Acute coronary syndrome pathological typing diagnostic kit and diagnostic system based on methylation and transcriptome

Abstract

The invention relates to the technical field of biological diagnosis, and provides an acute coronary syndrome pathological typing diagnostic kit and diagnostic system based on methylation and transcriptome. The invention provides application of a reagent for jointly detecting the expression levels of DEFA1B and AZU1 and the methylation level of a cg15610437 site in preparation of a diagnostic kit for pathological typing of acute coronary syndrome, and further provides a corresponding diagnostic kit and a diagnostic system. The invention provides an in-vitro pathological typing diagnosis scheme based on methylation and transcriptome sequencing aiming at clinical requirements of acute coronary syndromes, which is hopeful to accelerate and simplify the identification process of PE and PR, also provides important technical support for making personalized treatment strategies, and has good clinical application prospect and popularization value.

Description

Technical Field

[0001] The present invention belongs to the field of biological detection technology and relates to the typing diagnosis of acute coronary syndrome (ACS), and specifically to a diagnostic marker, diagnostic kit and diagnostic system for pathological typing of ACS based on methylation and transcriptome. Background Art

[0002] Acute coronary syndrome (ACS) is one of the leading cardiovascular diseases causing high mortality and morbidity worldwide. Its primary clinical manifestations include unstable angina and acute myocardial infarction. Existing studies have shown that the pathogenesis of ACS is heterogeneous, with two main pathological types: plaque rupture (PR) and plaque erosion (PE).

[0003] Plaque rupture (PR) commonly occurs in lesions with a thin fibrous cap, a rich lipid core, and a high inflammatory burden. When the fibrous cap ruptures, the exposed lipid core can come into contact with blood and form thrombi, leading to acute coronary artery occlusion. Plaque erosion (PE), manifested by endothelial cell shedding or damage without obvious fibrous cap rupture, can also form thrombi and cause acute coronary events. Current research suggests that PE is often clinically associated with lower levels of inflammation, may have a better prognosis than PR, and may benefit from personalized treatment strategies other than conventional stent implantation.

[0004] In clinical practice, efforts are underway to accurately differentiate the pathological subtypes (PE or PR) of ACS patients, but difficulties remain. For example, comprehensive scoring models have been constructed using a combination of multiple conventional indicators (e.g., age, gender, and risk factors associated with diabetes) with serum markers (e.g., cTn, DD dimer) to predict the risk of short-term or long-term adverse events in ACS. However, most of these models lack the ability to differentiate between pathological subtypes and are unable to provide a definitive diagnosis of PE or PR. Although optical coherence tomography (OCT) is considered the "gold standard" for distinguishing PE from PR in imaging-assisted diagnosis, it remains difficult to widely adopt in resource-limited areas due to its high cost, complex operation, and high technical requirements for both equipment and operators. Furthermore, prolonged catheterization during emergency PCI may also increase patient risk.

[0005] In summary, there are currently few comprehensive solutions on the market that combine high sensitivity, non-invasiveness, and specific PE / PR typing. Therefore, a blood test combining multi-omics (such as transcriptomics and DNA methylation) is urgently needed to overcome the limitations of existing technologies and provide more reliable support for the clinical differentiation of plaque rupture and plaque erosion. The development of a rapid, simple, accurate, and cost-effective non-invasive test to distinguish PE from PR in ACS patients is of great significance for guiding individualized clinical treatment.

[0006] In recent years, with the rapid development of high-throughput sequencing technology, peripheral blood analysis of gene expression profiles (transcriptomes) and epigenetic information such as DNA methylation has shown promising promise in the early diagnosis, subtype differentiation, and prognostic assessment of various diseases (such as tumors and inflammatory diseases). However, systematic multi-omics analyses and diagnostic models for the different pathological subtypes (PE and PR) of ACS patients are currently lacking. This present invention addresses this technological gap by integrating transcriptome sequencing with DNA methylation testing to establish a novel in vitro pathological typing diagnostic method capable of distinguishing PE and PR at the peripheral blood level, providing a new technical approach for the precise and personalized treatment of ACS. Summary of the Invention

[0007] The present invention addresses the difficulty in ACS classification diagnosis, and aims to provide diagnostic markers, diagnostic kits and diagnostic systems for acute coronary syndrome pathological classification based on methylation and transcriptome.

