CN113528636B - Calbindin gene methylation as a potential marker for early diagnosis of cardiovascular and cerebrovascular diseases - Google Patents
Calbindin gene methylation as a potential marker for early diagnosis of cardiovascular and cerebrovascular diseases Download PDFInfo
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Abstract
The invention discloses calbindin gene methylation as a potential marker for early diagnosis of cardiovascular and cerebrovascular diseases. The invention provides an application of a methylation S100P gene serving as a marker in preparation of a product, wherein the application of the product is at least one of the following: auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical symptoms; auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical symptoms; auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical symptoms; auxiliary distinguishing coronary heart disease and cerebral apoplexy; auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical symptoms; auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical symptoms. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of the cardiovascular and cerebrovascular diseases and reducing the death rate.
Description
Technical Field
The invention relates to the field of medicine, in particular to blood calbindin gene methylation as a potential marker for early diagnosis of cardiovascular and cerebrovascular diseases.
Background
Cardiovascular and cerebrovascular diseases are the general terms of cardiovascular and cerebrovascular diseases, and refer broadly 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 number of people dying from cardiovascular and cerebrovascular diseases worldwide is up to 1500 ten thousand people each year. The incidence rate and the death rate of cardiovascular and cerebrovascular diseases in China generally rise, the number of people dying from the cardiovascular and cerebrovascular diseases every year is 350 ten thousand, and the people account for the first place of various death reasons.
Coronary heart disease refers to heart disease caused by myocardial ischemia, hypoxia or necrosis due to stenosis, spasm or blockage of a lumen by coronary atherosclerosis, collectively referred to as 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) occult 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 necrosis 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 incidence rate of coronary heart disease in more than 10 years is obviously rising in China, and the incidence rate of coronary heart disease is generally represented by myocardial infarction incidence rate. The main diagnosis method of the coronary heart disease 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) imaging method: 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 reflects only a certain disease mechanism of a disease, the clinical significance of the markers is not widely accepted.
Cerebral apoplexy is commonly called as apoplexy, and is an acute cerebrovascular disease, including ischemic cerebral apoplexy and hemorrhagic cerebral apoplexy. Ischemic cerebral apoplexy accounts for 60% -70% of all cerebral strokes, mainly due to cerebral vascular stenosis or occlusion caused by atherosclerosis, thereby causing cerebral ischemia and hypoxia, further causing ischemic necrosis or softening of localized cerebral tissue, patients are more than 40 years old, men are more female, and serious men can cause death. Hemorrhagic stroke is classified into cerebral hemorrhage and subarachnoid hemorrhage, and is mainly caused by long-term hypertension, aneurysm or congenital weakness of cerebral blood vessels, and the like, which cause cerebral blood vessel rupture and hemorrhage, and the blood presses normal brain tissues in the brain, so that the brain cannot perform normal functions, namely 'cerebral hemorrhage', and the death rate is higher. At present, imaging methods are often used for diagnosing cerebral apoplexy, such as CT and nuclear magnetic resonance examination, the sensitivity of CT to the cerebral arterial thrombosis is higher, but the sensitivity to the cerebral arterial thrombosis is only 16%, and the cerebral arterial thrombosis is not suitable for frequent use due to radiation; nuclear magnetic resonance examination has higher sensitivity to ischemic stroke than CT and no radiation effect, but has the disadvantage of lower feasibility, practicality and accessibility (equipment and trained personnel).
Coronary heart disease and cerebral apoplexy both belong to cardiovascular and cerebrovascular diseases. Most cardiovascular diseases can be prevented and treated, and are generally prevented by improving consciousness through popularization knowledge, avoiding exogenous stimulus factors and reasonably dietary moderate exercise, and the treatment effect is greatly dependent on early diagnosis and corresponding intervention measures. At present, the sensitivity and specificity of diagnostic markers for coronary heart disease and cerebral apoplexy are limited clinically, and particularly markers for early diagnosis are lacking, so that more sensitive and specific early molecular markers are urgently needed to be discovered. DNA methylation is a chemical modification important on genes that affects the regulatory process of gene transcription and nuclear structure. The study is analyzed in a plurality of groups of samples by a time-of-flight mass spectrum DNA methylation analysis technology, and the obvious difference between the blood DNA methylation of the cardiovascular and cerebrovascular diseases and the blood DNA methylation of the healthy control group is found. Therefore, DNA methylation signals of blood abnormality possibly break through for in vitro early diagnosis of cardiovascular and cerebrovascular diseases. In addition, the blood is easy to collect, and the DNA methylation is stable at normal temperature, so that the DNA methylation kit has unique advantages in clinical application. Therefore, the exploration and development of sensitive and specific blood DNA methylation diagnosis technology suitable for clinical detection has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of cardiovascular and cerebrovascular diseases and reducing the death rate.
Disclosure of Invention
The invention aims to provide a calcium-binding protein (S100P) methylation marker and a kit for assisting in diagnosing cardiovascular and cerebrovascular diseases.
In a first aspect, the invention claims the use of a methylated S100P 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 symptoms;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical symptoms;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical symptoms;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical symptoms;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical symptoms.
Further, the diagnosis-assisting cardiovascular and cerebrovascular diseases described in (1) can be embodied as at least one of the following: assisting in distinguishing coronary heart disease patients from healthy controls, cerebral apoplexy patients from healthy controls. Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases and cancers are affected at present and once and the blood routine indexes are within the reference range.
In a specific embodiment of the present invention, the auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical symptoms described in (5) is specifically embodied as at least one of the following: can help to distinguish between latent or asymptomatic myocardial ischemia patients and healthy controls, can help to distinguish between angina patients and healthy controls, can help to distinguish between myocardial infarction patients and healthy controls, can help to distinguish between ischemic cardiomyopathy patients and healthy controls, can help to distinguish between sudden death patients and healthy controls. Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases and cancers are affected at present and once and the blood routine indexes are within the reference range.
In a specific embodiment of the present invention, the stroke of (6) that aids in distinguishing between different clinical features or pre-warning of a stroke of different clinical features prior to clinical symptoms is embodied as at least one of: can help to distinguish ischemic cerebral apoplexy from healthy control, and can help to distinguish ischemic cerebral apoplexy from healthy control. The healthy control can be understood as having no cardiovascular and cerebrovascular diseases and cancers at present and once and blood routine indexes are within the reference range.
In the above (1) - (6), the cardiovascular and cerebrovascular diseases may be diseases capable of causing a decrease in the methylation level of the S100P gene in the body, such as coronary heart disease and cerebral apoplexy. The clinical symptoms are preceded by a2 year or 1 year period prior to clinical onset.
In a second aspect, the invention claims the use of a substance for detecting the methylation level of the S100P gene for the preparation of a product. The use of the product may be at least one of the foregoing (1) to (6).
In a third aspect, the invention claims the use of a substance for detecting the methylation level of the S100P gene and a medium storing mathematical modeling methods and/or usage methods for the preparation of a product. The use of the product may be at least one of the foregoing (1) to (6).
The mathematical model may be obtained by a method comprising the steps of:
(A1) Detecting S100P gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the A type and the B type, and determining the threshold value of classification judgment.
Wherein, n1 and n2 in (A1) can be positive integers more than 50.
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of an S100P gene of a sample to be detected;
(B2) Substituting the S100P 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 A type and the B type are determined according to a specific mathematical model without convention.
