Wang Biao, Chen Jianhong, Wang Leili, Liu Yanli, Miao Liu
Department of Cardiology, Liuzhou People's Hospital.
Department of Oncology, Liuzhou People's Hospital.
Int Heart J. 2025;66(4):639-650. doi: 10.1536/ihj.24-510.
The incidence of acute myocardial infarction (AMI) is increasing, and existing diagnostic techniques exhibit limited capacity for early AMI diagnosis. Given the robust association between Ca levels and the AMI initiation, calcium-related genes represent promising biomarkers for the early diagnosis of AMI.The expression data of patients with AMI and normal samples were obtained from the gene expression omnibus database. Weighted correlation network analysis (WGCNA) was applied to identify genes associated with AMI. Signature genes were screened using the least absolute shrinkage and selection operator, support vector machine-recursive feature elimination (SVM-RFE), and random forest algorithm. A diagnostic model based on the signature gene was established and evaluated. The CIBERSORT algorithm was used to determine the levels of immune cell infiltration, and the single-sample gene set enrichment analysis (ssGSEA) scores of the immune cells were calculated. The regulatory network of competing endogenous RNA (ceRNA) based on the signature genes was constructed using cytoscape. The DGIdb database was used to identify potential drugs for AMI that may interact with the signature genes.A high-performance diagnostic model based on four signature genes was established. The CIBERSORT algorithm and ssGSEA analysis revealed differences in immune cells between the patients with AMI and normal groups. The ceRNA regulatory network revealed multiple lncRNA and miRNA targeting signature genes. Niacin, nitroglycerin, arsenic disulfide, and quercetin are potential drugs that interact with the signature genes.Four signature genes were selected as calcium-related biomarkers of AMI that could serve as diagnostic markers for the disease. Additionally, the predicted ceRNA network and drug interaction network associated with these genes offer new perspectives for the treatment of AMI.
急性心肌梗死（AMI）的发病率正在上升，而现有的诊断技术在早期AMI诊断方面能力有限。鉴于钙水平与AMI发病之间存在密切关联，与钙相关的基因有望成为早期诊断AMI的生物标志物。从基因表达综合数据库中获取AMI患者和正常样本的表达数据。应用加权基因共表达网络分析（WGCNA）来识别与AMI相关的基因。使用最小绝对收缩和选择算子、支持向量机递归特征消除（SVM-RFE）和随机森林算法筛选特征基因。建立并评估基于特征基因的诊断模型。使用CIBERSORT算法确定免疫细胞浸润水平，并计算免疫细胞的单样本基因集富集分析（ssGSEA）分数。使用Cytoscape构建基于特征基因的竞争性内源性RNA（ceRNA）调控网络。利用DGIdb数据库识别可能与特征基因相互作用的AMI潜在药物。建立了基于四个特征基因的高性能诊断模型。CIBERSORT算法和ssGSEA分析揭示了AMI患者与正常组之间免疫细胞的差异。ceRNA调控网络揭示了多个靶向特征基因的lncRNA和miRNA。烟酸、硝酸甘油、二硫化砷和槲皮素是与特征基因相互作用的潜在药物。选择四个特征基因作为AMI的钙相关生物标志物，可作为该疾病的诊断标志物。此外，预测的与这些基因相关的ceRNA网络和药物相互作用网络为AMI的治疗提供了新的视角。
