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. 2025 Mar;36(1):66-82.
doi: 10.1007/s00335-024-10090-z. Epub 2024 Dec 10.

Biological mechanism and functional verification of key genes related to major depressive disorder and type 2 diabetes mellitus

Affiliations

Biological mechanism and functional verification of key genes related to major depressive disorder and type 2 diabetes mellitus

Tao Fang et al. Mamm Genome. 2025 Mar.

Abstract

Major depressive disorder (MDD) and type 2 diabetes (T2D) have been shown to be linked, but a comprehensive understanding of the underlying molecular mechanisms remains elusive. The purpose of this study was to explore the biological relationship between MDD and T2D and verify the functional roles of key genes. We used the Gene Expression Omnibus database to investigate the targets associated with MDD and T2D. Using linear models for microarray data, differentially expressed genes associated with MDD and T2D were identified in GSE76826 and GSE95849, respectively, and 126 shared genes were significantly upregulated. Weighted gene coexpression network analysis identified modules associated with MDD and T2D in the GSE38206 and GSE20966 datasets and identified 8 common genes. Functional enrichment analysis revealed that these genes were enriched in cell signaling, enzyme activity, cell structure and amino acid biosynthesis and involved in cell death pathways. Finally, combined with the CTD and GeneCards databases, lysophosphatidylglycerol acyltransferase 1 (LPGAT1) was identified as a key gene. LPGAT1 was validated in GSE201332 and GSE182117, and the subject operating characteristic curve showed good diagnostic potential for MDD and T2D. Additionally, we used an in vitro model of MDD related to T2D to verify the expression of LPGAT1. A subsequent gene knockdown assay revealed that the downregulation of LPGAT1 improved mitochondrial function and reduced apoptosis in damaged neurons. Taken together, our results highlight the role of LPGAT1 in the connection between MDD and T2D, and these findings provide new insights into potential therapeutic targets for depression associated with diabetes.

Keywords: Bioinformatics analysis; Hub gene; Major depressive disorder; Type 2 diabetes; Verification of functionality.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

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