Bioinformatics Analysis of the Role of Epicardial Adipose Tissue in Coronary Artery Disease - CHAI Yan, ZHAO Yuqing, GUO Xunan, WANG Dongying, BIAN Yunfei (Zhongguo quanke yixue, 2023)
Background Cardiovascular disease (CVD) is a common and frequently occurring disease, and the prevalence and mortality of which are increasing rapidly. Atherosclerosis (AS) is the pathological basis of ischemic CVD. Studies have shown that epicardial adipose tissue (EAT) promotes the progression of AS by secreting exosomes and bioactive substances, but the mechanism of action still needs to be further studied. Objective To perform a bioinformatics analysis of role of EAT in coronary artery disease (CAD) at cellular and molecular levels by identifying the differentially expressed genes (DEGs) in EAT to explore the status of immune cell infiltration, and to assess and verify whether EAT is derived from DEGs in exosomes in CAD patients. Methods We downloaded GSE64554 and GSE120774 datasets about EAT from the GEO database and performed a bioinformatics analysis using R language and related packages. We first used R language to screen the DEGs in EAT and CAD patients, then used GO/KEGG enrichment analysis to establish a protein interaction network to explore biological functions of the screened genes and transcription factors potentially involved in their regulation process. After that, we conducted a weighted gene co-expression network analysis (WGCNA) of EAT in GSE64554 dataset to obtain a gene module related to CAD phenotype, then crossed the hub genes in this module and DEGs in EAT to obtain the key common genes. We used Cibersort to characterize the immune cell infiltration in EAT. Then we obtained DEGs from blood exosomes of CAD patients and healthy controls included in the exoRbase database, crossed DEGs in EAT and blood exosomes to identify the common genes to be used as diagnostic and therapeutic markers for CAD, and their values were tested by qRT-PCR measurement of clinical samples. The selected genes were analyzed by GO/KEGG and Metascape enrichment analyses. Results A total of 1 511 DEGs in EAT of CAD patients were identified, including 956 with up-regulated expression and 555 with down-regulated expression. By crossing the DEGs in EAT and hub genes in modules associated with CAD closely, we identified DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, and CHCHD4 as key genes in the development of CAD. Immune cell infiltration analysis showed that the abundance of immature CD4+ T cells increased while expression abundance of resting dendritic cells decreased in EAT of CAD patients (P<0.05). A total of 1 658 DEGs in exosomes of CAD patients, including 278 with up-regulated expression and 1 380 with down-regulated expression. One hundred and twenty-nine common DEGs were obtained by cross-tabbing DEGs in EAT and exosomes of CAD patients, among which BPI, BIRC5, CXCL12, RNASE1 and F2R with higher expression abundance were selected as potential diagnostic and therapeutic markers for CAD. By qRT-PCR detection, CAD patients were found with increased mRNA expression levels of BPI, BIRC5, CXCL12, RNASE1 (P>0.05), and decreased F2RmRNA expression level (P<0.05) than controls. GO/KEGG enrichment analysis showed that DEGs in EAT were mainly involved in the cytosol, MHC protein complex, RNA degradation, antigen processing and presentation. A PPI network was built, in which RPS27A gene was identified as a gene with the highest degree of connectivity by use of Cytoscape plugin CytoHubba with MCC algorithm. Metascape enrich analysis indicated that DEGs enriched mainly in cellular response to DNA damage, RNA metabolism, regulation of cell stress responses, and adaptive immune system. By an analysis of TRRUST datasets, we predicted that transcription factor CIITA may play a role in the regulation of DEGs in EAT influencing CAD. Conclusion EAT may be involved in the development of CAD through proinflammatory and immune pathways, in which DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, CHCHD4 and RPS27A may play a vital role as the key genes. The abundance of naive CD4+ T cells significantly increased while that of resting dendritic cells decreased obviously in EAT from CAD patients. BPI, BIRC5, CXCL12, RNASE1 and F2R may be excreted by EAT and have the potential as markers in CAD diagnosis and treatment.