Study on Immune-Related Genes and Clinical Validation of Acute Myocardial Infarction Based on Bioinformatics

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.