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Network-based approach to identify molecular signatures in the brains of depressed suicides

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BACKGROUND Suicide is a serious and global health problem that has a strong association with major depressive disorder (MDD). Weighted gene co-expression network analysis (WGCNA) was performed for the construction… Click to show full abstract

BACKGROUND Suicide is a serious and global health problem that has a strong association with major depressive disorder (MDD). Weighted gene co-expression network analysis (WGCNA) was performed for the construction of a co-expression network to get important gene modules associated with depressed suicide. METHODS Transcriptome sequencing data from dorsolateral prefrontal cortex was used, which included 29 non-psychiatric controls (CON), 21 MDD suicides (MDD-S) and 9 MDD non-suicides (MDD-NS) of medication-free sudden death individuals. RESULTS The highest correlation in the module-traits relationship was discovered between the black module and suicide (r = -0.30, p = 0.024) as well as MDD (r = -0.34, p = 0.010).Furthermore, the expression levels of genes decreased progressively across the three groups (CON>MDD-NS>MDD-S). Therefore, the genes in the black module was selected for subsequent analyses. Protein-Protein Interaction Network found that the top 10 hub genes were somehow involved in depressed suicide including JUN, FOS, ATF3, MYC, EGR1, FOSB, DUSP1, NFKBIA, TLR2, NR4A1. Most of the GO terms were enriched in cell death and apoptosis and KEGG was mainly enriched in MAPK pathway. Cell Type-Specific Analysis found these genes were significantly enriched in endothelial and microglia (p<0.000) cell types. In addition, 92 genes in this module had at least one highly significant differentially methylated positions between MDD-S and controls. CONCLUSION Cell death and apoptosis may participate in the interplay between depressed suicide and neuro-inflammation system.

Keywords: depressed suicide; network based; network; suicide; approach identify; based approach

Journal Title: Psychiatry Research
Year Published: 2020

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