The type a sample and the type B sample may be any one of the following:
(C1) Potential patients with coronary heart disease in the next 2 years and healthy controls;
(C2) Potential patients with stroke in the next 2 years and healthy controls;
(C3) A potential patient suffering from coronary heart disease in the next 2 years and a potential patient suffering from cerebral apoplexy in the next 2 years;
(C4) Potential patients and healthy controls with different clinical characteristics of coronary heart disease in the next 2 years;
(C5) Potential patients and healthy controls for developing different clinical characteristics of stroke in the next 2 years;
(C6) Potential patients with coronary heart disease in the next 1 year and healthy controls;
(C7) Potential patients with stroke in the next 1 year and healthy controls;
(C8) A potential patient suffering from coronary heart disease in the next 1 year and a potential patient suffering from cerebral apoplexy in the next 1 year;
(C9) Potential patients and healthy controls with different clinical characteristics of coronary heart disease occurring within the next 1 year;
(C10) Potential patients and healthy controls for developing different clinical characteristics of stroke within the next 1 year.
Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases and cancers are affected at present and once and the blood routine index is within the reference range.
In a fourth aspect, the invention claims the use of a medium storing a mathematical model building method and/or a use method as described in the third aspect above for the manufacture of a product. The use of the product may be at least one of the foregoing (1) to (6).
In a fifth aspect, the invention claims a kit.
The kit claimed in the present invention comprises a substance for detecting the methylation level of the S100P gene. The use of the kit may be at least one of the foregoing (1) to (6). The clinical symptoms are preceded by a2 year or 1 year period prior to clinical onset.
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 claimed system includes:
(D1) Reagents and/or instrumentation for detecting the methylation level of the S100P gene;
(D2) A device comprising 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 used for acquiring S100P gene methylation level data of n 1A type samples and n 2B type samples obtained by the detection of the (D1);
Wherein, n1 and n2 can be positive integers more than 50.
The data analysis processing module can establish a mathematical model through a two-class logistic regression method according to the classification mode of the A type and the B type based on the S100P gene methylation level data of the n 1A type samples and the n 2B type samples acquired by the data acquisition module, and determine the threshold value of classification judgment;
the model output module is used for outputting the mathematical model established by the data analysis processing module;
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 used for inputting the S100P gene methylation level data of the to-be-detected person obtained by the detection of (D1);
The data operation module is used for substituting the S100P gene methylation level data of the testee into the mathematical model, and calculating to obtain a detection index;
The data comparison module is used for comparing the detection index with a threshold value;
the conclusion output module is used for outputting a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result of the data comparison module;
the type a sample and the type B sample are any one of the following:
(C1) Potential patients with coronary heart disease in the next 2 years and healthy controls;
(C2) Potential patients with stroke in the next 2 years and healthy controls;
(C3) A potential patient suffering from coronary heart disease in the next 2 years and a potential patient suffering from cerebral apoplexy in the next 2 years;
(C4) Potential patients and healthy controls with different clinical characteristics of coronary heart disease in the next 2 years;
(C5) Potential patients and healthy controls for developing different clinical characteristics of stroke in the next 2 years;
(C6) Potential patients with coronary heart disease in the next 1 year and healthy controls;
(C7) Potential patients with stroke in the next 1 year and healthy controls;
(C8) A potential patient suffering from coronary heart disease in the next 1 year and a potential patient suffering from cerebral apoplexy in the next 1 year;
(C9) Potential patients and healthy controls with different clinical characteristics of coronary heart disease occurring within the next 1 year;
(C10) Potential patients and healthy controls for developing different clinical characteristics of stroke within the next 1 year.
Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases and cancers are affected at present and once and the blood routine index is within the reference range.
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 A type and the B type are determined according to a specific mathematical model without convention.
In the foregoing aspects, the methylation level of the S100P gene may be the methylation level of all or part of CpG sites in the fragment of the S100P gene as shown in (e 1) - (e 2) below. The methylated S100P gene can be all or part of CpG sites in the fragments shown in (e 1) - (e 2) below in the S100P gene.
(E1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(e2) The DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto.
Further, the "all or part of CpG sites" may be all CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1) and all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2).
Or, the "all or part of CpG sites" may be all 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 points or any 1 of all CpG sites in the DNA fragment shown in the SEQ ID No. 2.
Or, the "all or part of CpG sites" may be all 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 eleven CpG sites in the DNA fragment shown in SEQ ID No. 2:
(f1) The DNA fragment shown in SEQ ID No.2 contains CpG sites (S100deg.P_B_1, 2) shown in positions 41-42 and 44-45 from the 5' end;
(f2) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (S100deg.P_B_3) at the 128-129 th position from the 5' end;
(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites (S100deg.P_B_5) from 278 to 279 positions of the 5' end;
(f4) The DNA fragment shown in SEQ ID No.2 has CpG sites (S100deg.P_B_7, 8) shown at positions 362-363 and 372-373 from the 5' end;
(f5) The CpG site (S100deg.P_B_9) shown in 379-380 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites (S100deg.P_B_12) from 473 th to 474 th positions of the 5' end;
(f7) The CpG site (S100deg.P_B_13) shown in 491-492 of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f8) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (S100deg.P_B_14) from 516-517 from the 5' end;
(f9) The CpG sites shown in the 542-543, 551-552 and 554-555 of the DNA fragment shown in SEQ ID No.2 (S100deg.P_B_15, 16, 17) from the 5' end;
(f10) The DNA fragment shown in SEQ ID No.2 shows CpG sites (S100deg.P_B_19) from 649-650 th position of the 5' end;
(f11) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 774-775 of the 5' end (S100deg.P_B_20).
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 4), and thus the methylation level analysis is performed, and related mathematical models are constructed and used. This is the case with (f 1), (f 4) and (f 9) described above.
In the above aspects, the substance for detecting the methylation level of the S100P gene comprises a primer combination for amplifying a full or partial fragment of the S100P gene. The reagent for detecting the methylation level of the S100P gene comprises a primer combination for amplifying the full or partial fragment of the S100P gene; the instrument for detecting the methylation level of the S100P gene 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 reagents for detecting the methylation level of the S100P 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 having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 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.2 or a DNA fragment comprising the same.
In the present invention, the primer combination may specifically be primer pair a and/or primer pair B;
the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 can be specifically a single-stranded DNA shown in SEQ ID No.3 or 11-35 nucleotides of SEQ ID No. 3; the primer A2 can be specifically a single-stranded DNA shown in SEQ ID No.4 or 32-56 nucleotides of SEQ ID No. 4;
The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 can be specifically single-stranded DNA shown in SEQ ID No.5 or 11-35 nucleotides of SEQ ID No. 5; the primer B2 can be specifically a single-stranded DNA shown in SEQ ID No.6 or 32-56 nucleotides of SEQ ID No. 6.
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:
(A1) Detecting S100P gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the A type and the B type, 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 S100P gene of the sample to be detected;
(B2) Substituting the S100P 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 A type and the B type are determined according to a specific mathematical model without convention.