[0008] The present invention strives to solve the following technical problems:

[0009] The goal is to quickly and accurately identify the plaque type in ACS patients without relying on expensive and invasive OCT examinations, and to provide a clinical diagnosis scheme that can effectively determine whether the patient has plaque rupture (PR) or plaque erosion (PE) using peripheral blood.

[0010] Realize the integrated application of multi-omics data: Simultaneously detect the expression levels and DNA methylation status of key genes in peripheral blood, and comprehensively judge the results through model algorithms to improve diagnostic sensitivity and specificity.

[0011] Promote the implementation of personalized treatment for ACS: By differentiating different pathological subtypes, provide scientific basis for PE patients who are more suitable for intensive antithrombotic or non-stent treatment, so as to avoid excessive or inappropriate stent implantation and maximize patient prognosis.

[0012] In order to solve the above technical problems, the present invention has the following innovations and improvements:

[0013] (1) Combination of multiple omics: Traditional methods mainly rely on single-angle biomarkers or imaging examinations. The present invention is based on dual high-throughput detection of transcriptome and DNA methylation, combining "gene expression characteristics" with "epigenetic characteristics", thereby significantly improving the accuracy of distinguishing PE from PR.

[0014] (2) New discoveries of key biological pathways: Through large-scale differential analysis and enrichment analysis, the present invention discovered a series of important genes related to neutrophil activation and degranulation (such as DEFA1B, AZU1, etc.) and their specific methylation sites (such as cg15610437, etc.), which provided new ideas for further understanding the differences in molecular mechanisms between PE and PR.

[0015] (3) Non-invasive and easy to operate: The present invention is based on peripheral blood testing, does not require additional invasive operations, is friendly to patient compliance, and the detection cost is significantly lower than conventional invasive imaging technologies such as OCT, making it more suitable for promotion and application in medical institutions with limited resources.

[0016] (4) Accurate model construction: A variety of feature screening algorithms and a 10-fold cross-validation strategy were used in model training. The final model showed good discrimination ability (high AUC value) in both the internal training set and the external independent validation set, and had good versatility and stability.

[0017] The specific technical solutions of the present invention are as follows:

[0018] In the first aspect of the present invention, DEFA1B, AZU1, and the cg15610437 locus are diagnostic markers for the pathological classification of acute coronary syndrome. Feature screening and differential analysis revealed that the combination of these two mRNAs and one methylation site demonstrated excellent sensitivity and specificity as diagnostic markers (AUC: 0.967, 95% CI: 0.818-1.000).

[0019] The second aspect of the present invention provides the use of a reagent for jointly detecting the expression levels of DEFA1B and AZU1 and the methylation level of the cg15610437 site in a biological sample in the preparation of a diagnostic kit for pathological typing of acute coronary syndrome.

[0020] Preferably, the reagents for detecting the expression levels of DEFA1B and AZU1 and the methylation level of the cg15610437 site are selected from primers or high-throughput detection chips that have detection specificity for DEFA1B, AZU1 and cg15610437.

[0021] More preferably, the primer sequences with detection specificity for DEFA1B, AZU1 and cg15610437 are shown in Table 1 below:

[0022] Table 1 Summary of primer sequences

[0023]

[0024] Preferably, the biological sample is selected from the peripheral blood of the test subject.

[0025] In a third aspect, the present invention provides a diagnostic kit for acute coronary syndrome pathological typing, comprising reagents for jointly detecting the expression levels of DEFA1B and AZU1 and the methylation level of the cg15610437 site in peripheral blood. Specifically, the kit comprises an amplification system and a primer system, wherein the primer system comprises the primer sequences set forth in SEQ ID NOs. 1 to 7.

[0026] Preferably, when the methylation level of the cg15610437 site is detected, it is combined with a pyrophosphate sequencing method to output the methylation level detection value in the form of a percentage.

[0027] In a fourth aspect, the present invention provides an acute coronary syndrome pathological classification and diagnosis system, comprising an input and display module, an analysis module, a storage module, a communication module, and a control module.

[0028] The input display module receives the test results for DEFA1B and AZU1 expression levels, as well as the methylation level of the cg15610437 site, along with basic patient information. After analysis, the module displays the pathological classification results. The test results are obtained using the aforementioned test kits, or using a chip or sequencing platform. Basic patient information is imported from the patient's electronic medical record, including the patient's name, gender, medical record number, and preliminary clinical diagnosis, determined based on actual needs.

