RNA sequencing and bioinformatics analysis of tissue biopsy of abdominal fat in obesity associated with cardio-metabolic diseases

Obesity associated with cardiometabolic diseases is a major metabolic disorder and a significant global health issue. However, the specific molecular mechanisms of obesity associated with cardiometabolic diseases remain unclear. This study aims to identify key genes and signaling pathways associated with obesity using bioinformatics. Next generation sequencing (NGS) dataset (GSE244118) including those from 39 obesity volunteers and 15 lean volunteers was downloaded from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were screened using DESeq2. To better understand the functions and possible pathways of DEGs, we performed Gene Ontology (GO) and REACTOME pathway enrichment analysis. Protein-protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drug molecules associated with hub genes. The receiver operating characteristic (ROC) analyses were conducted to explore the value of hub genes for obesity diagnosis. GO and REACTOME pathway enrichment results showed that these genes were closely associated with multicellular organismal process, immune system process, Metabolism of water-soluble vitamins and cofactors and immune system. Hub genes (ESR1, MET, FKBP5, RPL9, MAP3K5, HTRA4, C3AR1, CEP55, TAFA3 and LAMP3), miRNAs (hsa-mir-30c-2-3p, hsa-miR-3149, hsa-miR-3119 and hsa-mir-449a) and TFs (TEAD1, BRCA1, SOX5 and RUNX2) were ultimately determined as common diagnostic markers for obesity associated with cardiometabolic diseases. Drug molecules (Methotrimeprazine, Dexfenfluramine, Clobazam and Eluxadoline) were predicted for treatment of obesity associated with cardiometabolic disease. ROC curve analysis also showed good diagnostic performance. After a series of bioinformatics analysis and validation, ESR1, MET, FKBP5, RPL9, MAP3K5, HTRA4, C3AR1, CEP55, TAFA3 and LAMP3 were identified as hub genes for the development of OA and AS. This study provides a new perspective on the common molecular mechanisms between OA and AS, and offers new insights into the potential pathogenesis obesity associated with cardiometabolic diseases and the direction of treatment.