CN117568457A

CN117568457A - Methylation markers for diagnosing coronary heart disease

Info

Publication number: CN117568457A
Application number: CN202210615823.XA
Authority: CN
Inventors: 王俊; 狄飞飞
Original assignee: Tengchen Biotechnology Shanghai Co ltd
Current assignee: Tengchen Biotechnology Shanghai Co ltd
Priority date: 2022-06-01
Filing date: 2022-06-01
Publication date: 2024-02-20

Abstract

The invention discloses a methylation marker for diagnosing coronary heart disease. The invention provides an application of a methylated ACTB gene as a marker in preparing a product; the product is used for assisting in diagnosing cardiovascular and cerebrovascular diseases or early warning the cardiovascular and cerebrovascular diseases before clinical onset; auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset; auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset. The invention can distinguish coronary heart disease and healthy control by taking blood as a sample and distinguish coronary heart disease patients with different clinical characteristics and healthy control. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of coronary heart disease and reducing the death rate.

Description

Methylation markers for diagnosing coronary heart disease

Technical Field

The invention relates to the field of medicine, in particular to a methylation marker for diagnosing coronary heart disease.

Background

Cardiovascular and cerebrovascular diseases are collectively called cardiovascular and cerebrovascular diseases, and refer generally to ischemic or hemorrhagic diseases of heart, brain and systemic tissues caused by hyperlipidemia, blood viscosity, atherosclerosis, hypertension, etc. Cardiovascular and cerebrovascular diseases are common diseases seriously threatening the health of human beings, especially middle-aged and elderly people over 50 years old, and have the characteristics of high morbidity, high disability rate and high mortality rate. At present, the worldwide number of people dying from cardiovascular and cerebrovascular diseases is up to 1500 ten thousand people each year.

Coronary heart disease refers to heart disease caused by coronary atherosclerosis, which causes stenosis, spasm or blockage of a lumen, and myocardial ischemia, hypoxia or death, and is collectively called coronary heart disease or coronary artery disease. Coronary heart disease is classified into 5 types according to clinical characteristics such as lesion sites, ranges, degrees and the like: 1. latent or asymptomatic myocardial ischemia: asymptomatic, but showing myocardial ischemia changes under resting, dynamic or loading electrocardiogram, or radionuclide myocardial imaging suggesting myocardial hypoperfusion, no tissue morphology changes; 2. angina pectoris: posttraumatic sternal pain caused by myocardial ischemia; 3. myocardial infarction: severe ischemic symptoms, acute ischemic necrosis of the myocardium due to coronary occlusion; 4. ischemic cardiomyopathy: chronic myocardial ischemia or death causes myocardial fibrosis, manifested by increased heart, heart failure and cardiac arrhythmias; 5. sudden death: death due to sudden cardiac arrest is often caused by severe arrhythmias resulting from local electrophysiological disturbances in the ischemic myocardium. The main coronary heart disease diagnosis method at present comprises the following steps: 1. clinical characteristics: typically, the combination of the medical history and physical examination status of the inspector is used for preliminary diagnosis, but the specificity is very low; 2. the imaging method comprises the following steps: electrocardiography, echocardiography, and coronary angiography, but are often affected by physician experience and instrumentation; 3. the most commonly used coronary heart disease markers at present are as follows: myocardial injury markers, inflammatory factors, adhesion molecules and cytokine markers, plasma lipoprotein and apolipoprotein markers, coagulation related protein markers, and the like. Because a certain marker can only reflect a certain disease mechanism of a disease, these markers are not widely recognized clinically.

Coronary heart disease belongs to cardiovascular and cerebrovascular diseases, and most cardiovascular diseases can be prevented and treated, and the prevention is generally carried out by improving consciousness through popularization knowledge, avoiding exogenous stimulus factors and reasonably diet and moderately moving, and the treatment effect is greatly dependent on early diagnosis and corresponding intervention measures. Currently, the sensitivity and specificity of diagnostic markers for coronary heart disease are very limited clinically, especially markers for early diagnosis are lacking, so that the development of more sensitive and specific early molecular markers is urgently needed. DNA methylation is a genetically important chemical modification that affects the regulation of gene transcription and nuclear structure.

Disclosure of Invention

The invention aims to provide application of ACTB gene methylation level in auxiliary diagnosis of cardiovascular and cerebrovascular diseases.

In a first aspect, the invention claims the use of a methylated ACTB gene as a marker in the preparation of a product; the application of the product is at least one of the following:

(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;

(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;

(3) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset.

Further, the cardiovascular and cerebrovascular diseases described in (1) may be diseases capable of causing altered methylation levels of ACTB genes in the body, such as coronary heart disease, etc.

In the invention, the auxiliary diagnosis of cardiovascular and cerebrovascular diseases described in (1) can be embodied as auxiliary differentiation of coronary heart disease patients and healthy controls. Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases or cancers are affected at present and once and no cardiovascular and cerebrovascular diseases are affected in the next 2 years, and the blood routine indexes are all within the reference range. The "healthy control" appearing hereinafter is synonymous.

In the invention, the early warning of cardiovascular and cerebrovascular diseases before clinical onset in (1) is as follows: coronary heart disease is pre-warned before clinical onset.

Further, the testee who early warns of coronary heart disease before clinical onset is a potential patient of coronary heart disease or a healthy control. The potential patients with coronary heart disease are potential patients with coronary heart disease which is happened in the next 2 years or potential patients with coronary heart disease which is happened in the next 1 year (i.e. coronary heart disease can be clinically diagnosed in 2 years or 1 year). The "potential patients for coronary heart disease" appearing hereinafter are synonymous.

In the present invention, the coronary heart disease of different clinical characteristics described in (3) is: latent or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy or sudden death. The "coronary heart disease of different clinical characteristics" appearing hereinafter is synonymous.

In the present invention, the coronary heart disease of the type described in (3) that assists in diagnosing different clinical characteristics is embodied as at least one of the following: can help to distinguish between latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls. The "diagnosis of coronary heart disease with different clinical features assisted" appearing hereinafter is synonymous.

In the invention, the testees of coronary heart disease with different clinical characteristics, which are early-warned before clinical onset, in the step (3) are coronary heart disease potential patients or healthy controls with different clinical characteristics.

In a second aspect, the invention claims the use of a substance for detecting the methylation level of the ACTB gene for the preparation of a product; the use of the product is at least one of the foregoing (1) - (3).

