or necrosis leading to myocardial fibrosis, manifested as cardiac enlargement, heart failure and arrhythmia; (5) sudden death: sudden cardiac arrest caused by death, mostly caused by severe arrhythmia caused by local ischemic myocardium electrical physiological disorder.

[0003] The current main diagnostic methods of coronary heart disease are as follows: (1) clinical features: generally combined with the medical history and physical examination condition of the examiner, which is used for preliminary diagnosis, but the specificity is very low; (2) imaging methods: electrocardiogram, echocardiogram and coronary angiography, but often affected by the experience of doctors and instrument equipment; (3) the most commonly used coronary heart disease markers mainly include the following categories: myocardial injury markers, inflammatory factors and adhesion molecules and cytokine markers, plasma lipoprotein and apolipoprotein markers and coagulation related protein markers, etc. Because a certain marker only reflects a certain disease mechanism of the disease, these markers have not been widely recognized in clinical practice. Epigenetics is a gene expression regulation method that does not involve DNA sequence changes but can be inherited, and can be passed on to the next generation [Nicoglou A, Merlin F. Epigenetics: A way to bridge the gap between biological fields. Stud Hist Philos Biol Biomed Sci. 2017; 66: 73-82]. DNA methylation is one of the important ways of epigenetic regulation, which refers to the covalent bond of a methyl group to the 5' carbon of cytosine in the CpG dinucleotide of the genome under the action of DNA methyltransferase [Bird A. Perceptions of epigenetics. Nature. 2007; 447: 396-398]. A large number of studies have shown that DNA methylation can cause changes in chromatin structure, DNA conformation, DNA stability and DNA-protein interaction mode, thereby controlling gene expression [Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013; 38: 23-38].

[0004] The DNA methylation marker is the best early warning in vitro molecular marker for coronary heart disease at present. At present, the sensitivity and specificity of the early warning marker for coronary heart disease in clinical practice are very limited, especially the lack of early warning markers, so more sensitive and specific early molecular markers are urgently needed.

[0005] The present application discloses a system for early warning of coronary heart disease.

[0006] The purpose of the present application is to provide a system for early warning of coronary heart disease.

[0007] The present application claims a computer device.

[0008] The computer device claimed in the present application can include a memory, a processor and a computer program stored on the memory; the processor executes the computer program to implement the following steps:

[0009] Receiving the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples;

[0010] Establishing a mathematical model for each gene in the DNA methylation marker combination of the n1 known A type samples and the n2 known B type samples by binary classification logistic regression method according to the classification mode of A type and B type, and determining the threshold of classification determination;

[0011] Wherein, n1 and n2 can be positive integers greater than 50.

[0012] In an embodiment of the present application, the threshold is set to 0.5. Greater than 0.5 is classified as one class, less than 0.5 is classified as another class, and equal to 0.5 is regarded as an uncertain gray area. Wherein A type and B type are corresponding two classifications, the grouping of binary classification, which group is A type and which group is B type, is determined according to the specific mathematical model, without being required to be agreed.

[0013] In practical application, the threshold can also be determined according to the maximum Youden index (specifically, the numerical value corresponding to the maximum Youden index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold is regarded as an uncertain gray area. Wherein A type and B type are corresponding two classifications, the grouping of binary classification, which group is A type and which group is B type, is determined according to the specific mathematical model, without being required to be agreed.

[0014] The DNA methylation marker combination consists of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene, or consists of any several (such as 5-9) genes thereof;

[0015] Inputting the methylation level data of each gene in the DNA methylation marker combination of the to-be-tested person;

[0016] Substituting the methylation level data of each gene in the DNA methylation marker combination of the to-be-tested person into the mathematical model to obtain a detection index;

[0017] Comparing the detection index with the threshold to obtain a comparison result;

[0018] Outputting the conclusion of whether the type of the to-be-tested sample is A type or B type according to the comparison result.

[0019] The A type sample and the B type sample are any one of the following:

[0020] (1) potential patients with coronary heart disease within 2 years and healthy controls;

[0021] (2) potential patients with coronary heart disease within 1 year and healthy controls.

[0022] Among them, the healthy control is a person who has never had coronary heart disease and cancer now and in the past, and will not have coronary heart disease and cancer within 2 years in the future, and the blood routine indicators are within the reference range. The same below.

[0023] That is, the computer device provided by the application can be used to warn coronary heart disease before clinical symptoms. Among them, the before clinical symptoms is within 2 years or 1 year earlier than the clinical onset time.

[0024] The application claims a computer program product.

[0025] The computer program product claimed by the application can include a computer program; the computer program is executed by a processor to realize the steps described above.

[0026] Among them, the computer program product can be a software product that mainly realizes its solution through a computer program.

[0027] That is, the computer program product provided by the application can be used to warn coronary heart disease before clinical symptoms. Among them, the before clinical symptoms is within 2 years or 1 year earlier than the clinical onset time.

[0028] The application claims a computer readable storage medium.

[0029] The computer readable storage medium claimed by the application stores a computer program; the computer program is executed by a processor to realize the steps described above.

[0030] Among them, the computer readable storage medium refers to a carrier for storing data, which can be a magnetic tape, a magnetic disk, a floppy disk, an optical disk, a magneto-optical disk, a ROM, a PROM, a VCD, a DVD, a hard disk, a flash memory, a U disk, a CF card, an SD card, an MMC card, an SM card, a memory stick (Memory Stick) or an xD card, etc.

[0031] That is, the computer readable storage medium provided by the application can be used to warn coronary heart disease before clinical symptoms. Among them, the before clinical symptoms is within 2 years or 1 year earlier than the clinical onset time.

[0032] The present application claims a system for early warning of coronary heart disease. Among them, the early warning of coronary heart disease is 2 years or 1 year earlier than the clinical onset time of coronary heart disease.

[0033] The system for early warning of coronary heart disease claimed in the present application can include:

[0034] (A1) a substance for detecting the methylation level of each gene in the DNA methylation marker combination;

[0035] (A2) a device, the device includes unit X and unit Y;

[0036] The unit X is used to establish a mathematical model, including a data receiving module, a data analysis processing module and a model output module;

[0037] The data receiving module is configured to receive the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples;

[0038] The data analysis processing module is configured to receive the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples sent from the data receiving module, and establish a mathematical model by binary classification logistic regression method according to the classification method of A type and B type, and determine the threshold of classification decision;

[0039] Among them, n1 and n2 can be positive integers greater than 50.

[0040] In an embodiment of the present application, the threshold is set to 0.5. Greater than 0.5 is classified as one class, less than 0.5 is classified as another class, and equal to 0.5 is as an uncertain gray area. Among them, A type and B type are two corresponding classifications, the grouping of binary classification, which group is A type and which group is B type is determined according to the specific mathematical model, without being required to be agreed.

[0041] In practical application, the threshold can also be determined according to the maximum Youden index (specifically, the numerical value corresponding to the maximum Youden index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold is as an uncertain gray area. Among them, A type and B type are two corresponding classifications, the grouping of binary classification, which group is A type and which group is B type is determined according to the specific mathematical model, without being required to be agreed.

[0042] The DNA methylation marker combination consists of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene or any several (such as 5-9) genes thereof;

[0043] The model output module is configured to receive the mathematical model established by the data analysis processing module and output;

[0044] The unit Y is used for determining the type of the sample to be tested, comprising a data input module, a data operation module, a data comparison module and a conclusion output module;

[0045] The data input module is configured to input the methylation level data of each gene in the DNA methylation marker combination of the person to be tested;

[0046] The data operation module is configured to receive the methylation level data of each gene in the DNA methylation marker combination of the person to be tested sent by the data input module, and substitute the methylation level data of each gene in the DNA methylation marker combination of the person to be tested into the mathematical model established by the data analysis processing module in the unit X to calculate a detection index;

[0047] The data comparison module is configured to receive the detection index calculated by the data operation module and compare the detection index with the threshold value determined in the data analysis processing module in the unit X;

[0048] The conclusion output module is configured to receive the comparison result from the data comparison module and output the conclusion that the type of the sample to be tested is type A or type B according to the comparison result;

[0049] The A type sample and the B type sample are any one of the following:

[0050] (A1) potential patients with coronary heart disease within 2 years and healthy controls;

[0051] (A2) potential patients with coronary heart disease within 1 year and healthy controls.

[0052] That is, the system provided by the present application can be used to warn coronary heart disease before clinical symptoms. Among them, the before clinical symptoms is within 2 years or 1 year before clinical onset time.

[0053] The present application claims a method for distinguishing or assisting in distinguishing type A sample and type B sample, wherein the type A sample and the type B sample are any one of the following:

[0054] (1) potential patients of coronary heart disease in the next 2 years and healthy controls;

[0055] (2) potential patients of coronary heart disease in the next 1 year and healthy controls.

[0056] The method for distinguishing or assisting in distinguishing the A-type sample and the B-type sample claimed in the present application can comprise the following steps:

[0057] detecting the methylation level of each gene in the DNA methylation marker combination of n1 known A-type samples and n2 known B-type samples; the DNA methylation marker combination consists of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene, or consists of any one or more of the genes; establishing a mathematical model by binary classification logistic regression according to the classification mode of A-type and B-type, and determining the threshold value of classification determination, by using the data of the methylation level of each gene in the DNA methylation marker combination of the n1 known A-type samples and the n2 known B-type samples obtained by detection;

[0058] detecting the methylation level of each gene in the DNA methylation marker combination of the subject to be tested; and substituting the data of the methylation level of each gene in the DNA methylation marker combination of the subject to be tested into the mathematical model to obtain a detection index by calculation;

[0059] comparing the detection index with the threshold value, so as to determine whether the type of the sample to be tested is A-type or B-type.

[0060] The present application claims any of the following applications:

[0061] Application I: application of the computer device described above or the computer program product described above or the computer readable storage medium described above or the system described above in distinguishing or assisting in distinguishing A-type samples and B-type samples;

[0062] The A-type sample and the B-type sample are any of the following:

[0063] (A1) potential patients of coronary heart disease in the next 2 years and healthy controls;

[0064] (A2) potential patients of coronary heart disease in the next 1 year and healthy controls.

[0065] Application II: application of the computer device described above or the computer program product described above or the computer readable storage medium described above or the system described above in early warning of coronary heart disease.

[0066] The present application claims a method for distinguishing or assisting in distinguishing A-type samples and B-type samples by using a computer.

[0067] The present application claims a method for distinguishing or assisting in distinguishing A-type samples and B-type samples by using a computer, which can comprise the following steps:

[0068] (B1) Data receiving: receiving the methylation level data of each gene in the DNA methylation marker combination of n1 known A-type samples and n2 known B-type samples;

[0069] (B2) Data analysis and processing: the methylation level data of each gene in the DNA methylation marker combination of the n1 known A-type samples and the n2 known B-type samples is processed according to the classification method of A-type and B-type, a mathematical model is established by using the binary classification logistic regression method, and the threshold value of classification determination is determined;

[0070] Wherein, n1 and n2 are both positive integers greater than 50.

[0071] In an embodiment of the present application, the threshold value is set to 0.5. Greater than 0.5 is classified as one type, less than 0.5 is classified as another type, and equal to 0.5 is classified as an uncertain gray area. Wherein A-type and B-type are corresponding two classifications, the grouping of two classifications, which group is A-type and which group is B-type, is determined according to the specific mathematical model, and does not need to be agreed.

