Abstract
BACKGROUND: Coronary artery disease (CAD) remains a significant global contributor to morbidity and mortality, necessitating a deeper understanding of its molecular mechanisms. This study aimed to identify and analyze differentially expressed genes associated with CAD, assess their functional enrichment, and construct protein-protein interaction (PPI) networks to elucidate the molecular mechanisms underlying the disease. METHODS: Data integration from two Gene Expression Omnibus datasets (GSE66360 and GSE42148) was performed using bioinformatics tools, with batch effects corrected via the R package sva. Differential gene expression analysis was conducted using the limma package in R, enabling identification of genes with statistically significant expression differences between patients with CAD and healthy controls. POSTN (periostin) expression was validated through receiver operating characteristic curve analysis. Functional enrichment was assessed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses via clusterProfiler package, in addition to Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). PPI networks were constructed using GeneMANIA, while regulatory networks were visualized with Cytoscape. RESULTS: A total of 704 differentially expressed genes were identified, with 476 upregulated and 228 downregulated genes in CAD samples. POSTN demonstrated significant differential expression indicating potential diagnostic relevance. Functional enrichment analyses highlighted key biological processes such as regulation of cell junction assembly and wound healing. GSEA highlighted the enrichment of apoptosis-related pathways, while GSVA showed notable differences in epithelial-mesenchymal transition pathways between groups with high and low POSTN expression. Immune infiltration analysis revealed distinct immune cell correlations based on POSTN expression levels. CONCLUSION: This integrated bioinformatics analysis provided valuable insights into the molecular landscape of CAD, highlighting POSTN as a potential biomarker for diagnosis and prognosis. Future research is needed to validate these findings in clinical settings and explore therapeutic targets within the identified pathways.