Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

Abstract Background Major depressive disorder (MDD) is a leading psychiatric that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in network connectivity of different brain tissues under pathological conditions, analyzed pathways genes are significantly related disease progression, further predicted potential therapeutic drug targets. Methods Expression differentially expressed (DEGs) were with postmortem cingulate cortex (ACC) prefrontal (PFC) mRNA expression profile datasets downloaded from Gene Omnibus (GEO) database, including 76 MDD patients healthy subjects ACC 63 PFC. The co-expression construction was based on system analysis. function annotated by Kyoto Encyclopedia Genes Genomes (KEGG) pathway Human Protein Reference Database (HPRD, http://www.hprd.org/ ) used for gene interaction relationship mapping. Results We filtered 586 DEGs 616 PFC By constructing network, we found reduced conditions ( P = 0.04 1.227e?09 ACC). Crosstalk analysis showed CD19, PTDSS2 NDST2 patients. Among them, CD19 have been targeted several drugs Drugbank database. KEGG demonstrated enriched Glycerophospholipid metabolism T cell receptor signaling pathway. Conclusion Co-expression tissue comparing can identify cross talk MDD, which may provide novel insight understanding molecular mechanisms MDD.