[0029] The analysis module has been trained and validated in advance, and analyzes whether the test sample belongs to the plaque erosion (PE) or plaque rupture (PR) type based on the detection values ​​of DEFA1B and AZU1 expression levels and the methylation level of the cg15610437 site.

[0030] The training and verification process is as follows:

[0031] Before modeling, multi-omics feature screening was performed: differential gene expression and differential methylation sites were comprehensively analyzed to exclude highly correlated or redundant features; a variety of feature importance algorithms (such as random forest, LASSO, XGBoost, etc.) were used for screening to obtain the top-ranked differentially expressed genes between the PE and PR groups.

[0032] Model training and internal validation: The data were randomly divided into a training set and an internal validation set. 10-fold cross-validation was used to optimize model hyperparameters and avoid overfitting. The area under the ROC curve (AUC) was selected as the primary evaluation metric, and indicators such as sensitivity, specificity, and accuracy were calculated for verification.

[0033] The results showed that in the PE group, the expression of DEFA1B and AZU1 was significantly upregulated compared with the PR group, but the methylation levels were significantly lower than those in the PR group. Therefore, when the expression levels of DEFA1B and AZU1 were higher than the PR type mean and the methylation percentage of the cg15610437 site was lower than the PR type mean, the pathological type of the sample was determined to be PE; when the expression levels of DEFA1B and AZU1 were lower than the PE type mean and the methylation percentage of the cg15610437 site was higher than the PE type mean, the pathological type of the sample was determined to be PR.

[0034] The functions of the storage module, communication module and control module are the same as those in the prior art, and are respectively used for storing relevant data, communicating with external devices and controlling the normal operation of the system.

[0035] According to a fifth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the functions of the system described above are realized.

[0036] Functions and effects of the invention

[0037] (1) Non-invasiveness and economic feasibility: Compared with high-cost, invasive imaging examinations such as OCT, the present invention only requires peripheral blood to complete the test, reducing the dependence on high-end instruments and professional technicians, and has a broader application prospect.

[0038] (2) High specificity and high sensitivity: The multi-omics diagnostic model showed excellent discrimination ability in both internal and external cohorts, with a high AUC index, which can effectively reduce the misclassification of PE and PR, thereby improving the diagnostic accuracy.

[0039] (3) Personalized treatment decision support: Based on the detection results of the present invention, some PE patients can avoid unnecessary stent implantation and instead choose intensive antithrombotic therapy or more conservative intervention measures, which improves the accuracy of treatment and may improve patient prognosis.

[0040] (4) The key molecules (DEFA1B, AZU1) and methylation sites (cg15610437) discovered in this invention provide important clues for further studying the pathogenesis and potential therapeutic targets of plaque erosion.

[0041] (5) Scalability: The methodology of the present invention also has potential reference and transfer value in the research and diagnosis of other diseases related to vascular lesions (such as peripheral atherosclerosis, etc.).

[0042] In summary, the present invention addresses the clinical needs of acute coronary syndrome and provides an in vitro pathological typing diagnostic scheme based on methylation and transcriptome sequencing. It is not only expected to accelerate and simplify the differentiation process between PE and PR, but also provides important technical support for the formulation of personalized treatment strategies. It has good clinical application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The transcriptome landscapes of plaque erosion and plaque rupture cohorts in the Discovery set are shown: (A) principal component analysis (PCA) of PE and PR groups; (B) volcano plot of differentially expressed genes between PE and PR groups; (C) GO and KEGG functional enrichment results of the top 20 differentially expressed genes between PE and PR groups; (D) ridge plot of enriched terms obtained by gene set enrichment analysis (GSEA) between PE and PR groups; (E) protein-protein interaction (PPI) network of the top 18 genes significantly upregulated in the PE group compared with the PR group.

[0044] Figure 2 A flowchart of the feature screening and modeling process is shown, where PE, plaque erosion; PR, plaque rupture; AUC, area under the curve; mRNA, messenger RNA; ROC, receiver operating characteristic; qPCR, quantitative polymerase chain reaction; and DCA, decision curve analysis.

[0045] Figure 3 Figure 3. Upregulated mRNAs in the erosion versus rupture groups are shown to aid in the identification of PE in the PR cohort. (A) Receiver operating characteristic (ROC) curves for the two markers used in the trained model to distinguish PE from PR. (B) ROC curves for the internal validation set.

