AUTHOR=Yang Huiping , Xiong Bingquan , Xiong Tianhua , Wang Dinghui , Yu Wenlong , Liu Bin , She Qiang 

TITLE=Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.927397

DOI=10.3389/fcvm.2022.927397

ISSN=2297-055X

ABSTRACT=Background: In recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic participants and explored the possible mechanisms using various bioinformatic tools.
Methods: RNA-seq datasets GSE108971 and GSE179455 for EAT between diabetic and non-diabetic patients were obtained from public functional genomics database Gene Expression Omnibus (GEO). The DEGs (differential expressed genes) were identified using R package DESeq2, then Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment were analyzed. Next, PPI (Protein-Protein Interaction) network was constructed and hub genes were mined using STRING and Cytoscape. Additionally, CIBERSORT was used to analyze the immune cells infiltration and key transcription factors were predicted based on ChEA3.
Results: By comparing EAT samples between diabetic and non-diabetic patients, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes. Ten genes (IL-1β, CD274, PDCD1, ITGAX, PRDM1, LAG3, TNFRSF18, CCL20, IL1RN, SPP1) were selected as hub genes. GO and KEGG analysis showed that DEGs were mainly enriched in inflammatory response, cytokine activity. Immune cell infiltration analysis indicated that macrophages M2 and T cells CD4 memory resting accounted for the largest proportion in these immune cells. CSRNP1, RELB, NFKB2, SNAI1, FOSB were detected as potential transcription factors. 
Conclusion: Comprehensive bioinformatic analysis were used to compare the difference of EAT between diabetic and non-diabetic patients. Several hub genes, TFs, immune cells infiltration were identified. Diabetic EAT is significant different in inflammatory response, cytokine activity. These findings may provide new targets for the diagnosis and treatment of diabetes, as well as reducing potential cardiovascular complications in diabetic patients through EAT modification.
Keywords: bioinformatic analysis, diabetic, EAT, IL-1β, CD274, inflammatory response, NF-κB, immune infiltration