In a third aspect, the invention claims the use of a substance for detecting the methylation level of the ACTB gene and a medium storing a mathematical model building method and/or a use method for the preparation of a product; the use of the product is at least one of the foregoing (1) - (3).

The mathematical model is obtained according to a method comprising the following steps:

(A1) Detecting ACTB gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;

(A2) Taking the ACTB gene methylation level data of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the type A and the type B.

Wherein, n1 and n2 can be positive integers more than 50.

The using method of the mathematical model comprises the following steps:

(B1) Detecting the methylation level of ACTB gene of a sample to be detected;

(B2) Substituting the ACTB gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.

In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.

In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the two classifications are determined according to a specific mathematical model without convention.

The type a sample and the type B sample are any one of the following:

(C1) Potential patients with coronary heart disease and healthy controls for onset within the next 2 years;

(C2) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;

(C3) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;

(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year.

In a fourth aspect, the invention claims the use of a medium storing a mathematical model building method and/or a use method for the manufacture of a product; the use of the product is at least one of the foregoing (1) - (3).

Wherein, n1 and n2 can be positive integers more than 50.

The using method of the mathematical model comprises the following steps:

(B1) Detecting the methylation level of ACTB gene of a sample to be detected;

The type a sample and the type B sample are any one of the following:

In a fifth aspect, the invention claims a kit.

The claimed kit comprises a substance for detecting the methylation level of the ACTB gene; the use of the kit is at least one of the above (1) - (3).

Further, the kit may further comprise a medium storing the mathematical model creation method and/or the use method described in the third or fourth aspect.

In a sixth aspect, the invention claims a system.

The system claimed by the present invention may include:

(D1) Reagents and/or instrumentation for detecting the methylation level of the ACTB gene;

(D2) The device comprises a unit X and a unit Y.

The unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module;

The data acquisition module is configured to acquire (D1) ACTB gene methylation level data of the detected n 1A-type samples and n 2B-type samples.

The data analysis processing module is configured to receive ACTB gene methylation level data of n 1A type samples and n 2B type samples sent by the data acquisition module, establish a mathematical model according to a classification mode of the A type and the B type by a classification logistic regression method, and determine a threshold value of classification judgment.

Wherein, n1 and n2 can be positive integers more than 50.

The model output module is configured to receive the mathematical model transmitted from the data analysis processing module and output the mathematical model.

The unit Y is used for determining the type of the sample to be detected and comprises 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 (D1) the detected methylation level data of the ACTB gene of the person to be tested.

The data operation module is configured to receive the ACTB gene methylation level data of the testee sent by the data input module, and substitute the ACTB gene methylation level data of the testee into the mathematical model to calculate a detection index.

The data comparison module is configured to receive the detection index transmitted from the data operation module and compare the detection index with the threshold determined in the data analysis processing module in the unit X.

The conclusion output module is configured to receive the comparison result sent by the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result.

The type a sample and the type B sample are any one of the following:

In the foregoing aspects, the period of time prior to clinical onset may be in particular within 2 years of the period of time prior to clinical onset or within 1 year of the period of time prior to clinical onset. The potential coronary heart disease patient with onset within 2 years (or within 1 year) in the future is a potential coronary heart disease patient with onset within 2 years (or within 1 year) earlier than the clinical onset time.

In the foregoing aspects, the ACTB gene methylation level may be the methylation level of all or part of CpG sites in fragments of the ACTB gene as shown in (e 1) - (e 3) below. The methylated ACTB gene may be all or part of CpG site methylation in fragments of the ACTB gene as shown in (e 1) - (e 3) below.

(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;

(e2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;

(e3) The DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto.

Further, the "all or part of CpG sites" may be any one or more CpG sites among 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the ACTB gene. The upper limit of "a plurality of CpG sites" as used herein is all CpG sites in the 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the ACTB gene. All CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1), all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2), and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).

Alternatively, the "all or part of the CpG sites" may be all CpG sites on the DNA fragment shown in SEQ ID No.2 (see Table 2) and all CpG sites on the DNA fragment shown in SEQ ID No.1 (see Table 1) in the ACTB gene.

Alternatively, the "all or part of the CpG sites" may be all CpG sites on the DNA fragment shown in SEQ ID No.2 (see Table 2) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (see Table 3) in the ACTB gene.

Alternatively, the "all or part of the CpG sites" may be all CpG sites on the DNA fragment shown in SEQ ID No.1 (see Table 1) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (see Table 3) in the ACTB gene.

Alternatively, the "all or part of the CpG sites" may be all CpG sites on the DNA fragment shown in SEQ ID No.1 (see Table 1), all CpG sites on the DNA fragment shown in SEQ ID No.2 (see Table 2), and all CpG sites on the DNA fragment shown in SEQ ID No.3 (see Table 3) in the ACTB gene.

Or, the "all or part of CpG sites" may be all CpG sites or any 30 or any 29 or any 28 or any 27 or any 26 or any 25 or any 24 or any 23 or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG sites in the DNA fragment shown in SEQ ID No.2 in the ACTB gene.

Or, the "all or part of the CpG sites" may be all or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the CpG sites shown in the following 15 on the DNA fragment shown in SEQ ID No.2 in the ACTB gene:

(f1) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (ACTB_B_12) from 435 to 436 of the 5' end;

(f2) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB_B_13) from 453 th to 454 th positions of the 5' end;

(f3) The CpG site (ACTB_B_14) shown in 488-489 of the 5' -end of the DNA fragment shown in SEQ ID No. 2;

(f4) The DNA fragment shown in SEQ ID No.2 contains CpG sites (ACTB_B_15.16) shown at positions 492-493 and 494-495 from the 5' end;

(f5) The CpG sites (ACTB_B_17.18) shown in the positions 514-515 and 518-519 of the DNA fragment shown in SEQ ID No.2 from the 5' end;

(f6) The CpG site (ACTB_B_19) shown in 522-523 of the 5' -end of the DNA fragment shown in SEQ ID No. 2;

(f7) The CpG sites (ACTB_B_20.21) shown in the 5' -end positions 530-531 and 534-535 of the DNA fragment shown in SEQ ID No. 2;

(f8) The DNA fragment shown in SEQ ID No.2 contains CpG sites (ACTB_B_22.23) shown in positions 560-561 and 563-564 of the 5' end;

(f9) The CpG site (ACTB_B_24) shown in the 575-576 position of the 5' end of the DNA fragment shown in SEQ ID No. 2;

(f10) The DNA fragment shown in SEQ ID No.2 shows the CpG site (ACTB_B_25) from 592 to 593 of the 5' end;

(f11) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB_B_26) from 626-627 of the 5' end;

(f12) The CpG site (ACTB_B_27) shown in 638-639 of the DNA fragment shown in SEQ ID No.2 from the 5' end;

(f13) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB_B_28.29) from 666-667 and 672-673 of the 5' end;

(f14) The CpG site (ACTB_B_30) shown in 728-729 th position of the DNA fragment shown in SEQ ID No.2 from the 5' end;

(f15) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (ACTB_B_31) at 757-758 th positions from the 5' end.

