Identification of Potential Key Biomarkers for Comorbid Depression and Obesity Through Integrated Bioinformatics Analysis and Experimental Verification.

PURPOSE: Depression and obesity impair quality of life and burden economies. Growing evidence shows a strong link between these two diseases. The objective of this study is to identify the shared core genes associated with both depression and obesity and to evaluate their diagnostic potential. METHODS: Gene expression profile data were retrieved from the Gene Expression Omnibus (GEO) database to analyze the shared differentially expressed genes (DEGs) in major depression and obesity. Weighted Gene Co-expression Network Analysis (WGCNA) identified co-expression modules. STRING was used to construct and analyze the protein-protein interaction (PPI) network. Six cytoHubba algorithms identified key genes. Two different machine learning methods were used to identify core genes and develop diagnostic nomograms. The association between core genes and immune cells was assessed by immune infiltration analysis. Finally, RT-qPCR tested how inflammation affects Mmp8 and Ltf expression in mast cells and how they influence inflammatory factor expression. RESULTS: Integration of DEGs and WGCNA yielded 98 key genes. The results of PPI network analysis were imported into the Cytoscape software, and 14 hub genes were screened using the cytoHubba plugin. Five core genes-MMP8, LTF, LCN2, CEACAM8, and ITGB3-were selected through machine learning. The previous four genes were strongly linked to different functions of immune cells in cases of coexisting depression and obesity. In the validation cohort, LTF, LCN2, CEACAM8 were downregulated after bariatric surgery, while ITGB3 unchanged and MMP8 undetected. In P815 cells, Lipopolysaccharide (LPS) induced MMP8 and LTF, and knockdown of either reduced proinflammatory cytokines. CONCLUSION: This study suggests that MMP8, LTF, LCN2, and CEACAM8 may be associated with therapeutic the comorbid mechanisms of depression and obesity and could potentially be used as candidates for treatment. The results of this research could offer new directions for future investigations into the causes and treatments of depression and obesity.