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pmid:32214815

Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression.

Guangyin Zhang · Shixin Xu · Zhuo Yuan · Li Shen

Neuropsychiatric disease and treatment · 2020 doi:10.2147/NDT.S244452

Paper

complete

Experiment

complete

Data

partial

Analysis

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title
Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression.
doi
10.2147/NDT.S244452
pmid
32214815
authors array · 4 items
[0]
Guangyin Zhang
[1]
Shixin Xu
[2]
Zhuo Yuan
[3]
Li Shen
journal
Neuropsychiatric disease and treatment
year
2020
abstract
PURPOSE: Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. PATIENTS AND METHODS: Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. RESULTS: We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. CONCLUSION: The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.
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To characterize DEGs, the control group and the MDD group were analyzed using the LIMMA (linear models for microarray data) package20 in the R/Bioconductor platform.
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All of the data were freely available online, and this study did not involve any experiments on humans or animals performed by any of the authors.
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Gene expression analysis in whole blood samples obtained from donors diagnosed with MDD compared to healthy controls
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PPI Network Construction and Analysis of Selected Modules
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PPI Network Construction and Analysis of Selected Modules
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To characterize DEGs, the control group and the MDD group were analyzed using the LIMMA (linear models for microarray data) package20 in the R/Bioconductor platform.
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enesis of the disease. PATIENTS AND METHODS: Microarray data from the GSE98793 dataset were download
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To characterize DEGs, the control group and the MDD group were analyzed using the LIMMA (linear models for microarray data) package20 in the R/Bioconductor platform.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32214815", "kind": "science-record", "revision": 8, "content_hash": "sha256:fd790293c8d8c53fef99b221070460bd84f0f044ccf2e9fa4a93bd3b1d8a9cef", "created_at": "2026-11-01T15:40:58Z", "updated_at": "2026-11-01T22:44:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression.", "doi": "10.2147/NDT.S244452", "pmid": "32214815", "authors": ["Guangyin Zhang", "Shixin Xu", "Zhuo Yuan", "Li Shen"], "journal": "Neuropsychiatric disease and treatment", "year": 2020, "abstract": "PURPOSE: Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease.\nPATIENTS AND METHODS: Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well.\nRESULTS: We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function.\nCONCLUSION: The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.", "claims": [{"id": "claim-method", "statement": "To characterize DEGs, the control group and the MDD group were analyzed using the LIMMA (linear models for microarray data) package20 in the R/Bioconductor platform.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32214815", "locator": "sections[4] \"Data Preprocessing and DEG Screening\" paragraph 3", "quote": "To characterize DEGs, the control group and the MDD group were analyzed using the LIMMA (linear models for microarray data) package20 in the R/Bioconductor platform."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All of the data were freely available online, and this study did not involve any experiments on humans or animals performed by any of the authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32214815", "locator": "sections[3] \"Microarray Data\" paragraph 2", "quote": "All of the data were freely available online, and this study did not involve any experiments on humans or animals performed by any of the authors."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:32214815"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE98793"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE98793"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32214815", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/NDT.S244452", "sha256": 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