In particular embodiments of the invention, some adjacent methylation sites are treated as one methylation site when analyzed for DNA methylation using time-of-flight mass spectrometry, because several CpG sites are located on one methylation fragment, the peak pattern is indistinguishable (indistinguishable sites are set forth in Table 5), and thus the methylation level analysis is performed, and related mathematical models are constructed and used. This is the case with (f 4), (f 5), (f 7), (f 8) and (f 13) described above.

In the above aspects, the substance for detecting the methylation level of the ACTB gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ACTB gene. The reagent for detecting the methylation level of the ACTB gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ACTB gene; the instrument for detecting ACTB gene methylation level may be a time-of-flight mass spectrometry detector. Of course, other conventional reagents for performing time-of-flight mass spectrometry may also be included in the reagent for detecting the methylation level of the ACTB gene.

Further, the partial fragment may be at least one fragment of:

(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g4) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g5) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same.

Still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C;

the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 5.

The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown in 32-57 nucleotides of SEQ ID No. 7.

The primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is single-stranded DNA shown in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer C2 is SEQ ID No.9 or single-stranded DNA shown in 32-55 th nucleotide of SEQ ID No. 9.

In addition, the invention also discloses a method for distinguishing whether the sample to be detected is an A type sample or a B type sample. The method may comprise the steps of:

(A) The mathematical model may be built as a method comprising the steps of:

(A2) Taking the ACTB gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model according to the classification modes of the A type and the B type by a two-classification logistic regression method, and determining the threshold value of classification judgment.

Wherein, n1 and n2 in (A1) can be positive integers more than 50.

(B) The sample to be tested may be determined as a type a sample or a type B sample according to a method comprising the steps of:

(B1) Detecting the methylation level of the ACTB gene of the sample to be detected;

The type a sample and the type B sample are any one of:

In practical applications, any of the above mathematical models may be changed according to the detection method and the fitting method of DNA methylation, and the mathematical model is determined according to a specific mathematical model without any convention.

In the embodiment of the invention, the model is specifically log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting a methylation value of one or more methylation sites of a sample to be tested into the model as a dependent variable, b0 is a constant, x1-xn is an independent variable which is a methylation value of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is a weight given by the model to the methylation value of each site.

In the embodiment of the invention, the model can be established by adding known parameters such as age, sex, white blood cell count and the like as appropriate to improve the discrimination efficiency. The specific model established in the embodiment of the invention is used for assisting in distinguishing coronary heart disease from health control. The model is specifically as follows: log (y/(1-y))=1.672+0.241 actb_b_12-1.744 actb_b_13+1.642 actb_b_b_14+2.576 actb_b_15.16-2.114 actb_b_17.18+1.163 actb_b_19-2.399 actb_b_20.21-8.181 actb_b_22.23-2.500 actb_b_24+2.840 actb_b_25-0.806 actb_b_26-2.840 actb_b_27+3.210 actb_b_28.29+0.178 actb_b_30+3.321 actb_b_31-0.025 (integer) +0.098 male female (0.029) is assigned a number of 0.029 to 0 female (0.029) ⁹ /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 15 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The model is provided withThe threshold is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were coronary heart disease patients, and patient candidates with less than 0.5 were healthy controls. ACTB_B_12 in the model is the methylation level of CpG sites shown in 435-436 th position of a DNA fragment shown in SEQ ID No.2 from a 5' end; the ACTB_B_13 is the methylation level of CpG sites shown in the 453 th-454 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_14 is the methylation level of CpG sites shown in the 488-489 th position of the 5' -end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_15.16 is the methylation level of CpG sites shown in 492-493 bits and 494-495 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB_B_17.18 is the methylation level of CpG sites shown in 514-515 and 518-519 of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB_B_19 is the methylation level of CpG sites shown in 522-523 from the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_20.21 is the methylation level of CpG sites shown in 530 th to 531 th and 534 th to 535 th positions of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB_B_22.23 is the methylation level of CpG sites shown in 560 th to 561 th and 563 th to 564 th from the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_24 is the methylation level of CpG sites shown in the 575-576 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB_B_25 is the methylation level of the CpG site shown in the 592 th-593 rd position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_26 is the methylation level of CpG sites shown in the 626-627 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_27 is the methylation level of CpG sites shown in the 638-639 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB_B_28.29 is the methylation level of CpG sites shown in 666-667 and 672-673 of a DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB_B_30 is the methylation level of CpG sites shown in 728-729 th position of a DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB_B_31 is the methylation level of CpG sites shown in 757-758 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2.

In the above aspects, the detecting the ACTB gene methylation level is detecting an ACTB gene methylation level in blood.

The ACTB gene described above may specifically include Genbank accession No.: NM-001101.5 (2018, 11, 23).

The invention provides a hypomethylation phenomenon of ACTB gene in coronary heart disease blood. Experiments prove that the coronary heart disease and the healthy control can be distinguished by taking blood as a sample, and the coronary heart disease patients with different clinical characteristics and the healthy control can be distinguished. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of coronary heart disease and reducing the death rate.

Drawings

FIG. 1 is a schematic diagram of a mathematical model.

Fig. 2 is an illustration of a mathematical model of coronary heart disease.

Detailed Description

The following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.

The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.

The beta Actin (ACTB) gene quantification assays in the following examples were all set up in triplicate and the results averaged.

Example 1 primer design for detecting methylation site of ACTB Gene

Three fragments (actb_a, ACTB and actb_c) of the ACTB gene were selected for methylation level and coronary heart disease correlation analysis, through a number of sequence and functional analyses.

