Identification of potential biomarkers and pathways related to major depressive disorder by integrated bioinformatic analysis and experimental validation

Objective: To identify promising biomarkers for the pathogenesis of major depressive disorder (MDD). Methods: Microarray chips of MDD patients, including the GSE98793, GSE52790, and GSE39653 datasets, were obtained from the Gene Expression Omnibus database. The biological processes and pathways related to MDD were investigated using the GO and KEGG pathway tools. Weighted gene coexpression network analysis was conducted to identify modules related to MDD. The hub genes associated with MDD were obtained via protein-protein interaction analysis. Finally, the expression of hub genes in the hippocampal tissues of depression-like rats was detected by reverse transcription-polymerase chain reaction and Western blotting. Results: A total of 658 differentially expressed genes were identified from the Gene Expression Omnibus datasets; thus, these genes and the GSE98793 dataset were used to conduct weighted gene coexpression network analysis. A total of 244 module-related genes were identified and these genes were highly correlated with MDD. These genes were involved in the Ras signaling pathway, regulation of the actin cytoskeleton, and axon guidance according to the KEGG analysis. Hub genes, including MAPK14, SOCS1, TLR2, PTK2B , and GRB2 , were obtained via protein-protein interaction analysis. All these hub genes showed better diagnostic efficiency in the GSE52790, GSE39653, and GSE98793 datasets. In vivo experiments revealed that compared with those in control rats, SOCS1 and MAPK14 expression was significantly decreased; while GRB2, TLR2 , and PTK2B expression was increased in the hippocampi of depression-like rats. Conclusions: Our study demonstrates that GRB2, TLR2, SOCS1, PTK2B , and MAPK14 are promising hub genes, and targeting these five genes may be an effective treatment strategy for MDD.