The type a sample and the type B sample are any one of:
(C1) Potential patients with coronary heart disease in the next 2 years and healthy controls;
(C2) Potential patients with stroke in the next 2 years and healthy controls;
(C3) A potential patient suffering from coronary heart disease in the next 2 years and a potential patient suffering from cerebral apoplexy in the next 2 years;
(C4) Potential patients and healthy controls with different clinical characteristics of coronary heart disease in the next 2 years;
(C5) Potential patients and healthy controls for developing different clinical characteristics of stroke in the next 2 years;
(C6) Potential patients with coronary heart disease in the next 1 year and healthy controls;
(C7) Potential patients with stroke in the next 1 year and healthy controls;
(C8) A potential patient suffering from coronary heart disease in the next 1 year and a potential patient suffering from cerebral apoplexy in the next 1 year;
(C9) Potential patients and healthy controls with different clinical characteristics of coronary heart disease occurring within the next 1 year;
(C10) Potential patients and healthy controls for developing different clinical characteristics of stroke within the next 1 year.
Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases and cancers are affected at present and once and the blood routine index is within the reference range.
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 to xn are independent variables which are 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 to bn are weights given to the methylation values of each site by the model.
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. Two specific models established in the embodiments of the present invention are for assisting in distinguishing potential patients (coronary heart disease or cerebral stroke) with cardiovascular and cerebrovascular diseases in the next 2 years from healthy controls. The model one was specifically ：log(y/(1-y))＝-0.291-3.546*S100P_B_1,2+2.753*S100P_B_3-2.636*S100P_B_5+3.546*S100P_B_7,8+2.636*S100P_B_9+3.621*S100P_B_12-0.286*S100P_B_13-11.986*S100P_B_14+11.004*S100P_B_15,16,17-3.132*S100P_B_19-2.753*S100P_B_20-0.025* years +0.282 x sex (male assigned 1, female assigned 0) -0.075 x white blood cell count. The threshold for model one is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were potential patients with coronary heart disease in the next 2 years, and patient candidates less than 0.5 were healthy controls. The second model is specifically as follows: log (y/(1-y)) = -1.129+1.556 s100p_b_1,2+2.812 s100p_b_3-1.686
S100P_B_5-2.812*S100P_B_7,8+1.671*S100P_B_9-1.343*S100P_B_12-3.797*S100P_B_13+5.816*S100P_B_14-4.768*S100P_B_15,16,17+1.311*S100P_B_19-1.556*S100P_B_20+0.021* Age-0.175 x sex (male assigned 1, female assigned 0) -0.075 x white blood cell number. The threshold of the second model is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model are potential patients for stroke in the next 2 years, and patient candidates less than 0.5 are healthy controls. In the first model and the second model, S100deg.P_B_1, 2 is methylation level of CpG sites shown in positions 41-42 and 44-45 of the 5' end of the DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_3 is the methylation level of CpG sites shown in 128-129 th position of 5' end of DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_5 is methylation level of CpG sites shown in 278-279 position of 5' end of DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_7, 8 is the methylation level of the CpG sites shown in 362 th to 363 th and 372 th to 373 th of the 5' end of the DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_9 is the methylation level of CpG sites shown in 379-380 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_12 is methylation level of CpG sites shown in the 473 th-474 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_13 is the methylation level of the CpG site shown in 491-492 from the 5' end of the DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_14 is methylation level of CpG sites shown in 516-517 from 5' end of DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_15, 16,17 is the methylation level of CpG sites shown in 542 th to 543 th, 551 th to 552 th and 554 th to 555 th of the DNA fragment shown in SEQ ID No.2 from the 5' end; the S100deg.P_B_19 is the methylation level of CpG sites shown in 649-650 th position of 5' end of DNA fragment shown in SEQ ID No. 2; the S100deg.P_B_20 is methylation level of CpG sites shown in 774-775 th position of 5' end of DNA fragment shown in SEQ ID No. 2.
In the above aspects, the detecting the methylation level of the S100P gene is detecting the methylation level of the S100P gene in blood.
In the above aspects, when the type a sample or the type B sample is a coronary heart disease patient with different clinical characteristics in (C4) and (C9), the type a sample or the type B sample may specifically be any one of a occult or asymptomatic myocardial ischemia sample, an angina sample, a myocardial infarction patient sample, an ischemic cardiomyopathy patient sample, and a sudden death sample.
In the above aspects, when the type a sample or the type B sample is a stroke patient with different clinical characteristics in (C5) and (C10), the type a sample or the type B sample may be an ischemic stroke or an hemorrhagic stroke sample.
The S100P gene described above may specifically include Genbank accession No.: NM-005980.3 gene.
The invention provides a hypomethylation phenomenon of an S100P gene in blood of coronary heart disease and cerebral apoplexy. Experiments prove that the cardiovascular and cerebrovascular diseases (coronary heart disease or cerebral apoplexy) and the healthy control can be distinguished by taking blood as a sample, coronary heart disease patients with different clinical characteristics and the healthy control can be distinguished, and cerebral apoplexy 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 cardiovascular and cerebrovascular diseases 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 occurring in the next 2 years.
Fig. 3 illustrates a mathematical model of the occurrence of cerebral apoplexy in the next 2 years.
Detailed Description
The experimental methods used in the following examples are conventional methods unless otherwise specified.
Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
Example 1 primer design for detection of methylation site of S100P Gene
Through a number of sequence and functional analyses, two fragments (s100deg.P_A fragment and s100deg.P_B fragment) of the calcium-binding protein (S100P) gene were selected for methylation level and cardiovascular and cerebrovascular disease correlation analysis.
The S100deg.P_A fragment is located on the sense strand of hg19 reference genome chr4: 6694355-6695352.
The S100deg.P_B fragment is located on the sense strand of hg19 reference genome chr4: 6695337-6696281.
CpG site information in the S100deg.P_A fragment (SEQ ID No. 1) is shown in Table 1.
CpG site information in the S100deg.P_B fragment (SEQ ID No. 2) is shown in Table 2.
TABLE 1 CpG site information in S100P_A fragment
TABLE 2 CpG site information in S100P_B fragment
| CpG sites | Position of CpG sites in the sequence |
| S100P_B_1 | SEQ ID No.2 from positions 41-42 of the 5' end |
| S100P_B_2 | SEQ ID No.2 from position 44 to 45 of the 5' end |
| S100P_B_3 | SEQ ID No.2 from position 128-129 of the 5' end |
| S100P_B_4 | SEQ ID No.2 from 139 th to 140 th position of 5' end |
| S100P_B_5 | 278 Th to 279 th positions of SEQ ID No.2 from 5' end |
| S100P_B_6 | SEQ ID No.2 from position 328 to 329 of the 5' end |
| S100P_B_7 | 362 Th bit to 363 th bit of SEQ ID No.2 from 5' end |
| S100P_B_8 | 372-373 Th bit of SEQ ID No.2 from 5' end |
| S100P_B_9 | SEQ ID No.2 from 379 th to 380 th position of 5' end |
| S100P_B_10 | SEQ ID No.2 shows positions 386-387 from the 5' end |
| S100P_B_11 | SEQ ID No.2 from 5' end position 397-398 |
| S100P_B_12 | SEQ ID No.2 from position 473-474 of the 5' end |
| S100P_B_13 | SEQ ID No.2 from the 5' end at positions 491-492 |
| S100P_B_14 | SEQ ID No.2 from positions 516-517 of the 5' end |
| S100P_B_15 | SEQ ID No.2 from position 542-543 of the 5' end |
| S100P_B_16 | 551 St to 552 nd from the 5' end of SEQ ID No.2 |
| S100P_B_17 | SEQ ID No.2 from position 554-555 of the 5' end |
| S100P_B_18 | SEQ ID No.2 from position 618-619 of the 5' end |
| S100P_B_19 | SEQ ID No.2 from position 649-650 of the 5' end |
| S100P_B_20 | SEQ ID No.2 from positions 774-775 of the 5' end |
| S100P_B_21 | From the 5' end, positions 835-836 of SEQ ID No.2 |
| S100P_B_22 | SEQ ID No.2 from positions 857 to 858 of the 5' end |
| S100P_B_23 | SEQ ID No.2 shows positions 867-868 from the 5' end |
| S100P_B_24 | SEQ ID No.2 from position 893 to 894 of the 5' end |
Specific PCR primers were designed for two fragments (S100 P_A fragment and S100P_B fragment) as shown in Table 3. Wherein SEQ ID No.3 and SEQ ID No.5 are forward primers, and SEQ ID No.4 and SEQ ID No.6 are reverse primers; the 1 st to 10 th positions of the 5' end in SEQ ID No.3 and SEQ ID No.5 are nonspecific labels, and the 11 th to 35 th positions are specific primer sequences; SEQ ID No.4 and SEQ ID No.6 show non-specific tags at positions 1 to 31 from the 5' end and specific primer sequences at positions 32 to 56. The primer sequences do not contain SNPs and CpG sites.