[0046] Figure 4Figure 2 shows the DNA methylation landscape of plaque erosion and plaque rupture in the discovery cohort: (A) PCA plot of the global methylation signature in peripheral blood between the PE and PR groups. (B) Comparison of methylation levels within the 1500 bp upstream and downstream of the transcription start site between the PE and PR groups, revealing significantly decreased methylation levels in the PE group compared to the PR group. (C) Heat map showing the top differentially methylated sites (DMPs) between PE and PR. (D) Volcano plot of significantly differentially methylated sites between PE and PR, defined as sites with |delta β (mean methylation level in PE samples - mean methylation level in PR samples)| > 0.1 and a corrected P value < 0.05. Red indicates CpG sites that are significantly hypermethylated in the PE cohort compared to the PR cohort; green indicates CpG sites that are significantly hypomethylated in the PE cohort compared to the PR cohort. The genes regulated by the significantly downregulated DMPs are annotated in the figure. (E) Lollipop plots of GO and KEGG analysis of regulated genes corresponding to methylation probes between PE and PR cohorts. (F) Significantly differentially methylated regions (DMRs) upstream and downstream of the transcription start sites of AZU1 and S100P genes were found between PE and PR cohorts.

[0047] Figure 5 Differential DNA methylation sites were shown to help distinguish PE and PR cohorts: (A) ROC analysis results of three methylation sites that distinguish PE from PR in the training set. (B) ROC analysis results in the internal validation set.

[0048] Figure 6 The results show that the hybrid diagnostic model combining methylation signatures and transcriptomes has higher diagnostic efficacy in the discovery cohort: (A) ROC curve of the training set after combining 2 mRNAs and 1 methylation site modeling in the discovery cohort; (B) ROC curve of the internal validation set.

[0049] Figure 7 Methodological validation by qPCR and pyrosequencing in an independent validation set shows consistent expression patterns for differentially expressed features: (A) Scatter plot of relative expression of DEFA1B between PE and PR cohorts using qPCR (quantitative real-time polymerase chain reaction), with GAPDH as the internal control; (B) Scatter plot of relative expression of AZU1 between PE and PR using qPCR, with GAPDH as the internal control; (C) Scatter plot of beta values ​​for methylation probe cg15610437 in PE and PR using pyrosequencing. PE, n = 92; PR, n = 132.

[0050] Figure 8The results showed that the PE / PR typing diagnostic model combining differential methylation signatures and mRNA had good diagnostic efficacy in an independent validation set: (A) Receiver operating characteristic (ROC) curves showed that the Logistic model had strong discriminatory power in the independent validation set; (B) DCA (decision curve analysis) showed that the Logistic model had higher net benefits compared with the full treatment and no treatment strategies over a wide range of threshold probabilities; (C) Calibration curve results showed acceptable consistency between predicted probabilities and observed outcomes; (D) Confusion matrix results in the independent validation set suggested that the model had high accuracy.

[0051] Figure 9 The SHAP summary plot and SHAP explanation plot reveal the feature interpretability and single-sample interpretability of the combined diagnostic model: (A) The SHAP summary plot reveals that cg15610437, AZU1, and DEFA1B contribute to the model in descending order; (B) The SHAP explanation plot reveals the single-sample interpretability. The predicted probability of a PR diagnosis for this sample is 0.11, which is derived from the interaction between cg15610437 and AZU1.

[0052] Figure 10 Shows the structural block diagram of the acute coronary syndrome pathological classification diagnosis system. DETAILED DESCRIPTION

[0053] The following examples and experimental examples further illustrate the present invention and should not be construed as limiting the present invention. The examples do not include detailed descriptions of conventional methods, which are well known to those skilled in the art and are described in numerous publications.

[0054] Unless otherwise defined, all professional and scientific terms used herein have the same meanings as those familiar to those skilled in the art. In addition, any methods and materials similar or equivalent to those described herein can be applied to the present invention, and the preferred implementation methods and materials described in the specific embodiments are for illustrative purposes only.

