WGCNA-Based Identification of Hub Genes and Key Pathways Involved in Obesity

Abstract

The prevalence of obesity is increasing, yet its pathogenesis and therapeutic targets are still unclear, and the efficacy of existing therapeutic modalities is limited. Therefore, it is crucial to elucidate the molecular mechanisms underlying the pathogenesis of obesity and to explore potential molecular targets for obesity drug therapy. The expression dataset (GSE73304) was downloaded from the Gene Expression Omnibus database (GEO), and the data were divided into C and P groups, which were normalized for differentially expressed genes analysis (DEGs), Gene Set Enrichment Analysis (GSEA) and weighted gene co-expression network analysis (WGCNA). The intersecting genes obtained by DEGs and WGCNA were analyzed by three machine learning methods (LASSO, RandomForest, SVM-REF), and the gene ROC curves, genetic differences between groups and correlations between genes were analyzed after obtaining the major genes. Based on WGCNA and machine learning, this study found that several genes were significantly different between healthy and obese populations and closely associated with multiple molecular mechanisms, and these genes may serve as potential targets for drug therapy and diagnostic biomarkers, which need further studies to elucidate.