CN116287196A - Cardiovascular and cerebrovascular disease identification marker and application thereof - Google Patents
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Abstract
The invention discloses a cardiovascular and cerebrovascular disease identification marker and application thereof. The cardiovascular and cerebrovascular disease identification marker provided by the invention is a methylation biomarker, and the application of the marker is at least one of the following: auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases (such as coronary heart disease and cerebral apoplexy) before clinical onset, auxiliary evaluation of heart injury and cardiovascular and cerebrovascular prognosis monitoring. The invention discloses hypomethylation of RPTOR genes in blood of coronary heart disease patients, heart injury patients and coronary heart disease recurrence patients and hypermethylation of RPTOR genes in blood of cerebral apoplexy patients and cerebral apoplexy recurrence patients, and the blood is taken as a sample to early warn and distinguish cardiovascular and cerebrovascular patients with different clinical characteristics from healthy controls, heart injury and cardiovascular and cerebrovascular disease recurrence and non-recurrence with different clinical characteristics before clinical onset.
Description
Technical Field
The invention relates to the field of medicine, in particular to a cardiovascular and cerebrovascular disease identification marker and application thereof.
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.
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 can be 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 caused by coronary occlusion; (4) ischemic cardiomyopathy: myocardial fibrosis due to chronic myocardial ischemia or necrosis manifests as increased heart, heart failure and arrhythmia; (5) sudden death: death due to sudden cardiac arrest is often caused by severe arrhythmia due to local electrophysiological disturbances in the ischemic myocardium. 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 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, these markers are not widely accepted clinically.
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 reasons; 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 coronary heart disease diseases can be prevented and treated, and are generally prevented by improving consciousness through popularization knowledge, avoiding exogenous stimulus factors and reasonably diet and moderately moving, and the treatment effect is greatly dependent on early diagnosis and corresponding intervention measures. 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.
Disclosure of Invention
The invention aims to provide a cardiovascular and cerebrovascular disease identification marker and application thereof.
In a first aspect, the invention claims a methylation biomarker.
The methylation biomarker disclosed by the invention has a nucleotide sequence of all or part of fragments shown in the following (A1) - (A3) in an RPTOR gene:
(A1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(A2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;
(A3) The DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto.
The methylation marker comprises a CpG site located on the nucleotide sequence of the methylation marker as shown in any one of the following (B1) - (B7):
(B1) Any one or more CpG sites in 3 DNA fragments shown as SEQ ID No.1, SEQ ID No.2 and SEQ ID No.3 in the RPTOR gene;
the upper limit of "multiple CpG sites" as used herein is all CpG sites in the 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the RPTOR gene. All CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1), all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2), and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).
(B2) All CpG sites on the DNA fragment shown in SEQ ID No.1 (Table 1) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) in the RPTOR gene;
(B3) All CpG sites on the DNA fragment shown in SEQ ID No.2 (Table 2) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) in the RPTOR gene;
(B4) All CpG sites on the DNA fragment shown in SEQ ID No.1 (Table 1) and all CpG sites on the DNA fragment shown in SEQ ID No.2 (Table 3) in the RPTOR gene;
(B5) All CpG sites on the DNA fragment shown in SEQ ID No.1 (Table 1), all CpG sites on the DNA fragment shown in SEQ ID No.2 (Table 2) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) in the RPTOR gene;
(B6) All CpG sites (table 2) or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG sites in the DNA fragment shown in SEQ ID No.2 in the RPTOR gene;
(B7) All 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 10 CpG sites on the DNA fragment shown in SEQ ID No.2 in the RPTOR gene:
item 1: the DNA fragment shown in SEQ ID No.2 shows CpG sites (RPTOR_B_1.2.3) from 26 th to 27 th, 34 th to 35 th and 36 th to 37 th of the 5' end;
item 2: the CpG site (RPTOR_B_4) shown in 61-62 positions of the DNA fragment shown in SEQ ID No.2 from the 5' end;
item 3: the DNA fragment shown in SEQ ID No.2 shows the CpG site (RPTOR_B_5) from 97 th to 98 th positions of the 5' end;
item 4: the CpG sites (RPTOR_B_6) shown in the 133 th to 134 th positions of the DNA fragment shown in SEQ ID No.2 from the 5' end;
item 5: the CpG site (RPTOR_B_7) shown in 152 th-153 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2;
Item 6: the CpG site (RPTOR_B_8) shown in 232-233 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end;
item 7: the CpG site (RPTOR_B_9) shown in the position 259-260 of the DNA fragment shown in SEQ ID No.2 from the 5' end;
item 8: the CpG site (RPTOR_B_10) shown in 280-281 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end;
item 9: the CpG site (RPTOR_B_11) shown in 313 th-314 th position of the DNA fragment shown in SEQ ID No.2 from the 5' end;
item 10: the DNA fragment shown in SEQ ID No.2 shows the CpG sites (RPTOR_B_12) at positions 418-419 from the 5' end.
In particular embodiments of the invention, some adjacent methylation sites are treated as one methylation site when analyzed for DNA methylation using time-of-flight mass spectrometry, because several CpG sites are located on one methylation fragment, the peak pattern is indistinguishable (indistinguishable sites are set forth in Table 5), and thus the methylation level analysis is performed, and related mathematical models are constructed and used.
The use of the methylation biomarker may be at least one of:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
Further, the cardiovascular and cerebrovascular diseases described in (1) can be cardiovascular and cerebrovascular diseases such as coronary heart disease and cerebral apoplexy, etc. which can cause the methylation level of RPTOR gene in the organism to be changed.
In the present invention, the auxiliary diagnosis of cardiovascular and cerebrovascular diseases described in (1) may 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 or cancers are affected at present and once and no cardiovascular and cerebrovascular diseases are affected in the next 2 years, and the blood routine indexes are all within the reference range. The "healthy control" appearing hereinafter is synonymous.
In the invention, the early warning of cardiovascular and cerebrovascular diseases before clinical onset in (1) is any one of the following: early warning coronary heart disease before clinical onset, early warning cerebral apoplexy before clinical onset, early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset.
Further, the testee who early warns of coronary heart disease before clinical onset is a potential patient of coronary heart disease or a healthy control. The potential patients with coronary heart disease are potential patients with coronary heart disease which is happened in the next 2 years or potential patients with coronary heart disease which is happened in the next 1 year (i.e. coronary heart disease can be clinically diagnosed in 2 years or 1 year). The "potential patients for coronary heart disease" appearing hereinafter are synonymous.
Further, the person to be detected who pre-warns of cerebral apoplexy before clinical onset is a potential cerebral apoplexy patient or a healthy control. The potential cerebral apoplexy patient is a potential cerebral apoplexy patient with a future 2 years of onset or a potential cerebral apoplexy patient with a future 1 year of onset (i.e. cerebral apoplexy can be clinically diagnosed within 2 years or within 1 year). The "potential stroke patient" appearing hereinafter is synonymous.
Further, the detected person who early warns and distinguishes coronary heart disease and cerebral apoplexy before clinical onset is a potential patient of coronary heart disease or a potential patient of cerebral apoplexy.
In the present invention, the coronary heart disease of different clinical characteristics described in (5) is: latent or asymptomatic myocardial ischemia, angina, myocardial infarction, ischemic cardiomyopathy or sudden death. The "coronary heart disease of different clinical characteristics" appearing hereinafter is synonymous.
In the present invention, the coronary heart disease of the type described in (5) that assists in diagnosing different clinical characteristics is embodied as at least one of the following: can help to distinguish between patients with latent or asymptomatic myocardial ischemia and healthy controls, can help to distinguish between patients with angina and healthy controls, can help to distinguish between patients with myocardial infarction and healthy controls, can help to distinguish between patients with ischemic myocardial ischemia and healthy controls, can help to distinguish between sudden death and healthy controls. The "diagnosis of coronary heart disease with different clinical features assisted" appearing hereinafter is synonymous.
In the invention, the testees of coronary heart disease with different clinical characteristics, which are early-warned before clinical onset, in the step (5) are coronary heart disease potential patients or healthy controls with different clinical characteristics.
In the present invention, the stroke with different clinical characteristics described in (6) is: ischemic stroke or hemorrhagic stroke. The occurrence of "stroke of different clinical characteristics" hereinafter is synonymous.
In the present invention, the stroke described in (6) that aids in distinguishing different clinical characteristics is embodied as at least one of the following: can help to distinguish ischemic cerebral apoplexy from healthy control, and can help to distinguish ischemic cerebral apoplexy from healthy control. The terms "stroke assisting in differentiating different clinical characteristics" appear synonymously hereinafter.
In the invention, the subjects with pre-alarm of cerebral apoplexy of different clinical characteristics before clinical onset in (6) are potential cerebral apoplexy patients or healthy controls of different clinical characteristics.
In the present invention, the auxiliary diagnosis of cardiac injury described in (7) is embodied as: auxiliary diagnosis of whether heart damage occurs or not a patient with tumor is treated by tumor drugs. The "assisted diagnosis of heart damage" appearing hereinafter is synonymous.
In the invention, the person to be tested who pre-warns of heart injury before clinical onset in (7) is a potential patient with heart injury caused by tumor drug treatment or a patient with heart injury caused by tumor drug treatment.
Further, the potential patient with heart damage caused by the tumor drug treatment is a patient with heart damage in the next 2 years (i.e. a patient with heart damage clinically diagnosed in 2 years) when the tumor patient is treated with the tumor drug. The patient with the tumor, which is not suffered from heart injury after being treated by the tumor drug, is a patient with the tumor, which is not suffered from heart injury in the next 2 years (i.e. the patient with heart injury is not clinically diagnosed in the 2 years).
In a specific embodiment of the invention, the tumor is in particular lung cancer or breast cancer; the drug is specifically doxorubicin, idarubicin and/or epirubicin.
In the invention, the auxiliary cardiovascular and cerebrovascular disease recurrence monitoring in (8) is specifically embodied as any one of the following: and the coronary heart disease recurrence monitoring and the cerebral apoplexy recurrence monitoring are assisted. The "aiding in the monitoring of recurrence of cardiovascular and cerebrovascular diseases" appearing hereinafter is synonymous.
In the invention, the early warning of the recurrence of cardiovascular and cerebrovascular diseases before clinical onset described in (8) is any one of the following: coronary heart disease recurrence is early-warned before clinical onset, and cerebral apoplexy recurrence is early-warned before clinical onset.
Further, the patient to be tested who early warns of the recurrence of the coronary heart disease before the clinical onset is a potential patient with the recurrence of the coronary heart disease (i.e. a patient with the recurrence of the coronary heart disease which is clinically diagnosed within 2 years of prognosis) or a patient without the recurrence of the coronary heart disease. Wherein, the non-recurrent coronary heart disease patient can be understood as the recurrent coronary heart disease patient which is not clinically diagnosed within 2 years of prognosis. The "non-recurrent patient with coronary heart disease" appearing hereinafter is synonymous.