[0055] 1. Screening of differentially expressed genes and methylation sites between PE and PR

[0056] (1) Research subjects and sample collection

[0057] 1. Subject inclusion / exclusion criteria

[0058] Eligible patients: Patients aged 18 to 85 years with a clinical diagnosis of acute coronary syndrome (including NSTE-ACS or STE-ACS) who were scheduled for urgent coronary angiography (CAG) and percutaneous coronary intervention (PCI). All patients included in the analysis had de novo coronary artery lesions, meaning they were the first occurrence of primary coronary artery stenosis or occlusion, rather than "secondary" lesions such as restenosis or stent thrombosis in pre-existing stents.

[0059] Exclusion criteria included patients with two or more coronary artery disease, prior stent implantation, cardiogenic shock, prior coronary artery bypass grafting, stent thrombosis, left main coronary artery disease, congestive heart failure, life-threatening arrhythmias, severe hepatic or renal insufficiency, sepsis, leukopenia, hematologic disorders, autoimmune diseases, active inflammatory conditions, or malignant diseases with an estimated survival of less than 2 years; patients with complex anatomy (e.g., significant vascular tortuosity, calcification), making satisfactory OCT images difficult to obtain, or patients in whom coronary artery disease could not be identified. Furthermore, to exclude the influence of circadian rhythms on peripheral blood immune cells, all patients were stratified to occur between 8:00 AM and 6:00 PM Beijing time (8:00 AM to 6:00 PM).

[0060] 2. Blood sample collection

[0061] Before or during interventional therapy, blood samples (approximately 3–5 mL) were collected from a peripheral vein and placed in EDTA anticoagulant tubes or PAXgene tubes. Plasma / blood cell separation was completed within 2 hours after collection or stored at -80°C.

[0062] (2) Feature screening and modeling process

[0063] See also Figure 2 Blood samples were collected from the PE and PR cohorts and analyzed using DNA methylation and transcriptome data. The biomarker selection workflow included four main stages: feature selection, model training and internal validation, methodological validation, and independent dataset validation. Each stage ensured the identification and validation of reliable biomarkers through systematic and comprehensive analysis. Specifically:

[0064] 1. Multi-omics feature screening

[0065] Comprehensively analyze differential gene expression and differential methylation sites, exclude highly correlated or redundant features, and use a variety of feature importance algorithms (such as random forest, LASSO, XGBoost, etc.) for screening.

[0066] 2. Model training and internal validation

[0067] The data were randomly divided into a training set and an internal validation set, and 10-fold cross-validation was used to optimize the model hyperparameters and avoid overfitting. The area under the ROC curve (AUC) was selected as the main evaluation indicator, and indicators such as sensitivity, specificity, and accuracy were calculated.

[0068] 3. External independent verification

[0069] The predictive performance of the model was validated using independent cohorts (such as peripheral blood samples collected from hundreds of additional ACS patients). For key candidate molecules (such as DEFA1B, AZU1, and cg15610437), their expression and methylation levels were tested using qRT-PCR and pyrophosphate methylation assays, and the results were input into the model to calculate the final discrimination accuracy.

[0070] (III) Transcriptome sequencing and data analysis

[0071] 1. RNA extraction and quality testing

[0072] Blood cell RNA was extracted using the PAXgene Blood miRNA Kit (or other equivalent kits). RNA integrity was tested using a bioanalyzer (such as the Agilent Bioanalyzer 2100), and samples with an RNA integrity (RIN value) ≥ 7.0 were selected for subsequent sequencing analysis.

[0073] 2. Library construction and sequencing

[0074] Use a eukaryotic mRNA sequencing library construction kit (such as VAHTS Total RNA-Seq) to build the library;

[0075] High-throughput sequencing was performed using the Illumina platform to obtain raw sequencing data.

[0076] 3. Sequencing data processing and differential expression analysis

[0077] FastQC and Trimmomatic were used to perform quality control on raw data and remove low-quality sequences;

[0078] Use STAR or HISAT2 to align to the reference genome and quantify;

[0079] Differential expression analysis was performed using R language limma or DESeq2, and the threshold was set as follows: adjusted p value (FDR) < 0.05 and |log2(FC)| > 1.5;

[0080] The differentially expressed genes were further subjected to functional enrichment analysis (GO / KEGG) and GSEA analysis to explore possible biological mechanisms. The results are shown in Figure 1Further screening showed that the expression of DEFA1B and AZU1PE in the PE group was significantly upregulated compared with the PR group. The ROC analysis of the training model showed that the AUC was 0.946, and it was verified by the internal validation set ( Figure 3 Upregulated mRNAs in the plaque erosion group versus the plaque rupture group may help identify PE in the PR cohort.