[0072] In practical application, the threshold value can also be determined according to the maximum Youden index (specifically, the numerical value corresponding to the maximum Youden index). Greater than the threshold value is classified as one type, less than the threshold value is classified as another type, and equal to the threshold value is classified as an uncertain gray area. Wherein A-type and B-type are corresponding two classifications, the grouping of two classifications, which group is A-type and which group is B-type, is determined according to the specific mathematical model, and does not need to be agreed.

[0073] The DNA methylation marker combination consists of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene, or consists of any several (such as 5-9) genes thereof;

[0074] (B3) Data input: inputting the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested;

[0075] (B4) data operation: inputting the methylation level data of each gene in the DNA methylation marker combination of the subject into the mathematical model to obtain a detection index;

[0076] (B5) data comparison: comparing the detection index with the threshold value to obtain a comparison result;

[0077] (B6) conclusion output: outputting a conclusion that the type of the sample to be tested is type A or type B according to the comparison result;

[0078] The A type sample and the B type sample are any one of the following:

[0079] (1) potential patients with coronary heart disease within 2 years and healthy controls;

[0080] (2) potential patients with coronary heart disease within 1 year and healthy controls.

[0081] That is, the method provided by the application is to use a computer to early warn of coronary heart disease before clinical symptoms. Among them, the early warning before clinical symptoms is within 2 years or 1 year before the clinical onset time.

[0082] The application claims the use of a substance for detecting the methylation level of each gene in the DNA methylation marker combination described above in distinguishing or assisting in distinguishing the A type sample and the B type sample, or in preparing a product for distinguishing or assisting in distinguishing the A type sample and the B type sample.

[0083] The A type sample and the B type sample are any one of the following:

[0084] (1) potential patients with coronary heart disease within 2 years and healthy controls;

[0085] (2) potential patients with coronary heart disease within 1 year and healthy controls.

[0086] That is, the application claimed by the application is the use of a substance for detecting the methylation level of each gene in the DNA methylation marker combination described above in preparing a product for early warning of coronary heart disease before clinical symptoms; wherein the early warning before clinical symptoms is within 2 years or 1 year before the clinical onset time.

[0087] The application claims the use of a substance for detecting the methylation level of each gene in the DNA methylation marker combination described above in distinguishing or assisting in distinguishing the A type sample and the B type sample, or in preparing a product for distinguishing or assisting in distinguishing the A type sample and the B type sample.

[0088] The mathematical model is obtained according to the method comprising the following steps:

[0089] (C1) detecting the methylation level of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples respectively;

[0090] (C2) taking the methylation level data of each gene in the DNA methylation marker combination of all samples obtained in step (C1), establishing a mathematical model by binary classification logistic regression method according to the classification of A type and B type, and determining the threshold of classification determination;

[0091] Wherein, n1 and n2 are both positive integers greater than or equal to 50.

[0092] In an embodiment of the present application, the threshold is set to 0.5. Greater than 0.5 is classified as one type, less than 0.5 is classified as another type, and equal to 0.5 is the uncertain gray area. Wherein A type and B type are the corresponding two classifications, the grouping of binary classification, which group is A type and which group is B type, is determined according to the specific mathematical model, without being required to be agreed.

[0093] In practical application, the threshold can also be determined according to the maximum Youden index (specifically, the numerical value corresponding to the maximum Youden index). Greater than the threshold is classified as one type, less than the threshold is classified as another type, and equal to the threshold is the uncertain gray area. Wherein A type and B type are the corresponding two classifications, the grouping of binary classification, which group is A type and which group is B type, is determined according to the specific mathematical model, without being required to be agreed.

[0094] The use method of the mathematical model comprises the following steps:

[0095] (D1) detecting the methylation level of each gene in the DNA methylation marker combination of the sample to be tested;

[0096] (D2) substituting the methylation level data of each gene in the DNA methylation marker combination of the sample to be tested obtained in step (D1) into the mathematical model to obtain a detection index; then comparing the size of the detection index and the threshold, and determining whether the type of the sample to be tested is A type or B type according to the comparison result;

[0097] The A type sample and the B type sample are any of the following:

[0098] (1) potential patients with coronary heart disease within 2 years and healthy controls;

[0099] (2) potential patients with coronary heart disease within 1 year and healthy controls.

[0100] The application claimed in the present application is the use of substances for detecting the methylation level of each gene in the DNA methylation marker combination and media recording the mathematical model establishment method and / or use method in the preparation of products for early warning of coronary heart disease before clinical symptoms; wherein the early warning before clinical symptoms is within 2 years or 1 year before the clinical onset time.

[0101] In the present application, the DNA methylation marker combination can be any of the following:

[0102] (E1) consisting of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene;

[0103] (E2) consisting of SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene;

[0104] (E3) consisting of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene and ABCG1 gene;

[0105] (E4) consisting of RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene;

[0106] (E5) consisting of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene and FUT7 gene;

[0107] (E6) consisting of DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene;

[0108] (E7) consisting of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene and MGRN1 gene;

[0109] (E8) consisting of MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene;

[0110] (E9) consisting of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene and DYRK4 gene.

[0111] In the present application, the methylation level of the HYAL2 gene in the DNA methylation marker combination refers to the methylation levels of the following 4 CpG sites: the CpG site at positions 73-74 from the 5' end, the CpG site at positions 135-136, the CpG site at positions 160-161, and the CpG site at positions 183-184 in the DNA fragment represented by SEQ ID No. 1 in the HYAL2 gene.

[0112] In the present application, the methylation level of the S100P gene in the DNA methylation marker combination refers to the methylation levels of the following 9 CpG sites: the CpG site at positions 64-65 from the 5' end, the CpG site at positions 74-75, the CpG site at positions 81-82, the CpG site at positions 175-176, the CpG site at positions 193-194, the CpG site at positions 218-219, the CpG site at positions 244-245, the CpG site at positions 253-254, and the CpG site at positions 256-257 in the DNA fragment represented by SEQ ID No. 2 in the S100P gene.

[0113] In the present application, the methylation level of the S100P gene in the DNA methylation marker combination refers to the methylation levels of the following 9 CpG sites: the CpG site at positions 64-65 from the 5' end, the CpG site at positions 74-75, the CpG site at positions 81-82, the CpG site at positions 175-176, the CpG site at positions 193-194, the CpG site at positions 218-219, the CpG site at positions 244-245, the CpG site at positions 253-254, and the CpG site at positions 256-257 in the DNA fragment represented by SEQ ID No. 2 in the S100P gene.

[0114] In the present application, the methylation level of the RPTOR gene in the DNA methylation marker combination refers to the methylation levels of the following 7 CpG sites: the CpG site at positions 25-26 from the 5' end, the CpG site at positions 61-62, the CpG site at positions 97-98, the CpG site at positions 116-117, the CpG site at positions 196-197, the CpG site at positions 223-224, and the CpG site at positions 277-278 in the DNA fragment represented by SEQ ID No. 4 in the RPTOR gene.

[0115] In the present application, the methylation level of the DYRK4 gene in the DNA methylation marker combination refers to the methylation levels of the following 3 CpG sites: the CpG site at positions 41-42 from the 5' end, the CpG site at positions 255-256, and the CpG site at positions 357-358 in the DNA fragment represented by SEQ ID No. 5 in the DYRK4 gene.

[0116] In the present application, the methylation level of the MGRN1 gene in the DNA methylation marker combination refers to the methylation levels of the following 24 CpG sites: the CpG sites from 54-55, 60-61, 75-76, 82-83, 99-100, 101-102, 106-107, 108-109, 130-131, 139-140, 148-149, 184-185, 189-190, 198-199, 200-201, 205-206, 228-229, 231-232, 240-241, 249-250, 254-255, 277-278, 307-308, and 330-331 of the DNA fragment shown in SEQ ID No. 6 in the MGRN1 gene.

[0117] In the present application, the methylation level of the FUT7 gene in the DNA methylation marker combination refers to the methylation levels of the following 7 CpG sites: the CpG sites from 35-36, 118-119, 134-135, 156-157, 174-175, 255-256, and 287-288 of the DNA fragment shown in SEQ ID No. 7 in the FUT7 gene.

[0118] In the present application, the methylation level of the ABCG1 gene in the DNA methylation marker combination refers to the methylation levels of the following 8 CpG sites: the CpG sites from 27-28, 45-46, 58-59, 112-113, 160-161, 279-280, 299-300, and 327-328 of the DNA fragment shown in SEQ ID No. 8 in the ABCG1 gene.

[0119] In the present application, the methylation level of the THRA1 gene in the DNA methylation marker combination refers to the methylation levels of the following 22 CpG sites: the CpG sites at positions 27-28, the CpG sites at positions 34-35, the CpG sites at positions 39-40, the CpG sites at positions 179-180, the CpG sites at positions 183-184, the CpG sites at positions 194-195, the CpG sites at positions 202-203, the CpG sites at positions 210-211, the CpG sites at positions 216-217, the CpG sites at positions 225-226, the CpG sites at positions 227-228, the CpG sites at positions 234-235, the CpG sites at positions 240-241, the CpG sites at positions 250-251, the CpG sites at positions 252-253, the CpG sites at positions 260-261, the CpG sites at positions 268-269, the CpG sites at positions 289-290, the CpG sites at positions 315-316, the CpG sites at positions 320-321, the CpG sites at positions 348-349, and the CpG sites at positions 362-363 on the DNA fragment represented by SEQ ID No. 9 in the THRA1 gene.

[0120] In the present application, the methylation level of the ACTB gene in the DNA methylation marker combination refers to the methylation levels of the following 16 CpG sites: the CpG sites at positions 39-40, the CpG sites at positions 41-42, the CpG sites at positions 61-62, the CpG sites at positions 65-66, the CpG sites at positions 69-70, the CpG sites at positions 77-78, the CpG sites at positions 81-82, the CpG sites at positions 107-108, the CpG sites at positions 110-111, the CpG sites at positions 122-123, the CpG sites at positions 139-140, the CpG sites at positions 185-186, the CpG sites at positions 213-214, the CpG sites at positions 219-220, the CpG sites at positions 275-276, and the CpG sites at positions 304-305 on the DNA fragment represented by SEQ ID No. 10 in the ACTB gene.

[0121] In the detailed description of the present application, some adjacent methylation sites cannot be distinguished in the DNA methylation analysis using time-of-flight mass spectrometry because several CpG sites are located on one methylation fragment (the undistinguishable sites are listed in Table 4), and thus they are treated as one methylation site when analyzing the methylation level and constructing and using the relevant mathematical model.

[0122] In the present application, the substance for detecting the methylation level of each gene in the DNA methylation marker combination comprises (or is) a primer pair combination capable of specifically amplifying the full-length or partial fragment of each gene in the DNA methylation marker combination.

[0123] wherein the HYAL2 gene fragment is the fragment shown in SEQ ID No. 1; the S100P gene fragment is the fragment shown in SEQ ID No. 2; the SLC22A18 gene fragment is the fragment shown in SEQ ID No. 3; the RPTOR gene fragment is the fragment shown in SEQ ID No. 4; the DYRK4 gene fragment is the fragment shown in SEQ ID No. 5; the MGRN1 gene fragment is the fragment shown in SEQ ID No. 6; the FUT7 gene fragment is the fragment shown in SEQ ID No. 7; the ABCG1 gene fragment is the fragment shown in SEQ ID No. 8; the THRA1 gene fragment is the fragment shown in SEQ ID No. 9; and the ACTB gene fragment is the fragment shown in SEQ ID No. 10.

