Jin Shuang, Wu Zhang
Department of Emergency, The Wenzhou Third Clinical Institute Affiliated To Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, Wenzhou, 325000, Zhejiang, China.
Biochem Genet. 2025 Jan 16. doi: 10.1007/s10528-025-11029-y.
Acute myocardial infarction (AMI) is a cardiovascular disease featuring the narrowing and hardening of coronary arteries triggered by a combination of factors, which ultimately leads to the death of heart muscle. We retrieved the GSE109048 and GSE123342 datasets from the Gene Expression Omnibus (GEO) database. After integrating these datasets, we selected 154 module key genes with the help of weighted correlation network analysis (WGCNA). After that, we used protein-protein interaction networks (PPI) analysis to screen out 18 core genes in the protein interaction network from 154 genes. Finally, we used three machine learning algorithms to jointly identify three genes (CLEC4D, CLEC4E and LY96) that may predict or influence the progression of AMI. In the dataset, CLEC4D, CLEC4E and LY96 were significantly overexpressed in AMI patients. Immune infiltration analysis revealed that CLEC4D, CLEC4E and LY96 could affect the extent of immune cell infiltration. For further verification, we found that the expression levels of CLEC4D, CLEC4E and LY96 in the AMI cohort were significantly higher than those in coronary heart disease (CAD) patients by qRT-PCR. This finding corroborated the results derived from bioinformatics analysis. In summary, CLEC4D, CLEC4E and LY96 can be used to predict the occurrence of AMI.
急性心肌梗死（AMI）是一种心血管疾病，其特征是冠状动脉变窄和硬化，由多种因素共同引发，最终导致心肌死亡。我们从基因表达综合数据库（GEO）中检索了GSE109048和GSE123342数据集。整合这些数据集后，我们借助加权基因共表达网络分析（WGCNA）选择了154个模块关键基因。之后，我们使用蛋白质-蛋白质相互作用网络（PPI）分析从154个基因中筛选出蛋白质相互作用网络中的18个核心基因。最后，我们使用三种机器学习算法共同鉴定出三个可能预测或影响AMI进展的基因（CLEC4D、CLEC4E和LY96）。在数据集中，CLEC4D、CLEC4E和LY96在AMI患者中显著过表达。免疫浸润分析显示，CLEC4D、CLEC4E和LY96可影响免疫细胞浸润程度。为进一步验证，我们通过qRT-PCR发现，AMI队列中CLEC4D、CLEC4E和LY96的表达水平显著高于冠心病（CAD）患者。这一发现证实了生物信息学分析的结果。综上所述，CLEC4D、CLEC4E和LY96可用于预测AMI的发生。
