Genome-wide study of key genes and scoring system as potential noninvasive biomarkers for detection of suicide behavior in major depression disorder.

Although some progress has been made in the molecular biological detection of major depression disorder (MDD), its specificity and accuracy are still insufficient. This study is aimed to find hub genes, which could contribute to MDD related suicide and provide potential therapeutic targets for diagnosis and treatment. We downloaded RNA expression and clinical information from Gene Expression Omnibus (GEO) Dataset. Then, weighted gene co-expression network analysis (WGCNA) was applied to find core modules. Logistic regression was performed to identify the independent risk factors, and a scoring system was constructed based on these independent risk factors. As a result, a total of 16487 genes were selected to further conducted WGCNA analysis. We found that tan and green functional modules were exhibited high correlation with suicide behavior. 309 genes were identified in tan modules that were the strongest positively correlated with suicide behavior. Functional analysis in tan module indicated that activation of enzymes including nitric-oxide synthase and endoribonuclease, estrogen signaling pathway, glucagon signaling pathway, and legionellosis pathway were most enriched in MDD. Furthermore, we applied protein-protein interaction (PPI) analysis to select the hub genes and 10 genes were found in the core area of network. Then, we identified three-gene base independent risk signature by logistic regression model, including HSPA1A, RASEF, TBC1D8B. In conclusion, our study suggests that the tan module genes are closely related to suicide behaviors, which is mainly caused by multiple signaling pathway activation. The three-genes-based signature could provide a better efficacy to predict suicidal behavior in MDD patients.