{"title":"急性心肌梗死组蛋白乙酰化相关诊断标志物的综合分析。","authors":"Man Li, Lifeng Yang, Yan Wang, Lei Zhang","doi":"10.1186/s12920-025-02135-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients.</p><p><strong>Methods: </strong>Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively.</p><p><strong>Results: </strong>Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol).</p><p><strong>Conclusion: </strong>AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.</p>","PeriodicalId":8915,"journal":{"name":"BMC Medical Genomics","volume":"18 1","pages":"75"},"PeriodicalIF":2.1000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.\",\"authors\":\"Man Li, Lifeng Yang, Yan Wang, Lei Zhang\",\"doi\":\"10.1186/s12920-025-02135-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients.</p><p><strong>Methods: </strong>Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively.</p><p><strong>Results: </strong>Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol).</p><p><strong>Conclusion: </strong>AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.</p>\",\"PeriodicalId\":8915,\"journal\":{\"name\":\"BMC Medical Genomics\",\"volume\":\"18 1\",\"pages\":\"75\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Genomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12920-025-02135-2\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Genomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12920-025-02135-2","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
摘要
背景：急性心肌梗死（AMI）已成为危害人类健康的严重疾病，具有较高的发病率和死亡率。大量研究报道组蛋白乙酰化可导致心血管疾病的发生。本文旨在探讨AMI患者组蛋白乙酰化调节基因（ARGs）的潜在生物标志物。方法：从Gene Expression Omnibus （GEO）数据库下载5个AMI数据集。接下来，通过基因集变异分析（GSVA）和Spearman相关分析收集arg相关基因。随后，采用加权基因共表达网络分析（WGCNA）鉴定组蛋白乙酰化调控相关模块基因。在GSE60993和GSE48060数据集中，筛选AMI和对照样本之间的共同差异表达基因（DEGs）。重要的是，交叉基因是通过重叠args相关基因、共同DEGs和模块基因获得的。然后，通过机器学习、受试者工作特征（ROC）曲线和定量PCR （qPCR）确定AMI的生物标志物。此外，还分别分析了免疫分析、药物预测、分子对接以及靶向生物标志物的lncRNA-miRNA-mRNA调控网络。结果：通过重叠的7349个args相关基因、5565个模块基因和25个共同deg，共鉴定出18个交叉基因。进一步获得5个生物标记物（AQP9、HLA-DQA1、MCEMP1、NKG7和S100A12），并基于这些生物标记物构建了nomogram，并进行了验证。值得注意的是，这些生物标志物与CD8 T细胞和中性粒细胞显著相关。此外，对生物标志物相关药物进行了预测，ATOGEPANT与分子靶点（S100A12）具有较高的结合亲和力（对接评分= -10 kcal/mol）。结论：AQP9、HLA-DQA1、MCEMP1、NKG7、S100A12是AMI中与ARGs相关的生物标志物，为研究ARGs与AMI的关系提供了新的视角。
Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.
Background: Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients.
Methods: Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively.
Results: Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol).
Conclusion: AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.
期刊介绍：
BMC Medical Genomics is an open access journal publishing original peer-reviewed research articles in all aspects of functional genomics, genome structure, genome-scale population genetics, epigenomics, proteomics, systems analysis, and pharmacogenomics in relation to human health and disease.
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