The ACTB-A fragment (SEQ ID No. 1) is located in the sense strand of the hg19 reference genome chr7: 5567016-5567713.

The ACTB-B fragment (SEQ ID No. 2) is located in the hg19 reference genome chr7:5567834-5568619, antisense strand.

The ACTB-C fragment (SEQ ID No. 3) is located in the sense strand of the hg19 reference genome chr7: 5568885-5569416.

CpG site information in the ACTB_A fragment is shown in Table 1.

CpG site information in the ACTB-B fragment is shown in Table 2.

CpG site information in the ACTB-C fragment is shown in Table 3.

Table 1 CpG site information in ACTB_A fragment

CpG sites	Position of CpG sites in the sequence
		ACTB_A_1	SEQ ID No.1 from the 97 th to 98 th position of the 5' end
ACTB_A_2	153 th to 154 th positions of SEQ ID No.1 from 5' end
		ACTB_A_3	SEQ ID No.1 from the 5' end at positions 182-183
ACTB_A_4	191 th to 192 th positions from the 5' end of SEQ ID No.1
		ACTB_A_5	SEQ ID No.1 from position 210 to 211 at the 5' end
ACTB_A_6	SEQ ID No.1 from position 311 to 312 of the 5' end
		ACTB_A_7	SEQ ID No.1 from the 5' end at positions 343-344
ACTB_A_8	361 st to 362 th position from 5' end of SEQ ID No.1
		ACTB_A_9	377-378 of SEQ ID No.1 from the 5' end
ACTB_A_10	The 384 th to 385 th positions of SEQ ID No.1 from the 5' end
		ACTB_A_11	SEQ ID No.1 shows positions 396-397 from the 5' end
ACTB_A_12	SEQ ID No.1 from position 402-403 of the 5' end
		ACTB_A_13	SEQ ID No.1 from 5' end position 448-449
ACTB_A_14	465-466 th position from 5' end of SEQ ID No.1
		ACTB_A_15	SEQ ID No.1 from position 468-469 of the 5' end
ACTB_A_16	SEQ ID No.1 from positions 477-478 of the 5' end
		ACTB_A_17	From 5' end 488-48 of SEQ ID No.19 bits
ACTB_A_18	581-582 from 5' end of SEQ ID No.1
		ACTB_A_19	SEQ ID No.1 from position 673-674 of the 5' end

Table 2 CpG site information in ACTB_B fragment

TABLE 3 CpG site information in ACTB_C fragment

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Specific PCR primers were designed for three fragments (ACTB_A fragment, ACTB_B fragment and ACTB_C fragment) as shown in Table 4. Wherein SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 are forward primers, and SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 are reverse primers; positions 1 to 10 of SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 from 5' are nonspecific tags, and positions 11 to 35 are specific primer sequences; the 1 st to 31 st positions of SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 from 5' are non-specific labels, the 32 nd to 56 th positions of SEQ ID No.5 are specific primer sequences, the 32 nd to 57 th positions of SEQ ID No.7 are specific primer sequences, and the 32 nd to 55 th positions of SEQ ID No.9 are specific primer sequences. The primer sequences do not contain SNPs and CpG sites.

TABLE 4 ACTB methylation primer sequences

Example 2 ACTB Gene methylation detection and analysis of results

1. Study sample

The research sample adopts an epidemiological whole group sampling method, and the follow-up investigation is carried out on community groups over 18 years old in a certain city through the period of more than 2 years. The study was reviewed by the ethics committee and all panelists signed informed consent. Cardiovascular and cerebrovascular diseases and cancer incidence information are recorded annually through local hospitals, disease control center chronic disease management systems, community health service centers and workstation chronic disease routine registration projects and social security center reimbursement data. The starting time of the queue is the baseline investigation date, the ending variable is the cardiovascular and cerebrovascular diseases, and the follow-up time of the study subjects without visit is uniformly calculated according to half of the follow-up ending time. By 7 months along with the date of the interview 2018, 342 patients suffering from coronary heart disease are counted, and new coronary heart disease patients in 2 years after being queued into the group are selected as case groups. After age and sex matching, a population without coronary heart disease during follow-up period (the follow-up time is more than 2 years) and with blood routine index in the reference range is selected as health control, and 375 cases are counted.

All patient ex vivo blood samples were collected prior to onset. The disease condition is confirmed by imaging and pathology in the subsequent disease.

342 patients suffering from coronary heart disease within 2 years after the group are classified according to clinical typing: 45 cases of latent 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. Wherein 137 cases of coronary heart disease occur within 1 year after the administration, including 20 cases of latent 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.

The median of the ages of healthy controls and coronary heart disease patients was 65 and 64, respectively, and the ratio of men and women in each of these 2 populations was about 1:1. Median age of patients with coronary heart disease within 1 year after the group is 65 and 63 years, respectively, and the ratio of men and women in the group is about 1:1.

2. Methylation detection

1. Total DNA of the blood sample is extracted.

2. The total DNA of the blood samples prepared in step 1 was subjected to bisulfite treatment (see DNA methylation kit instructions for Qiagen). After bisulfite treatment, unmethylated cytosines (C) in the original CpG sites are converted to uracil (U), while methylated cytosines remain unchanged.

3. And (3) performing PCR amplification by using the DNA treated by the bisulfite in the step (2) as a template and adopting 3 pairs of specific primer pairs in the table (4) through DNA polymerase according to a reaction system required by a conventional PCR reaction, wherein all primers adopt a conventional standard PCR reaction system, and the primers are amplified according to the following procedure.

The PCR reaction procedure was: 95 ℃,4 min- & gt (95 ℃,20 s- & gt 56 ℃,30 s- & gt 72 ℃ 2 min) 45 cycles- & gt 72 ℃,5 min- & gt 4 ℃ for 1h.

4. Taking the amplified product of the step 3, and carrying out DNA methylation analysis by a time-of-flight mass spectrum, wherein the specific method is as follows:

(1) Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [ 0.5U) was added to 5. Mu.l of PCR product]+1.7ml H ₂ O) then incubated in a PCR apparatus (37 ℃,20 min. Fwdarw. 85 ℃,5 min. Fwdarw. 4 ℃,5 min) according to the following procedure;

(2) Taking out 2 mu.l of the SAP treated product obtained in the step (1), adding the product into a 5 mu l T-clear reaction system according to the instruction, and then incubating for 3 hours at 37 ℃;

(3) Taking the product of the step (2), adding 19 mu l of deionized water, and then carrying out deionized incubation on a rotary shaking table for 1h by using 6 mu g of Resin;

(4) Centrifuging at 2000rpm at room temperature for 5min, and loading 384SpectroCHIP with the micro supernatant by a Nanodispenser mechanical arm;

(5) Time-of-flight mass spectrometry; the data obtained were collected with the spectroacquisition v3.3.1.3 software and visualized by MassArray EpiTyper v 1.2.1.2 software.

