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

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 i...

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Autores principales: Zhang, Guangyin, Xu, Shixin, Yuan, Zhuo, Shen, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079285/
https://www.ncbi.nlm.nih.gov/pubmed/32214815
http://dx.doi.org/10.2147/NDT.S244452
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author Zhang, Guangyin
Xu, Shixin
Yuan, Zhuo
Shen, Li
author_facet Zhang, Guangyin
Xu, Shixin
Yuan, Zhuo
Shen, Li
author_sort Zhang, Guangyin
collection PubMed
description 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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spelling pubmed-70792852020-03-25 Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression Zhang, Guangyin Xu, Shixin Yuan, Zhuo Shen, Li Neuropsychiatr Dis Treat Original Research 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. Dove 2020-03-12 /pmc/articles/PMC7079285/ /pubmed/32214815 http://dx.doi.org/10.2147/NDT.S244452 Text en © 2020 Zhang et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Zhang, Guangyin
Xu, Shixin
Yuan, Zhuo
Shen, Li
Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
title Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
title_full Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
title_fullStr Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
title_full_unstemmed Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
title_short Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
title_sort weighted gene coexpression network analysis identifies specific modules and hub genes related to major depression
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079285/
https://www.ncbi.nlm.nih.gov/pubmed/32214815
http://dx.doi.org/10.2147/NDT.S244452
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