Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, is a major cause of morbidity and mortality worldwide. Macrophages play a critical role in ventricular remodeling after AMI. The regulatory mechanisms of the AMI progression remain unclear. This study aimed to identify hub regulators of macrophage-related modules and provide translational experiments with potential therapeutic targets.
The GSE59867 dataset was downloaded from the Gene Expression Omnibus (GEO) database for bioinformatics analysis. The expression patterns of 22 types of immune cells were determined using CIBERSORT. GEO2R was used to identify differentially expressed genes (DEGs) through the limma package. Then, DEGs were clustered into different modules, and relationships between modules and macrophage types were analyzed using weighted gene correlation network analysis (WGCNA). Further functional enrichment analysis was performed using significantly associated modules. The module most significantly associated with M2 macrophages (Mϕ2) was chosen for subsequent analysis. Co-expressed DEGs of AMI were identified in the GSE123342 and GSE97320 datasets and module candidate hub genes. Additionally, hub gene identification was performed in GSE62646 dataset and clinical samples.
A total of 8,760 DEGs were identified and clustered into ten modules using WGCNA analysis. The blue and turquoise modules were significantly related to Mϕ2, and 482 hub genes were discerned from two hub modules that conformed to module membership values > 0.8 and gene significance values > 0.25. Subsequent analysis using a Venn diagram assessed 631 DEGs in GSE123342, 1457 DEGs in GSE97320, and module candidate hub genes for their relationship with Mϕ2 in the progression of AMI. Finally, four hub genes (CSF2RB, colony stimulating factor 2 receptor subunit beta; SIGLEC9, sialic acid-binding immunoglobulin-like lectin 9; LRRC25, leucine-rich repeat containing 25; and CSF3R, colony-stimulating factor-3 receptor) were validated to be differentially expressed and to have high diagnostic value in both GSE62646 and clinical samples.
Using comprehensive bioinformatics analysis, we identified four novel genes that may play crucial roles in the pathophysiological mechanism of AMI. This study provides novel insights into the impact of macrophages on the progression of AMI and directions for Mϕ2-targeted molecular therapies for AMI.