Further, the subject who is to be tested for early warning of the recurrence of the cerebral stroke before the clinical onset is a potential patient for the recurrence of the cerebral stroke (i.e., a patient diagnosed clinically as having the recurrence of the cerebral stroke within 2 years of prognosis) or a non-recurrence patient of the cerebral stroke. Wherein, the cerebral apoplexy non-recurrence patient can be understood as cerebral apoplexy recurrence which is not clinically diagnosed within 2 years after the cerebral apoplexy patient. The term "stroke non-recurrent patient" appearing hereinafter is synonymous.
In the present invention, the recurrence of coronary heart disease of the different clinical characteristics described in (11) is: asymptomatic myocardial ischemia recurrence, angina pectoris recurrence, myocardial infarction recurrence, and ischemic cardiomyopathy. The "recurrence of coronary heart disease with different clinical characteristics" appearing hereinafter is synonymous.
Further, the monitoring of coronary heart disease recurrence as described in (11) in assisting with different clinical characteristics is embodied as at least one of the following: can help to distinguish asymptomatic myocardial ischemia recurrence from asymptomatic myocardial ischemia non-recurrence, can help to distinguish angina pectoris recurrence from angina pectoris non-recurrence, can help to distinguish myocardial infarction recurrence from myocardial infarction non-recurrence, can help to distinguish ischemic cardiomyopathy recurrence from ischemic cardiomyopathy non-recurrence. The following is synonymous with "aiding in the monitoring of recurrence of coronary heart disease with different clinical profiles".
In the present invention, the recurrence of stroke with different clinical features described in (12) is: ischemic stroke recurrence and hemorrhagic stroke recurrence. The occurrence of "stroke recurrence of different clinical characteristics" hereinafter is synonymous.
Further, the monitoring of stroke recurrence as described in (12) to aid in the progression of different clinical characteristics is embodied as at least one of: can help to distinguish the recurrence of ischemic cerebral apoplexy from the non-recurrence of ischemic cerebral apoplexy, and can help to distinguish the recurrence of hemorrhagic cerebral apoplexy from the non-recurrence of hemorrhagic cerebral apoplexy. The following is synonymous with "aiding in the monitoring of recurrence of stroke with different clinical features".
In a second aspect, the invention claims the use of a methylation biomarker as described in the first aspect above in the preparation of a product; the application of the product is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
In a third aspect, the invention claims the use of a substance for detecting the methylation level of a methylation biomarker as described in the first aspect above for the preparation of a product; the application of the product is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
In a fourth aspect, the invention claims the use of a substance for detecting the methylation level of a methylation biomarker as described in the first aspect above and a medium storing a mathematical model and/or a method of using a mathematical model for the preparation of a product; the application of the product is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting the methylation level of the methylation biomarker in the first aspect above for n1 type a samples and n2 type B samples, respectively;
(A2) Taking methylation level data of the methylation biomarkers in the first aspect of the previous all samples obtained in the step (A1), and establishing a mathematical model through a two-classification logistic regression method according to classification modes of A type and B type;
Wherein, n1 and n2 can be positive integers more than 10.
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of the methylation biomarker described in the first aspect above in the test sample;
(B2) Substituting methylation level data of the methylation biomarker in the first aspect of the sample to be tested 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 the following:
(C1) Potential patients with coronary heart disease and healthy controls for onset within the next 2 years;
(C2) Potential patients with stroke and healthy controls that developed within the next 2 years;
(C3) Coronary heart disease potential patients with onset in the next 2 years and cerebral apoplexy potential patients with onset in the next 2 years;
(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;
(C5) Stroke potential patients and healthy controls of different clinical characteristics of onset within the next 2 years;
(C6) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;
(C7) Potential patients with stroke and healthy controls that developed within the next 1 year;
(C8) Coronary heart disease potential patients with onset in the next 1 year and cerebral apoplexy potential patients with onset in the next 1 year;
(C9) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year;
(C10) Stroke potential patients and healthy controls of different clinical characteristics of the onset within the next 1 year;
(C11) Patients with and without heart injury due to treatment with tumor drugs in the next 2 years;
(C12) Patients with recurrent cardiovascular and cerebrovascular diseases and non-recurrent patients within 2 years of prognosis;
(C13) Coronary heart disease recurrent patients and non-recurrent patients within 2 years of prognosis;
(C14) Stroke relapsing patients and non-relapsing patients within 2 years of prognosis;
(C15) Coronary heart disease recurrent patients and non-recurrent patients with different clinical characteristics within 2 years of prognosis;
(C16) Cerebral stroke relapsing patients and non-relapsing patients with different clinical characteristics within 2 years of prognosis.
In a fifth aspect, the invention claims a kit.
The kit as claimed in the present invention comprises a substance for detecting the methylation level of the methylation biomarker described in the first aspect hereinbefore; the use of the kit may be at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
Further, the kit may further comprise a medium storing a mathematical model and/or a method for using a mathematical model as described in the fourth aspect.
In a sixth aspect, the invention claims a system.
The system claimed in the present invention may include:
(D1) Reagents and/or instrumentation for detecting the methylation level of the methylation biomarker described in the first aspect hereinbefore;
(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 configured to acquire (D1) methylation level data of the methylation biomarkers in the first aspect of the foregoing of n1 type a samples and n2 type B samples detected;
the data analysis processing module is configured to receive methylation level data of the methylation biomarker in the first aspect of the n1 type A samples and the n2 type B samples sent by the data acquisition module, establish a mathematical model through a two-classification logistic regression method according to classification modes of the type A and the type B, and determine a threshold value of classification judgment;
wherein, n1 and n2 can be positive integers more than 10.
The model output module is configured to receive the mathematical model sent by the data analysis processing module and output the mathematical model;
the unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;
the data input module is configured to input (D1) the methylation level data of the methylation biomarker in the previous first aspect of the tested person detected;
the data operation module is configured to receive methylation level data of the methylation biomarker in the first aspect of the person to be tested, sent by the data input module, and substitute the methylation level data of the methylation biomarker in the first aspect of the person to be tested into the mathematical model to calculate a detection index;
The data comparison module is configured to receive the detection index sent from the data operation module and compare the detection index with the threshold value determined in the data analysis processing module in the unit X;
the conclusion output module is configured to receive the comparison result sent by the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result;
the type a sample and the type B sample are any one of the following:
(C1) Potential patients with coronary heart disease and healthy controls for onset within the next 2 years;
(C2) Potential patients with stroke and healthy controls that developed within the next 2 years;
(C3) Coronary heart disease potential patients with onset in the next 2 years and cerebral apoplexy potential patients with onset in the next 2 years;
(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;
(C5) Stroke potential patients and healthy controls of different clinical characteristics of onset within the next 2 years;
(C6) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;
(C7) Potential patients with stroke and healthy controls that developed within the next 1 year;
(C8) Coronary heart disease potential patients with onset in the next 1 year and cerebral apoplexy potential patients with onset in the next 1 year;
(C9) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year;
(C10) Stroke potential patients and healthy controls of different clinical characteristics of the onset within the next 1 year;
(C11) Patients with and without heart injury due to treatment with tumor drugs in the next 2 years;
(C12) Patients with recurrent cardiovascular and cerebrovascular diseases and non-recurrent patients within 2 years of prognosis;
(C13) Coronary heart disease recurrent patients and non-recurrent patients within 2 years of prognosis;
(C14) Stroke relapsing patients and non-relapsing patients within 2 years of prognosis;
(C15) Coronary heart disease recurrent patients and non-recurrent patients with different clinical characteristics within 2 years of prognosis;
(C16) Cerebral stroke relapsing patients and non-relapsing patients with different clinical characteristics within 2 years of prognosis.
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 period of time before clinical onset may be specifically within 2 years of the period of time before clinical onset or within 1 year of the period of time before clinical onset or within 2 years of recurrence of the clinical condition. The potential coronary heart disease patients with the onset within 2 years (or within 1 year) in the future are the potential coronary heart disease patients which are found within 2 years (or within 1 year) earlier than the clinical onset time; the potential cerebral apoplexy patients with the future 2 years (or 1 year) are the potential cerebral apoplexy patients which are found 2 years (or 1 year) earlier than the clinical onset time. The patient diagnosed with heart damage within the next 2 years from receiving oncological drug therapy may be a patient diagnosed with heart damage within 2 years of the clinical onset time. The coronary heart disease patient with recurrence within 2 years is earlier than the clinical coronary heart disease recurrence time within 2 years. The cerebral apoplexy patient with recurrence within 2 years of prognosis is earlier than the clinical cerebral apoplexy recurrence time within 2 years.
In each of the above aspects, the substance for detecting the methylation level of the methylation biomarker described in the first aspect above comprises (or is) primer pair a and/or primer pair B and/or primer pair C;
the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 5;
The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 7;
the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is single-stranded DNA shown in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer C2 is SEQ ID No.9 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 9.
In the above aspects, the apparatus for detecting the methylation level of the methylation biomarker described in the first aspect above 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 methylation biomarkers described in the first aspect hereinbefore.
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 methylation levels of the methylation biomarkers described in the foregoing first aspect of n1 type a samples and n2 type B samples, respectively (training set);
(A2) Taking methylation level data of the methylation biomarkers in the first aspect of all samples obtained in the step (A1), establishing a mathematical model according to classification modes of A type and B type by a two-classification logistic regression method, and determining a threshold value of classification judgment.
Wherein, n1 and n2 in (A1) can be positive integers more than 10.
(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 methylation biomarker in the first aspect above of the test sample;
(B2) Substituting methylation level data of the methylation biomarker in the first aspect of the sample to be tested 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 and healthy controls for onset within the next 2 years;
(C2) Potential patients with stroke and healthy controls that developed within the next 2 years;
(C3) Coronary heart disease potential patients with onset in the next 2 years and cerebral apoplexy potential patients with onset in the next 2 years;
(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;
(C5) Stroke potential patients and healthy controls of different clinical characteristics of onset within the next 2 years;
(C6) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;
(C7) Potential patients with stroke and healthy controls that developed within the next 1 year;
(C8) Coronary heart disease potential patients with onset in the next 1 year and cerebral apoplexy potential patients with onset in the next 1 year;
(C9) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year;
(C10) Stroke potential patients and healthy controls of different clinical characteristics of the onset within the next 1 year;
(C11) Patients with and without heart injury due to treatment with tumor drugs in the next 2 years;
(C12) Patients with recurrent cardiovascular and cerebrovascular diseases and non-recurrent patients within 2 years of prognosis;
(C13) Coronary heart disease recurrent patients and non-recurrent patients within 2 years of prognosis;
(C14) Stroke relapsing patients and non-relapsing patients within 2 years of prognosis;
(C15) Coronary heart disease recurrent patients and non-recurrent patients with different clinical characteristics within 2 years of prognosis;
(C16) Cerebral stroke relapsing patients and non-relapsing patients with different clinical characteristics within 2 years of prognosis.