[0124] Further, the primer pair for amplifying the fragment shown in SEQ ID No. 1 in the HYAL2 gene is composed of primer a1 and primer a2, wherein the primer a1 is a single-stranded DNA shown in SEQ ID No. 11 or nucleotides 11-37 of SEQ ID No. 11, and the primer a2 is a single-stranded DNA shown in SEQ ID No. 12 or nucleotides 32-56 of SEQ ID No. 12.

[0125] Further, the primer pair for amplifying the fragment shown in SEQ ID No. 2 in the S100P gene is composed of primer b1 and primer b2, wherein the primer b1 is a single-stranded DNA shown in SEQ ID No. 13 or nucleotides 11-35 of SEQ ID No. 13, and the primer b2 is a single-stranded DNA shown in SEQ ID No. 14 or nucleotides 32-56 of SEQ ID No. 14.

[0126] Further, the primer pair for amplifying the fragment shown in SEQ ID No. 3 in the SLC22A18 gene is composed of primer c1 and primer c2, wherein the primer c1 is a single-stranded DNA shown in SEQ ID No. 15 or nucleotides 11-35 of SEQ ID No. 15, and the primer c2 is a single-stranded DNA shown in SEQ ID No. 16 or nucleotides 32-56 of SEQ ID No. 16.

[0127] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 4 in the RPTOR gene consists of primer d1 which is a single-stranded DNA represented by SEQ ID No. 17 or nucleotides 11-34 of SEQ ID No. 17, and primer d2 which is a single-stranded DNA represented by SEQ ID No. 18 or nucleotides 32-56 of SEQ ID No. 18.

[0128] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 5 in the DYRK4 gene consists of primer e1 which is a single-stranded DNA represented by SEQ ID No. 19 or nucleotides 11-38 of SEQ ID No. 19, and primer e2 which is a single-stranded DNA represented by SEQ ID No. 20 or nucleotides 32-54 of SEQ ID No. 20.

[0129] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 6 in the MGRN1 gene consists of primer f1 which is a single-stranded DNA represented by SEQ ID No. 21 or nucleotides 11-35 of SEQ ID No. 21, and primer f2 which is a single-stranded DNA represented by SEQ ID No. 22 or nucleotides 32-60 of SEQ ID No. 22.

[0130] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 7 in the FUT7 gene consists of primer g1 which is a single-stranded DNA represented by SEQ ID No. 23 or nucleotides 11-35 of SEQ ID No. 23, and primer g2 which is a single-stranded DNA represented by SEQ ID No. 24 or nucleotides 32-56 of SEQ ID No. 24.

[0131] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 8 in the ABCG1 gene consists of primer h1 which is a single-stranded DNA represented by SEQ ID No. 25 or nucleotides 11-35 of SEQ ID No. 25, and primer h2 which is a single-stranded DNA represented by SEQ ID No. 26 or nucleotides 32-56 of SEQ ID No. 26.

[0132] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 9 in the THRA1 gene is composed of primer i1 and primer i2, wherein the primer i1 is a single-stranded DNA represented by SEQ ID No. 27 or nucleotides 11-35 of SEQ ID No. 27, and the primer i2 is a single-stranded DNA represented by SEQ ID No. 28 or nucleotides 32-55 of SEQ ID No. 28.

[0133] Further, the primer pair for amplifying the fragment represented by SEQ ID No. 10 in the ACTB gene is composed of primer l1 and primer l2, wherein the primer l1 is a single-stranded DNA represented by SEQ ID No. 29 or nucleotides 11-37 of SEQ ID No. 29, and the primer l2 is a single-stranded DNA represented by SEQ ID No. 30 or nucleotides 32-56 of SEQ ID No. 30.

[0134] The present application claims a kit for distinguishing or assisting in distinguishing type A samples and type B samples, wherein the type A samples and the type B samples are any of the following:

[0135] (1) potential patients with coronary heart disease within 2 years and healthy controls;

[0136] (2) potential patients with coronary heart disease within 1 year and healthy controls;

[0137] The kit for distinguishing or assisting in distinguishing the type A samples and the type B samples claimed by the present application contains the primer pair combinations described above.

[0138] Further, the kit can also contain the media described above recording the mathematical model establishment method and / or the use method.

[0139] In the present application, the coronary heart disease can be different clinical types, such as occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death.

[0140] In the present application, detecting the methylation level of each gene in the DNA methylation marker combination is detecting the methylation level of each gene in the DNA methylation marker combination in a blood sample.

[0141] Any of the above mathematical models can be changed in actual application according to different DNA methylation detection methods and fitting methods, which is determined according to the specific mathematical model and does not need to be agreed.

[0142] In the embodiments of the present application, the model is specifically ln(y / (1-y))=b0+b1x1+b2x2+b3x3+…+bnXn, wherein y is the detection index obtained after substituting the methylation values of one or more methylation sites of the sample to be tested into the model, b0 is a constant, x1-xn are the methylation values of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn are the weights of the methylation values of each site given by the model.

[0143] In an embodiment of the present application, the mathematical model is specifically: ln(y / (1-y)) = 0.681-1.229xHYAL2_CpG_1+0.591xHYAL2_CpG_2-1.092xHYAL2_CpG_3+0.663xHYAL2_CpG_4-3.106xS100P_CpG_2.3+2.028xS100P_CpG_4-1.593xS100P_CpG_7-0.470xS100P_CpG_8-0.503xS100P_CpG_9+1.007xS100P_CpG_10.11.12-3.016xSLC22A18_CpG_1-1.803xSLC22A18_CpG_3+3.509xSLC22A18_CpG_4+0.469xSLC22A18_CpG_5+1.003xSLC22A18_CpG_6-2.197xSLC22A18_CpG_8+0.394xRPTOR_CpG_1+1.806xRPTOR_CpG_2+0.327xRPTOR_CpG_3-1.309xRPTOR_CpG_4+2.880xRPTOR_CpG_5+2.001xRPTOR_CpG_6-3.115xRPTOR_CpG_8+0.149xDYRK4_CpG_1-4.102xDYRK4_CpG_2+2.968xDYRK4_CpG_3. Wherein, the HYAL2_CpG_1 is CpG site from 73-74th site from 5'end in DNA fragment of SEQ ID No. 1 in HYAL2 gene; the HYAL2_CpG_2 is CpG site from 135-136th site from 5'end in DNA fragment of SEQ ID No. 1 in HYAL2 gene; the HYAL2_CpG_3 is CpG site from 160-161th site from 5'end in DNA fragment of SEQ ID No. 1 in HYAL2 gene; the HYAL2_CpG_4 is CpG site from 183-184th site from 5'end in DNA fragment of SEQ ID No. 1 in HYAL2 gene; the S100P_CpG_2.3 is CpG site from 64-65th and 74-75th site from 5'end in DNA fragment of SEQ ID No. 2 in S100P gene; the S100P_CpG_4 is CpG site from 81-82th site from 5'end in DNA fragment of SEQ ID No. 2 in S100P gene; the S100P_CpG_7 is CpG site from 81-82th site from 5'end in DNA fragment of SEQ ID No.2; S100P_CpG_8 is the CpG site at 193-194 of SEQ ID No. 2; S100P_CpG_9 is the CpG site at 218-219 of SEQ ID No. 2; S100P_CpG_10.11.12 is the CpG site at 244-245, 253-254 and 256-257 of SEQ ID No. 2; SLC22A18_CpG_1 is the CpG site at 45-46 of SEQ ID No. 3; SLC22A18_CpG_3 is the CpG site at 75-76 of SEQ ID No. 3; SLC22A18_CpG_4 is the CpG site at 99-100 of SEQ ID No. 3; SLC22A18_CpG_5 is the CpG site at 117-118 of SEQ ID No. 3; SLC22A18_CpG_6 is the CpG site at 129-130 of SEQ ID No. 3; SLC22A18_CpG_8 is the CpG site at 300-301 of SEQ ID No. 3; RPTOR_CpG_1 is the CpG site at 25-26 of SEQ ID No. 4; RPTOR_CpG_2 is the CpG site at 61-62 of SEQ ID No. 4; RPTOR_CpG_3 is the CpG site at 97-98 of SEQ ID No. 4; RPTOR_CpG_4 is the CpG site at 116-117 of SEQ ID No. 4; RPTOR_CpG_5 is the CpG site at 196-197 of SEQ ID No. 4; RPTOR_CpG_6 is the CpG site at 218-219 of SEQ ID No. 4; RPTOR_CpG_7 is the CpG site at 245-246 of SEQ ID No. 4; RPTOR_CpG_8 is the CpG site at 253-254 of SEQ ID No. 4; RPTOR_CpG_9 is the CpG site at 256-257 of SEQ ID No. 4; RPTOR_CpG_10 is the CpG site at 266-267 of SEQ ID No. 4; RPTOR_CpG_11 is the CpG site at 275-276 of SEQ ID No. 4; RPTOR_CpG_12 is the CpG site at 283-284 of SEQ ID No. 4; RPTOR_CpG_13 is the CpG site at 300-301 of SEQ ID No. 4; RPTOR_CpG_14 is the CpG site at 323-324 of SEQ ID No. 4; RPTOR_CpG_15 is the CpG site at 337-338 of SEQ ID No. 4; RPTOR_CpG_16 is the CpG site at 352-353 of SEQ ID No. 4; RPTOR_CpG_17 is the CpG site at 363-364 of SEQ ID No. 4; RPTOR_CpG_18 is the CpG site at 379-380 of SEQ ID No. 4; RPTOR_CpG_19 is the CpG site at 389-390 of SEQ ID No. 4; RPTOR_CpG_20 is the CpG site at 405-406 of SEQ ID No. 4; RPTOR_CpG_21 is the CpG site at 421-422 of SEQ ID No. 4; RPTOR_CpG_22 is the CpG site at 437-438 of SEQ ID No. 4; RPTOR_CpG_23 is the CpG site at 453-454 of SEQ ID No. 4; RPTOR_CpG_24 is the CpG site at 469-470 of SEQ ID No. 4; RPTOR_CpG_25 is the CpG site at 485-486 of SEQ ID No. 4; RPTOR_CpG_26 is the CpG site at 501-502 of SEQ ID No. 4; RPTOR_CpG_27 is the CpG site at 517-518 of SEQ ID No. 4; RPTOR_CpG_28 is the CpG site at 533-534 of SEQ ID No. 4; RPTOR_CpG_29 is the CpG site at 549-550 of SEQ ID No. 4; RPTOR_CpG_30 is the CpG site at 565-566 of SEQ ID No. 4; RPTOR_CpG_31 is the CpG site at 581-582 of SEQ ID No. 4; RPTOR_CpG_32 is the CpG site at 597-598 of SEQ ID No. 4; RPTOR_CpG_33 is the CpG site at 613-614 of SEQ ID No. 4; RPTOR_CpG_34 is the CpG site at 629-630 of SEQ ID No. 4; RPTOR_CpG_35 is the CpG site at 645-646 of SEQ ID No. 4; RPTOR_CpG_36 is the CpG site at 661-662 of SEQ ID No. 4; RPTOR_CpG_37 is the CpG site at 677-678 of SEQ ID No. 4; RPTOR_CpG_38 is the CpG site at 693-694 of SEQ ID No. 4; RPTOR_CpG_39 is the CpG site at 709-710 of SEQ ID No. 4; RPTOR_CpG_40 is the CpG site at 725-726 of SEQ ID No. 4; RPTOR_CpG_41 is the CpG site at 741-742 of SEQ ID No. 4; RPTOR_CpG_42 is the CpG site at 757-758 of SEQ ID No. 4; RPTOR_CpG_43 is the CpG site at 773-774 of SEQ ID No. 4; RPTOR_CpG_44 is the CpG site at 789-790 of SEQ ID No. 4; RPTOR_CpG_45 is the CpG site at 805-806 of SEQ ID No. 4; RPTOR_CpG_46 is the CpG site at 821-822 of SEQ ID No. 4; RPTOR_CpG_47 is the CpG site at 837-838 of SEQ ID No. 4; RPTOR_CpG_48 is the CpG site at 853-854 of SEQ ID No. 4; RPTOR_CpG_49 is the CpG site at 869-870 of SEQ ID No. 4; RPTOR_CpG_50 is the CpG site at 885-886 of SEQ ID No. 4; RPTOR_CpG_51 is the CpG site at 901-902 of SEQ ID No. 4; RPTOR_CpG_52 is the CpG site at 917-918 of SEQ ID No. 4; RPTOR_CpG_53 is the CpG site at 933-934 of SEQ ID No. 4; RPTOR_CpG_54 is the CpG site at 949-950 of SEQ ID No. 4; RPTOR_CpG_55 is the CpG site at 965-966 of SEQ ID No. 4; RPTOR_CpG_56 is the CpG site at 981-982 of SEQ ID No. 4; RPTOR_CpG_57 is the CpG site at 997-998 of SEQ ID No. 4; RPTOR_CpG_58 is the CpG site at 1013-1014 of SEQ ID No. 4; RPTOR_CpG_59 is the CpG site at 1029-1030 of SEQ ID No. 4; RPTOR_CpG_60 is the CpG site at 1045-1046 of SEQ ID No. 4; RPTOR_CpG_61 is the CpG site at 1061-1062 of SEQ ID No. 4; RPTOR_CpG_62 is the CpG site at 1077-1078 of SEQ ID No. 4; RPTOR_CpG_63 is the CpG site at 1093-1094 of SEQ ID No. 4; RPTOR_CpG_64 is the CpG site at 1109-1110 of SEQ ID No. 4; RPTOR_CpG_65 is the CpG site at 1125-1126 of SEQ ID No. 4; RPTOR_CpG_66 is the CpG site at 1141-1142 of SEQ ID No. 4; RPTOR_CpG_67 is the CpG site at 1157-1158 of SEQ ID No. 4; RPTOR_CpG_68 is the CpG site at 1173-1174 of SEQ ID No4; the RPTOR_CpG_8 is a CpG site shown as 277-278 from the 5' end of the DNA fragment shown in SEQ ID No. 4 in the RPTOR gene; the DYRK4_CpG_1 is a CpG site shown as 41-42 from the 5' end of the DNA fragment shown in SEQ ID No. 5 in the DYRK4 gene; the DYRK4_CpG_2 is a CpG site shown as 255-256 from the 5' end of the DNA fragment shown in SEQ ID No. 5 in the DYRK4 gene; the DYRK4_CpG_3 is a CpG site shown as 357-358 from the 5' end of the DNA fragment shown in SEQ ID No. 5 in the DYRK4 gene. The threshold of the model is 0.5. The subjects with the detection index greater than 0.5 calculated by the model are or are candidate for the potential patients who will develop coronary heart disease in the next 2 years, and the subjects with the detection index less than 0.5 are or are candidate for the healthy controls.

