Bioinformatics Analysis of Key Differentially Expressed Genes in Pericoronary Adipose Tissue Associated with the Diagnosis of Coronary Artery Disease

Abstract Objective: To investigate the potential key biomarkers for the diagnosis of coronary artery disease (CAD) in pericoronal adipose tissue using bioinformatics analysis, and to explore the mechanism underlying the occurrence and progression of CAD. Methods: Two datasets were downloaded from the Gene Expression Omnibus (GEO) database for bioinformatics analysis, the differentially expressed genes (DEGs) were identified, and the relevant biological pathways of these genes were functionally annotated and enriched by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Meanwhile, functional enrichment and protein–protein interaction (PPI) network analyses. Pericoronary adipose tissue and subcutaneous adipose tissue of patients with CAD(n=60) were analyzed and verified by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Results: A total of 82 DEGs from CAD patients and healthy individuals. The results of enrichment analysis showed that the top DEGs were mainly enriched in the retinol metabolism, carbon metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Among them, the PPAR signaling pathway with the strongest correlation with the key genes was associated with the downstream target protein Janus kinase (JAK), and verification using RT-qPCR revealed that the expression levels of JAK, monocyte chemoattractant protein-1 (MCP-1), platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31), IL-6, and leptin in pericoronary PVAT tissue were significantly upregulated. In contrast, the expression level of PPAR was significantly reduced (P<0.05). Conclusion: This study revealed 4 DEGs in pericoronal adipose tissue for diagnosing CAD, which may improve understanding of CAD and assist scholars to explore the molecular mechanism of CAD.