In practical applications, any of the above mathematical models may be changed according to the detection method and the fitting method of DNA methylation, and the mathematical model is determined according to a specific mathematical model without any convention.
In the embodiment of the invention, the model is specifically log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting a methylation value of one or more methylation sites of a sample to be tested into the model as a dependent variable, b0 is a constant, x1-xn is an independent variable which is a methylation value of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is a weight given by the model to the methylation value of each site.
In the embodiment of the invention, the model can be established by adding known parameters such as age, sex, white blood cell count and the like as appropriate to improve the discrimination efficiency. Five specific models established in the embodiment of the invention are used for assisting in distinguishing potential cardiovascular and cerebrovascular disease patients (coronary heart disease and cerebral apoplexy) with the onset within 2 years from healthy controls, heart damage and no heart damage caused by the treatment of tumor medicaments within the next 2 years, recurrence and non-recurrence of coronary heart disease within the next 2 years and recurrence and non-recurrence of cerebral apoplexy within the next 2 years. The model one is specifically as follows: log (y/(1-y))= 4.599-2.119 rptor_b_1.2.3-3.628 rptor_b_4-0.622 rptor_b_5-0.706 rptor_b_6+1.589 rptor_b_7-4.617 rptor_b_8-1.806 rptor_b_9+0.692 rptor_b_10-0.032 rptor_b_11+2.139 rptor_b_12-0.026 (integer) +0.225 sex (male assignment 1, female assignment 0) -0.054 white blood cell count (unit 10) 9 /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold for model one is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were potential coronary heart disease patients with onset within 2 years, and patient candidates less than 0.5 were healthy controls. The second model is specifically as follows: log (y/(1-y)) = -4.015-1.901 rptor_b_1.2.3-1.796 rptor_b_4+4.143 rptor_b_5-0.955 rptor_b_6-2.117 rptor_b_7+1.834 rptor_b_8+1.970 rptor_b_9+5.125 rptor_b_10-0.066 rptor_b_11+1.853 rptor_b_12+0.022 (male assigned 1, female assigned 0) +0.104 white blood cell number (unit 10) 9 /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold of the second model is 0.5. Patient with detection index greater than 0.5 calculated by modelCandidates for potential stroke patients who developed within 2 years, less than 0.5 patient candidates were healthy controls. The model three is specifically as follows: log (y/(1-y))=2.518-1.003×rptor_b_1.2.3-0.679×rptor_b_4+3.418×rptor_b_5-0.592×rptor_b_6+1.214×rptor_b_7-0.941×rptor_b_8+1.023×rptor_b_9+4.609×rptor_b_10+0.217×rptor_b_11-0.927×rptor_b_12-0.031×age (integer number of female+0.703×white blood cell number (unit 10) with a male assignment of 1+0.384×female assignment of 0.703×white blood cell number 9 /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold for model three was 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model are patients with heart damage within 2 years, and patient candidates with less than 0.5 are patients with no heart damage within 2 years. The model IV specifically comprises: log (y/(1-y))=4.175-0.932 rptor_b_1.2.3-2.716 rptor_b_4+2.118 rptor_b_5-1.651 rptor_b_6+0.487 rptor_b_7-1.774 rptor_b_8-0.839 rptor_b_9+3.671 rptor_b_10-0.149 rptor_b_11+1.550 rptor_b_12+0.036 (male assigned 1, female assigned 0) +0.093 white blood cell number (unit 10) 9 /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold for model four is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were patients with recurrent coronary heart disease within 2 years, and patient candidates with less than 0.5 were patients with non-recurrent coronary heart disease within 2 years. The model five is specifically as follows: log (y/(1-y)) = -2.993+0.896 rptor_b_1.2.3+1.531 rptor_b_4-3.016 rptor_b_5+0.675 rptor_b_6+1.237 rptor_b_7+1.504 rptor_b_8-1.368 rptor_b_9-2.657 rptor_b_10+0.167 rptor_b_11+1.553 rptor_b_12+0.013 age (integer) +0.315 sex (male assigned 1, female assigned 0) +0.251 white blood cell number (unit 10) 9 /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold value of the model five is0.5. Patient candidates with a detection index greater than 0.5 calculated by the model are recurrent patients with stroke within 2 years, and patient candidates with less than 0.5 are non-recurrent patients with stroke within 2 years. In the first to fifth models, the RPTOR_B_1.2.3 is the methylation level of the CpG sites shown in 26 th to 27 th, 34 th to 35 th and 36 th to 37 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the RPTOR_B_4 is the methylation level of CpG sites shown in 61-62 positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the RPTOR_B_5 is the methylation level of CpG sites shown in 97-98 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the RPTOR_B_6 is the methylation level of CpG sites shown in the 133 th-134 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the RPTOR_B_7 is the methylation level of CpG sites shown in 152 th-153 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the RPTOR_B_8 is the methylation level of CpG sites shown in 232 th-233 th positions of a DNA fragment shown in SEQ ID No.2 from a 5' end; the RPTOR_B_9 is the methylation level of CpG sites shown in the 259 th to 260 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the RPTOR_B_10 is the methylation level of the CpG site shown in 280-281 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end; the RPTOR_B_11 is the methylation level of CpG sites shown in 313-314 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the RPTOR_B_12 is the methylation level of CpG sites shown in 418-419 from the 5' end of the DNA fragment shown in SEQ ID No. 2.
In the above aspects, detecting the methylation level of the methylation biomarker of the first aspect of the present disclosure is detecting the methylation level of the methylation biomarker of the first aspect of the present disclosure in a blood sample. I.e. the sample to be measured is a blood sample.
The RPTOR gene described above may specifically include Genbank accession No.: NM-020761.3 (GI: 1519244773), transcript variant 1; NM-001163034.2 (GI: 1676318601), transcript variant 2.
The invention provides hypomethylation of RPTOR gene in blood of coronary heart disease patients and heart injury patients and hypermethylation of RPTOR gene in blood of cerebral apoplexy patients, and performs prognosis evaluation on cardiac and cerebral blood vessels. Experiments prove that the blood can be used as a sample to early warn and distinguish cardiovascular and cerebrovascular diseases (coronary heart disease and cerebral apoplexy) and healthy controls before clinical onset, early warn and distinguish coronary heart disease patients and healthy controls with different clinical characteristics before clinical onset, early warn and distinguish cerebral apoplexy patients and healthy controls with different clinical characteristics before clinical onset, distinguish heart injury patients and heart injury patients without heart injury caused by being treated by tumor medicaments, distinguish recurrence and non-recurrence of coronary heart disease patients, and distinguish recurrence and non-recurrence of cerebral apoplexy patients. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment and prognosis evaluation effects of the cardiovascular and cerebrovascular diseases and reducing the death rate of the cardiovascular and cerebrovascular diseases.
Drawings
FIG. 1 is a schematic diagram of a mathematical model.
Fig. 2 is an illustration of a mathematical model of coronary heart disease.
Fig. 3 is an illustration of a mathematical model of cerebral apoplexy.
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.
The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
The quantitative test of the gene of the protein related to the regulation of the target protein complex 1 (Regulatory Associated Protein Of mTOR, complex 1, RPTOR) in the following examples was performed in three replicates, and the results were averaged.
Example 1 primer design for detection of methylation site of RPTOR Gene
Three fragments (RPTOR_A fragment, RPTOR_B fragment, RPTOR_C fragment) of the RPTOR gene were selected for methylation level and cardiovascular and cerebrovascular disease correlation analysis through a number of sequence and functional analyses.
The RPTOR_A fragment (SEQ ID No. 1) is located on the sense strand of the hg19 reference genome chr17: 78754894-78755296.
The RPTOR_B fragment (SEQ ID No. 2) is located on the sense strand of hg19 reference genome chr17: 78755346-78756065.
The RPTOR_C fragment (SEQ ID No. 3) is located on the sense strand of hg19 reference genome chr17: 78756232-78756874.
CpG site information in the RPTOR_A fragment is shown in Table 1.
CpG site information in the RPTOR_B fragment is shown in Table 2.
CpG site information in the RPTOR_C fragment is shown in Table 3.
Table 1 CpG site information in RPTOR_A fragment
| CpG sites | Position of CpG sites in the sequence |
| RPTOR_A_1 | SEQ ID No.1 from positions 26-27 of the 5' end |
| RPTOR_A_2 | SEQ ID No.1 from positions 75-76 of the 5' end |
| RPTOR_A_3 | SEQ ID No.1 from positions 87-88 of the 5' end |
| RPTOR_A_4 | SEQ ID No.1 from positions 235-236 of the 5' end |
| RPTOR_A_5 | SEQ ID No.1 from position 247 to 248 of the 5' end |
| RPTOR_A_6 | SEQ ID No.1 from positions 260-261 of the 5' end |
| RPTOR_A_7 | 293 th to 294 th positions from 5' end of SEQ ID No.1 |
| RPTOR_A_8 | 299 th to 300 th positions from 5' end of SEQ ID No.1 |
| RPTOR_A_9 | 306-307 th bit from 5' end of SEQ ID No.1 |
| RPTOR_A_10 | 321 th to 322 th positions of SEQ ID No.1 from 5' end |
| RPTOR_A_11 | SEQ ID No.1 from position 334-335 of the 5' end |
| RPTOR_A_12 | SEQ ID No.1 from position 354-355 of the 5' end |
Table 2 CpG site information in RPTOR_B fragment
Table 3 CpG site information in RPTOR_C fragment
Specific PCR primers were designed for three fragments (RPTOR_A fragment, RPTOR_B fragment, RPTOR_C fragment) as shown in Table 4. Wherein, SEQ ID No.4, SEQ ID No.6, SEQ ID No.8 forward primer, SEQ ID No.5, SEQ ID No.7, SEQ ID No.9 reverse primer; the 1 st to 10 th positions of the 5' in SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 are nonspecific labels, and the 11 th to 35 th positions are specific primer sequences; the non-specific tags are arranged at positions 1 to 31 of SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 from 5', and the specific primer sequences are arranged at positions 32 to 56. The primer sequences do not contain SNPs and CpG sites.