TABLE 3 S100P methylation primer sequences
Example 2, detection of methylation of S100P Gene 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 time of 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 the following date of 2018 and 7 months, the incidence of cardiovascular and cerebrovascular diseases is 620, new cardiovascular and cerebrovascular diseases patients in 2 years after the patients are queued as case groups, wherein 342 cases are coronary heart disease patients, and 278 cases are cerebral apoplexy patients. After age and sex matching, the population without cardiovascular and cerebrovascular diseases and cancers and with blood routine indexes within the reference range is selected as healthy control, and total is 612.
All patient ex vivo blood samples were collected at the time of group entry and 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.
278 Patients suffering from cerebral apoplexy within 2 years after group entry are classified according to clinical typing: 112 cases of cerebral arterial thrombosis and 166 cases of cerebral arterial thrombosis. Of these, 110 cases developed cerebral apoplexy within 1 year after the group, including 49 cases of hemorrhagic cerebral apoplexy and 61 cases of ischemic cerebral apoplexy.
The median of the ages of healthy controls, coronary heart disease and stroke patients were 65, 64 and 65 years, respectively, and the ratio of men and women in each of these 3 populations was about 1:1. Median age of patients suffering from coronary heart disease and cerebral apoplexy within 1 year after the administration is 65 and 64 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 cytosine (C) is converted to uracil (U), while methylated cytosine remains unchanged, i.e., the C base of the original CpG site is converted to C or U after bisulfite treatment.
3. And 2 pairs of specific primers in the table 3 are adopted to carry out PCR amplification by using the DNA treated by the bisulfite in the step 2 as a template according to a reaction system required by a conventional PCR reaction by DNA polymerase, 2 pairs of primers are all adopted by the same conventional PCR system, and 2 pairs of primers are all 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) To 5. Mu.l of the PCR product was added 2. Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [0.5U ] +1.7ml H 2 O) and 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 a5 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 from Nanodispenser robot;
(5) Time-of-flight mass spectrometry; the data obtained were collected with SpectroACQUIRE v3.3.1.3 software and visualized by MASSARRAY EPITYPER V1.2 software.
Reagents used for the time-of-flight mass spectrometry were all from a Kit (T-CLEAVAGE MASSCLEAVE REAGENT Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection isAnalyzer Chip Prep Module 384, model number 384: 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 23.0.0.
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, a total of 39 distinguishable peak patterns of methylated fragments were obtained. Methylation levels were calculated using SpectroACQUIRE v3.3.1.3 software based on peak area comparisons of methylated and unmethylated fragments (SpectroACQUIRE v3.3.1.3 software can automatically calculate peak areas to obtain methylation levels for each sample at each CpG site).
3. Analysis of results
1. Healthy controls, coronary heart disease and cerebral apoplexy patients with blood methylation level difference of S100P gene (2 years earlier than clinical onset time)
The methylation level of all CpG sites in the S100P gene was analyzed using blood of 342 coronary heart disease patients, 278 cerebral apoplexy patients and 612 healthy controls as a study material (Table 4), wherein both coronary heart disease and cerebral apoplexy patients were asymptomatic when they were put into the group, and developed within 2 years after the group was put into the group. The results show that the methylation level median of the S100P gene of the healthy control is 0.55 (IQR=0.41-0.72), the methylation level median of the S100P gene of cerebral apoplexy is 0.51 (IQR=0.39-0.67), and the methylation level median of coronary heart disease patients is 0.49 (IQR=0.37-0.65). As a result of comparative analysis of the methylation levels of the S100P gene among the three, it was found that the methylation levels of all CpG sites in the S100P gene of the cerebral apoplexy patient and the coronary heart disease patient were significantly lower than that of the healthy control (P <0.05, table 4), respectively. Furthermore, the methylation level of all CpG sites in the S100P gene was significantly lower in patients with coronary heart disease than in patients with stroke (P <0.05, table 4). Therefore, the methylation level of the S100P gene can be used for screening potential patients with cerebral apoplexy and coronary heart disease in the future 2 years in the population, and is a molecular marker with high clinical value.
2. Healthy controls, coronary heart disease and cerebral apoplexy patients with blood S100P gene methylation level difference (1 year earlier than clinical onset time)
Blood of 137 patients with coronary heart disease, 110 patients with cerebral apoplexy and 612 healthy controls are used as research materials to analyze methylation level differences of all CpG sites in S100P genes among the three (table 5), wherein the patients with coronary heart disease and cerebral apoplexy have no symptoms when entering the group, and the patients with coronary heart disease and cerebral apoplexy are ill within 1 year after entering the group. The results show that the methylation level median of the S100P gene of the healthy control is 0.55 (IQR=0.41-0.72), the methylation level median of the S100P gene of cerebral apoplexy is 0.52 (IQR=0.40-0.67), and the methylation level median of coronary heart disease patients is 0.48 (IQR=0.37-0.65). As a result of comparative analysis of methylation levels of the S100P genes of the three, it was found that methylation levels of all CpG sites in the S100P genes of the patients suffering from cerebral apoplexy and coronary heart disease were significantly lower than those of the healthy control (P <0.05, table 5). Furthermore, the methylation level of all CpG sites in the S100P gene was significantly lower in patients with coronary heart disease than in patients with stroke (P <0.05, table 5). Therefore, the methylation level of the S100P gene can be used for screening potential patients suffering from cerebral apoplexy and coronary heart disease within 1 year in the population, and is a molecular marker with high clinical value.
3. Methylation level differences between healthy controls and coronary heart disease and cerebral stroke of different clinical characteristics (2 years earlier than clinical onset time)
We compared and analyzed the methylation level difference of S100P genes of 342 patients with coronary heart disease, 278 patients with cerebral apoplexy and 612 healthy controls with different clinical characteristics, wherein the patients with coronary heart disease and cerebral apoplexy are asymptomatic when entering the group, and the patients with coronary heart disease and cerebral apoplexy are ill within 2 years after entering the group. 342 patients with coronary heart disease are classified according to clinical characteristics: 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. 278 cerebral apoplexy patients are divided according to clinical typing: 112 cases of cerebral arterial thrombosis and 166 cases of cerebral arterial thrombosis. As a result of comparative analysis of methylation levels of the S100P gene in 342 patients with coronary heart disease having different clinical characteristics and 612 healthy controls, it was found that methylation levels of all CpG sites of the S100P gene in patients with coronary heart disease having different clinical characteristics (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) were significantly different from those in healthy controls (P <0.05, table 6). Furthermore, we found that methylation levels of all CpG sites in the S100P gene were significantly different from healthy controls in stroke patients with different clinical characteristics (hemorrhagic stroke, ischemic stroke) (P <0.05, table 6).
