Identifying Ferroptosis-Related Genes Associated with Coronary Heart Disease Based on Bioinformatics Analysis

Background: Coronary heart disease (CHD) is a prevalent type of organ disease, caused by atherosclerosis, and is the leading cause of global human mortality. Ferroptosis, a recently discovered form of programmed cell death, is known to be involved in the pathogenesis of various diseases. In this study, we aimed to investigate the differential expression of ferroptosis-related genes (FRGs) and their potential relationship with CHD. Methods: The CHD datasets were obtained from the Gene Expression Omnibus (GEO) database and microarray data was normalized and analyzed for differential expression genes (DEGs) using R software. FRGs were retrieved from the FerrDb and GeneCards databases, and their intersection with DEGs was determined using Venn diagrams. The differential expression FRGs were subjected to further analyses, including GO, KEGG, GSEA, transcription factor analysis, and PPI network analysis. Immune infiltration analysis was performed on both control and CHD groups, and diagnostic biomarkers for CHD were identified by constructing ROC curves. Finally, the expression of the diagnostic biomarkers at the gene level in mice was validated using qRT-PCR assay. Results: Our study identified 25 DEGs associated with ferroptosis, which were enriched in various biological processes, including response to extracellular stimulus, regulation of protein ubiquitination, DNA-templated, and autophagy of mitochondrion. The KEGG pathway and GSEA indicated that these DEGs were mainly involved in cytokine-cytokine receptor interaction and the NF-kappa B signaling pathway. The PPI network analysis identified 6 key genes in the network. The correlation analysis between the expression of these key genes and immune cells showed a significant relationship. Diagnostic biomarkers for CHD, including ICTSB, GABARAPL1, and VDR, were identified using ROC analysis. The expression trends of these biomarkers were further validated using qRT-PCR, which confirmed the consistency with the bioinformatics analysis. Conclusions: Our study identified CTSB, GABARAPL1, and VDR as potential diagnostic biomarkers for CHD. In the future, in-vivo studies are necessary to validate our findings and confirm their clinical utility.