TABLE 4 RPTOR methylation primer sequences
Example 2 methylation detection of RPTOR Gene and analysis of results
1. Study sample
1. Research sample for assisting early screening of cardiovascular and cerebrovascular diseases
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 for more than 2 years. The study was reviewed by the ethics committee and all panelists signed informed consent. Cardiovascular and cerebrovascular diseases and cancer incidence information are recorded annually through local hospitals, disease control center chronic disease management systems, community health service centers and workstation chronic disease routine registration projects and social security center reimbursement data. The starting time of the queue is the baseline investigation date, the ending variable is the cardiovascular and cerebrovascular diseases, and the follow-up time of the study subjects without visit is uniformly calculated according to half of the follow-up ending time.
The invention selects new cardiovascular and cerebrovascular disease 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, and the total cardiovascular and cerebrovascular disease onset is 620 persons after 2018 and 7 months after the follow-up date. After age and sex matching, the population without cardiovascular and cerebrovascular diseases and cancers in the follow-up period (the follow-up time is more than 2 years) and with blood routine indexes within the reference range is selected as health control, and total 612 cases are selected.
All patient ex vivo blood samples were collected prior to onset. The disease condition is confirmed by imaging and pathology in the subsequent disease.
342 patients suffering from coronary heart disease within 2 years after the group are classified according to clinical typing: 45 cases of latent or asymptomatic myocardial ischemia, 64 cases of angina pectoris, 83 cases of myocardial infarction, 74 cases of ischemic cardiomyopathy and 76 cases of sudden death. Wherein 137 cases of coronary heart disease occur within 1 year after the administration, including 20 cases of latent or asymptomatic myocardial ischemia, 21 cases of angina pectoris, 33 cases of myocardial infarction, 30 cases of ischemic cardiomyopathy and 33 cases of sudden death.
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.
Healthy controls had a median age of 65 years, patients with coronary heart disease and stroke who developed within 2 years of the group had median ages of 64 and 65 years, respectively, and the ratio of men to women in each of these 3 groups 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. Research sample for assisting in diagnosing cardiac injury condition of tumor patient after drug administration
The study selects two patient groups of lung cancer and breast cancer as follow-up targets, and the taken tumor drugs are doxorubicin, idarubicin and/or epirubicin.
After age and sex matching, a total of 147 patients and 105 patients without heart injury in 2 years during the selected follow-up period (the follow-up time is longer than 2 years) due to receiving the tumor treatment drug.
All patient ex vivo blood samples were collected before cardiac injury occurred after tumor drug administration. The disease condition is confirmed by imaging and pathology in the subsequent disease.
The median ages of the patients who developed cardiac injury and did not develop cardiac injury within 2 years after the group were 65 and 63 years, respectively, and the ratio of men and women in each of these 2 groups was about 1:1.
3. Research sample for auxiliary diagnosis of cardiovascular and cerebrovascular prognosis evaluation
The research sample adopts an epidemiological whole group sampling method, and carries out follow-up investigation on community groups over 18 years old in a certain city for more than 2 years, wherein the follow-up time is 2016 years 2 months to 2018 years 7 months. Recurrent cardiovascular and cerebrovascular disease information is recorded in local hospitals, chronic disease management systems of disease control centers, community health service centers and common registration projects of chronic diseases of workstations and reimbursement data of social security centers. The starting time of the queue is the baseline investigation date, and the ending variable is the recurrence of cardiovascular and cerebrovascular diseases. For the follow-up time of the study subjects, the follow-up time is uniformly calculated according to half of the follow-up end time. And after the end of the follow-up visit, counting 216 recurrent cardiovascular and cerebrovascular diseases as a case group, wherein 118 recurrent coronary heart disease patients and 98 recurrent cerebral apoplexy patients. After age and sex matching, the population which does not relapse cardiovascular diseases within 2 years (the follow-up time is more than 2 years) and has blood routine indexes within the reference range is selected as a control, and 286 cases are counted, wherein the number of the cases is 165 coronary heart diseases and the number of the cases is 121.
All patient ex vivo blood samples were collected prior to disease recurrence. The disease recurrence is confirmed by imaging and pathology at the time of subsequent onset.
118 patients with recurrence of coronary heart disease within 2 years after group entry were classified according to clinical typing: 25 cases of latent or asymptomatic myocardial ischemia, 26 cases of angina pectoris, 35 cases of myocardial infarction and 32 cases of ischemic cardiomyopathy.
98 patients with recurrence of cerebral stroke within 2 years after group entry were classified according to clinical typing: 42 cases of cerebral arterial thrombosis and 56 cases of cerebral arterial thrombosis.
The median age of the cardiovascular and cerebrovascular non-recurrence is 64 years, the median of the ages of the patients suffering from coronary heart disease recurrence and the patients suffering from cerebral apoplexy recurrence within 2 years after the patients are respectively 65 and 66 years, and the ratio of men and women in the groups is about 1:1.
2. Methylation detection
1. Total DNA of the blood sample is extracted.
2. The total DNA of the blood samples prepared in step 1 was subjected to bisulfite treatment (see DNA methylation kit instructions for Qiagen). After bisulfite treatment, unmethylated cytosines (C) in the original CpG sites are converted to uracil (U), while methylated cytosines remain unchanged.
3. The DNA treated by the bisulfite in the step 2 is used as a template, 3 pairs of specific primer pairs in the table 4 are adopted to carry out PCR amplification through DNA polymerase according to a reaction system required by a conventional PCR reaction, and all primers adopt a conventional standard PCR reaction system and are amplified according to the following procedure.
The PCR reaction procedure was: 95 ℃,4 min- & gt (95 ℃,20 s- & gt 56 ℃,30 s- & gt 72 ℃ 2 min) 45 cycles- & gt 72 ℃,5 min- & gt 4 ℃ for 1h.
4. Taking the amplified product of the step 3, and carrying out DNA methylation analysis by a time-of-flight mass spectrum, wherein the specific method is as follows:
(1) Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [ 0.5U) was added to 5. Mu.l of PCR product]+1.7ml H 2 O) then incubated in a PCR apparatus (37 ℃,20 min. Fwdarw. 85 ℃,5 min. Fwdarw. 4 ℃,5 min) according to the following procedure;
(2) Taking out 2 mu.l of the SAP treated product obtained in the step (1), adding the product into a 5 mu l T-clear reaction system according to the instruction, and then incubating for 3 hours at 37 ℃;
(3) Taking the product of the step (2), adding 19 mu l of deionized water, and then carrying out deionized incubation on a rotary shaking table for 1h by using 6 mu g of Resin;
(4) Centrifuging at 2000rpm at room temperature for 5min, and loading 384SpectroCHIP with the micro supernatant by a Nanodispenser mechanical arm;
(5) Time-of-flight mass spectrometry; the data obtained were collected with the spectroacquisition v3.3.1.3 software and visualized by MassArray EpiTyper v 1.2.1.2 software.
Reagents used for the time-of-flight mass spectrometry detection are all kits (T-Cleavage MassCLEAVE Reagent Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection is Massary O R Analyzer Chip Prep Module 384, model: 41243; the data analysis software is self-contained software of the detection instrument.
5. And (5) analyzing the data obtained in the step (4).
Statistical analysis of the data was performed by SPSS Statistics 23.0.
Non-parametric tests were used for comparative analysis between the two groups.
The identification effect of a combination of multiple CpG sites on different sample groupings is achieved by logistic regression and statistical methods of the subject curves.
All statistical tests were double-sided, with p-values <0.05 considered statistically significant.
A total of 46 distinguishable peak patterns were obtained by mass spectrometry experiments. The methylation level at each CpG site of each sample can be automatically obtained by calculating the peak area according to the "methylation level=peak area of methylated fragments/(peak area of unmethylated fragments+peak area of methylated fragments)" formula using SpectroAcquin v3.3.1.3 software.
3. Analysis of results
1. Healthy controls, differences in the methylation levels of the RPTOR gene in the blood of patients with coronary heart disease and cerebral apoplexy (2 years earlier than the clinical onset time)
The methylation level of all CpG sites in the RPTOR gene was analyzed by taking blood of 342 coronary heart disease patients, 278 cerebral apoplexy patients and 612 healthy controls as study materials (Table 5), wherein the coronary heart disease and cerebral apoplexy patients are asymptomatic when they are put into the group, and the patients develop within 2 years after the group is put into the group. The result shows that the methylation level median of the RPTOR gene of the healthy control is 0.53 (IQR=0.35-0.66), the methylation level median of the RPTOR gene of cerebral apoplexy is 0.57 (IQR=0.36-0.70), and the methylation level median of the coronary heart disease patient is 0.50 (IQR=0.33-0.63). As a result of comparative analysis of the methylation levels of the RPTOR genes among the three, the methylation levels of all CpG sites in the RPTOR genes of the cerebral apoplexy patients are found to be significantly higher than those of the healthy controls (p <0.05, table 5), and the methylation levels of all CpG sites in the RPTOR genes of the coronary heart disease patients are found to be significantly lower than those of the healthy controls (p <0.05, table 5). Furthermore, the methylation level of all CpG sites in the RPTOR 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 RPTOR gene can be used for screening potential patients with cerebral apoplexy and coronary heart disease in 2 years in the population, and is a molecular marker with high clinical value.
Table 5, comparative healthy controls, methylation level differences between patients with coronary heart disease and cerebral apoplexy (2 years earlier than clinical onset time)
2. Healthy controls, differences in the methylation levels of the RPTOR gene in the blood of patients with coronary heart disease and cerebral apoplexy (1 year earlier than the clinical onset time)
Blood of 137 patients with coronary heart disease, 110 patients with cerebral apoplexy and 612 healthy controls is used as a research material to analyze methylation level differences of all CpG sites in RPTOR genes among the three (table 6), 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 RPTOR gene of the healthy control is 0.53 (IQR=0.35-0.66), the methylation level median of the RPTOR gene of cerebral apoplexy is 0.59 (IQR=0.38-0.72), and the methylation level median of the coronary heart disease patient is 0.48 (IQR=0.32-0.62). By comparing and analyzing the methylation levels of the RPTOR genes of the three, the methylation levels of all CpG sites in the RPTOR genes of the cerebral apoplexy patients are found to be significantly higher than that of the healthy control (p <0.05, table 6), and the methylation levels of all CpG sites in the RPTOR genes of the coronary heart disease patients are found to be significantly lower than that of the healthy control (p <0.05, table 6). Furthermore, the methylation level of all CpG sites in the RPTOR gene was significantly lower in patients with coronary heart disease than in patients with stroke (p <0.05, table 6). Therefore, the methylation level of the RPTOR 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.
