Integrated bioinformatics approach reveals methylation-regulated differentially expressed genes in obesity

OBJECTIVE: To identify DNA methylation and gene expression profiles involved in obesity by implementing an integrated bioinformatics approach. MATERIALS AND METHODS: Gene expression (GSE94752, GSE55200, and GSE48964) and DNA methylation (GSE67024 and GSE111632) datasets were obtained from the GEO database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in subcutaneous adipose tissue of patients with obesity were identified using GEO2R. Methylation-regulated DEGs (MeDEGs) were identified by overlapping DEGs and DMGs. The protein–protein interaction (PPI) network was constructed with the STRING database and analyzed using Cytoscape. Functional modules and hub-bottleneck genes were identified by using MCODE and CytoHubba plugins. Functional enrichment analyses were performed based on Gene Ontology terms and KEGG pathways. To prioritize and identify candidate genes for obesity, MeDEGs were compared with obesity-related genes available at the DisGeNET database. RESULTS: A total of 54 MeDEGs were identified after overlapping the lists of significant 274 DEGs and 11,556 DMGs. Of these, 25 were hypermethylated-low expression genes and 29 were hypomethylated-high expression genes. The PPI network showed three hub-bottleneck genes (PTGS2, TNFAIP3, and FBXL20) and one functional module. The 54 MeDEGs were mainly involved in the regulation of fibroblast growth factor production, the molecular function of arachidonic acid, and ubiquitin-protein transferase activity. Data collected from DisGeNET showed that 11 of the 54 MeDEGs were involved in obesity. CONCLUSION: This study identifies new MeDEGs involved in obesity and assessed their related pathways and functions. These results data may provide a deeper understanding of methylation-mediated regulatory mechanisms of obesity.