4. Methylation level differences between healthy controls and coronary heart disease and cerebral stroke of different clinical characteristics (1 year earlier than clinical onset time)
We compared and analyzed the methylation level difference of S100P genes of 137 patients with coronary heart disease, 110 patients with cerebral apoplexy and 612 healthy controls with different clinical characteristics, wherein the patients with coronary heart disease and cerebral apoplexy are asymptomatic when entering the group, and the patients with coronary heart disease and cerebral apoplexy are ill within 1 year after entering the group. 137 patients with coronary heart disease are classified according to clinical characteristics: hidden or asymptomatic myocardial ischemia 20 cases, angina 21 cases, myocardial infarction 33 cases, ischemic cardiomyopathy 30 cases, and sudden death 33 cases. 110 cerebral stroke patients are classified according to clinical typing: cerebral arterial thrombosis 49 cases and cerebral arterial thrombosis 61 cases. As a result of comparative analysis of methylation levels of the S100P gene in 137 patients with coronary heart disease having different clinical characteristics and 612 healthy controls, it was found that methylation levels of all CpG sites of the S100P gene in patients with coronary heart disease having different clinical characteristics (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) were significantly different from those in healthy controls (P <0.05, table 7). Furthermore, we found that methylation levels of all CpG sites in the S100P gene were significantly different from healthy controls in stroke patients with different clinical characteristics (hemorrhagic stroke, ischemic stroke) (P <0.05, table 7). Thus, the methylation level of the S100P gene can be used to predict the likelihood of developing coronary heart disease and stroke disease of different clinical characteristics over a1 year period.
5. Establishment of mathematical model for assisting cardiovascular and cerebrovascular disease diagnosis
The mathematical model established by the invention can be used for achieving the following purposes:
(1) Before clinical onset, individuals with coronary heart disease onset risk in the crowd are pre-warned.
(2) Before clinical onset, individuals with coronary heart disease onset risk in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases.
(3) Before clinical onset, individuals with cerebral apoplexy incidence risks in the crowd are pre-warned.
(4) Before clinical onset, individuals with cerebral apoplexy incidence risks in the crowd are pre-warned, and the method is suitable for cerebral apoplexy of various types.
(5) Before clinical onset, individuals with risks of cerebral apoplexy and coronary heart disease are pre-warned, and coronary heart disease patients and cerebral apoplexy patients are distinguished.
Wherein, the individuals with coronary heart disease incidence risk can be specifically coronary heart disease patients with coronary heart disease which are earlier than clinical incidence time within 2 years or within 1 year (namely, coronary heart disease can be clinically diagnosed within 2 years or within 1 year). The individual at risk of developing cerebral apoplexy may specifically be a cerebral apoplexy patient who is earlier than the clinical onset time by 2 years or within 1 year (i.e., a cerebral apoplexy can be clinically diagnosed within 2 years or within 1 year).
The mathematical model is established as follows:
(A) Data sources: the methylation level of the target CpG sites (combination of one or more of tables 1-2) of the isolated blood samples of 342 coronary heart disease patients, 278 cerebral stroke patients and 612 healthy controls listed in step one (detection method is the 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, i.e., training sets, are selected as needed, (e.g.: the method comprises the steps of controlling potential patients and healthy controls of coronary heart disease in the future 2 years, controlling potential patients and healthy controls of cerebral apoplexy in the future 2 years, controlling potential patients and healthy controls of coronary heart disease in the future 2 years, controlling potential patients and healthy controls of latent or asymptomatic myocardial ischemia in the future 2 years, controlling potential patients and healthy controls of angina pectoris in the future 2 years, controlling potential patients and healthy controls of myocardial infarction in the future 2 years, controlling potential patients and healthy controls of ischemic cardiomyopathy in the future 2 years, controlling potential patients and healthy controls of sudden death in the future 2 years, controlling potential patients and healthy controls of ischemic brain stroke in the future 2 years, controlling potential patients and healthy controls of coronary heart disease in the future 1 year, controlling potential patients and ischemic myocardial infarction in the future 1 year, controlling potential patients and healthy controls of ischemic heart disease in the future 1 year, controlling potential patients and ischemic heart disease in the future 1 year, controlling potential patients and healthy controls of ischemic heart disease in the future 1 year, controlling potential patients and healthy controls of ischemic heart disease in the future 1 year; wherein, the healthy control can be understood as that cardiovascular and cerebrovascular diseases and cancers are not affected at present and once and blood routine indexes are in a reference range), and statistical software such as SAS, R, SPSS and the like is used for establishing a mathematical model through formulas by using a statistical method of two-class logistic regression. The numerical value corresponding to the maximum approximate dengue index calculated by the mathematical model formula is a threshold value or is directly set to be 0.5 as the threshold value, the detection index obtained by the sample to be tested after the sample is tested and substituted into the model calculation is more than the threshold value and is classified into one type (B type), less than the threshold value and is 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 belongs to, firstly, detecting methylation levels of one or more CpG sites on an S100P 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 step simultaneously substitutes specific numerical values of corresponding parameters of the sample to be detected into a model formula), calculating to obtain a detection index corresponding to the sample to be detected, and then comparing the detection index corresponding to the sample to be detected with a threshold value, and determining which type of sample the sample to be detected belongs to according to a comparison result.