TABLE 6 comparative healthy controls, methylation level differences between coronary heart disease and cerebral apoplexy patients (1 year earlier than clinical onset time)
3. Methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (within 2 years earlier than clinical onset time)
We compared and analyzed the methylation level difference of RPTOR 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. As a result, it was found that the methylation level of all CpG sites of the RPTOR gene in patients with coronary heart disease (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) with different clinical characteristics was significantly different from that of healthy controls (p <0.05, table 7). Furthermore, we found that methylation levels of all CpG sites in RPTOR genes of stroke patients (hemorrhagic stroke, ischemic stroke) with different clinical characteristics were significantly different from healthy controls (p <0.05, table 7).
Table 7, compares methylation level differences between healthy controls and coronary heart disease, cerebral stroke of different clinical character (2 years earlier than clinical onset time)
4. Methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (within 1 year earlier than clinical onset time)
We compared and analyzed the methylation level difference of RPTOR 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. As a result, it was found that the methylation level of all CpG sites of the RPTOR gene in patients with coronary heart disease (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) with different clinical characteristics was significantly different from that of healthy controls (p <0.05, table 8). Furthermore, we found that methylation levels of all CpG sites in RPTOR genes of stroke patients (hemorrhagic stroke, ischemic stroke) with different clinical characteristics were significantly different from healthy controls (p <0.05, table 8). Thus, the methylation level of the RPTOR gene can be used to predict the likelihood of developing coronary heart disease and stroke disease of different clinical characteristics over a 1 year period.
Table 8, comparison of methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (1 year earlier than clinical onset time)
5. Methylation level differences between patients with and without cardiac injury due to receipt of anti-tumor drugs within 2 years of the group
The methylation levels of all CpG sites in the RPTOR gene were analyzed using blood of 147 and 105 non-heart-injured patients as a study material (Table 9), wherein both the heart-injured and non-heart-injured patients were asymptomatic in the group, and were clinically diagnosed within 2 years after the group. The results showed that the median methylation level was 0.46 (iqr=0.33-0.63) and 0.51 (iqr=0.36-0.67) in patients without heart injury, and that the methylation level of the RPTOR gene in the blood of patients with heart injury due to treatment with antitumor drugs was significantly lower than that in the blood of patients without heart injury (p <0.05, table 9). Thus, the methylation level of the RPTOR gene can be used to predict the likelihood of heart damage occurring over a period of 2 years from receiving a tumor therapeutic.
TABLE 9 comparison of methylation level differences between heart injury and non-heart injury patients over the next two years
6. Methylation level differences between patients with recurrent and non-recurrent cardiovascular and cerebrovascular diseases within the next 2 years
The methylation level of all CpG sites in the RPTOR gene was analyzed by taking blood of 118 patients with recurrent coronary heart disease and 165 non-recurrent coronary heart disease groups as a study material (Table 10), wherein the recurrent coronary heart disease patients and the non-recurrent coronary heart disease patients are asymptomatic when they are in the group, and are clinically diagnosed within 2 years after the group. The results showed that the methylation level of patients with recurrent coronary heart disease was median of 0.49 (iqr=0.31-0.62), the methylation level of patients with non-recurrent coronary heart disease was median of 0.52 (iqr=0.34-0.65), and the methylation level of RPTOR gene in the blood of patients with recurrent coronary heart disease was found to be significantly lower than that of patients with non-recurrent coronary heart disease by comparing the methylation levels of the RPTOR gene with the methylation level of the RPTOR gene in the blood of patients with recurrent coronary heart disease (p <0.05, table 10). Thus, the methylation level of the RPTOR gene can be used to predict the likelihood of recurrence of coronary heart disease in 2 years (p <0.05, table 10).
The methylation level of all CpG sites in the RPTOR gene was analyzed using blood of 98 patients with recurrent stroke and 121 non-recurrent stroke groups as study materials (Table 10), wherein both recurrent stroke patients and non-recurrent stroke groups were asymptomatic when they were enrolled, and clinically diagnosed within 2 years after enrolling. The results show that the methylation level of the cerebral apoplexy recurrence patients is median of 0.58 (IQR=0.38-0.73), the methylation level of the cerebral apoplexy non-recurrence people is median of 0.55 (IQR=0.34-0.71), and the methylation level of the RPTOR gene in the blood of the cerebral apoplexy recurrence patients is found to be significantly higher than that of the cerebral apoplexy non-recurrence people by comparing the methylation levels of the RPTOR gene (p <0.05, table 10). Thus, the methylation level of the RPTOR gene can be used to predict the likelihood of recurrence of stroke disease over a 2 year period.
TABLE 10 comparison of methylation level differences between patients with recurrent and non-recurrent coronary heart disease and patients with recurrent and non-recurrent cerebral apoplexy within two years of prognosis
7. Methylation level differences between non-recurrence of cardiovascular and cerebrovascular disease and recurrence of different clinical characteristics (prognosis within 2 years)
We compared and analyzed the methylation level difference of RPTOR gene of 286 patients with cardiovascular and cerebrovascular diseases and 216 patients with cardiovascular and cerebrovascular diseases. As a result, it was found that methylation levels of all CpG sites of the RPTOR gene in patients with coronary heart disease of different clinical characteristics (latent or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy) were significantly different from those of non-recurrent controls of coronary heart disease of different clinical characteristics (p <0.05, table 12). Furthermore, we found that methylation levels of all CpG sites in RPTOR genes of stroke patients with different clinical characteristics (hemorrhagic stroke, ischemic stroke) were significantly different from the non-recurrent control of stroke with different clinical characteristics (p <0.05, table 12). Thus, the methylation level of the RPTOR gene can be used to predict the likelihood of recurrence of coronary heart disease and stroke disease of different clinical characteristics over a 2 year period.
Table 11, compares the methylation level differences between non-recurrence of cardiovascular and cerebrovascular disease and recurrence of cardiovascular and cerebrovascular disease of different clinical characteristics (prognosis within 2 years)
8. 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, early warning is carried out on individuals with coronary heart disease onset risks within 2 years in the crowd;
(2) Before clinical onset, individuals with coronary heart disease onset risks within 2 years in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases;
(3) Before clinical onset, individuals with cerebral apoplexy incidence risks within 2 years of the population are pre-warned.
(4) Before clinical onset, individuals with cerebral apoplexy incidence risks within 2 years of the population are pre-warned, and the method is suitable for cerebral apoplexy of various types;
(5) Before clinical onset, early warning is carried out on individuals with cerebral apoplexy and coronary heart disease onset risks within 2 years of the population, and coronary heart disease patients and cerebral apoplexy patients are distinguished;
(6) Before clinical onset, early warning is carried out on individuals with coronary heart disease onset risks within 1 year in the crowd;
(7) Before clinical onset, individuals with coronary heart disease onset risks within 1 year in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases;
(8) Before clinical onset, early warning is carried out on individuals with cerebral apoplexy onset risks within 1 year in the crowd;
(9) Before clinical onset, individuals with cerebral apoplexy incidence risks within 1 year in the crowd are pre-warned, and the method is suitable for cerebral apoplexy of various types;
(10) Before clinical onset, early warning is carried out on individuals with cerebral apoplexy and coronary heart disease onset risks within 1 year in the crowd, and coronary heart disease patients and cerebral apoplexy patients are distinguished;
(11) Before clinical onset, early warning is carried out on individuals in the crowd, which are at risk of heart injury due to treatment of tumor medicaments within 2 years;
(12) Early warning individuals with coronary heart disease recurrence risks within 2 years in prognosis population before clinical onset;
(13) Before clinical onset, early warning is carried out on individuals with recurrence risk of cerebral apoplexy within 2 years in prognosis population;
(14) Before clinical onset, individuals with recurrence risk of coronary heart disease within 2 years in prognosis crowd are pre-warned, and the method is suitable for various types of coronary heart disease;
(15) Before clinical onset, individuals with recurrence risk of cerebral apoplexy within 2 years in the prognosis population are pre-warned, and the method is applicable to cerebral apoplexy of various types.
The mathematical model is established as follows:
(A) Data sources: the methylation levels of the target CpG sites of the isolated blood samples of the 342 coronary heart disease patients, 278 cerebral stroke patients and 612 healthy controls or 147 cardiac injury patients and 105 non-cardiac injury people or 118 coronary heart disease recurrent patients and 165 non-recurrent patients or 98 cerebral stroke recurrent patients and 121 non-recurrent patients (combination of one or more of tables 1-3) 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
Selecting any two different types of patient data, namely training sets, according to requirements (for example, coronary heart disease patients and healthy controls, cerebral apoplexy patients and healthy controls, coronary heart disease patients and cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral apoplexy patients and healthy controls, ischemic cerebral apoplexy patients and healthy controls, cardiac injury patients and non-occurrence cardiac injury patients, and the collection of the above patient samples is earlier than the clinical onset time of the disease for 2 years; alternatively, coronary heart disease patients and healthy controls, cerebral apoplexy patients and healthy controls, coronary heart disease patients and cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral apoplexy patients and healthy controls, ischemic cerebral apoplexy patients and healthy controls, and the above patient samples are collected earlier than the clinical onset time of the disease by 1 year, or coronary heart disease recurrence patients and non-recurrence patients, cerebral apoplexy recurrence patients and non-recurrence patients, and the above patient samples are collected within the 2 years later) are used as data for establishing a model, and statistical software such as SAS, R, SPSS and the like is used for establishing a mathematical model by a formula 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 the RPTOR 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 RPTOR gene in the training set 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 statistical software such as SAS, R, SPSS. The mathematical model is herein a two-class logistic regression model, specifically: log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting a methylation value of one or more methylation sites of a sample to be tested into a model as a dependent variable, b0 is a constant, x1-xn is an independent variable which is a methylation value of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is a weight given 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 is class a and which is class B, to be determined according to a specific mathematical model, no convention is made herein), such as: coronary heart disease patients and healthy controls, cerebral stroke patients and healthy controls, coronary heart disease patients and cerebral stroke patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral stroke patients and healthy controls, ischemic cerebral stroke patients and healthy controls, heart damage patients and non-heart damage patients, and the collection of the above patient samples is earlier than the clinical onset time of the disease for 2 years; alternatively, coronary heart disease patients and healthy controls, cerebral stroke patients and healthy controls, coronary heart disease patients and cerebral stroke patients, occult or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral stroke patients and healthy controls, ischemic cerebral stroke patients and healthy controls, and the collection of the above patient samples is within 1 year earlier than the clinical onset time of the disease; alternatively, patients with recurrent coronary heart disease and non-recurrent patients, patients with recurrent cerebral apoplexy and non-recurrent patients, and the samples of the patients are collected within 2 years after prognosis. 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 RPTOR 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 RPTOR 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 RPTOR 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 smaller than the threshold value in the training set; if the methylation level data of one or more CpG sites of the RPTOR 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 is exemplified byMethylation of the preferred CpG sites of RPTOR_B (RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11, and RPTOR_B_12) and mathematical modeling are used to pre-warn whether coronary heart disease will occur within 2 years: the methylation level data of 10 distinguishable CpG site combinations of coronary heart disease patients (earlier than clinical onset time less than or equal to 2 years) and health control training sets (342 coronary heart disease patients and 612 health controls here) and the age (integer) of the patient, sex (male assignment 1, female assignment 0), white blood cell count (unit 10) 9 L) a mathematical model for distinguishing coronary heart disease patients from healthy controls was established by R software using a formula of a two-class logistic regression.