Reagents used for the time-of-flight mass spectrometry detection are all kits (T-Cleavage MassCLEAVE Reagent Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection is Analyzer Chip Prep Module 384, model: 41243; the data analysis software is self-contained software of the detection instrument.

5. And (5) analyzing the data obtained in the step (4).

Statistical analysis of the data was performed by SPSS Statistics 23.0.

Non-parametric tests were used for comparative analysis between the two groups.

The identification effect of a combination of multiple CpG sites on different sample groupings is achieved by logistic regression and statistical methods of the subject curves.

All statistical tests were double-sided, with P values <0.05 considered statistically significant.

Through mass spectrometry experiments, 69 distinguishable peak patterns were obtained in total. The methylation level at each CpG site of each sample can be automatically obtained by calculating the peak area according to the "methylation level=peak area of methylated fragments/(peak area of unmethylated fragments+peak area of methylated fragments)" formula using SpectroAcquin v3.3.1.3 software.

3. Analysis of results

1. Methylation level difference of ACTB gene in blood of healthy control and coronary heart disease patients (earlier than clinical onset time less than or equal to 2 years)

The methylation level of all CpG sites in the ACTB gene was analyzed using blood of 342 patients with coronary heart disease and 375 healthy controls as study material (Table 5), wherein the patients with coronary heart disease were asymptomatic when they were enrolled, and developed within 2 years after enrolling. The results showed that healthy controls had a median methylation level of the ACTB gene of 0.38 (iqr=0.18-0.67) and patients with coronary heart disease had a median methylation level of 0.35 (iqr=0.16-0.63). As a result of comparative analysis of the methylation level of the ACTB gene between the two, it was found that the methylation level of all CpG sites in the ACTB gene was significantly lower than that in the healthy control (p <0.05, table 5) for patients with coronary heart disease. Therefore, the methylation level of the ACTB gene can be used in a population to screen potential patients who will develop coronary heart disease in 2 years, and is a very clinically valuable molecular marker.

2. Methylation level difference of ACTB gene in blood of healthy control and coronary heart disease patients (earlier than clinical onset time less than or equal to 1 year)

The difference in methylation levels of all CpG sites in ACTB gene between 137 patients with coronary heart disease and 375 healthy controls was analyzed using blood of both as a study material (table 6), wherein the coronary heart disease patients were asymptomatic when they were enrolled, and developed within 1 year after enrolling. The results showed that healthy controls had a median methylation level of ACTB gene of 0.38 (iqr=0.18-0.67) and coronary heart disease patients had a median methylation level of 0.32 (iqr=0.12-0.61). As a result of comparative analysis of the methylation levels of the ACTB gene in both cases, it was found that the methylation levels of all CpG sites in the ACTB gene were significantly lower than those in healthy controls (p <0.05, table 6). Therefore, the methylation level of the ACTB gene can be used in a population to screen potential patients who will develop coronary heart disease within 1 year, and is a very clinically valuable molecular marker.

3. Methylation level differences between healthy controls and coronary heart disease of different clinical characteristics (earlier than clinical onset time less than or equal to 2 years)

We compared and analyzed differences in methylation levels of ACTB gene between 342 patients with coronary heart disease and 375 healthy controls with different clinical characteristics, wherein patients with coronary heart disease were asymptomatic in the group, and developed within 2 years after the group. As a result, it was found that methylation levels of all CpG sites of the ACTB gene in patients with coronary heart disease (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) with different clinical characteristics were significantly different from those of healthy controls (p <0.05, table 7).

4. Methylation level differences between healthy controls and coronary heart disease of different clinical characteristics (earlier than clinical onset time less than or equal to 1 year)

We compared and analyzed the methylation level difference of ACTB genes of 137 patients with coronary heart disease and 375 healthy controls with different clinical characteristics, wherein the patients with coronary heart disease have no symptoms when being in the group, and the patients with coronary heart disease are ill within 1 year after being in the group. As a result, it was found that methylation levels of all CpG sites of the ACTB gene in patients with coronary heart disease (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) with different clinical characteristics were significantly different from those of healthy controls (p <0.05, table 8). Thus, the methylation level of the ACTB gene can be used to predict the likelihood of developing a coronary heart disease of different clinical characteristics within 1 year.

5. Establishment of mathematical model for assisting coronary heart disease diagnosis

The mathematical model established by the invention can be used for achieving the following purposes:

(1) Before clinical onset, early warning is carried out on individuals with coronary heart disease onset risks within 2 years in the crowd;

(2) Before clinical onset, individuals with coronary heart disease onset risk within 2 years in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases;

(3) Before clinical onset, early warning is carried out on individuals with coronary heart disease onset risks within 1 year in the crowd;

(4) Before clinical onset, individuals with coronary heart disease onset risks within 1 year in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases;

the mathematical model is established as follows:

(A) Data sources: methylation levels of target CpG sites (combinations of one or more of tables 1-3) in isolated blood samples of 342 coronary heart disease patients and 375 healthy controls listed in step one (detection method same as step two).

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

(B) Model building

Any two different types of patient data, namely training sets, are selected according to the needs, (for example, coronary heart disease patients and healthy controls, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic myocardial patients and healthy controls, sudden death patients and healthy controls, and the collection of the patient samples is earlier than the clinical onset time of diseases for 2 years, or coronary heart disease patients and healthy controls, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic myocardial patients and healthy controls, sudden death patients and healthy controls, and the collection of the patient samples is earlier than the clinical onset time of diseases for 1 year) are used as data for establishing models, and statistical models of SAS, R, SPSS and other statistical software are established through formulas by using statistical methods of two types of logistic regression. The numerical value corresponding to the maximum approximate dengue index calculated by the mathematical model formula is a threshold value or directly set 0.5 as the threshold value, the detection index obtained by the sample to be tested after the test and the calculation of the substitution model is more than the threshold value and is classified into one type (B type), less than the threshold value and classified into the other type (A type), and the detection index is equal to the threshold value and is used as an uncertain gray area. When a new sample to be detected is predicted to judge which type is the sample, firstly, detecting methylation levels of one or more CpG sites on an ACTB gene of the sample to be detected by a DNA methylation determination method, then substituting data of the methylation levels into the mathematical model (if known parameters such as age, sex, white cell count and the like are included in the model construction, the specific numerical value of the corresponding parameter of the sample to be detected is substituted into a model formula at the same time in the step), calculating to obtain a detection index corresponding to the sample to be detected, comparing the detection index corresponding to the sample to be detected with the magnitude of a threshold value, and determining which type of sample the sample to be detected is the sample to be detected according to the comparison result.