[0144] The HYAL2 gene described in any of the above can specifically include Genbank Accession No: NM_003773.5 (GI: 1519311938), transcript variant 1; NM_033158.5 (GI: 1890343426), transcript variant 2.

[0145] The S100P gene described in any of the below can specifically include Genbank Accession No: NM_005980.3.

[0146] The SLC22A18 gene described in any of the above can specifically include Genbank Accession No: NM_002555.6 (GI: 1677501124), transcript variant 1; Genbank Accession No: NM_183233.3 (GI: 1677531469), transcript variant 2; NM_001315501.1 (GI: 937500800), transcript variant 3; Genbank Accession No: NM_001315502.2 (GI: 1677531744), transcript variant 4.

[0147] The RPTOR gene described in any of the above can specifically include Genbank Accession No: NM_020761.3 (GI: 1519244773), transcript variant 1; Genbank Accession No: NM_001163034.2 (GI: 1676318601), transcript variant 2.

[0148] The DYRK4 gene mentioned above may specifically include GenBank accession number: NM_003845.2 (GI:537361020), transcript variant 1; GenBank accession number: NM_001282285.1 (GI:537361021), transcript variant 2; GenBank accession number: NM_001282286.1 (GI:537361023), transcript variant 3; GenBank accession number: NR_104115.2 (GI:1700660554), transcript variant 4; GenBank accession number: NM_001371301.1 (GI:1700447575), transcript variant 5.

[0149] The MGRN1 gene mentioned above may specifically include GenBank accession number: NM_015246.4 (GI:1519311542), transcript variant 1; GenBank accession number: NM_001142289.2 (GI:334883177), transcript variant 2; GenBank accession number: NM_001142290.2 (GI:334883179), transcript variant 3; GenBank accession number: NM_001142291.2 (GI:334883181), transcript variant 4; GenBank accession number: NR_102267.1 (GI:456367266), transcript variant 5.

[0150] The FUT7 gene mentioned above may specifically include Genbank accession number: NM_004479.4, transcript variant 1.

[0151] The ABCG1 gene mentioned above may specifically include GenBank accession number NM_016818.2 (GI:46592897), transcript variant 2; GenBank accession number NM_207174.1 (GI:46592955), transcript variant 3; GenBank accession number NM_004915.3 (GI:46592914), transcript variant 4; GenBank accession number NM_207627.1 (GI:46592963), transcript variant 5; GenBank accession number NM_207628.1 (GI:46592970), transcript variant 6; GenBank accession number NM_207629.1 (GI:46592977), transcript variant 7.

[0152] Any of the above-mentioned THRA1 genes can specifically include Genbank Accession No. NM_001190919.2 (GI: 1890263803), transcript variant 4; NM_199334.5 (GI: 1752423552), transcript variant 1; NM_003250.6 (GI: 1677499490), transcript variant 2; NM_001190918.2 (GI: 1677498565), transcript variant 3.

[0153] Any of the above-mentioned ACTB genes can specifically include Genbank Accession No. NM_001101.5, transcript variant 1. BRIEF DESCRIPTION OF DRAWINGS

[0154] FIG. 1 is a computer flow chart for implementing the present application to assist in distinguishing between A-type samples and B-type samples.

[0155] FIG. 2 is a schematic diagram of a mathematical model.

[0156] FIG. 3 is an illustration of a mathematical model.

[0157] BEST MODE FOR CARRYING OUT THE INVENTION

[0158] The following examples facilitate a better understanding of the present application, but do not limit the present application. In the following examples, the experimental methods are conventional methods unless otherwise specified. In the following examples, the test materials used are commercially available from conventional biochemical reagent stores unless otherwise specified. In the following examples, the quantitative tests are set up in triplicate, and the results are averaged.

[0159] FIG. 1 is a computer flow chart for implementing the present application to assist in distinguishing between A-type samples and B-type samples.

[0160] In step S1, the methylation level data of each gene in the DNA methylation marker combination (consisting of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene, and the ACTB gene, or consisting of any one or more of these genes) of n1 known A-type samples and n2 known B-type samples is received;

[0161] In step S2, the methylation level data of each gene in the DNA methylation marker combination of the n1 known A-type samples and the n2 known B-type samples in S1 is used to establish a mathematical model by binary classification logistic regression according to the classification method of A-type and B-type, and a threshold value for classification determination is determined;

[0162] In step S3, the methylation level data of each gene in the DNA methylation marker combination of the to-be-tested person is inputted;

[0163] In step S4, the methylation level data of each gene in the DNA methylation marker combination of the to-be-tested person in S3 is substituted into the mathematical model in S2, and a detection index is calculated;

[0164] In step S5, the detection index in S4 is compared with the threshold value in S2 to obtain a comparison result;

[0165] In step S6, according to the comparison result in S5, a conclusion is outputted that the type of the to-be-tested sample is type A or type B.

[0166] Embodiment 1, primer design for detecting combination gene methylation sites

[0167] The present application detects the correlation analysis of the methylation level and the early warning of coronary heart disease on the CpG sites on HYAL2_A fragment, S100P_B fragment, SLC22A18_C fragment, RPTOR_D fragment, DYRK4_E fragment, MGRN1_F fragment, FUT7_G fragment, ABCG1_H fragment, THRA1_I fragment and ACTB_J fragment.

[0168] HYAL2_A fragment (SEQ ID No. 1) is located at hg19 reference genome chr3:50360508-50360856, positive strand;

[0169] S100P_B fragment (SEQ ID No. 2) is located at hg19 reference genome chr4:6695635-6695920, positive strand;

[0170] SLC22A18_C fragment (SEQ ID No. 3) is located at hg19 reference genome chr11:2920536-2920864, negative strand;

[0171] RPTOR_D fragment (SEQ ID No. 4) is located at hg19 reference genome chr17:78755382-78755760, positive strand

[0172] DYRK4_E fragment (SEQ ID No. 5) is located at hg19 reference genome chr12:4699192-4699641, positive strand;

[0173] MGRN1_F fragment (SEQ ID No. 6) is located at hg19 reference genome chr16:4730182-4730540, negative strand;

[0174] FUT7_G fragment (SEQ ID No. 7) is located at hg19 reference genome chr9: 139927462-139927780, negative strand;

[0175] ABCG1_H fragment (SEQ ID No. 8) is located at hg19 reference genome chr21: 43642309-43642664, positive strand;

[0176] THRA1_I fragment (SEQ ID No. 9) is located at hg19 reference genome chr10: 124221654-124222051, negative strand;

[0177] ACTB_J fragment (SEQ ID No. 10) is located at hg19 reference genome chr10: 124221654-124222051, negative strand.

[0178] The CpG site information to be studied in the present application on the HYAL2_A fragment, S100P_B fragment, SLC22A18_C fragment, RPTOR_D fragment, DYRK4_E fragment, MGRN1_F fragment, FUT7_G fragment, ABCG1_H fragment, THRA1_I fragment and ACTB_J fragment is shown in Table 1.

