Senescence-related genomic signature reflecting epicardial adipose tissue and immune infiltration associated with diagnosis and treatment outcomes after myocardial infarction

Abstract Background After coronary artery disease (CAD)-related myocardial injury, reactivation of the epicardium results in cardiac remodeling via paracrine secretion. Cellular senescence is characterized by stable cell cycle arrest correlated with typical morphological cellular changes and a unique secretome called senescence-associated secretory phenotype (SASP). However, the senescence-related genomic signature that reflects epicardial adipose tissue (EAT) and immune infiltration is not well understood.Methods Adipocyte-related differentially expressed genes (DEGs) were identified in EAT and subcutaneous adipose tissue (SAT) from patients with and without CAD. Immune cells and senescence-related DEGs in EATs were identified, and the enrichment pathways were analyzed. A protein-protein interaction network was used to determine the hub genes. To validate these genes, a Gene Expression Omnibus (GEO) dataset investigation and quantitative polymerase chain reaction analysis of human sub-epicardial adipose samples were performed.Results Thirty-five up-regulated and 122 down-regulated DEGs were obtained between the EAT of patients with and without CAD. In patients with CAD, 222 up-regulated and 188 down-regulated DEGs were obtained between the EAT and SAT. A Venn diagram was used to obtain 82 senescence-related DEGs, and the top 15-degree hub genes were explored. After validating using the GEO datasets and human sub-epicardial adipose samples, STAT3, SERPINE1, CDKN2A, DLG4, PTGS2, MDM2, LRP1, IRS2, PRKCD, CCND2, and CISH were found to be significantly expressed in the group with severe CAD.Conclusion In this study, a bioinformatics analysis was conducted to determine whether senescence-related DEGs in human sub-epicardial adipose tissue could be used to predict myocardial infarction progression. These screened SASP hub genes may be biomarkers for the prediction of and therapeutic targets for the prognosis of MI progression.