The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: log (y/(1-y))= 4.599-2.119 rptor_b_1.2.3-3.628 rptor_b_4-0.622 rptor_b_5-0.706 rptor_b_6+1.589 rptor_b_7-4.617 rptor_b_8-1.806 rptor_b_9+0.692 rptor_b_10-0.032 rptor_b_11+2.139 rptor_b_12-0.026 (integer) +0.225 sex (male assignment 1, female assignment 0) -0.054 white blood cell count (unit 10) 9 /L). Wherein y is the detection index obtained by substituting the methylation values of 10 distinguishable methylation sites of the sample to be detected into the model according to the dependent variables such as age, sex and white blood cell count. Under the condition that 0.5 is set as a threshold value, the methylation level of 10 distinguishable CpG sites, namely RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11 and RPTOR_B_12 of the sample to be tested, is tested and then calculated together with the age, sex and white blood cell count information substitution model, the obtained detection index, namely y value, is more than 0.5 and is classified as a potential coronary heart disease patient with onset within 2 years, less than 0.5 is classified as a health control, and the methylation level of the CpG sites is not determined as a potential coronary heart disease patient with onset within 2 years or a health control. The area under the curve (AUC) calculation for this model was 0.72 (table 18).
Blood was collected from two subjects (subject No. 1, subject No. 2), DNA was extracted from the blood, and after the extracted DNA was converted by bisulfite, the methylation level of 10 distinguishable CpG sites of rptor_b_1.2.3, rptor_b_4, rptor_b_5, rptor_b_6, rptor_b_7, rptor_b_8, rptor_b_9, rptor_b_10, rptor_b_11, and rptor_b_12 of the subjects was detected by a DNA methylation assay. The methylation level data obtained from the test are then combined with the age (integer), sex (male assigned 1, female assigned 0) and white blood cell count (unit 10) 9 and/L) substituting the information into the mathematical model for early warning whether coronary heart disease can occur within 2 years. The value calculated by the mathematical model of the subject No. 1 is greater than 0.81 and is greater than 0.5, and the subject No. 1 is judged to be a potential patient of coronary heart disease (clinical onset within 2 years in the future); and if the value calculated by the mathematical model of the coronary heart disease of the subject No. 2 is less than 0.36, the subject No. 2 is judged to be healthy control (the standard of the healthy control is still met in the next 2 years). The detection result is consistent with the actual situation.
Examples: the schematic diagram of FIG. 3 illustrates methylation of the preferred CpG sites RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11, and RPTOR_B_12) of RPTOR_B for a mathematical modeling to pre-warn of whether a stroke will occur within 2 years: data on methylation levels of the 10 distinguishable preferred CpG site combinations that have been detected in the cerebral stroke patient (clinical onset time. Ltoreq.2 years) and healthy control as training sets (here: 278 cerebral stroke patients and 612 healthy control), age (integer), sex (male assignment 1, female assignment 0), white blood cell count (unit 10) 9 L) a mathematical model for distinguishing stroke patients from healthy controls is established by R software using a formula of a two-class logistic regression. The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: log (y/(1-y)) = -4.015-1.901 x rptor_b_1.2.3-1.796 x rptor_b_4+4.143 x rptor_b_5-0.955 x rptor_b_6-2.117 x rptor_b_7+1.834 x rptor_b_8+1.970 x rptor_b_9+5.125 x rptor_b 10-0.066 rptor_b_11+1.853 rptor_b_12+0.022 age (integer) -0.284 sex (male assigned 1, female assigned 0) +0.104 white blood cell count (unit 10) 9 /L). Wherein y is the detection index obtained by substituting the methylation values of 10 distinguishable methylation sites of the sample to be detected into the model according to the dependent variables such as age, sex and white blood cell count. Under the condition that 0.5 is set as a threshold value, 10 methylation levels of the RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11 and RPTOR_B_12 of the sample to be tested can be tested and then calculated together with information of age, sex and white blood cell count of the sample to be tested and substituted into a model, the obtained detection index, namely y value, is more than 0.5 and is classified as a potential cerebral apoplexy patient with a disease in 2 years, less than 0.5 is classified as a healthy control, and the methylation level of the 10 distinguishable CpG sites is not determined as a potential cerebral apoplexy patient with a disease in 2 years or a healthy control. The area under the curve (AUC) calculation for this model was 0.71 (table 18).
Blood was collected from two subjects (subject No. 3, subject No. 4), DNA was extracted from the blood, and after transformation of the extracted DNA with bisulfite, the methylation level of 10 distinguishable CpG sites of rptor_b_1.2.3, rptor_b_4, rptor_b_5, rptor_b_6, rptor_b_7, rptor_b_8, rptor_b_9, rptor_b_10, rptor_b_11, and rptor_b_12 of the subjects was detected by a DNA methylation assay. The methylation level data obtained from the test are then combined with the age (integer), sex (male assigned 1, female assigned 0) and white blood cell count (unit 10) 9 and/L) substituting the information into the mathematical model for early warning whether cerebral apoplexy can occur within 2 years. The value calculated by the mathematical model of the subject No. 3 is greater than 0.83 and is greater than 0.5, and the subject No. 3 is judged to be a potential cerebral apoplexy patient (clinical onset within 2 years in the future); and if the value calculated by the cerebral apoplexy mathematical model of the subject No. 4 is less than 0.33 and less than 0.5, the subject No. 4 is judged to be healthy control (the healthy control standard is still met in the next 2 years). The detection result is consistent with the actual situation.
Examples: preferred CpG sites for RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10. Methylation of rptor_b_11 and rptor_b_12) and mathematical modeling was used to determine whether cardiac injury occurred within 2 years of tumor patient treatment with tumor drugs: the methylation level data of the 10 distinguishable preferred CpG site combinations detected in the heart injury patients (clinical onset time. Ltoreq.2 years) and the heart injury-free patients as training sets (147 heart injury patients and 105 heart injury-free patients in this case) and the age (integer), sex (male assignment 1, female assignment 0), white blood cell count (unit 10) 9 L) a mathematical model for distinguishing between heart damaged patients and non-heart damaged patients is established by R software using a formula of a two-class logistic regression. The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: the model three is specifically as follows: log (y/(1-y))=2.518-1.003×rptor_b_1.2.3-0.679×rptor_b_4+3.418×rptor_b_5-0.592×rptor_b_6+1.214×rptor_b_7-0.941×rptor_b_8+1.023×rptor_b_9+4.609×rptor_b_10+0.217×rptor_b_11-0.927×rptor_b_12-0.031×age (integer number of female+0.703×white blood cell number (unit 10) with a male assignment of 1+0.384×female assignment of 0.703×white blood cell number 9 /L). Wherein y is the detection index obtained by substituting the methylation values of 10 distinguishable methylation sites of the sample to be detected into the model according to the dependent variables such as age, sex and white blood cell count. Under the condition that 0.5 is set as a threshold value, the methylation level of the 10 distinguishable CpG sites of the RPTOR_B_1.2.3, the RPTOR_B_4, the RPTOR_B_5, the RPTOR_B_6, the RPTOR_B_7, the RPTOR_B_8, the RPTOR_B_9, the RPTOR_B_10, the RPTOR_B_11 and the RPTOR_B_12 of the sample to be tested is tested and then is calculated together with the information substitution model of age, sex and white cell count of the two different CpG sites, the obtained detection index, namely y value, is more than 0.5, and is classified as a patient suffering from heart injury in 2 years after being treated by a tumor drug, the patient suffering from heart injury in 2 years after being treated by the tumor drug is less than 0.5, and whether the heart injury occurs in 2 years after being treated by the tumor drug is not determined. The area under the curve (AUC) calculation for this model was 0.72 (table 22).
Separately collected from two subjects (subject No. 5, subject No. 6)Blood is pooled to extract DNA, the extracted DNA is converted by bisulfite, and the methylation level of 10 distinguishable CpG sites, namely RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11 and RPTOR_B_12, of the subject is detected by a DNA methylation assay. The methylation level data obtained from the test are then combined with the age (integer), sex (male assigned 1, female assigned 0) and white blood cell count (unit 10) 9 and/L) substituting the information into the mathematical model of the heart injury. The value calculated by the heart injury mathematical model of the subject No. 5 is 0.81 to be more than 0.5, and the subject No. 5 is judged to be a patient with heart injury within 2 years after being treated by the tumor drug (clinical onset within 2 years in the future); and if the calculated value of the number 6 subject through the cerebral apoplexy mathematical model is less than 0.36 and is less than 0.5, the number 6 subject is judged to be a patient with heart injury within 2 years after being treated by the tumor medicament (the prognosis accords with the non-recurrence standard within 2 years). The detection result is consistent with the actual situation.
Examples: for example, methylation of preferred CpG sites RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11, and RPTOR_B_12) of RPTOR_B is used to predict whether coronary heart disease will recur within 2 years of prognosis: the methylation level data of the 10 distinguishable preferred CpG site combinations detected in the patients with recurrent coronary heart disease (recurrent time less than or equal to 2 years) and non-recurrent coronary heart disease as the training set (118 patients with recurrent coronary heart disease and 165 non-recurrent coronary heart disease) and the age (integer), sex (male assignment 1 and female assignment 0), white blood cell count (unit 10) 9 L) a mathematical model for distinguishing between patients with recurrent coronary heart disease and patients without recurrent coronary heart disease within 2 years of prognosis was established by R software using a formula of a two-class logistic regression. The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: log (y/(1-y))=4.175-0.932×rptor_b_1.2.3-2.716×rptor_b_4+2.118×rptor_b_5-1.651×rptor_b_6+0.487×rptor_b_7-1.774×rptor_b_8-0.839×rptor_b_9+3.67Rptor_b_10-0.149 rptor_b_11+1.550 rptor_b_12+0.036 age (integer) -0.410 sex (male assigned 1, female assigned 0) +0.093 white blood cell count (unit 10) 9 /L). Wherein y is the detection index obtained by substituting the methylation values of 10 distinguishable methylation sites of the sample to be detected into the model according to the dependent variables such as age, sex and white blood cell count. Under the condition that 0.5 is set as a threshold value, the methylation level of the 10 distinguishable CpG sites of the RPTOR_B_1.2.3, the RPTOR_B_4, the RPTOR_B_5, the RPTOR_B_6, the RPTOR_B_7, the RPTOR_B_8, the RPTOR_B_9, the RPTOR_B_10, the RPTOR_B_11 and the RPTOR_B_12 of the sample to be tested is tested and then calculated together with the age, sex and white blood cell count information substitution model, and the obtained detection index, namely y value, is more than 0.5 and is classified as a patient suffering from recurrent coronary heart disease in 2 years, less than 0.5 is classified as a patient suffering from non-recurrent coronary heart disease in 2 years, and the condition that the methylation level of the 10 distinguishable CpG sites is not determined as a patient suffering from recurrent coronary heart disease in 2 years after the prognosis or a patient suffering from non-recurrent coronary heart disease. The area under the curve (AUC) calculation for this model was 0.70 (table 22).
