Coronary artery disease (CAD) is a primarily inflammatory condition caused by atherosclerosis in the coronary arteries. Increasing evidence suggests that endoplasmic reticulum (ER) stress is closely involved in the development of CAD. In the arteries of CAD patients, the degree of ER stress may increase due to endothelial cell damage and the presence of inflammatory reactions. The aim of this study was to identify ERS-related genes (ERSRGs) and potential drugs for treating CAD. Gene expression data for CAD patients and healthy controls were retrieved from the GEO database, while the ERs-related gene set was sourced from GeneCards for analysis. Differentially expressed genes (DEGs) between CAD and control groups were identified, followed by GO, KEGG, GSEA, and GSVA analyses to explore differences. WGCNA and differential expression analysis were applied to identify key modules and genes associated with CAD, and the CIBERSORT algorithm was used to assess immune cell infiltration. Nomogram is used for gene risk scoring in CAD. Additionally, a TF-miRNA-mRNA network was built to explore the regulatory mechanisms of central genes and identify upstream regulators. The DGIdb database was used to identify potential therapeutic drugs or compounds targeting these central genes. The drug predicted to be most closely associated with ERSRGs using the BATMAN-TCM database is Ginkgo biloba extract(GBE), and the strong effect of GBE in regulating ER stress was verified in cell experiments. Single-cell RNA sequencing analysis of ERSRGs was also conducted. In this study, 833 DEGs and 10 ERSRGs were identified from CAD patients and healthy controls, which showed good diagnostic value and high correlation. Functional enrichment and immune infiltration analyses revealed notable differences in gene expression, biological functions, and enriched pathways between CAD patients and healthy individuals. Drug prediction analysis identified 177 potential therapeutic drugs for CAD, especially GBE, and verified the strong effect of GBE in regulating ER stress in cell experiments. Finally, real-time quantitative PCR of human peripheral blood validated our findings. In single-cell RNA sequencing analysis, the cell source specificity of ERSRGs in transcriptome changes has been validated, providing a cellular context basis for its functional mechanism in CAD. The results indicate that ER stress plays an important role in the pathogenesis of CAD, and several promising feature genes were identified, forming a genetic risk score model for CAD, while a strong effect of GBE in the regulation of ER stress has been detected.This study provides a new comprehensive perspective on the pathways and interaction networks of ERSRGs, and lays the foundation for future personalized diagnosis and treatment.
Zhao et al. (Sun,) studied this question.