ArticlePeerJ2019
Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Identification and Analyses of Crucial Genes Associated with Pathogenesis of Major Depressive Disorder.Psychiatry and clinical psychopharmacology · 2023Article
- EEG alpha band characteristics in patients with a depressive episode within recurrent and bipolar depression.Consortium psychiatricum · 2023Article
- The Complexity and Multiplicity of the Specific cAMP Phosphodiesterase Family: PDE4, Open New Adapted Therapeutic Approaches.International journal of molecular sciences · 2022Review
- The Role of Mitochondria in Mood Disorders: From Physiology to Pathophysiology and to Treatment.Frontiers in psychiatry · 2021Review
- Stanniocalcin-1 Overexpression Prevents Depression-Like Behaviors Through Inhibition of the ROS/NF-κB Signaling Pathway.Frontiers in psychiatry · 2021Article
- Article
- Protein Kinase A Is Involved in Neuropathic Pain by Activating the p38MAPK Pathway to Mediate Spinal Cord Cell Apoptosis.Mediators of inflammation · 2020Article
- Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression.Neuropsychiatric disease and treatment · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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