[0179] Table 1, CpG site information in combined gene fragments

[0180] Specific PCR primers were designed for 10 fragments (HYAL2_A fragment, S100P_B fragment, SLC22A18_C fragment, RPTOR_D fragment, DYRK4_E fragment, MGRN1_F fragment, FUT7_G fragment, ABCG1_H fragment, THRA1_I fragment and ACTB_J fragment), as shown in Table 2. SEQ ID No. 11, SEQ ID No. 13, SEQ ID No. 15, SEQ ID No. 17, SEQ ID No. 19, SEQ ID No. 21, SEQ ID No. 23, SEQ ID No. 25, SEQ ID No. 27 and SEQ ID No. 29 are forward primers; SEQ ID No. 12, SEQ ID No. 14, SEQ ID No. 16, SEQ ID No. 18, SEQ ID No. 20, SEQ ID No. 22, SEQ ID No. 24, SEQ ID No. 26, SEQ ID No. 28 and SEQ ID No. 30 are reverse primers. In SEQ ID No. 11, SEQ ID No. 13, SEQ ID No. 15, SEQ ID No. 17, SEQ ID No. 19, SEQ ID No. 21, SEQ ID No. 23, SEQ ID No. 25, SEQ ID No. 27 and SEQ ID No. 29, the lowercase letters are non-specific tags, and the capital letters are specific primer sequences. In SEQ ID No. 12, SEQ ID No. 14, SEQ ID No. 16, SEQ ID No. 18, SEQ ID No. 20, SEQ ID No. 22, SEQ ID No. 24, SEQ ID No. 26, SEQ ID No. 28 and SEQ ID No. 30, the lowercase letters are non-specific tags, and the capital letters are specific primer sequences. The primer sequences do not contain SNPs and CpG sites.

[0181] Table 2, Methylation detection primer sequences of DNA methylation marker combination of the application

[0182] Example 2, Methylation detection and result analysis of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB gene combination

[0183] I. Research samples

[0184] The research sample was obtained by epidemiological cluster sampling method. The community population over 18 years old in a city was followed up for 2 years. The study was reviewed by the ethics committee, and all the subjects signed the informed consent form. The incidence information of coronary heart disease and cancer was recorded through the local hospital, disease control center chronic disease management system, community health service center and work station chronic disease routine registration project, and social security center reimbursement data. The starting time of the cohort was the baseline survey date, and the outcome variable was the incidence of coronary heart disease. The follow-up time of the lost subjects was calculated according to half of the end of follow-up time. By July 2018, we selected the new coronary heart disease patients within 2 years after the cohort entered as the case group, a total of 342 people (according to the clinical classification: 45 cases of occult or asymptomatic myocardial ischemia, 64 cases of angina pectoris, 83 cases of myocardial infarction, 74 cases of ischemic cardiomyopathy, and 76 cases of sudden death), of which 137 cases occurred within 1 year (including 20 cases of occult or asymptomatic myocardial ischemia, 21 cases of angina pectoris, 33 cases of myocardial infarction, 30 cases of ischemic cardiomyopathy, and 33 cases of sudden death). After age and sex matching, we selected people who had not occurred coronary heart disease and cancer during the follow-up period and whose blood routine indexes were within the reference range as healthy controls, a total of 612 cases.

[0185] All patient ex vivo blood samples were collected at the time of enrollment and before the onset of disease. The disease was diagnosed by imaging and pathology after the subsequent onset.

[0186] The median age of healthy controls and coronary heart disease was 65 and 64 years old, respectively, and the male to female ratio in each of the two populations was about 1:1. The median age of coronary heart disease that occurred within 1 year after enrollment was 65 years old, and the male to female ratio in the population was about 1:1.

[0187] II. Methylation detection

[0188] 1. Extract total DNA from blood tissue.

[0189] 2. Perform bisulfite conversion on the DNA extracted above to obtain bisulfite-converted DNA, according to the Qiagen DNA Methylation Kit Instructions. Here, according to the principle that bisulfite can convert all unmethylated cytosine to uracil, while methylated cytosine remains unchanged.

[0190] 3. Using the DNA treated with bisulfite in step 2 as a template, 10 pairs of specific primers in Table 2 were used to perform PCR amplification on the reaction system according to the conventional PCR reaction requirements of DNA polymerase. All primers used the conventional standard PCR reaction system, and the amplification was performed according to the program shown in Table 3 below.

[0191] Table 3, amplification program

[0192] 4. Take the amplified product of step 3, and perform DNA methylation analysis by time-of-flight mass spectrometry, according to the following method:

[0193] (1) Add 2 μl shrimp alkaline phosphatase (SAP) solution (0.3 ml SAP [0.5 U] + 1.7 ml H2O) to 5 μl PCR product, then incubate in a PCR instrument according to the following program (37°C, 20 min→ 85°C, 5 min→ 4°C, 5 min);

[0194] (2) Take out 2 μl of the SAP-treated product obtained in step (1), and add 5 μl of T-Cleavage reaction system according to the instructions, then incubate at 37°C for 3 h;

[0195] (3) Take the product of step (2), add 19 μl of deionized water, and then deionize with 6 μg of Resin on a rotary shaker for 1 h;

[0196] (4) Centrifuge at 2000 rpm for 5 min at room temperature, and load the micro-volume supernatant onto a 384 SpectroCHIP by a Nanodispenser robotic arm;

[0197] (5) Time-of-flight mass spectrometry analysis; the obtained data is collected by SpectroACQUIRE v3.3.1.3 software, and visualization is achieved by MassArray EpiTyper v1.2 software.

[0198] The reagents used in the above time-of-flight mass spectrometry detection are from a kit (T-Cleavage MassCLEAVE Reagent Auto Kit, item number: 10129A); the detection instrument used in the above time-of-flight mass spectrometry detection is MassARRAY○R Analyzer Chip Prep Module 384, model number: 41243; and the data analysis software is the software provided with the detection instrument.

[0199] 5. Analyze the data obtained in step 4.

[0200] Data statistical analysis is performed by SPSS Statistics 23.0.

[0201] Non-parametric test is used for comparative analysis between two groups.

[0202] The identification effect of the combination of multiple CpG sites for different sample groups is achieved by statistical methods of logistic regression and receiver operating curve.

[0203] All statistical tests are bilateral, and p value < 0.05 is considered statistically significant.

[0204] Through mass spectrometry experiments, a total of 82 distinguishable peak maps were obtained from the 10 joint genes to be studied CpG sites (Table 1). The SpectroACQUIRE v3.3.1.3 software was used to automatically calculate the peak area according to the formula "methylation level = peak area of methylated fragments / (peak area of non-methylated fragments + peak area of methylated fragments)" to obtain the methylation level of each sample at each CpG site.

[0205] III. Statistical analysis

[0206] The median was used to represent the methylation level, and the non-parametric test was used to compare the methylation level differences between two groups or multiple groups. The receiver operating characteristic curve (ROC curve) was used to evaluate the diagnostic value of single CpG site and multiple CpG site combinations. P<0.05 was considered statistically significant, and all data were analyzed by SPSS 25.0.

[0207] IV. Results analysis

[0208] 1. Differences in methylation levels of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes in healthy controls and potential patients with coronary heart disease (2 years earlier than the clinical onset time)

[0209] The methylation levels of all CpG sites in the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes were analyzed using blood samples from 342 patients with coronary heart disease and 612 healthy controls (Table 1). The patients were asymptomatic at the time of enrollment and developed the disease within 2 years after enrollment. By comparing the methylation levels of all CpG sites in the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes between the two groups, it was found that there was a significant difference between the median methylation levels of the combined genes in the healthy controls and the median methylation levels of the patients with coronary heart disease, and the P values between the healthy controls and the coronary heart disease were all less than 0.05 (Table 4). Therefore, the methylation levels of the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes can be used to screen potential patients who will develop coronary heart disease in the future 2 years in the population, and are very valuable molecular markers in clinical practice.

[0210] 2. The methylation level difference of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes in blood of healthy control and potential coronary heart disease patients (1 year earlier than clinical onset time)

[0211] The methylation level difference of all CpG sites (Table 1) in HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes between 137 coronary heart disease patients and 612 healthy controls was analyzed by using blood as research material. The coronary heart disease patients were all asymptomatic at the time of enrollment, and the onset was within 1 year after enrollment. The results showed that the median of the methylation level of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes of the healthy control was obviously different from the median of the methylation level of the coronary heart disease patients, and the P value between them was less than 0.05 (Table 4). Therefore, the methylation level of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes can be used in the population to screen potential patients who will have coronary heart disease within 1 year, and is a very clinically valuable molecular marker.

[0212] Table 4. Comparison of methylation level difference between healthy control and potential coronary heart disease patients (2 years earlier than clinical onset time and 1 year earlier than clinical onset time) Note: "Coronary heart disease (1 year)" refers to potential coronary heart disease patients 1 year earlier than clinical onset time; "Coronary heart disease (2 years)" refers to potential coronary heart disease patients 2 years earlier than clinical onset time.

[0213] 3. Establishment of a mathematical model for assisting diagnosis of cardiovascular and cerebrovascular diseases

[0214] The mathematical model established by the present application can be used to achieve the following purposes:

[0215] (1) Early warning of individuals with coronary heart disease risk in the population before clinical onset.

[0216] (2) Early warning of individuals with coronary heart disease risk in the population before clinical onset, and suitable for various types of coronary heart disease.

[0217] The individual with the risk of developing coronary heart disease can be a patient with coronary heart disease within 2 years or 1 year before the clinical onset time (i.e. within 2 years or 1 year before being clinically diagnosed as coronary heart disease).

[0218] The method for establishing the mathematical model is as follows:

[0219] (A) Data source: the methylation levels of the target CpG sites of the ex vivo blood samples of the 342 patients with coronary heart disease and the 612 healthy controls listed in step one (the combination of the CpG sites of all genes or part of the genes in Table 1) (the detection method is step two).

[0220] The data can be added with known parameters such as age, gender, and white blood cell count according to actual needs to improve the discrimination efficiency.

[0221] (B) Model establishment

[0222] According to the needs, two types of patient data, i.e. the training set (i.e. the potential patients with coronary heart disease within 2 years in the future and the healthy controls, or the potential patients with coronary heart disease within 1 year in the future and the healthy controls) are selected as the data for establishing the model, and the statistical method of binary classification logistic regression is used to establish the mathematical model by formula using statistical software such as SAS, R, and SPSS. The value corresponding to the maximum Youden index calculated by the mathematical model formula is the threshold value or the threshold value is directly set to 0.5. After the test sample is tested and substituted into the model for calculation, the detection index greater than the threshold value is classified as one class (class B), the detection index less than the threshold value is classified as another class (class A), and the detection index equal to the threshold value is classified as an uncertain gray area. When predicting a new test sample to determine which class it belongs to, first, the methylation level of the CpG site of any one or all of the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1, and ACTB combination genes in the test sample is detected by the DNA methylation detection method, and then the methylation level data is substituted into the above mathematical model to calculate the detection index corresponding to the test sample. Then, the detection index corresponding to the test sample is compared with the threshold value, and according to the comparison result, it is determined which class the test sample belongs to.