Examples: as shown in fig. 1, the methylation level of a single CpG site or the methylation level of a combination of multiple CpG sites in the training set S100P gene is used to establish a mathematical model for distinguishing between class a and class B by using a formula of two classification logistic regression through SAS, R, SPSS et al statistical software. 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 after substituting a dependent variable, i.e., a methylation value of one or more methylation sites of a sample to be tested, into a model, b0 is a constant, x1 to xn are independent variables, i.e., methylation values (each value is a value between 0 and 1) of one or more methylation sites of the sample to be tested, and b1 to bn are weights given to each methylation 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 classifications, which group a is class B, which group is to be determined according to a specific mathematical model, no convention is made here), such as: the method comprises the steps of controlling potential patients and healthy controls of coronary heart disease in the future 2 years, controlling potential patients and healthy controls of cerebral apoplexy in the future 2 years, controlling potential patients and healthy controls of coronary heart disease in the future 2 years, controlling potential patients and healthy controls of latent or asymptomatic myocardial ischemia in the future 2 years, controlling potential patients and healthy controls of angina pectoris in the future 2 years, controlling potential patients and healthy controls of myocardial infarction in the future 2 years, controlling potential patients and healthy controls of ischemic cardiomyopathy in the future 2 years, controlling potential patients and healthy controls of sudden death in the future 2 years, controlling potential patients and healthy controls of ischemic brain stroke in the future 2 years, controlling potential patients and healthy controls of coronary heart disease in the future 1 year, controlling potential patients and ischemic myocardial infarction in the future 1 year, controlling potential patients and healthy controls of ischemic heart disease in the future 1 year, controlling potential patients and ischemic heart disease in the future 1 year, controlling potential patients and healthy controls of ischemic heart disease in the future 1 year, controlling potential patients and healthy controls of ischemic heart disease in the future 1 year; wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases and cancers are affected at present and once and the blood routine index is within the reference range. 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 S100P 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 S100P gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is larger than the 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 S100P 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 S100P 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 (S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and s100deg.P_B_20) of s100deg.P_B and mathematical modeling for discrimination of coronary heart disease: the data of methylation levels of the 11 distinguishable CpG site combinations of S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and S100deg.P_B_20 detected in the potential patients with coronary heart disease in the next 2 years (earlier than the clinical onset time is less than or equal to 2 years) and the healthy control training set (342 coronary heart disease patients and 612 healthy controls here) and the ages, sexes (male assigned 1 and female assigned 0) of the patients are used, and the white blood cell count is used for establishing a mathematical model for distinguishing the potential patients with coronary heart disease in the next 2 years and the healthy controls by using a formula of a two-class logistic regression through R software. The mathematical model is here a two-class logistic regression model, from which the constants b0 of the mathematical model and the weights b1 to bn of the individual methylation sites are determined, in this case in particular ：log(y/(1-y))＝-0.291-3.546*S100P_B_1,2+2.753*S100P_B_3-2.636*S100P_B_5+3.546*S100P_B_7,8+2.636*S100P_B_9+3.621*S100P_B_12-0.286*S100P_B_13-11.986*
S100p_b_14+11.004×s100p_b_15,16,17-3.132×s100p_b_19-2.753×s100p_b_20-0.025×age+0.282×sex (male assigned 1 and female assigned 0) -0.075×white blood cell count, where y is the detection index obtained after substituting the methylation value of 11 distinguishable methylation sites of the sample to be tested and the age, sex, white blood cell count into the model as a function of the variables. Under the condition that 0.5 is set as a threshold value, the methylation levels of the S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and S100deg.P_B_20 distinguishable CpG sites of the sample to be tested are tested and then are substituted into the model together with information of age, sex and white blood cell count of the sample to be tested, the obtained detection index, namely y value, is more than 0.5 and is classified as a potential patient suffering from coronary heart disease in the coming 2 years, less than 0.5 is classified as a healthy control, and if the y value is equal to 0.5, the potential patient suffering from coronary heart disease in the coming 2 years is not determined as the healthy control. The area under the curve (AUC) calculation for this model was 0.72 (table 12).
Examples: the schematic diagram of fig. 3 illustrates methylation of the preferred CpG sites (S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and s100deg.P_B_20) of s100deg.P_B and mathematical modeling for stroke discrimination: the data of methylation levels of the 11 distinguishable preferred CpG site combinations that have been detected in the potential patients with stroke in the next 2 years (earlier than the clinical onset time less than or equal to 2 years) and the healthy control training set (here: 278 stroke patients and 612 healthy controls), as well as the age, sex (male assigned 1, female assigned 0) of the patients, and white blood cell count were used to build a mathematical model for distinguishing stroke patients from healthy controls by R software using a formula of two-class logistic regression. The mathematical model is here a two-class logistic regression model, from which the constants b0 of the mathematical model and the weights b1 to bn of the individual methylation sites are determined, in this case in particular ：log(y/(1-y))＝-1.129+1.556*S100P_B_1,2+2.812*S100P_B_3-1.686*S100P_B_5-2.812*S100P_B_7,8+1.671*S100P_B_9-1.343*S100P_B_12-3.797*S100P_B_13
+5.816×S100p_b_14-4.768×s100p_b_15,16,17+1.311×s100p_b_19-1.556×s100p_b_20+0.021×age-0.175×sex (male assigned 1, female assigned 0) -0.075×white blood cell count, where y is the detection index obtained by substituting the methylation value of 11 distinguishable methylation sites of the sample to be tested, and the age, sex, white blood cell count into the model as a function of the variables. Under the condition that 0.5 is set as a threshold value, the methylation levels of the S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and S100deg.P_B_20 distinguishable CpG sites of the sample to be tested are tested and then are substituted into the model together with information of age, sex and white blood cell count of the sample to be tested, the obtained detection index, namely y value, is more than 0.5 and is classified as a potential patient suffering from cerebral apoplexy in the coming 2 years, less than 0.5 is classified as a healthy control, and if the y value is equal to 0.5, the potential patient suffering from cerebral apoplexy in the coming 2 years is not determined as the healthy control. The area under the curve (AUC) calculation for this model was 0.71 (table 12).
Blood was collected from two subjects (a, B) and DNA was extracted, and after the extracted DNA was converted by bisulfite, methylation levels of 11 distinguishable CpG sites, S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and s100deg.P_B_20, were measured in subjects using a 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 more than 0.81 and is more than 0.5, and the first test subject is judged to be a potential patient with coronary heart disease or cerebral apoplexy in the next 2 years (clinical onset in 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 (clinical onset is not caused in the next 2 years). The detection result is consistent with the actual situation.
(C) Model Effect evaluation
According to the above method, a potential patient and a healthy control for the occurrence of coronary heart disease in the coming 2 years, a potential patient and a healthy control for the occurrence of cerebral apoplexy in the coming 2 years, a potential patient and a healthy control for the occurrence of coronary heart disease in the coming 2 years, a potential patient and a healthy control for the occurrence of latent or asymptomatic myocardial ischemia in the coming 2 years, a potential patient and a healthy control for the occurrence of angina pectoris in the coming 2 years, a potential patient and a healthy control for the occurrence of myocardial infarction in the coming 2 years, a potential patient and a healthy control for the occurrence of ischemic cardiomyopathy in the coming 2 years, a potential patient and a healthy control for the occurrence of sudden death in the coming 2 years, a potential patient and a healthy control for the occurrence of ischemic cerebral apoplexy in the coming 2 years are established, respectively, potential patients and healthy controls for developing coronary heart disease in the coming 1 year, potential patients and healthy controls for developing cerebral stroke in the coming 1 year, potential patients and cerebral stroke patients for developing coronary heart disease in the coming 1 year, potential patients and healthy controls for developing latent or asymptomatic myocardial ischemia in the coming 1 year, potential patients and healthy controls for developing angina in the coming 1 year, potential patients and healthy controls for developing myocardial infarction in the coming 1 year, potential patients and healthy controls for developing ischemic cardiomyopathy in the coming 1 year, potential patients and healthy controls for developing sudden death in the coming 1 year, potential patients and healthy controls for developing ischemic stroke in the coming 1 year (wherein, the healthy control can be understood as a mathematical model that does not suffer from cardiovascular and cerebrovascular diseases and cancers at present and has been free from blood routine index within the reference range), and its effectiveness is evaluated by a subject curve (ROC curve). The larger the area under the curve (AUC) from the ROC curve, the better the differentiation of the model, the more efficient the molecular marker. The evaluation results after construction of mathematical models using different CpG sites are shown in tables 8, 9, 10 and 11. In tables 8, 9, 10 and 11, 1 CpG site represents the site of any one CpG site in the S100deg.P_B amplified fragment, 2 CpG sites represent the combination of any 2 CpG sites in S100deg.P_B, 3 CpG sites represent the combination of any 3 CpG sites in S100deg.P_B, … … 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 S100P gene for each group (potential patients and healthy controls for coronary heart disease in the coming 2 years, potential patients and healthy controls for cerebral apoplexy in the coming 2 years, potential patients and healthy controls for coronary heart disease in the coming 2 years, potential patients and healthy controls for latent or asymptomatic myocardial ischemia in the coming 2 years, potential patients and healthy controls for angina pectoris in the coming 2 years, potential patients and healthy controls for myocardial infarction in the coming 2 years, potential patients and healthy controls for ischemic cardiomyopathy in the coming 2 years, potential patients and healthy controls for sudden death in the coming 2 years, potential patients and healthy controls for ischemic cerebral apoplexy in the coming 2 years, potential patients and healthy controls for developing coronary heart disease in the coming 1 year, potential patients and healthy controls for developing cerebral stroke in the coming 1 year, potential patients and cerebral stroke patients for developing coronary heart disease in the coming 1 year, potential patients and healthy controls for developing latent or asymptomatic myocardial ischemia in the coming 1 year, potential patients and healthy controls for developing angina in the coming 1 year, potential patients and healthy controls for developing myocardial infarction in the coming 1 year, potential patients and healthy controls for developing ischemic cardiomyopathy in the coming 1 year, potential patients and healthy controls for developing sudden death in the coming 1 year, potential patients and healthy controls for developing ischemic stroke in the coming 1 year; wherein, the healthy controls can be understood as having no cardiovascular and cerebrovascular disease and cancer and blood normative indicators within the reference range) both now and once) increased with increasing number of sites.