Blood was collected from two subjects (subject No. 7, subject No. 8), DNA was extracted from the blood, and after transformation of the extracted DNA with bisulfite, the methylation level of 10 distinguishable CpG sites of rptor_b_1.2.3, rptor_b_4, rptor_b_5, rptor_b_6, rptor_b_7, rptor_b_8, rptor_b_9, rptor_b_10, rptor_b_11, and rptor_b_12 of the subjects was detected by a DNA methylation assay. The methylation level data obtained from the test are then combined with the age (integer), sex (male assigned 1, female assigned 0) and white blood cell count (unit 10) 9 and/L) substituting the information into the mathematical model for evaluating the recurrence risk of coronary heart disease. The number 7 subject is judged to be a patient with recurrent coronary heart disease (recurrent coronary heart disease within two years after the prognosis) if the value calculated by the mathematical model for evaluating recurrent coronary heart disease risk is 0.86 to be more than 0.5; and if the value calculated by the mathematical model of the coronary heart disease recurrence risk assessment of the subject No. 8 is less than 0.5, the subject No. 8 is judged to be a coronary heart disease non-recurrence patient (the non-recurrence standard is still met within 2 years after prognosis). The detection result is consistent with the actual situation.
Examples: preferred CpG sites for RPTOR_B_1.2 Methylation of 3, rptor_b_4, rptor_b_5, rptor_b_6, rptor_b_7, rptor_b_8, rptor_b_9, rptor_b_10, rptor_b_11, and rptor_b_12) and mathematical modeling are used to pre-warn the discrimination of whether a stroke recurs within 2 years of prognosis: the methylation level data of the 10 distinguishable preferred CpG site combinations which have been detected in the training set (98 patients with recurrent cerebral stroke and 121 patients with non-recurrent cerebral stroke are used as controls) and the age (integer), sex (male assignment 1, female assignment 0), white blood cell count (unit 10) 9 L) establishing a mathematical model for distinguishing between stroke relapsing patients and stroke non-relapsing patients by R software using a formula of a two-class logistic regression. The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: log (y/(1-y)) = -2.993+0.896 rptor_b_1.2.3+1.531 rptor_b_4-3.016 rptor_b_5+0.675 rptor_b_6+1.237 rptor_b_7+1.504 rptor_b_8-1.368 rptor_b_9-2.657 rptor_b_10+0.167 rptor_b_11+1.553 rptor_b_12+0.013 age (integer) +0.315 sex (male assigned 1, female assigned 0) +0.251 white blood cell number (unit 10) 9 /L). Wherein y is the detection index obtained by substituting the methylation values of 10 distinguishable methylation sites of the sample to be detected into the model according to the dependent variables such as age, sex and white blood cell count. Under the condition that 0.5 is set as a threshold value, 10 methylation levels of the CpG sites of the RPTOR_B_1.2.3, the RPTOR_B_4, the RPTOR_B_5, the RPTOR_B_6, the RPTOR_B_7, the RPTOR_B_8, the RPTOR_B_9, the RPTOR_B_10, the RPTOR_B_11 and the RPTOR_B_12 of the sample to be tested are tested and then calculated together with information substitution models of age, sex and white cell count of the methylation levels, and the obtained detection index, namely y value, is more than 0.5 and is classified as a patient with recurrence of cerebral apoplexy within 2 years after prognosis, less than 0.5 is classified as a patient with non-recurrence of cerebral apoplexy within 2 years after prognosis, and the methylation levels of the 10 different CpG sites are not determined as a patient with recurrence of cerebral apoplexy within 2 years after the detection index, namely y value is more than 0.5. The area under the curve (AUC) calculation for this model was 0.71 (table 22).
From two subjects (subject No. 9, subject No. 10)Blood is pooled to extract DNA, the extracted DNA is converted by bisulfite, and the methylation level of 10 distinguishable CpG sites, namely RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11 and RPTOR_B_12, of the subject is detected by a DNA methylation assay. The methylation level data obtained from the test are then combined with the age (integer), sex (male assigned 1, female assigned 0) and white blood cell count (unit 10) 9 and/L) substituting the information into the cerebral apoplexy recurrence risk assessment mathematical model. The value calculated by the mathematical model of the recurrence risk assessment of the cerebral apoplexy of the first-class subject is more than 0.5, and the first-class subject is judged to be a patient with recurrence of cerebral apoplexy (clinical onset within 2 years after prognosis); and if the value calculated by the cerebral apoplexy mathematical model of the second subject is less than 0.38 and less than 0.5, the second subject is judged to be a cerebral apoplexy non-recurrence patient (the non-recurrence control standard is still met within 2 years later). The detection result is consistent with the actual situation.
(C) Model Effect evaluation
According to the method, a method for distinguishing coronary heart disease patients from healthy controls, cerebral apoplexy patients from healthy controls, coronary heart disease patients from cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients from healthy controls, angina patients from healthy controls, myocardial infarction patients from healthy controls, ischemic cardiomyopathy patients from healthy controls, sudden death patients from healthy controls, hemorrhagic cerebral apoplexy patients from healthy controls, ischemic cerebral apoplexy patients from healthy controls, cardiac injury patients from cardiac injury patients, is established, and the collection of the above patient samples is earlier than the clinical onset time of the disease for 2 years; for distinguishing coronary heart disease patients from healthy controls, cerebral stroke patients from healthy controls, coronary heart disease patients from cerebral stroke patients, latent or asymptomatic myocardial ischemia patients from healthy controls, angina patients from healthy controls, myocardial infarction patients from healthy controls, ischemic myocardial patients from healthy controls, sudden death patients from healthy controls, hemorrhagic cerebral stroke patients from healthy controls, ischemic cerebral stroke patients from healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease by 1 year; for distinguishing between patients with recurrent coronary heart disease and non-recurrent patients, patients with recurrent cerebral stroke and non-recurrent patients, and the samples of the above patients were collected within a period of 2 years after prognosis, and the effectiveness thereof was 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 Table 12, table 13, table 14, table 15, table 16 and Table 17. Table 12, table 13, table 14, table 15, table 16 and table 17,1 CpG site represents the site of any one CpG site in the amplified fragment of rptor_b, 2 CpG sites represent the combination of any 2 CpG sites in rptor_b, 3 CpG sites represent the combination of any 3 CpG sites in rptor_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 ability of the RPTOR gene to discriminate between groups (coronary heart disease patient and healthy control, cerebral apoplexy patient and healthy control, coronary heart disease patient and healthy control, latent or asymptomatic myocardial ischemia patient and healthy control, angina patient and healthy control, myocardial infarction patient and healthy control, ischemic cardiomyopathy patient and healthy control, sudden death patient and healthy control, hemorrhagic stroke patient and healthy control, ischemic stroke patient and healthy control, cardiac injury patient and non-cardiac injury patient, and the sample of the above patient is collected earlier than the clinical onset time of the disease by 2 years, or coronary heart disease patient and healthy control, cerebral apoplexy patient and healthy control, coronary heart disease patient and cerebral apoplexy patient, latent or asymptomatic myocardial ischemia patient and healthy control, angina patient and healthy control, myocardial infarction patient and healthy control, ischemic cardiomyopathy patient and healthy control, dead patient and healthy control, hemorrhagic stroke patient and healthy control, ischemic stroke patient and healthy control, and sample of the above patient is collected earlier than the clinical onset time of the disease by 1 year, or the sample of the above patient is increased and the recurrence time of the disease by 2 years, and the recurrence time of the disease is increased in the clinical onset time of the disease and the non-disease.
In addition, among the CpG sites shown in tables 1 to 3, there are cases where combinations of a few preferred sites are better in discrimination ability than combinations of a plurality of non-preferred sites. For example, the 10 distinguishable CpG sites shown in tables 18, 19, 20, 21, 22 and 23, RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, RPTOR_B_11, RPTOR_B_12 are preferred sites for any 10 combinations in RPTOR_B.
In summary, cpG sites on the RPTOR gene and various combinations thereof, cpG sites on the RPTOR_A fragment and various combinations thereof, cpG sites on the RPTOR_B fragment and various combinations thereof, RPTOR_B_1.2.3, RPTOR_B_4, RPTOR_B_5, RPTOR_B_6, RPTOR_B_7, RPTOR_B_8, RPTOR_B_9, RPTOR_B_10, 4RPTOR_B_11 and RPTOR_B_12CpG sites and various combinations thereof, the methylation levels of the CpG sites and various combinations thereof on the rptor_c fragment and the CpG sites and various combinations thereof on the rptor_ A, RPTOR _ B, RPTOR _c were measured for each group of subjects (coronary heart disease patients and healthy controls, cerebral stroke patients and healthy controls, coronary heart disease patients and cerebral stroke patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral stroke patients and healthy controls, ischemic cerebral stroke patients and healthy controls, cardiac injury patients and non-occurrence of cardiac injury patients, and the collection of the above patient samples was earlier than the clinical onset time of the disease for 2 years; alternatively, coronary heart disease patients and healthy controls, cerebral apoplexy patients and healthy controls, coronary heart disease patients and cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina pectoris patients and healthy controls, myocardial infarction patients and healthy controls, ischemic myocardial patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral apoplexy patients and healthy controls, ischemic cerebral apoplexy patients and healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease by 1 year, alternatively, coronary heart disease recurrence patients and non-recurrence patients, patients with recurrent cerebral stroke and non-recurrent cerebral stroke, and the collection of the samples of the above patients is within 2 years of prognosis).