[0223] For example, as shown in FIG. 2, the methylation level data of the CpG sites (Table 1) on any one or all of the gene fragments of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combination genes in the training set is used to establish a mathematical model for distinguishing between Class A and Class B by using the formula of binary logistic regression through statistical software such as SAS, R, SPSS, etc. The mathematical model is a binary logistic regression model, specifically: log(y / (1-y)) = b0+b1x1+b2x2+b3x3+…+bnXn, wherein y is the dependent variable, i.e. the detection index obtained by substituting the methylation value of one or more methylation sites (Table 1) of the sample to be tested into the model, b0 is a constant, x1-xn are the independent variables, i.e. the methylation values of one or more methylation sites (each value is a number between 0 and 1), and b1-bn are the weights of each methylation site assigned by the model. In specific applications, a mathematical model is first established according to the methylation degree (x1-xn) of one or more DNA methylation sites (Table 1) of the samples that have been detected in the training set and their known classification (Class A or Class B, assigning 0 and 1 to y, respectively), thereby determining the constant b0 of the mathematical model and the weights b1-bn of each methylation site, and the value corresponding to the maximum Youden index calculated by the mathematical model is used as the threshold or 0.5 is directly set as the threshold for division. The detection index y obtained after the sample to be tested is tested and substituted into the model is greater than the threshold, which is classified as Class B, less than the threshold, which is classified as Class A, and equal to the threshold, which is classified as an uncertain gray area. Wherein Class A and Class B are the corresponding two classifications (two classification groups, which group is Class A and which group is Class B, to be determined according to the specific mathematical model, which is not specified here), for example: potential patients with coronary heart disease within 2 years and healthy controls, potential patients with coronary heart disease within 1 year and healthy controls; wherein the healthy control can be understood as having never suffered from coronary heart disease and cancer and having blood routine indexes within the reference range. When predicting the sample of a subject to determine which class it belongs to, the blood of the subject is first collected, and then the DNA is extracted therefrom. The extracted DNA is converted by bisulfite, and the methylation level of the CpG sites (Table 1) on any one or all of the gene fragments of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combination genes in the subject is detected by the DNA methylation detection method, and then the detected methylation data is substituted into the above mathematical model.If the methylation level data of any one or all of the CpG sites (Table 1) on the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes of the subject is substituted into the above mathematical model, and the value calculated, i.e. the detection index, is greater than the threshold value, then the subject is determined to belong to the same class (class B) as the subjects in the training set whose detection index is greater than the threshold value; if the methylation level data of any one or all of the CpG sites (Table 1) on the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes of the subject is substituted into the above mathematical model, and the value calculated, i.e. the detection index, is less than the threshold value, then the subject belongs to the same class (class A) as the subjects in the training set whose detection index is less than the threshold value; if the methylation level data of any one or all of the CpG sites (Table 1) on the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes of the subject is substituted into the above mathematical model, and the value calculated, i.e. the detection index, is equal to the threshold value, then the subject cannot be determined to be class A or class B.

[0224] For example, as shown in Figure 3, the methylation of 26 distinguishable CpG sites in five genes (HYAL2, S100P, SLC22A18, RPTOR and DYRK4) (the methylation level of HYAL2 gene is the methylation level of the following 4 distinguishable CpG sites: the CpG site at 73-74, the CpG site at 135-136, the CpG site at 160-161 and the CpG site at 183-184 of the DNA fragment of SEQ ID No. 1 from 5' end of HYAL2 gene; the methylation level of S100P gene is the methylation level of the following 6 distinguishable CpG sites: the CpG site at 64-65 and the CpG site at 74-75, the CpG site at 81-82, the CpG site at 175-176, the CpG site at 193-194, the CpG site at 218-219, the CpG site at 244-245 and the CpG site at 253-254 and the CpG site at 256-257 of the DNA fragment of SEQ ID No. 2 from 5' end of S100P gene; the methylation level of SLC22A18 gene is the methylation level of the following 6 CpG sites: the CpG site at 45-46, the CpG site at 75-76, the CpG site at 99-100, the CpG site at 117-118, the CpG site at 129-130 and the CpG site at 300-301 of the DNA fragment of SEQ ID No. 3 from 5' end of SLC22A18 gene; the methylation level of RPTOR gene is the methylation level of the following 7 CpG sites: the CpG site at 25-26, the CpG site at 61-62, the CpG site at 97-98, the CpG site at 116-117, the CpG site at 196-197, the CpG site at 223-224 and the CpG site at 277-278 of the DNA fragment of SEQ ID No. 4 from 5' end of RPTOR gene; the methylation level of DYRK4 gene is the methylation level of the following 3 CpG sites: the CpG site at 41-42, the CpG site at 255-256 and the CpG site at 357-358 of the DNA fragment of SEQ ID No. 5 from 5' end of DYRK4 gene) and mathematical modeling are used for the discrimination of coronary heart disease: the data of the methylation level of the combination of the 26 distinguishable CpG sites in the training set of potential patients of coronary heart disease (≤2 years earlier than the clinical onset time) and healthy controls detected in the future 2 years are used to establish a mathematical model for distinguishing the potential patients of coronary heart disease and healthy controls by R software using the formula of binary logistic regression.The mathematical model is a logistic regression model, and the constant b0 and the weight of each methylation site b1-bn are determined, which are specifically as follows: ln(y / (1-y)) = 0.681 - 1.229 x HYAL2_CpG_1 + 0.591 x HYAL2_CpG_2 - 1.092 x HYAL2_CpG_3 + 0.663 x HYAL2_CpG_4 - 3.106 x S100P_CpG_2.3 + 2.028 x S100P_CpG_4 - 1.593 x S100P_CpG_7 - 0.470 x S100P_CpG_8 - 0.503 x S100P_CpG_9 + 1.007 x S100P_CpG_10.11.12 - 3.016 x SLC22A18_CpG_1 - 1.803 x SLC22A18_CpG_3 + 3.509 x SLC22A18_CpG_4 + 0.469 x SLC22A18_CpG_5 + 1.003 x SLC22A18_CpG_6 - 2.197 x SLC22A18_CpG_8 + 0.394 x RPTOR_CpG_1 + 1.806 x RPTOR_CpG_2 + 0.327 x RPTOR_CpG_3 - 1.309 x RPTOR_CpG_4 + 2.880 x RPTOR_CpG_5 + 2.001 x RPTOR_CpG_6 - 3.115 x RPTOR_CpG_8 + 0.149 x DYRK4_CpG_1 - 4.102 x DYRK4_CpG_2 + 2.968 x DYRK4_CpG_3, wherein y is the dependent variable, i.e., the detection index obtained by substituting the methylation value of the 26 distinguishable methylation sites of HYAL2, S100P, SLC22A18, RPTOR and DYRK4 in the sample to be tested into the model. When the threshold value is set to 0.5, the methylation level of the 26 distinguishable CpG sites of the sample to be tested is substituted into the model for calculation after testing, and the detection index y value obtained is greater than 0.5, which is classified as a potential patient who will develop coronary heart disease within 2 years, less than 0.5 is classified as a healthy control, and equal to 0.5 is not determined as a potential patient who will develop coronary heart disease within 2 years or a healthy control. The area under the curve (AUC) of the model is 0.86 (Table 5).

[0225] (C) Model Effect Evaluation

[0226] According to the above method, mathematical models for distinguishing potential patients with coronary heart disease in the next 2 years and healthy controls, potential patients with coronary heart disease in the next 1 year and healthy controls (wherein the healthy controls can be understood as never having suffered from coronary heart disease and cancer and having blood routine indexes within the reference range) were established respectively, and their effectiveness was evaluated by receiver operating characteristic (ROC) curves. The larger the area under the curve (AUC) of the ROC curve, the better the discrimination of the model, and the more effective the molecular marker. The evaluation results after mathematical model construction using different CpG sites are shown in Table 5. In Table 5, each gene fragment refers to the combination of all CpG sites on the gene fragment shown in Table 1, and "HYAL2_A fragment + S100P_B fragment + SLC22A18_C fragment + RPTOR_D fragment + DYRK4_E fragment" refers to the combination of all CpG sites on the HYAL2_A fragment shown in Table 1, all CpG sites on the S100P_B fragment shown in Table 1, all CpG sites on the SLC22A18_C fragment shown in Table 1, all CpG sites on the RPTOR_D fragment shown in Table 1, and all CpG sites on the DYRK4_E fragment shown in Table 1, and so on.

[0227] Table 5, HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combination genes for distinguishing healthy controls and potential patients with coronary heart disease (2 years earlier than the clinical onset time and 1 year earlier than the clinical onset time) Note: "Coronary heart disease (1 year)" refers to potential patients with coronary heart disease 1 year earlier than the clinical onset time; "Coronary heart disease (2 years)" refers to potential patients with coronary heart disease 2 years earlier than the clinical onset time.

[0228] The above results show that the methylation levels of the CpG sites (Table 1) on any or all of the gene fragments of the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combination genes increase the discrimination ability of each group (potential patients with coronary heart disease in the next 2 years and healthy controls, potential patients with coronary heart disease in the next 1 year and healthy controls; wherein the healthy controls can be understood as never having suffered from coronary heart disease and cancer and having blood routine indexes within the reference range) with the increase of the number of genes and sites, and the model effect of the combination of 10 combination genes is the best.

[0229] Example 3, verification of experimental results

[0230] I. Verification set samples for assisting early screening of coronary heart disease

[0231] The verification set (completely different from the training set samples in Example 2) sampling method, sampling criteria and sampling follow-up time are the same as in Example 2.

[0232] The present application selects 175 cases of new coronary heart disease patients within 2 years after enrollment as the case group (according to the clinical classification, 23 cases of occult or asymptomatic myocardial ischemia, 33 cases of angina pectoris, 42 cases of myocardial infarction, 38 cases of ischemic cardiomyopathy, and 39 cases of sudden death), of which 85 cases (including 13 cases of occult or asymptomatic myocardial ischemia, 15 cases of angina pectoris, 19 cases of myocardial infarction, 18 cases of ischemic cardiomyopathy, and 20 cases of sudden death) occurred within 1 year after enrollment. After age and gender matching, people who did not develop coronary heart disease and cancer and whose blood routine indicators were within the reference range during the follow-up period (follow-up time greater than 2 years) were selected as healthy controls, totaling 300 cases.

[0233] All patient blood samples were collected at enrollment and before the onset of the disease. The disease was diagnosed by imaging and pathology at the time of the subsequent onset.

[0234] The median age of the healthy controls was 65 years, and the median age of the patients with coronary heart disease who developed the disease within 2 years after enrollment was 64 years, and the male-to-female ratio in each of these two populations was approximately 1:1. The median age of the patients with coronary heart disease who developed the disease within 1 year after enrollment was 65 and 64 years, respectively.

[0235] II. Methylation detection and data analysis

[0236] The same as in Example 2.

[0237] III. Results analysis

[0238] 1. Differences in HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB gene methylation levels in the blood of healthy controls and potential coronary heart disease patients (2 years or 1 year earlier than the clinical onset time)

[0239] As can be seen from Table 6, like the results of the training set, the results show that whether the potential coronary heart disease patients are 2 years or 1 year earlier than the clinical onset time, the median methylation level of the HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB combined genes is significantly different from the median methylation level of the healthy controls, and the P value between the healthy controls and the coronary heart disease patients is less than 0.05.

[0240] Table 6. Comparison of methylation level differences between healthy controls and coronary heart disease patients Note: "Coronary heart disease (1 year)" means the potential patient of coronary heart disease 1 year earlier than the clinical onset time; "Coronary heart disease (2 years)" means the potential patient of coronary heart disease 2 years earlier than the clinical onset time.

[0241] 2. Model verification

[0242] The models constructed in Example 2 were verified. As shown in Table 7, like the trend of the results of the training set, the 10 combined genes had the best ability to identify different types of samples of each group listed in the present application.

[0243] Table 7. CpG sites in the combined genes of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB used to distinguish healthy controls and potential patients of coronary heart disease Note: "Coronary heart disease (1 year)" means the potential patient of coronary heart disease 1 year earlier than the clinical onset time; "Coronary heart disease (2 years)" means the potential patient of coronary heart disease 2 years earlier than the clinical onset time.

