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Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder
Aiming at a more comprehensive understanding of the molecular biomarkers and potential mechanisms of major depressive disorder (MDD), from the Gene Expression Omnibus (GEO) database, we first obtained mRNA expression profiles and identified 585 differentially expressed genes (DEGs) through the R sof...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357473/ https://www.ncbi.nlm.nih.gov/pubmed/34394704 http://dx.doi.org/10.1155/2021/3036741 |
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author | Qi, Dong Chen, Kui |
author_facet | Qi, Dong Chen, Kui |
author_sort | Qi, Dong |
collection | PubMed |
description | Aiming at a more comprehensive understanding of the molecular biomarkers and potential mechanisms of major depressive disorder (MDD), from the Gene Expression Omnibus (GEO) database, we first obtained mRNA expression profiles and identified 585 differentially expressed genes (DEGs) through the R software, including 263 upregulated genes and 322 downregulated genes. Then, through the Kyoto Encyclopedia of Genome and Genome (KEGG) pathway and biological process (BP) analysis, we found that the upregulated and downregulated DEGs were abundant in different pathways, respectively. It was noteworthy that upregulated DEGs were the most significantly enriched in the mTOR signaling pathway. Subsequently, through the protein-protein interaction (PPI) network, we identified seven hub genes, namely, EXOSC2, CAMK2A, PRIM1, SMC4, TYMS, CDK6, and RPA2. Finally, through gene set enrichment analysis (GSEA), we obtained that hypoxia, epithelial-mesenchymal transition, hedgehog signaling, and reactive oxygen species pathway were the enriched pathways for MDD patients. The above data results would provide a new direction for the treatment of MDD patients. |
format | Online Article Text |
id | pubmed-8357473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-83574732021-08-12 Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder Qi, Dong Chen, Kui Comput Math Methods Med Research Article Aiming at a more comprehensive understanding of the molecular biomarkers and potential mechanisms of major depressive disorder (MDD), from the Gene Expression Omnibus (GEO) database, we first obtained mRNA expression profiles and identified 585 differentially expressed genes (DEGs) through the R software, including 263 upregulated genes and 322 downregulated genes. Then, through the Kyoto Encyclopedia of Genome and Genome (KEGG) pathway and biological process (BP) analysis, we found that the upregulated and downregulated DEGs were abundant in different pathways, respectively. It was noteworthy that upregulated DEGs were the most significantly enriched in the mTOR signaling pathway. Subsequently, through the protein-protein interaction (PPI) network, we identified seven hub genes, namely, EXOSC2, CAMK2A, PRIM1, SMC4, TYMS, CDK6, and RPA2. Finally, through gene set enrichment analysis (GSEA), we obtained that hypoxia, epithelial-mesenchymal transition, hedgehog signaling, and reactive oxygen species pathway were the enriched pathways for MDD patients. The above data results would provide a new direction for the treatment of MDD patients. Hindawi 2021-08-03 /pmc/articles/PMC8357473/ /pubmed/34394704 http://dx.doi.org/10.1155/2021/3036741 Text en Copyright © 2021 Dong Qi and Kui Chen. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Qi, Dong Chen, Kui Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder |
title | Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder |
title_full | Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder |
title_fullStr | Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder |
title_full_unstemmed | Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder |
title_short | Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder |
title_sort | bioinformatics analysis of potential biomarkers and pathway identification for major depressive disorder |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357473/ https://www.ncbi.nlm.nih.gov/pubmed/34394704 http://dx.doi.org/10.1155/2021/3036741 |
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