In addition, among the CpG sites shown in tables 1 to 2, 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 11 distinguishable CpG site combinations, such as S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and S100deg.P_B_20 shown in Table 12, table 13, table 14 and Table 15, are preferred sites for any eleven combinations in S100deg.P_B.
In summary, the CpG sites on the S100P gene and various combinations thereof, the CpG sites on the S100P_A fragment and various combinations thereof, the CpG sites on the S100P_B fragment and various combinations thereof, the S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17、S100P_B_19 and S100P_B_20 CpG sites on the S100P_B fragment and various combinations thereof, and the methylation levels of the CpG sites on the S100P_A and S100P_B are used for potential patients and healthy controls of coronary heart disease in the future 2 years, potential patients and healthy controls of cerebral apoplexy in the future 2 years, potential patients and healthy controls of latent or asymptomatic myocardial ischemia in the future 2 years, potential patients and healthy controls of angina pectoris in the future 2 years, potential patients and healthy controls of ischemic cardiomyopathy in the future 2 years, potential patients and healthy controls of sudden cerebral death in the future 2 years, potential patients and healthy controls of cerebral infarction in the future 2 years, potential patients and healthy controls for developing coronary heart disease in the future 1 year, potential patients and healthy controls for developing cerebral stroke in the future 1 year, potential patients and cerebral stroke patients for developing coronary heart disease in the future 1 year, potential patients and healthy controls for developing latent or asymptomatic myocardial ischemia in the future 1 year, potential patients and healthy controls for developing angina pectoris in the future 1 year, potential patients and healthy controls for developing myocardial infarction in the future 1 year, potential patients and healthy controls for developing ischemic cardiomyopathy in the future 1 year, potential patients and healthy controls for developing sudden death in the future 1 year, and, potential patients and healthy controls with the occurrence of the cerebral arterial thrombosis in the future 1 year, and potential patients and healthy controls with the occurrence of the cerebral arterial thrombosis in the future 1 year (wherein the healthy controls can be understood as having no cardiovascular and cerebrovascular diseases and cancers at present and once and blood routine indexes are within a reference range).
Table 4 compares methylation level differences between healthy controls, coronary heart disease and stroke patients (earlier than clinical onset time less than or equal to 2 years)
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Table 5 compares methylation level differences between healthy controls, coronary heart disease and stroke patients (earlier than clinical onset time less than or equal to 1 year)
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Table 6 compares methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (earlier than clinical onset time. Ltoreq.2 years)
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Table 7 compares methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (earlier than clinical onset time. Ltoreq.1 year)
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Table 8 CpG sites of S100P_B and combinations thereof for distinguishing healthy controls and cerebral apoplexy, healthy controls and coronary heart disease, cerebral apoplexy and coronary heart disease (earlier than clinical onset time less than or equal to 2 years)
Table 9 CpG sites of S100P_B and combinations thereof are used for distinguishing healthy controls and cerebral apoplexy, healthy controls and coronary heart disease, cerebral apoplexy and coronary heart disease (earlier than clinical onset time less than or equal to 1 year)
CpG sites of Table 10 S100P_B and combinations thereof are used for distinguishing healthy control and coronary heart disease patients with cerebral apoplexy with different clinical characteristics (earlier than clinical onset time less than or equal to 2 years)
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Note that: the data in the table are area under the curve (AUC).
Table 11 CpG sites of S100P_B and combinations thereof for distinguishing healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (earlier than clinical onset time less than or equal to 1 year)
Note that: the data in the table are area under the curve (AUC).
Table 12 optimal CpG sites of S100P_B and combinations thereof for distinguishing healthy controls and cerebral apoplexy, healthy controls and coronary heart disease, cerebral apoplexy and coronary heart disease (earlier than clinical onset time less than or equal to 2 years)
Table 13 S100P_B optimal CpG sites and combinations thereof for distinguishing healthy controls and cerebral apoplexy, healthy controls and coronary heart disease, cerebral apoplexy and coronary heart disease (earlier than clinical onset time less than or equal to 1 year)
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Table 14 S100P_B optimal CpG sites and combinations thereof for distinguishing healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (earlier than clinical onset time less than or equal to 2 years)
Note that: the data in the table are area under the curve (AUC).
Table 15 S100P_B optimal CpG sites and combinations thereof for distinguishing healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (earlier than clinical onset time less than or equal to 1 year)
Note that: the data in the table are area under the curve (AUC).