[0244] In summary, all CpG sites on the combined gene fragments of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB or CpG sites on any combination of gene fragments have the ability to distinguish between potential patients of coronary heart disease within 2 years and healthy controls, potential patients of coronary heart disease within 1 year and healthy controls (wherein the healthy controls can be understood as never having suffered from coronary heart disease and cancer and having blood routine indexes within the reference range).

[0245] The present application is described in detail above. For those skilled in the art, without departing from the purpose and scope of the present application, and without unnecessary experiments, the present application can be implemented in a wider range under equivalent parameters, concentrations and conditions. Although the present application gives a special example, it should be understood that further improvements can be made to the present application. In summary, according to the principle of the present application, the present application intends to include any changes, uses or improvements of the present application, including changes made by conventional techniques known in the art, which are outside the scope disclosed in the present application.

[0246] Industrial applications

[0247] The research of the present application has confirmed that the methylation level of the combined genes of HYAL2, S100P, SLC22A18, RPTOR, DYRK4, MGRN1, FUT7, ABCG1, THRA1 and ACTB can be used to warn coronary heart disease 1 year and 2 years in advance, and the combined potential marker for early warning of coronary heart disease has important scientific significance and clinical application value in the diagnosis and treatment of coronary heart disease.

Claims

1. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory, characterized in that: The processor executes the computer program to implement the following steps: Receiving the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples; The methylation level data of each gene in the DNA methylation marker combination of the n1 known A type samples and n2 known B type samples is established by binary classification logistic regression method according to the classification mode of A type and B type to determine the threshold value of classification judgment; The DNA methylation marker combination consists of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene or any of the genes; Input the methylation level data of each gene in the DNA methylation marker combination of the testee; The methylation level data of each gene in the DNA methylation marker combination of the testee is substituted into the mathematical model to obtain a detection index; The detection index is compared with the threshold value to obtain a comparison result; According to the comparison result, output the conclusion whether the type of the test sample is A type or B type; The A type sample and the B type sample are any of the following: (1) potential patients with coronary heart disease within 2 years and healthy controls; (2) potential patients with coronary heart disease within 1 year and healthy controls.

2. A computer program product comprising a computer program, characterized in that: The computer program is executed by the processor to implement the steps in claim 1.

3. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps in claim 1.

4. A system for early warning of coronary heart disease, comprising: (A1) a substance for detecting the methylation level of each gene in the DNA methylation marker combination; (A2) a device, the device comprising unit X and unit Y; The unit X is used to establish a mathematical model, comprising a data receiving module, a data analysis processing module and a model output module; The data receiving module is configured to receive the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples; The data analysis processing module is configured to receive the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples sent from the data receiving module, and establish a mathematical model by binary classification logistic regression method according to the classification mode of A type and B type to determine the threshold value of classification judgment; The DNA methylation marker combination consists of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene or any of the genes; The model output module is configured to receive the mathematical model established by the data analysis processing module and output; ​ The unit Y is used for determining the type of the sample to be tested, comprising a data input module, a data operation module, a data comparison module and a conclusion output module. The data input module is configured to input the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested. The data operation module is configured to receive the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested sent from the data input module, and substitute the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested into the mathematical model established by the data analysis processing module in the unit X to calculate a detection index. The data comparison module is configured to receive the detection index calculated from the data operation module, and compare the detection index with the threshold value determined in the data analysis processing module in the unit X. The conclusion output module is configured to receive the comparison result from the data comparison module, and output the conclusion that the type of the sample to be tested is the A type or the B type according to the comparison result. The A type sample and the B type sample are any one of the following: (A1) potential patients with coronary heart disease within 2 years and healthy controls; (A2) potential patients with coronary heart disease within 1 year and healthy controls.

5. Any one of the following applications: Application I: the computer device of claim 1 or the computer program product of claim 2 or the computer readable storage medium of claim 3 or the system of claim 4 is applied to distinguish or assist in distinguishing the A type sample and the B type sample; The A type sample and the B type sample are any one of the following: (A1) potential patients with coronary heart disease within 2 years and healthy controls; (A2) potential patients with coronary heart disease within 1 year and healthy controls; Application II: the computer device of claim 1 or the computer program product of claim 2 or the computer readable storage medium of claim 3 or the system of claim 4 is applied to early warning of coronary heart disease.

6. A method for distinguishing or assisting in distinguishing the A type sample and the B type sample by using a computer, comprising the following steps: (B1) data receiving: receiving the methylation level data of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples; (B2) data analysis processing: substituting the methylation level data of each gene in the DNA methylation marker combination of the n1 known A type samples and the n2 known B type samples into a mathematical model established by a binary classification logistic regression method according to the classification mode of the A type and the B type to determine the threshold value of the classification determination; The DNA methylation marker combination comprises HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene, or any one of the genes.

7. The computer device of claim 1, wherein the unit X is a computer device comprising a data analysis processing module and a data storage module.

8. The computer program product of claim 2, wherein the unit X is a computer program product comprising a data analysis processing module and a data storage module.

9. The computer readable storage medium of claim 3, wherein the unit X is a computer readable storage medium comprising a data analysis processing module and a data storage module.

10. The system of claim 4, wherein the unit X is a system comprising a data analysis processing module and a data storage module. (B3) data input: input the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested; (B4) data operation: input the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested into the mathematical model to calculate a detection index; (B5) data comparison: compare the detection index with the threshold value to obtain a comparison result; (B6) conclusion output: output the conclusion that the type of the sample to be tested is type A or type B according to the comparison result; The A type sample and the B type sample are any one of the following: (1) potential patients with coronary heart disease within 2 years and healthy controls; (2) potential patients with coronary heart disease within 1 year and healthy controls.

7. A method for distinguishing or assisting in distinguishing between A type samples and B type samples, the A type samples and the B type samples being any one of the following: (1) potential patients with coronary heart disease within 2 years and healthy controls; (2) potential patients with coronary heart disease within 1 year and healthy controls; characterized in that The method comprises the following steps: Detecting the methylation level of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples; the DNA methylation marker combination consists of HYAL2 gene, S100P gene, SLC22A18 gene, RPTOR gene, DYRK4 gene, MGRN1 gene, FUT7 gene, ABCG1 gene, THRA1 gene and ACTB gene, or consists of any one or more of the genes; the methylation level data of each gene in the DNA methylation marker combination of the n1 known A type samples and the n2 known B type samples obtained by detection is used to establish a mathematical model by binary classification logistic regression method according to the classification mode of A type and B type, and the threshold value of classification determination is determined; Detecting the methylation level of each gene in the DNA methylation marker combination of the subject to be tested; inputting the methylation level data of each gene in the DNA methylation marker combination of the subject to be tested into the mathematical model to calculate a detection index; Comparing the detection index with the threshold value to determine whether the type of the sample to be tested is A type or B type.

8. Any one of the following applications: Application III: use of a substance for detecting the methylation level of each gene in a DNA methylation marker combination in distinguishing or assisting in distinguishing between A type samples and B type samples or in preparing a product for distinguishing or assisting in distinguishing between A type samples and B type samples; The A type sample and the B type sample are any one of the following: (1) potential patients with coronary heart disease within 2 years and healthy controls; (2) potential patients with coronary heart disease within 1 year and healthy controls; Application IV: use of a substance for detecting the methylation level of each gene in a DNA methylation marker combination in early warning of coronary heart disease or in preparing a product for early warning of coronary heart disease; The DNA methylation marker combination consists of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene, or consists of any several genes among them.

9. Any of the following applications: Application V: the application of the substance for detecting the methylation level of each gene in the DNA methylation marker combination and the medium recording the method for establishing the mathematical model and / or the method for using in distinguishing or assisting in distinguishing the A type sample and the B type sample or in preparing the product for distinguishing or assisting in distinguishing the A type sample and the B type sample; Application IV: the application of the substance for detecting the methylation level of each gene in the DNA methylation marker combination and the medium recording the method for establishing the mathematical model and / or the method for using in early warning of coronary heart disease or in preparing the product for early warning of coronary heart disease; The mathematical model is obtained according to the method comprising the following steps: (C1) respectively detecting the methylation level of each gene in the DNA methylation marker combination of n1 known A type samples and n2 known B type samples; (C2) taking the methylation level data of each gene in the DNA methylation marker combination of all samples obtained in step (C1), establishing a mathematical model by binary classification logistic regression method according to the classification mode of A type and B type, and determining the threshold value of classification determination; The method for using the mathematical model comprises the following steps: (D1) detecting the methylation level of each gene in the DNA methylation marker combination of the sample to be tested; (D2) substituting the methylation level data of each gene in the DNA methylation marker combination of the sample to be tested obtained in step (D1) into the mathematical model to obtain a detection index; then comparing the size of the detection index and the threshold value, and determining whether the type of the sample to be tested is A type or B type according to the comparison result; The A type sample and the B type sample are any of the following: (1) potential patients with coronary heart disease within 2 years and healthy controls; (2) potential patients with coronary heart disease within 1 year and healthy controls; The DNA methylation marker combination consists of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene, or consists of any several genes among them.

10. The computer device or computer program product or computer readable storage medium or system or method or application of any of claims 1-9, wherein: The DNA methylation marker combination is any of the following: (E1) consisting of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene; (E2) consisting of the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene; (E3) consisting of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene and the ABCG1 gene; (E4) consisting of the RPTOR gene, the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene; (E5) consisting of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene, the MGRN1 gene and the FUT7 gene; (E6) consisting of the DYRK4 gene, the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene; (E7) consisting of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene, the DYRK4 gene and the MGRN1 gene; (E8) consisting of the MGRN1 gene, the FUT7 gene, the ABCG1 gene, the THRA1 gene and the ACTB gene; (E9) consisting of the HYAL2 gene, the S100P gene, the SLC22A18 gene, the RPTOR gene and the DYRK4 gene.