[0081] (IV) DNA methylation detection and data analysis

[0082] 1. DNA extraction and bisulfite conversion

[0083] Extract genomic DNA using a commercial kit (e.g., DNeasy Blood & Tissue Kit);

[0084] Bisulfite convert the DNA using a bisulfite conversion kit such as the EZ DNA Methylation Kit.

[0085] 2. Illumina 850K methylation chip detection

[0086] The converted DNA was loaded onto the Infinium MethylationEPIC 850K chip for hybridization detection;

[0087] Software packages such as ChAMP or minfi were used to perform quality control, batch effect correction, and methylation site (CpG) quantification on the raw data;

[0088] Common thresholds for screening differentially methylated sites (DMPs) and differentially methylated regions (DMRs) are: |Δβ|>0.1 and corrected p-value<0.05;

[0089] Functional enrichment analysis of genes near significant differential sites ( Figure 4 ), and further screening obtained candidate methylation markers related to PE and PR, cg02668773, cg15610437, and cg19401149 ( Figure 5 ).

[0090] The combined detection of DEFA1B, AZU1PE and the site with the highest methylation level, cg15610437, significantly improved the sensitivity and specificity compared with the use of genes or methylation sites alone ( Figure 6 ).

[0091] 2. qRT-PCR and DNA methylation focus verification

[0092] 1. qRT-PCR Validation

[0093] The most significant genes in the differential expression analysis (such as DEFA1B, AZU1, etc.) were selected and cDNA was synthesized using a high-capacity cDNA reverse transcription kit;

[0094] Real-time quantitative PCR was performed using SYBR Green or TaqMan probes (primer sequences are shown in Table 1 ), and relative expression levels (e.g., 2^-ΔΔCt) were calculated and compared with those in the PR group.

[0095] 2. Pyrosequencing analysis of DNA methylation

[0096] Specific primers were designed for the identified significant loci (e.g., cg15610437) (primer sequences are shown in Table 1) and PCR amplification was performed. Pyrosequencing was used to measure methylation levels, which were output as percentages. Methylation differences between the PE and PR groups were compared and verified for consistency with the microarray results.

[0097] The results showed that the methodological validation of qPCR and pyrosequencing in the independent validation set suggested that the differential features had consistent expression patterns ( Figure 7 The PE / PR typing diagnostic model combining differential methylation features and mRNA has good diagnostic efficacy in an independent validation set ( Figure 8 ).

[0098] SHAP summary plots and SHAP explanations were used to reveal the feature interpretability and single-sample interpretability of the joint diagnosis model. The SHAP summary plots revealed that cg15610437, AZU1, and DEFA1B were the features that contributed most to the model ( Figure 9 A) SHAP interpretation attempts to reveal single-sample interpretability. The predicted probability of diagnosing PR in this sample is 0.11, which is derived from the interaction between the two indicators cg15610437 and AZU1 ( Figure 9 B).

[0099] 3. Clinical Diagnostic Transformation

[0100] The present invention provides a diagnostic kit for acute coronary syndrome pathological typing, comprising reagents for jointly detecting the expression levels of DEFA1B and AZU1 and the methylation level of the cg15610437 site in peripheral blood. Specifically, the kit comprises an amplification system and a primer system, wherein the primer system includes the primer sequences shown in SEQ ID NOs. 1-6 in Table 1 above. When detecting the methylation level of the cg15610437 site, the kit is combined with pyrosequencing to output the methylation level as a percentage.

[0101] In addition, a pathological classification and diagnosis system for acute coronary syndrome can be formed. Figure 10 It includes an input and display module 1, an analysis module 2, a storage module 3, a communication module 4 and a control module 5.

[0102] Input and display module 1 is used to receive the test results of DEFA1B and AZU1 expression levels and cg15610437 methylation levels, as well as basic patient information, and display the pathological classification results after analysis. The test results are obtained based on the aforementioned test kit, or on the results of a chip or sequencing platform. The patient's basic information is imported from the patient's electronic medical record, including the patient's name, gender, medical record number, and preliminary clinical diagnosis, and is determined based on actual needs.

[0103] Analysis module 2 has been trained and verified in advance, and analyzes whether the test sample belongs to the plaque erosion (PE) or plaque rupture (PR) type based on the detection values ​​of DEFA1B and AZU1 expression levels and the methylation level of the cg15610437 site.