CpG sites of Table 12, RPTOR_B and combinations thereof for distinguishing healthy controls and cerebral stroke, healthy controls and coronary heart disease, cerebral stroke and coronary heart disease (2 years earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
CpG sites of Table 13, RPTOR_B and combinations thereof for distinguishing healthy controls and cerebral stroke, healthy controls and coronary heart disease, cerebral stroke and coronary heart disease (1 year earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
CpG sites of Table 14, RPTOR_B and combinations thereof for differentiating healthy controls from coronary heart disease and cerebral apoplexy patients with different clinical characteristics (2 years earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
CpG sites of Table 15, RPTOR_B and combinations thereof for differentiating healthy controls from coronary heart disease and cerebral apoplexy patients with different clinical characteristics (1 year earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
CpG sites of surface 16RPTOR_B and combinations thereof for distinguishing heart injury patients from heart injury-free patients, recurrence of coronary heart disease and non-recurrence of coronary heart disease, recurrence of cerebral apoplexy and non-recurrence of cerebral apoplexy
Note that: the data in the table are area under the curve (AUC), where heart damage is within 2 years prior to clinical onset and recurrence of coronary heart disease and stroke is within 2 years of prognosis.
Table 17, cpG sites of RPTOR_B and combinations thereof are used to distinguish patients with cardiovascular and cerebrovascular recurrence of different clinical characteristics from non-recurrence controls (within two years of prognosis)
Table 18, RPTOR_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 (2 years earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
Table 19, RPTOR_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 (1 year earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
Table 20, optimal CpG sites for RPTOR_B and combinations for differentiating healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (2 years earlier than clinical onset time)
Note that: the data in the table are area under the curve (AUC).
Table 21, best CpG sites of RPTOR_B and combinations for differentiating healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (1 year earlier than the clinical onset time)
Note that: the data in the table are area under the curve (AUC).
Table 22, optimal CpG sites of RPTOR_B and combinations for distinguishing heart injury from absence of heart injury, recurrence and non-recurrence of coronary heart disease, recurrence and non-recurrence of cerebral apoplexy
And (3) injection: the data in the table are area under the curve (AUC), where heart damage is within 2 years prior to clinical onset and recurrence of coronary heart disease and stroke is within 2 years of prognosis.
Table 23, best CpG sites of RPTOR_B and combinations for distinguishing patients with recurrent coronary heart disease with different clinical characteristics from non-recurrent controls (prognosis within 2 years)
Note that: the data in the table are area under the curve (AUC), where recurrence of coronary heart disease and stroke of different clinical characteristics is a recurrence within 2 years of prognosis.
The present invention is described in detail above. It will be apparent to those skilled in the art that the present invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with respect to specific embodiments, it will be appreciated that the invention may be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The application of some of the basic features may be done in accordance with the scope of the claims that follow.
Claims (10)
1. A methylation biomarker, characterized in that: the nucleotide sequence of the methylation biomarker is all or part of fragments shown in the following (A1) - (A3) in the RPTOR gene:
(A1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(A2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;
(A3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto;
the methylation marker comprises a CpG site located on the nucleotide sequence of the methylation marker as shown in any one of the following (B1) - (B7): (B1) Any one or more CpG sites in 3 DNA fragments shown as SEQ ID No.1, SEQ ID No.2 and SEQ ID No.3 in the RPTOR gene;
(B2) All CpG sites on the DNA fragment shown in SEQ ID No.1 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the RPTOR gene;
(B3) All CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the RPTOR gene;
(B4) All CpG sites on the DNA fragment shown in SEQ ID No.1 and all CpG sites on the DNA fragment shown in SEQ ID No.2 in the RPTOR gene;
(B5) All CpG sites on the DNA fragment shown in SEQ ID No.1, all CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the RPTOR gene;
(B6) All CpG sites or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG sites in the DNA fragment shown in SEQ ID No.2 in the RPTOR gene;
(B7) All 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 10 CpG sites on the DNA fragment shown in SEQ ID No.2 in the RPTOR gene:
item 1: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 26 th to 27 th, 34 th to 35 th and 36 th to 37 th of the 5' end;
item 2: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 61-62 positions of the 5' end;
item 3: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 97 th to 98 th positions of the 5' end;
item 4: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 133 th to 134 th positions of the 5' end;
item 5: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 152 th to 153 th positions of the 5' end;
item 6: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 232 th to 233 th positions of the 5' end;
Item 7: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 259 to 260 positions of the 5' end;
item 8: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 280 th to 281 th positions of the 5' end;
item 9: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 313 th to 314 th positions of the 5' end;
item 10: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 418 to 419 positions of the 5' end;
the methylation biomarker is used for at least one of the following purposes:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
2. Use of the methylation biomarker of claim 1 in the manufacture of a product; the application of the product is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
3. Use of a substance for detecting the methylation level of the methylation biomarker of claim 1 in the manufacture of a product; the application of the product is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
4. Use of a substance for detecting the methylation level of a methylation biomarker according to claim 1 and a medium storing a mathematical model and/or a method of using a mathematical model for the preparation of a product; the application of the product is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset;
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting the methylation level of the methylation biomarker of claim 1 of n1 type a samples and n2 type B samples, respectively;
(A2) Taking methylation level data of the methylation biomarkers of claim 1 of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type;
the using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of the methylation biomarker of claim 1 in a test sample;
(B2) Substituting methylation level data of the methylation biomarker of claim 1 of the sample to be tested 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 and healthy controls for onset within the next 2 years;
(C2) Potential patients with stroke and healthy controls that developed within the next 2 years;
(C3) Coronary heart disease potential patients with onset in the next 2 years and cerebral apoplexy potential patients with onset in the next 2 years;
(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;
(C5) Stroke potential patients and healthy controls of different clinical characteristics of onset within the next 2 years;
(C6) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;
(C7) Potential patients with stroke and healthy controls that developed within the next 1 year;
(C8) Coronary heart disease potential patients with onset in the next 1 year and cerebral apoplexy potential patients with onset in the next 1 year;
(C9) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year;
(C10) Stroke potential patients and healthy controls of different clinical characteristics of the onset within the next 1 year;
(C11) Patients with and without heart injury due to treatment with tumor drugs in the next 2 years;
(C12) Patients with recurrent cardiovascular and cerebrovascular diseases and non-recurrent patients within 2 years of prognosis;
(C13) Coronary heart disease recurrent patients and non-recurrent patients within 2 years of prognosis;
(C14) Stroke relapsing patients and non-relapsing patients within 2 years of prognosis;
(C15) Coronary heart disease recurrent patients and non-recurrent patients with different clinical characteristics within 2 years of prognosis;
(C16) Cerebral stroke relapsing patients and non-relapsing patients with different clinical characteristics within 2 years of prognosis.
5. A kit comprising a substance for detecting the methylation level of the methylation biomarker of claim 1; the application of the kit is at least one of the following:
(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;
(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;
(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;
(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;
(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;
(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;
(7) Auxiliary diagnosis of cardiac injury or early warning of cardiac injury prior to clinical onset;
(8) The cardiovascular and cerebrovascular disease recurrence monitoring is assisted or the cardiovascular and cerebrovascular disease recurrence is early-warned before clinical onset;
(9) Auxiliary coronary heart disease recurrence monitoring or early warning of coronary heart disease recurrence before clinical onset;
(10) Auxiliary cerebral apoplexy recurrence monitoring or cerebral apoplexy recurrence pre-warning before clinical onset;
(11) Auxiliary monitoring of coronary heart disease recurrence with different clinical characteristics or early warning of coronary heart disease recurrence with different clinical characteristics before clinical onset;
(12) Auxiliary monitoring of cerebral apoplexy recurrence with different clinical characteristics or early warning of cerebral recurrence with different clinical characteristics before clinical onset.
6. The kit of claim 5, wherein: the kit further comprises a medium storing a mathematical model and/or a method for using the mathematical model as described in claim 4.
7. A system, comprising:
(D1) Reagents and/or instrumentation for detecting the methylation level of the methylation biomarker of claim 1;
(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 configured to acquire (D1) methylation level data of the methylation biomarker of claim 1 of n1 type a samples and n2 type B samples detected;
the data analysis processing module is configured to receive methylation level data of the methylation biomarker of the claim 1 from n1 type A samples and n2 type B samples sent by the data acquisition module, and establish a mathematical model through a two-classification logistic regression method according to classification modes of the type A and the type B to determine a threshold value of classification judgment;
The model output module is configured to receive the mathematical model sent by the data analysis processing module and output the mathematical model;
the unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;
the data input module is configured to input (D1) the methylation level data of the methylation biomarker of claim 1 detected by the subject;
the data operation module is configured to receive methylation level data of the methylation biomarker of claim 1 of the person to be tested, sent by the data input module, and substitute the methylation level data of the methylation biomarker of claim 1 of the person to be tested into the mathematical model to calculate a detection index;
the data comparison module is configured to receive the detection index sent from the data operation module and compare the detection index with the threshold value determined in the data analysis processing module in the unit X;
the conclusion output module is configured to receive the comparison result sent by the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result;
The type a sample and the type B sample are any one of the following:
(C1) Potential patients with coronary heart disease and healthy controls for onset within the next 2 years;
(C2) Potential patients with stroke and healthy controls that developed within the next 2 years;
(C3) Coronary heart disease potential patients with onset in the next 2 years and cerebral apoplexy potential patients with onset in the next 2 years;
(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;
(C5) Stroke potential patients and healthy controls of different clinical characteristics of onset within the next 2 years;
(C6) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;
(C7) Potential patients with stroke and healthy controls that developed within the next 1 year;
(C8) Coronary heart disease potential patients with onset in the next 1 year and cerebral apoplexy potential patients with onset in the next 1 year;
(C9) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year;
(C10) Stroke potential patients and healthy controls of different clinical characteristics of the onset within the next 1 year;
(C11) Patients with and without heart injury due to treatment with tumor drugs in the next 2 years;
(C12) Patients with recurrent cardiovascular and cerebrovascular diseases and non-recurrent patients within 2 years of prognosis;
(C13) Coronary heart disease recurrent patients and non-recurrent patients within 2 years of prognosis;
(C14) Stroke relapsing patients and non-relapsing patients within 2 years of prognosis;
(C15) Coronary heart disease recurrent patients and non-recurrent patients with different clinical characteristics within 2 years of prognosis;
(C16) Cerebral stroke relapsing patients and non-relapsing patients with different clinical characteristics within 2 years of prognosis.
8. The use or kit or system according to any one of claims 2-7, wherein: the time of clinical onset is 2 years earlier than the time of clinical onset or 1 year earlier than the time of clinical onset.
9. The use or kit or system according to any one of claims 2-8, wherein: the substance for detecting the methylation level of the methylation biomarker of claim 1 comprising primer pair a and/or primer pair B and/or primer pair C;
the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 5;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 7;
The primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is single-stranded DNA shown in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer C2 is SEQ ID No.9 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 9.
10. The use or kit or system according to any one of claims 2-9, wherein: detecting the methylation level of the methylation biomarker of claim 1 is detecting the methylation level of the methylation biomarker of claim 1 in a blood sample.
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