Identification and analysis of obesity-cellular senescence associated key genes in metabolic syndrome based on transcriptomic data

The Intricate relationship between obesity, cellular senescence, and metabolic syndrome (MetS) remains elusive. This study aimed to elucidate mechanisms involving obesity-related genes (ORGs) and cellular senescence-related genes (CSRGs) in MetS. Transcriptomic profiles from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs) distinguishing MetS patients from healthy controls. We then implemented weighted gene co-expression network analysis (WGCNA) to determine pivotal module genes correlated with ORGs and CSRGs, followed by intersecting DEGs to obtain target genes. Nine CytoHubba algorithms and expression validation were employed to identify potential biomarkers. Based on potential biomarkers, a nomogram model were developed and assessed using receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA) and therapeutic target prediction were performed. Ultimately, potential biomarkers expression levels were confirmed through reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis. CCL2, CXCL2, HAVCR2, and TLR5 were identified as potential biomarkers, demonstrating significant associations with obesity and cellular senescence in MetS. The nomogram exhibited promising predictive capability for MetS risk. Furthermore, GSEA showed that NOD-like receptor signaling pathway was enriched by potential biomarkers. Several potential therapeutic drugs were predicted on potential biomarkers, including mifamurtide and plozalizumab. Finally, the expression of CCL2, CXCL2, and HAVCR2 was significantly lower in the MetS group, while TLR5 had the converse results. This study preliminarily suggests that CCL2, CXCL2, HAVCR2, and TLR5 are potential biomarkers for MetS and may be associated with obesity and cellular senescence. These findings provide candidate targets for subsequent functional validation and mechanistic studies. However, their clinical diagnostic value, risk predictive performance, and biological functions still require further validation in larger independent cohorts and functional experiments.