[0104] In the PE group, the expression of DEFA1B and AZU1 was significantly upregulated compared with the PR group, but the methylation levels were significantly lower than those in the PR group. Therefore, when the expression levels of DEFA1B and AZU1 were higher than the PR type mean and the methylation percentage of the cg15610437 site was lower than the PR type mean, the pathological type of the sample was determined to be PE; when the expression levels of DEFA1B and AZU1 were lower than the PE type mean and the methylation percentage of the cg15610437 site was higher than the PE type mean, the pathological type of the sample was determined to be PR.

[0105] The functions of the storage module 3, the communication module 4 and the control module 5 are the same as those in the prior art, and are respectively used for storing relevant data, communicating with external devices and controlling the normal operation of the system.

[0106] When conducting clinical application, the following steps are taken:

[0107] (1) Obtain a small amount of peripheral blood from patients during emergency PCI or early ACS diagnosis;

[0108] (2) After extracting RNA and DNA, quickly perform PCR or pyrosequencing to detect target genes and methylation sites, or use a chip / sequencing platform for deep scanning (if conditions permit).

[0109] (3) Interpretation and result reporting: The test results are imported into the pre-trained diagnostic system model. The analysis module analyzes and outputs the probability of each patient belonging to PE or PR, providing clinicians with a reference for personalized treatment strategies.

[0110] Any undescribed parts of the present invention are the same as or implemented using existing technologies. The applicant declares that the present invention uses the above-mentioned embodiments to illustrate the detailed methods of the present invention, but the present invention is not limited to the above-mentioned detailed methods, that is, it does not mean that the present invention must rely on the above-mentioned detailed methods to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent replacement of various raw materials of the product of the present invention, addition of auxiliary ingredients, selection of specific methods, etc., all fall within the scope of protection and disclosure of the present invention.

Claims

1. Application of reagents for the combined detection of DEFA1B and AZU1 expression levels and cg15610437 site methylation levels in the preparation of a diagnostic kit for acute coronary syndrome pathological typing.

2. The use according to claim 1, characterized in that The reagents for detecting the expression levels of DEFA1B and AZU1 and the methylation level of the cg15610437 site are selected from primers or high-throughput detection chips that have detection specificity for DEFA1B, AZU1 and cg15610437 in biological samples.

3. The use according to claim 2, characterized in that The primer sequences with detection specificity for DEFA1B, AZU1 and cg15610437 are shown in SEQ ID NOs. 1 to 7.

4. The use according to claim 2, characterized in that The biological sample is selected from the peripheral blood of the test subject.

5. A diagnostic kit for pathological typing of acute coronary syndrome, characterized in that: It comprises an amplification system and a primer system, wherein the primer system comprises primer sequences as shown in SEQ ID NO. 1 to 7.

6. The acute coronary syndrome pathological typing diagnostic kit according to claim 5, characterized in that: When detecting the methylation level of the cg15610437 site, it is combined with the pyrosequencing method to output the methylation level detection value in the form of a percentage.

7. A pathological classification and diagnosis system for acute coronary syndrome, characterized in that: It includes input display module, analysis module, storage module, communication module and control module. The input display module is used to receive the detection results of DEFA1B, AZU1 expression levels and cg15610437 site methylation levels, and display the pathological typing results after the analysis is completed; The analysis module has been trained and verified in advance, and analyzes whether the test sample belongs to the plaque erosion (PE) or plaque rupture (PR) type based on the detection values ​​of DEFA1B and AZU1 expression levels and the methylation level of the cg15610437 site.

8. The acute coronary syndrome pathological classification and diagnosis system according to claim 7, characterized in that: The detection results of DEFA1B and AZU1 expression levels and cg15610437 site methylation level are obtained based on the kit according to claim 6, or based on the detection results of a chip or sequencing platform.

9. The acute coronary syndrome pathological classification and diagnosis system according to claim 7, characterized in that: When the expression levels of DEFA1B and AZU1 are higher than the PR type mean and the methylation percentage of the cg15610437 site is lower than the PR type mean, the pathological type of the test sample is determined to be PE; when the expression levels of DEFA1B and AZU1 are lower than the PE type mean and the methylation percentage of the cg15610437 site is higher than the PE type mean, the pathological type of the test sample is determined to be PR.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the function of the system according to any one of claims 7 to 9 is realized.

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