11. The computer device or computer program product or computer readable storage medium or system or method or application of any of claims 1-10, wherein: in the DNA methylation marker combination, the methylation level of the HYAL2 gene is the methylation level of the following 4 CpG sites: the CpG site at positions 73-74 from the 5' end, the CpG site at positions 135-136, the CpG site at positions 160-161 and the CpG site at positions 183-184 on the DNA fragment as set forth in SEQ ID No. 1 in the HYAL2 gene; and / or in the DNA methylation marker combination, the methylation level of the S100P gene is the methylation level of the following 9 CpG sites: the CpG site at positions 64-65 from the 5' end, the CpG site at positions 74-75, the CpG site at positions 81-82, the CpG site at positions 175-176, the CpG site at positions 193-194, the CpG site at positions 218-219, the CpG site at positions 244-245, the CpG site at positions 253-254 and the CpG site at positions 256-257 on the DNA fragment as set forth in SEQ ID No. 2 in the S100P gene; and / or ​ In the DNA methylation marker combination, the methylation level of the SLC22A18 gene is the methylation level of the following 6 CpG sites: the CpG site at positions 45-46 from the 5' end, the CpG site at positions 75-76, the CpG site at positions 99-100, the CpG site at positions 117-118, the CpG site at positions 129-130, and the CpG site at positions 300-301 on the DNA fragment represented by SEQ ID No. 3 in the SLC22A18 gene; and / or In the DNA methylation marker combination, the methylation level of the RPTOR gene is the methylation level of the following 7 CpG sites: the CpG site at positions 25-26 from the 5' end, the CpG site at positions 61-62, the CpG site at positions 97-98, the CpG site at positions 116-117, the CpG site at positions 196-197, the CpG site at positions 223-224, and the CpG site at positions 277-278 on the DNA fragment represented by SEQ ID No. 4 in the RPTOR gene; and / or In the DNA methylation marker combination, the methylation level of the DYRK4 gene is the methylation level of the following 3 CpG sites: the CpG site at positions 41-42 from the 5' end, the CpG site at positions 255-256, and the CpG site at positions 357-358 on the DNA fragment represented by SEQ ID No. 5 in the DYRK4 gene; and / or In the DNA methylation marker combination, the methylation level of the MGRN1 gene is the methylation level of the following 24 CpG sites: the CpG site at positions 54-55 from the 5' end, the CpG site at positions 60-61, the CpG site at positions 75-76, the CpG site at positions 82-83, the CpG site at positions 99-100, the CpG site at positions 101-102, the CpG site at positions 106-107, the CpG site at positions 108-109, the CpG site at positions 130-131, the CpG site at positions 139-140, the CpG site at positions 148-149, the CpG site at positions 184-185, the CpG site at positions 189-190, the CpG site at positions 198-199, the CpG site at positions 200-201, the CpG site at positions 205-206, the CpG site at positions 228-229, the CpG site at positions 231-232, the CpG site at positions 240-241, the CpG site at positions 249-250, the CpG site at positions 254-255, the CpG site at positions 277-278, the CpG site at positions 307-308, and the CpG site at positions 330-331 on the DNA fragment represented by SEQ ID No. 6 in the MGRN1 gene; and / or In the DNA methylation marker combination, the methylation level of the MGRN1 gene is the methylation level of the following 24 CpG sites: the CpG site at positions 54-55 from the 5' end, the CpG site at positions 60-61, the CpG site at positions 75-76, the CpG site at positions 82-83, the CpG site at positions 99-100, the CpG site at positions 101-102, the CpG site at positions 106-107, the CpG site at positions 108-109, the CpG site at positions 130-131, the CpG site at positions 139-140, the CpG site at positions 148-149, the CpG site at positions 184-185, the CpG site at positions 189-190, the CpG site at positions 198-199, the CpG site at positions 200-201, the CpG site at positions 205-206, the CpG site at positions 228-229, the CpG site at positions 231-232, the CpG site at positions 240-241, the CpG site at positions 249-250, the CpG site at positions 254-255, the CpG site at positions 277-278, the CpG site at positions 307-308, and the CpG site at positions 330-331 on the DNA fragment represented by SEQ ID No. 6 in the MGRN1 gene; and / or In the DNA methylation marker combination, the methylation level of the FUT7 gene is the methylation level of the following 7 CpG sites: the CpG site at positions 35-36 from the 5' end, the CpG site at positions 118-119, the CpG site at positions 134-135, the CpG site at positions 156-157, the CpG site at positions 174-175, the CpG site at positions 255-256, and the CpG site at positions 287-288 in the DNA fragment represented by SEQ ID No. 7 in the FUT7 gene; and / or In the DNA methylation marker combination, the methylation level of the ABCG1 gene is the methylation level of the following 8 CpG sites: the CpG site at positions 27-28 from the 5' end, the CpG site at positions 45-46, the CpG site at positions 58-59, the CpG site at positions 112-113, the CpG site at positions 160-161, the CpG site at positions 279-280, the CpG site at positions 299-300, and the CpG site at positions 327-328 in the DNA fragment represented by SEQ ID No. 8 in the ABCG1 gene; and / or In the DNA methylation marker combination, the methylation level of the THRA1 gene is the methylation level of the following 22 CpG sites: the CpG site at positions 27-28 from the 5' end, the CpG site at positions 34-35, the CpG site at positions 39-40, the CpG site at positions 179-180, the CpG site at positions 183-184, the CpG site at positions 194-195, the CpG site at positions 202-203, the CpG site at positions 210-211, the CpG site at positions 216-217, the CpG site at positions 225-226, the CpG site at positions 227-228, the CpG site at positions 234-235, the CpG site at positions 240-241, the CpG site at positions 250-251, the CpG site at positions 252-253, the CpG site at positions 260-261, the CpG site at positions 268-269, the CpG site at positions 289-290, the CpG site at positions 315-316, the CpG site at positions 320-321, the CpG site at positions 348-349, and the CpG site at positions 362-363 in the DNA fragment represented by SEQ ID No. 9 in the THRA1 gene; and / or In the DNA methylation marker combination, the methylation level of the ACTB gene is the methylation level of the following 16 CpG sites: the CpG site at positions 39-40 from the 5' end, the CpG site at positions 41-42, the CpG site at positions 61-62, the CpG site at positions 65-66 ​ the CpG site at positions 69-70, the CpG site at positions 77-78, the CpG site at positions 81-82, the CpG site at positions 107-108, the CpG site at positions 110-111, the CpG site at positions 122-123, the CpG site at positions 139-140, the CpG site at positions 185-186, the CpG site at positions 213-214, the CpG site at positions 219-220, the CpG site at positions 275-276, and the CpG site at positions 304-305.

12. Use according to any one of claims 8 to 11, characterized in that: The substance for detecting the methylation level of each gene in the DNA methylation marker combination comprises a primer pair combination capable of specifically amplifying each gene fragment in the DNA methylation marker combination; The HYAL2 gene fragment is a fragment as shown in SEQ ID No. 1; the S100P gene fragment is a fragment as shown in SEQ ID No. 2; the SLC22A18 gene fragment is a fragment as shown in SEQ ID No. 3; the RPTOR gene fragment is a fragment as shown in SEQ ID No. 4; the DYRK4 gene fragment is a fragment as shown in SEQ ID No. 5; the MGRN1 gene fragment is a fragment as shown in SEQ ID No. 6; the FUT7 gene fragment is a fragment as shown in SEQ ID No. 7; the ABCG1 gene fragment is a fragment as shown in SEQ ID No. 8; the THRA1 gene fragment is a fragment as shown in SEQ ID No. 9; and the ACTB gene fragment is a fragment as shown in SEQ ID No.

10.

13. Use according to claim 12, characterized in that: The primer pair for amplifying the fragment as shown in SEQ ID No. 1 in the HYAL2 gene is composed of primer a1 and primer a2, the primer a1 is a single-stranded DNA as shown in SEQ ID No. 11 or nucleotides at positions 11-37 of SEQ ID No. 11, and the primer a2 is a single-stranded DNA as shown in SEQ ID No. 12 or nucleotides at positions 32-56 of SEQ ID No. 12; and / or The primer pair for amplifying the fragment as shown in SEQ ID No. 2 in the S100P gene is composed of primer b1 and primer b2, the primer b1 is a single-stranded DNA as shown in SEQ ID No. 13 or nucleotides at positions 11-35 of SEQ ID No. 13, and the primer b2 is a single-stranded DNA as shown in SEQ ID No. 14 or nucleotides at positions 32-56 of SEQ ID No. 14; and / or The primer pair for amplifying the fragment as shown in SEQ ID No. 3 in the SLC22A18 gene is composed of primer c1 and primer c2, the primer c1 is a single-stranded DNA as shown in SEQ ID No. 15 or nucleotides at positions 11-35 of SEQ ID No. 15, and the primer c2 is a single-stranded DNA as shown in SEQ ID No. 16 or nucleotides at positions 32-56 of SEQ ID No. 16; and / or The primer pair for amplifying the fragment as shown in SEQ ID No. 3 in the SLC22A18 gene is composed of primer c1 and primer c2, the primer c1 is a single-stranded DNA as shown in SEQ ID No. 15 or nucleotides at positions 11-35 of SEQ ID No. 15, and the primer c2 is a single-stranded DNA as shown in SEQ ID No. 16 or nucleotides at positions 32-56 of SEQ ID No. 16; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 4 in the RPTOR gene consists of primer d1 which is a single-stranded DNA represented by SEQ ID No. 17 or nucleotides 11-34 of SEQ ID No. 17, and primer d2 which is a single-stranded DNA represented by SEQ ID No. 18 or nucleotides 32-56 of SEQ ID No. 18; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 5 in the DYRK4 gene consists of primer e1 which is a single-stranded DNA represented by SEQ ID No. 19 or nucleotides 11-38 of SEQ ID No. 19, and primer e2 which is a single-stranded DNA represented by SEQ ID No. 20 or nucleotides 32-54 of SEQ ID No. 20; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 6 in the MGRN1 gene consists of primer f1 which is a single-stranded DNA represented by SEQ ID No. 21 or nucleotides 11-35 of SEQ ID No. 21, and primer f2 which is a single-stranded DNA represented by SEQ ID No. 22 or nucleotides 32-60 of SEQ ID No. 22; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 7 in the FUT7 gene consists of primer g1 which is a single-stranded DNA represented by SEQ ID No. 23 or nucleotides 11-35 of SEQ ID No. 23, and primer g2 which is a single-stranded DNA represented by SEQ ID No. 24 or nucleotides 32-56 of SEQ ID No. 24; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 8 in the ABCG1 gene consists of primer h1 which is a single-stranded DNA represented by SEQ ID No. 25 or nucleotides 11-35 of SEQ ID No. 25, and primer h2 which is a single-stranded DNA represented by SEQ ID No. 26 or nucleotides 32-56 of SEQ ID No. 26; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 9 in the THRA1 gene consists of primer i1 which is a single-stranded DNA represented by SEQ ID No. 27 or nucleotides 11-35 of SEQ ID No. 27, and primer i2 which is a single-stranded DNA represented by SEQ ID No. 28 or nucleotides 32-55 of SEQ ID No. 28; and / or The primer pair for amplifying the fragment represented by SEQ ID No. 9 in the THRA1 gene consists of primer i1 which is a single-stranded DNA represented by SEQ ID No. 27 or nucleotides 11-35 of SEQ ID No. 27, and primer i2 which is a single-stranded DNA represented by SEQ ID No. 28 or nucleotides 32-55 of SEQ ID No. 28; and / or The primer pair for amplifying the fragment shown in SEQ ID No. 10 in ACTB gene consists of primer l1 and primer l2, wherein the primer l1 is a single-stranded DNA shown in SEQ ID No. 29 or the 11th-37th nucleotide of SEQ ID No. 29, and the primer l2 is a single-stranded DNA shown in SEQ ID No. 30 or the 32nd-56th nucleotide of SEQ ID No.

30.

14. The computer device or computer program product or computer readable storage medium or system or application of any one of claims 1-13, wherein: The method for detecting the methylation level of each gene in the DNA methylation marker combination comprises detecting the methylation level of each gene in the DNA methylation marker combination in a blood sample.

15. A kit for distinguishing or assisting in distinguishing between A-type samples and B-type samples, wherein the A-type samples and the B-type samples are any of the following: (1) potential patients with coronary heart disease in the next 2 years and healthy controls; (2) potential patients with coronary heart disease in the next 1 year and healthy controls; characterized in that The kit contains the primer pair combination described in claim 12 or 13.

16. The kit of claim 15, wherein: The kit also contains the medium described in claim 9, which records the mathematical model establishment method and / or use method. The kit also contains the medium described in claim 9, which records the mathematical model establishment method and / or use method.

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