Examples: as shown in fig. 1, the methylation level of single CpG sites or the methylation level of a combination of multiple CpG sites in the ACTB gene in the training set was used to construct a mathematical model for distinguishing between class a and class B using a formula of two classification logistic regression through statistical software such as SAS, R, SPSS. The mathematical model is herein a two-class logistic regression model, specifically: log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained by substituting a dependent variable, i.e., a methylation value of one or more methylation sites of a sample to be tested, into a model and then converting the value, b0 is a constant, x1-xn is an independent variable, i.e., a methylation value (each value is a value between 0 and 1) of one or more methylation sites of the sample to be tested, and b1-bn are weights given to the methylation values of each site by the model. In specific application, a mathematical model is established according to methylation degrees (x 1-xn) of one or more DNA methylation sites of a sample detected in a training set and known classification conditions (class A or class B, respectively assigning 0 and 1 to y), so that a constant B0 of the mathematical model and weights B1-bn of each methylation site are determined, and a numerical value corresponding to a maximum approximate dengue index calculated by the mathematical model is used as a threshold value or a threshold value divided by 0.5 is directly set. And the detection index, namely the y value, obtained after the sample to be detected is tested and calculated by substituting the sample into the model is classified as B when the y value is larger than the threshold value, and classified as A when the y value is smaller than the threshold value, and the y value is equal to the threshold value and is used as an uncertain gray area. Where class a and class B are the corresponding two classifications (groupings of two classifications, which group is class a and which group is class B, to be determined according to a specific mathematical model, not specified herein), such as: coronary heart disease patients and healthy controls, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease for 2 years; alternatively, coronary heart disease patients and healthy controls, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease by 1 year. When predicting a sample of a subject to determine which category the sample belongs to, blood of the subject is collected first, and then DNA is extracted therefrom. After the extracted DNA is converted by bisulfite, the methylation level of single CpG sites or the methylation level of a plurality of CpG sites of the ACTB gene of a subject is detected by using a DNA methylation determination method, and methylation data obtained by detection are substituted into the mathematical model. If the methylation level of one or more CpG sites of the ACTB gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is larger than a threshold value, the subject judges the class (B class) with the detection index in the training set larger than the threshold value; if the methylation level data of one or more CpG sites of the ACTB gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is smaller than the threshold value, the subject belongs to the class (class A) with the detection index in the training set smaller than the threshold value; if the methylation level data of one or more CpG sites of the ACTB gene of the subject is substituted into the mathematical model, and the calculated value, i.e. the detection index, is equal to the threshold value, the subject cannot be judged to be A class or B class.

Examples: the schematic diagram of fig. 2 illustrates methylation of the preferred CpG sites of actb_b (actb_b_12, actb_b_13, actb_b_14, actb_b_15.16, actb_b_17.18, actb_b_19, actb_b_20.21, actb_b_22.23, actb_b_24, actb_b_25, actb_b_26, actb_b_27, actb_b_28.29, actb_b_30, and actb_b_31) and mathematical modeling for discrimination of coronary heart disease: the methylation level data of 15 distinguishable CpG site combinations of ACTB_B_12, ACTB_B_13, ACTB_B_14, ACTB_B_15.16, ACTB_B_17.18, ACTB_B_19, ACTB_B_20.21, ACTB_B_22.23, ACTB_B_24, ACTB_B_25, ACTB_B_26, ACTB_B_27, ACTB_B_28.29, ACTB_B_30 and ACTB_B_31 in the coronary heart disease patient (earlier than the clinical onset time is less than or equal to 2 years) and the healthy control training set (here: 342 coronary heart disease patients and 375 healthy controls) were tested, and the age, sex (male assignment 1, female assignment 0) of the patient, white blood cell count were used by R software to build a mathematical model for distinguishing coronary heart disease patients from healthy controls using a binary logistic regression equation. The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: log (y/(1-y))=1.672+0.241 actb_b_12-1.744 actb_b_13+1.642 actb_b_14+2.576 actb_b_15.16-2.114 actb_b_17.18+1.163 actb_b_19-2.399 actb_b_20.21-8.181 actb_b_22.23-2.500 actb_b_24+2.840 actb_b\u 25-0.806 actb_b_26-2.840 actb_b_27+3.210 actb_b_28.29+0.178 actb_b_30+3.321 actb_b_31-0.025 age (integer) +0.098 sex (male assigned 1 and female assigned 0) -0.029 white blood cell count (unit 10) ⁹ L), wherein y is the detection index obtained by conversion after substituting the methylation values of 15 distinguishable methylation sites of the sample to be tested and the age, sex and white blood cell count into the model. Under the condition that 0.5 is set as a threshold value, the methylation level of 15 distinguishable CpG sites of the ACTB_B_12, the ACTB_B_13, the ACTB_B_14, the ACTB_B_15.16, the ACTB_B_17.18, the ACTB_B_19, the ACTB_B_20.21, the ACTB_B_22.23, the ACTB_B_24, the ACTB_B_25, the ACTB_B_26, the ACTB_B_27, the ACTB_B_28.29, the ACTB_B_30 and the ACTB_B_31 of the sample to be tested is calculated through a test together with an information substitution model of age, gender and white blood cell count, and the obtained detection index, namely y value is more than 0.5 and classified as a coronary heart disease patient, and less than 0.5 are classified as a healthy control, and the coronary heart disease patient or a healthy control is not determined. The area under the curve (AUC) calculation for this model was 0.74 (table 12).