<110> Nanjing Techno Biotechnology Co., ltd
<120> Calbindin Gene methylation as a potential marker for early diagnosis of cardiovascular and cerebrovascular diseases
<130> GNCLN200786
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<210> 3
<211> 35
<212> DNA
<213> Artificial sequence
<400> 3
aggaagagag ggggtaggat gtgaatatag gtttt 35
<210> 4
<211> 56
<212> DNA
<213> Artificial sequence
<400> 4
cagtaatacg actcactata gggagaaggc tctcattcct aaccctccct aaataa 56
<210> 5
<211> 35
<212> DNA
<213> Artificial sequence
<400> 5
aggaagagag gagggttagg aatgaggatg ttatt 35
<210> 6
<211> 56
<212> DNA
<213> Artificial sequence
<400> 6
cagtaatacg actcactata gggagaaggc taaacccact aataccaaac aacaaa 56
Claims (10)
1. Use of a substance for detecting the methylation level of the S100P gene in the preparation of a product; the application of the product is at least one of the following:
(1) Early warning of coronary heart disease prior to clinical symptoms;
(2) Early warning of stroke prior to clinical symptoms;
(3) Auxiliary distinguishing of coronary heart disease potential patients and cerebral apoplexy potential patients;
The testee for early warning the coronary heart disease before clinical symptoms is a potential coronary heart disease patient or a healthy control;
The person to be detected for early warning of cerebral apoplexy before clinical symptoms is a potential cerebral apoplexy patient or a healthy control;
The potential patients with coronary heart disease are potential patients with coronary heart disease occurring within 2 years or 1 year in the future;
The potential cerebral apoplexy patient is a potential cerebral apoplexy patient occurring within 2 years or 1 year in the future;
the methylation level of the S100P gene is the methylation level of all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
2. Use of a substance for detecting the methylation level of the S100P 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:
(1) Early warning of coronary heart disease prior to clinical symptoms;
(2) Early warning of stroke prior to clinical symptoms;
(3) Auxiliary distinguishing of coronary heart disease potential patients and cerebral apoplexy potential patients;
The testee for early warning the coronary heart disease before clinical symptoms is a potential coronary heart disease patient or a healthy control;
The person to be detected for early warning of cerebral apoplexy before clinical symptoms is a potential cerebral apoplexy patient or a healthy control;
The potential patients with coronary heart disease are potential patients with coronary heart disease occurring within 2 years or 1 year in the future;
The potential cerebral apoplexy patient is a potential cerebral apoplexy patient occurring within 2 years or 1 year in the future;
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting S100P gene methylation levels of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P 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;
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of an S100P gene of a sample to be detected;
(B2) Substituting the S100P 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:
(C1) Potential patients with coronary heart disease in the next 2 years and healthy controls;
(C2) Potential patients with stroke in the next 2 years and healthy controls;
(C3) A potential patient suffering from coronary heart disease in the next 2 years and a potential patient suffering from cerebral apoplexy in the next 2 years;
(C4) Potential patients and healthy controls with different clinical characteristics of coronary heart disease in the next 2 years;
The coronary heart disease with different clinical characteristics is latent myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy or sudden death;
(C5) Potential patients and healthy controls for developing different clinical characteristics of stroke in the next 2 years;
the cerebral apoplexy with different clinical characteristics is ischemic cerebral apoplexy or hemorrhagic cerebral apoplexy;
(C6) Potential patients with coronary heart disease in the next 1 year and healthy controls;
(C7) Potential patients with stroke in the next 1 year and healthy controls;
(C8) A potential patient suffering from coronary heart disease in the next 1 year and a potential patient suffering from cerebral apoplexy in the next 1 year;
the methylation level of the S100P gene is the methylation level of all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
3. 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:
(1) Early warning of coronary heart disease prior to clinical symptoms;
(2) Early warning of stroke prior to clinical symptoms;
(3) Auxiliary distinguishing of coronary heart disease potential patients and cerebral apoplexy potential patients;
The testee for early warning the coronary heart disease before clinical symptoms is a potential coronary heart disease patient or a healthy control;
The person to be detected for early warning of cerebral apoplexy before clinical symptoms is a potential cerebral apoplexy patient or a healthy control;
The potential patients with coronary heart disease are potential patients with coronary heart disease occurring within 2 years or 1 year in the future;
The potential cerebral apoplexy patient is a potential cerebral apoplexy patient occurring within 2 years or 1 year in the future;
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting S100P gene methylation levels of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P 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;
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of an S100P gene of a sample to be detected;
(B2) Substituting the S100P 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:
(C1) Potential patients with coronary heart disease in the next 2 years and healthy controls;
(C2) Potential patients with stroke in the next 2 years and healthy controls;
(C3) A potential patient suffering from coronary heart disease in the next 2 years and a potential patient suffering from cerebral apoplexy in the next 2 years;
(C4) Potential patients and healthy controls with different clinical characteristics of coronary heart disease in the next 2 years;
The coronary heart disease with different clinical characteristics is latent myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy or sudden death;
(C5) Potential patients and healthy controls for developing different clinical characteristics of stroke in the next 2 years;
the cerebral apoplexy with different clinical characteristics is ischemic cerebral apoplexy or hemorrhagic cerebral apoplexy;
(C6) Potential patients with coronary heart disease in the next 1 year and healthy controls;
(C7) Potential patients with stroke in the next 1 year and healthy controls;
(C8) A potential patient suffering from coronary heart disease in the next 1 year and a potential patient suffering from cerebral apoplexy in the next 1 year;
the methylation level of the S100P gene is the methylation level of all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
4. A use according to any one of claims 1-3, characterized in that: the clinical symptoms are preceded by a2 year or 1 year period prior to clinical onset.
5. Use according to claim 1 or 2, characterized in that: the substance for detecting the methylation level of the S100P gene is a primer combination.
6. The use according to claim 5, characterized in that: the primer combination is a primer pair A and a primer pair B;
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.3 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 3; the primer A2 is SEQ ID No.4 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 4;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.5 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 5; the primer B2 is SEQ ID No.6 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 6.
7. A system, comprising:
(D1) Reagents and/or instrumentation for detecting the methylation level of the S100P gene;
(D2) A device comprising 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 used for acquiring S100P gene methylation level data of n 1A type samples and n 2B type samples obtained by the detection of the (D1);
The data analysis processing module can establish a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type based on S100P gene methylation level data of n 1A type samples and n 2B type samples acquired by the data acquisition module;
the model output module is used for outputting the mathematical model established by the data analysis processing module;
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 used for inputting the S100P gene methylation level data of the to-be-detected person obtained by the detection of (D1);
The data operation module is used for substituting the S100P gene methylation level data of the testee into the mathematical model, and calculating to obtain a detection index;
The data comparison module is used for comparing the detection index with a threshold value;
the conclusion output module is used for outputting a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result of the data comparison module;
the type a sample and the type B sample are any one of the following:
(C1) Potential patients with coronary heart disease in the next 2 years and healthy controls;
(C2) Potential patients with stroke in the next 2 years and healthy controls;
(C3) A potential patient suffering from coronary heart disease in the next 2 years and a potential patient suffering from cerebral apoplexy in the next 2 years;
(C4) Potential patients and healthy controls with different clinical characteristics of coronary heart disease in the next 2 years;
The coronary heart disease with different clinical characteristics is latent myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy or sudden death;
(C5) Potential patients and healthy controls for developing different clinical characteristics of stroke in the next 2 years;
the cerebral apoplexy with different clinical characteristics is ischemic cerebral apoplexy or hemorrhagic cerebral apoplexy;
(C6) Potential patients with coronary heart disease in the next 1 year and healthy controls;
(C7) Potential patients with stroke in the next 1 year and healthy controls;
(C8) A potential patient suffering from coronary heart disease in the next 1 year and a potential patient suffering from cerebral apoplexy in the next 1 year;
the methylation level of the S100P gene is the methylation level of all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
8. The system according to claim 7, wherein: the clinical symptoms are preceded by a2 year or 1 year period prior to clinical onset.
9. The system according to claim 7, wherein: the reagent for detecting the methylation level of the S100P gene is a primer combination.
10. The system according to claim 9, wherein: the primer combination is a primer pair A and a primer pair B;
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.3 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 3; the primer A2 is SEQ ID No.4 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 4;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.5 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 5; the primer B2 is SEQ ID No.6 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 6.
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| WO2011106322A2 (en) * | 2010-02-23 | 2011-09-01 | The Govt. Of The U.S.A. As Represented By The Secretary, Department Of Health And Human Services. | Biomarkers for acute ischemic stroke |
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| WO2011106322A2 (en) * | 2010-02-23 | 2011-09-01 | The Govt. Of The U.S.A. As Represented By The Secretary, Department Of Health And Human Services. | Biomarkers for acute ischemic stroke |
| EP2915883A1 (en) * | 2014-03-07 | 2015-09-09 | Ruprecht-Karls-Universität Heidelberg | Non-invasive assay for early detection of cancer |
| WO2019081507A1 (en) * | 2017-10-23 | 2019-05-02 | Universität Heidelberg | Novel blood-derived markers for the detection of cancer |
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