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

The prevalence of obesity is increasing year by year, but its characteristic molecular targets are still unclear, and the available therapeutic approaches are relatively 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 for between-group differential expression gene analyses (DEGs), genome enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) in healthy and obese populations. Intersecting genes obtained from DEGs and WGCNA difference modules were analyzed with three machine learning methods (LASSO, RandomForest, SVM-REF) to obtain obesity characteristic Genes. Analysis of ROC curves, intergroup differences, and intergene correlations for Genes characterizing obesity. The results of the study showed that 10 specimens and their Gene expression matrices were collected from each of the normal and obese patient groups, yielding 1937 DEGs. GSEA results showed that DEGs were enriched for 32 significant KEGG pathways. Forty gene co-expression modules of the gene expression matrix were constructed by WGCNA. Forty-five intersecting genes were obtained from DEGs and WGCNA significant difference module, which were associated with cellular differentiation, mitochondria, and a variety of endocrine factors and hormones. Eleven genes, including XLOC_004699, RIMBP2, COX6B2, OR5T1, RXFP2, XLOC_003676, XLOC_013038, VAX1, Q07610, XLOC_011515, and PTPN3, were obtained as the obesity characterization Genes through machine learning analysis of intersecting Genes. Based on WGCNA and machine learning, this study found that 11 genes, including RIMBP2, COX6B2, and OR5T1, differed significantly between healthy and obese populations and were closely associated with multiple molecular mechanisms, and these genes may be potential targets for drug therapy and diagnostic biomarkers.