Blood was collected from two subjects (a, B), DNA was extracted from each blood, and after transformation of the extracted DNA with bisulfite, the methylation level of 15 distinguishable CpG sites, actb_b_12, actb_b_13, actb_b_14, actb_b_15.16, actb_b_17.18, actb_b_19, actb_b_20.21, actb_b_22.23, actb_b_24, actb_b_25, actb_b_26, actb_b_27, actb_b_28.29, actb_b_30, and actb_b_31, was detected in the subjects using DNA methylation assay. The methylation level data obtained from the detection together with the information on age, sex and white blood cell count of the subject are then substituted into the mathematical model described above. The value calculated by the mathematical model of the first test subject is greater than 0.5, and the first test subject is judged to be a potential patient of coronary heart disease (clinical onset within the next 2 years); and if the value calculated by the mathematical model of the second subject is less than 0.5, the second subject is judged to be a healthy control (the healthy control standard is still met in the next 2 years). The detection result is consistent with the actual situation.

(C) Model Effect evaluation

According to the method, a method for distinguishing coronary heart disease patients from healthy controls, latent or asymptomatic myocardial ischemia patients from healthy controls, angina patients from healthy controls, myocardial infarction patients from healthy controls, ischemic cardiomyopathy patients from healthy controls, sudden death patients from healthy controls are established respectively, and the patients are 2 years earlier than the clinical onset time of the disease; mathematical models for distinguishing between patients suffering from coronary heart disease and healthy controls, patients suffering from latent or asymptomatic myocardial ischemia and healthy controls, angina patients and healthy controls, patients suffering from myocardial infarction and healthy controls, patients suffering from ischemic cardiomyopathy and healthy controls, sudden death patients and healthy controls, all of which are 1 year earlier than the clinical onset time of the disease, and evaluating their effectiveness by means of a subject curve (ROC curve). The larger the area under the curve (AUC) from the ROC curve, the better the discrimination of the model, the more efficient the molecular marker. The evaluation results after constructing mathematical models using different CpG sites are shown in tables 9, 10 and 11. In tables 9, 10 and 11, 1 CpG site represents the site of any one CpG site in the actb_c amplified fragment, 2 CpG sites represent the combination of any 2 CpG sites in actb_c, 3 CpG sites represent the combination of any 3 CpG sites in actb_c, … … and so on. The values in the table are the range of values for the combined evaluation of the different sites (i.e., the results for any combination of CpG sites are within this range).

The above results show that the discrimination ability of the ACTB gene for each group (coronary heart disease patient and healthy control, latent or asymptomatic myocardial ischemia patient and healthy control, angina pectoris patient and healthy control, myocardial infarction patient and healthy control, ischemic myocardial patient and healthy control, sudden death patient and healthy control, and all of which are earlier than the clinical onset time of the disease by 2 years or less; coronary heart disease patient and healthy control, latent or asymptomatic myocardial ischemia patient and healthy control, angina patient and healthy control, myocardial infarction patient and healthy control, ischemic myocardial patient and healthy control, sudden death patient and healthy control, and all of which are earlier than the clinical onset time of the disease by 1 year or less) increases with the increase of the number of sites.

In addition, among the CpG sites shown in tables 1 to 3, there are cases where combinations of a few preferred sites are better in discrimination ability than combinations of a plurality of non-preferred sites. The 15 distinguishable CpG sites, e.g., ACTB_B_12, ACTB_B_13, ACTB_B_14, ACTB_B_15.16, ACTB_B_17.18, ACTB_B_19, ACTB_B_20.21, ACTB_B_22.23, ACTB_B_24, ACTB_B_25, ACTB_B_26, ACTB_B_27, ACTB_B_28.29, ACTB_B_30, and ACTB_B_31 combinations shown in tables 12, 13, and 14 are preferred sites for any 15 combinations in ACTB_B.

In summary, the CpG sites on the ACTB gene and various combinations thereof, the CpG sites on the ACTB_A fragment and various combinations thereof, the CpG sites on the ACTB_B fragment and various combinations thereof, the CpG sites on the ACTB_B_12, the ACTB_B_13, the ACTB_B_14, the ACTB_B_15.16, the ACTB_B_17.18, the ACTB_B_19, the ACTB_B_20.21, the ACTB_B_22.23, the ACTB_B_24, the ACTB_B_25, the ACTB_B_26, the ACTB_B_27, the ACTB_B_28.29, the ACTB_B_30 and the ACTB_31, the 15 CpG sites on the ACTB_C fragment and various combinations thereof, and the methylation levels of the CpG sites on the ACTB_ A, ACTB _B and the ACTB_C and various combinations thereof are all healthy in patients and healthy controls, patients, ischemic or non-ischemic patients and patients, patients and patients with myocardial infarction and angina and other healthy conditions, and in healthy patients and healthy conditions, and patients with a clinical controls, and no or no ischemic or no more than the time, and healthy patients and healthy controls, and patients with a healthy condition and no acute or the heart disease, and the heart disease; coronary heart disease patients and healthy controls, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, and all of which are earlier than the clinical onset time of the disease by less than or equal to 1 year).

Table 5 compares methylation level differences between healthy controls and patients with coronary heart disease (earlier than clinical onset time. Ltoreq.2 years)

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Table 6, methylation level differences between healthy controls and patients with coronary heart disease (earlier than clinical onset time. Ltoreq.1 year)

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Table 7 compares methylation level differences between healthy controls and coronary heart disease of different clinical characteristics (earlier than clinical onset time less than or equal to 2 years)

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Table 8, comparison of methylation level differences between healthy controls and coronary heart disease of different clinical character (earlier than clinical onset time. Ltoreq.1 year)

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CpG sites of Table 9, ACTB_B and combinations thereof for distinguishing healthy controls from coronary heart disease

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CpG sites of Table 10, ACTB_B and combinations thereof for distinguishing healthy controls from patients with coronary heart disease of different clinical character (time of onset earlier than or equal to 2 years)

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Note that: the data in the table are area under the curve (AUC).

CpG sites of Table 11, ACTB_B and combinations thereof for distinguishing healthy controls from patients with coronary heart disease of different clinical character (earlier than clinical onset time. Ltoreq.1 year)

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Note that: the data in the table are area under the curve (AUC).

Table 12, optimal CpG sites of ACTB_B and combinations thereof for distinguishing healthy controls from coronary heart disease

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Note that: the data in the table are area under the curve (AUC).

