Abstract
BackgroundMajor depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD.MethodsIn this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein–protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes.ResultsWe identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD.ConclusionsOur findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.
Highlights
The prevalence and incidence of major depressive disorder (MDD), which is ranked as the leading cause of the global disease burden and death by suicide (Ferrari et al, 2013; Hasin et al, 2018), are continuously increasing
The aim of this study was to identify potential diagnostic biomarkers and biological functions related to Major depressive disorder (MDD) from the Gene Expression Omnibus (GEO; Edgar, Domrachev & Lash, 2002)
Expressed genes identification The MDD and control blood samples were divided into two groups for cross-validation
Summary
The prevalence and incidence of major depressive disorder (MDD), which is ranked as the leading cause of the global disease burden and death by suicide (Ferrari et al, 2013; Hasin et al, 2018), are continuously increasing. An interesting study identifies distinct “biotypes” of depression using fMRI, which could be diagnostic biomarkers and may predict treatment response (Wager & Woo, 2017). Investigation of the molecular mechanisms underlying MDD is crucial, and may contribute to identification of the precise targets and essential biomarkers for MDD diagnosis. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD. Protein–protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes. A total of 20 hub genes were selected via the PPI network
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