Integrative bioinformatics analysis revealing genetic and molecular mechanisms underlying obesity

Obesity is a complex disorder with significant genetic and molecular underpinnings. This study employed a multi-step bioinformatics approach, integrating genome-wide association study (GWAS) data, functional enrichment analyses, pathway mapping and network-based investigations to elucidate the genetic and molecular mechanisms underlying obesity. TargetScan, miRTarBase, Reactome Pathways, KEGG, Protein-protein interaction (PPI) analysis and Gene Ontology (GO) mapping were the bioinformatic tools used to perform enrichment analysis on GWAS data identified key obesity-associated genes. GWAS data identified key obesity-associated genes, were subjected to gene ontology (GO) analysis, revealing their involvement in biological processes, Reactome and KEGG pathway databases enriched analysis highlighted the dysregulation of lipid metabolism, inflammatory pathways and neuronal signalling. Notably, LDL clearance, plasma lipoprotein clearance and cholesterol metabolism pathways were significantly enriched. Protein-protein interaction network analysis identified central hub proteins including PRKACA and UBC, implicating dysregulated protein degradation and inflammatory signaling in obesity pathogenesis. miRNA enrichment analysis revealed the regulatory roles of hsa-miR-33a-5p and hsa-miR-877-5p in lipid metabolism and inflammation. Cell-type enrichment analysis highlighted neuroinflammation and immunological components. These findings underscore the intricate interplay between genetic, metabolic and regulatory factors in obesity, providing valuable insights into potential therapeutic targets and biomarkers. Future research should focus on validating these findings and exploring their applications in obesity prevention and management.