Table 13, ACTB_B optimal CpG sites and combinations thereof for discriminating healthy controls and patients with coronary heart disease with different clinical characteristics (earlier than clinical onset time less than or equal to 2 years)

And (3) injection: the data in the table are area under the curve (AUC).

Table 14, ACTB_B optimal CpG sites and combinations thereof for discriminating healthy controls and coronary heart disease of different clinical characteristics (earlier than clinical onset time. Ltoreq.1 year)

Note that: the data in the table are area under the curve (AUC).

The present invention is described in detail above. It will be apparent to those skilled in the art that the present invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with respect to specific embodiments, it will be appreciated that the invention may be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The application of some of the basic features may be done in accordance with the scope of the claims that follow.

<110> Nanjing Techno Biotechnology Co., ltd

<120> methylation markers for diagnosing coronary heart disease

<130> GNCLN221364

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<211> 35

<212> DNA

<213> Artificial sequence

<400> 6

aggaagagag ttttttggtg tttgtttttt tgatt 35

<210> 7

<211> 57

<212> DNA

<213> Artificial sequence

<400> 7

cagtaatacg actcactata gggagaaggc tacaaaactt aacttccaca acacaac 57

<210> 8

<211> 35

<212> DNA

<213> Artificial sequence

<400> 8

aggaagagag gaaggtgtgg tgttagattt ttttt 35

<210> 9

<211> 55

<212> DNA

<213> Artificial sequence

<400> 9

cagtaatacg actcactata gggagaaggc tacttccttt atccccaatc taaac 55

Claims

1. Application of methylation ACTB gene as a marker in preparation of products; the application of the product is at least one of the following:

2. Use of a substance for detecting the methylation level of the ACTB gene in the preparation of a product; the application of the product is at least one of the following:

3. Use of a substance for detecting the methylation level of the ACTB gene and a medium storing a mathematical model building method and/or a use method for the preparation of a product; the application of the product is at least one of the following:

(3) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;

(A1) Detecting ACTB gene methylation levels of n1 type a samples and n2 type B samples, respectively;

(A2) Taking ACTB gene methylation level data of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type;

the using method of the mathematical model comprises the following steps:

(B1) Detecting the methylation level of ACTB gene of a sample to be detected;

(B2) Substituting the ACTB gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a comparison result;

the type a sample and the type B sample are any one of the following:

4. Use of a medium storing a mathematical model building method and/or a use method for the preparation of a product; the application of the product is at least one of the following:

the using method of the mathematical model comprises the following steps:

(B1) Detecting the methylation level of ACTB gene of a sample to be detected;

The type a sample and the type B sample are any one of the following:

5. A kit comprising a substance for detecting the methylation level of the ACTB gene; the application of the kit is at least one of the following:

6. The kit of claim 5, wherein: the kit further comprises a medium storing a mathematical model establishing method and/or a using method as set forth in claim 3 or 4.

7. A system, comprising:

(D2) A device comprising a unit X and a unit Y;

the data acquisition module is configured to acquire ACTB gene methylation level data of n 1A type samples and n 2B type samples detected by the (D1);

the data analysis processing module is configured to receive ACTB gene methylation level data of n 1A type samples and n 2B type samples sent by the data acquisition module, establish a mathematical model through a two-classification logistic regression method according to classification modes of the A type and the B type, and determine a threshold value of classification judgment;

the model output module is configured to receive the mathematical model sent by the data analysis processing module and output the mathematical model;

the unit Y is used for determining the type of the sample to be detected and comprises 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 ACTB gene methylation level data of the tested person detected by the step (D1);

the data operation module is configured to receive the ACTB gene methylation level data of the testee, sent by the data input module, and substitutes the ACTB gene methylation level data of the testee into the mathematical model to calculate a detection index;

The data comparison module is configured to receive the detection index sent 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 sent by the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result;

the type a sample and the type B sample are any one of the following:

8. The use or kit or system according to any one of claims 1-7, wherein: the time of clinical onset is 2 years earlier than the time of clinical onset or 1 year earlier than the time of clinical onset.

9. The use or kit or system according to any one of claims 1-8, wherein: the methylation level of the ACTB gene is the methylation level of all or part of CpG sites in fragments shown in the following (e 1) - (e 3) in the ACTB gene;

The methylated ACTB gene is formed by methylation of all or part of CpG sites in fragments shown in the following (e 1) - (e 3) in the ACTB gene;

(e3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto;

further, the 'all or part of CpG sites' are any one or more CpG sites in 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the ACTB gene;

or, the whole or partial CpG sites are all CpG sites on the DNA fragment shown in SEQ ID No.2 in the ACTB gene;

or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.1 in the ACTB gene;

or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the ACTB gene;

or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.1 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the ACTB gene;

Or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.1, all CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the ACTB gene;

or, the whole or part of CpG sites are all CpG sites in the DNA fragment shown in SEQ ID No.2 in the ACTB gene or all CpG sites in any 30 or any 29 or any 28 or any 27 or any 26 or any 25 or any 24 or any 23 or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG sites;

or, the whole or part of CpG sites are all or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 15 CpG sites on the DNA fragment shown in SEQ ID No.2 in the ACTB gene:

(f1) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 435 to 436 of the 5' end;

(f2) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 453 th to 454 th positions of the 5' end;

(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 488-489 of the 5' end;

(f4) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 492-493 and 494-495 of the 5' end;

(f5) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 514-515 and 518-519 of the 5' end;

(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 522 to 523 of the 5' end;

(f7) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 530 to 531 and 534 to 535 of the 5' end;

(f8) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 560-561 and 563-564 of the 5' end;

(f9) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 575 to 576 positions of the 5' end;

(f10) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 592 to 593 of the 5' end;

(f11) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 626 th to 627 th positions of the 5' end;

(f12) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 638-639 positions of the 5' end;

(f13) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 666-667 and 672-673 of the 5' end;

(f14) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 728 to 729 th positions of the 5' end;

(f15) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 757 to 758 positions of the 5' end.

10. The use or kit or system according to any one of claims 1-9, wherein: the substance for detecting the methylation level of the ACTB gene comprises a primer combination for amplifying a full-length or partial fragment of the ACTB gene;

the reagent for detecting the methylation level of the ACTB gene comprises a primer combination for amplifying a full-length or partial fragment of the ACTB gene;

further, the partial fragment is at least one fragment of:

(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 5;

the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-57 th nucleotide of SEQ ID No. 7;