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Screening and identification of key biomarkers of depression using bioinformatics

We aimed to identify the molecular biomarkers of MDD disease progression to uncover potential mechanisms of major depressive disorder (MDD). In this study, three microarray data sets, GSE44593, GSE12654, and GSE54563, were cited from the Gene Expression Omnibus database for performance evaluation. T...

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Autores principales: Kong, Xinru, Wang, Chuang, Wu, Qiaolan, Wang, Ziyue, Han, Yu, Teng, Jing, Qi, Xianghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010653/
https://www.ncbi.nlm.nih.gov/pubmed/36914737
http://dx.doi.org/10.1038/s41598-023-31413-1
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author Kong, Xinru
Wang, Chuang
Wu, Qiaolan
Wang, Ziyue
Han, Yu
Teng, Jing
Qi, Xianghua
author_facet Kong, Xinru
Wang, Chuang
Wu, Qiaolan
Wang, Ziyue
Han, Yu
Teng, Jing
Qi, Xianghua
author_sort Kong, Xinru
collection PubMed
description We aimed to identify the molecular biomarkers of MDD disease progression to uncover potential mechanisms of major depressive disorder (MDD). In this study, three microarray data sets, GSE44593, GSE12654, and GSE54563, were cited from the Gene Expression Omnibus database for performance evaluation. To perform molecular functional enrichment analyses, differentially expressed genes (DEGs) were identified, and a protein–protein interaction network was configured using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. To assess multi-purpose functions and pathways, such as signal transduction, plasma membrane, protein binding, and cancer pathways, a total of 220 DEGs, including 143 upregulated and 77 downregulated genes, were selected. Additionally, six central genes were observed, including electron transport system variant transcription factor 6, FMS-related receptor tyrosine kinase 3, carnosine synthetase 1, solute carrier family 22 member 13, prostaglandin endoperoxide synthetase 2, and protein serine kinase H1, which had a significant impact on cell proliferation, extracellular exosome, protein binding, and hypoxia-inducible factor 1 signaling pathway. This study enhances our understanding of the molecular mechanism of the occurrence and progression of MDD and provides candidate targets for its diagnosis and treatment.
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spelling pubmed-100106532023-03-14 Screening and identification of key biomarkers of depression using bioinformatics Kong, Xinru Wang, Chuang Wu, Qiaolan Wang, Ziyue Han, Yu Teng, Jing Qi, Xianghua Sci Rep Article We aimed to identify the molecular biomarkers of MDD disease progression to uncover potential mechanisms of major depressive disorder (MDD). In this study, three microarray data sets, GSE44593, GSE12654, and GSE54563, were cited from the Gene Expression Omnibus database for performance evaluation. To perform molecular functional enrichment analyses, differentially expressed genes (DEGs) were identified, and a protein–protein interaction network was configured using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. To assess multi-purpose functions and pathways, such as signal transduction, plasma membrane, protein binding, and cancer pathways, a total of 220 DEGs, including 143 upregulated and 77 downregulated genes, were selected. Additionally, six central genes were observed, including electron transport system variant transcription factor 6, FMS-related receptor tyrosine kinase 3, carnosine synthetase 1, solute carrier family 22 member 13, prostaglandin endoperoxide synthetase 2, and protein serine kinase H1, which had a significant impact on cell proliferation, extracellular exosome, protein binding, and hypoxia-inducible factor 1 signaling pathway. This study enhances our understanding of the molecular mechanism of the occurrence and progression of MDD and provides candidate targets for its diagnosis and treatment. Nature Publishing Group UK 2023-03-13 /pmc/articles/PMC10010653/ /pubmed/36914737 http://dx.doi.org/10.1038/s41598-023-31413-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kong, Xinru
Wang, Chuang
Wu, Qiaolan
Wang, Ziyue
Han, Yu
Teng, Jing
Qi, Xianghua
Screening and identification of key biomarkers of depression using bioinformatics
title Screening and identification of key biomarkers of depression using bioinformatics
title_full Screening and identification of key biomarkers of depression using bioinformatics
title_fullStr Screening and identification of key biomarkers of depression using bioinformatics
title_full_unstemmed Screening and identification of key biomarkers of depression using bioinformatics
title_short Screening and identification of key biomarkers of depression using bioinformatics
title_sort screening and identification of key biomarkers of depression using bioinformatics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010653/
https://www.ncbi.nlm.nih.gov/pubmed/36914737
http://dx.doi.org/10.1038/s41598-023-31413-1
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