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Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses

Synovitis in osteoarthritis (OA) is a very common condition. However, its underlying mechanism is still not well understood. This study aimed to explore the molecular mechanisms of synovitis in OA. The gene expression profile GSE82107 (downloaded from the Gene Expression Omnibus database) included 1...

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Autores principales: Huang, Hui, Zheng, Jiaxuan, Shen, Ningjiang, Wang, Guangji, Zhou, Gang, Fang, Yehan, Lin, Jianping, Zhao, Jianning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030156/
https://www.ncbi.nlm.nih.gov/pubmed/29968759
http://dx.doi.org/10.1038/s41598-018-28280-6
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author Huang, Hui
Zheng, Jiaxuan
Shen, Ningjiang
Wang, Guangji
Zhou, Gang
Fang, Yehan
Lin, Jianping
Zhao, Jianning
author_facet Huang, Hui
Zheng, Jiaxuan
Shen, Ningjiang
Wang, Guangji
Zhou, Gang
Fang, Yehan
Lin, Jianping
Zhao, Jianning
author_sort Huang, Hui
collection PubMed
description Synovitis in osteoarthritis (OA) is a very common condition. However, its underlying mechanism is still not well understood. This study aimed to explore the molecular mechanisms of synovitis in OA. The gene expression profile GSE82107 (downloaded from the Gene Expression Omnibus database) included 10 synovial tissues of the OA patients and 7 synovial tissues of healthy people. Subsequently, differentially expressed gene (DEG) analysis, GO (gene ontology) enrichment analysis, pathway analysis, pathway network analysis, and gene signal network analysis were performed using Gene-Cloud of Biotechnology Information (GCBI). A total of 1,941 DEGs consisting of 1,471 upregulated genes and 470 downregulated genes were determined. Genes such as PSMG3, LRP12 MIA-RAB4B, ETHE1, SFXN1, DAZAP1, RABEP2, and C9orf16 were significantly regulated in synovitis of OA. In particular, the MAPK signalling pathway, apoptosis, and pathways in cancer played the most important roles in the pathway network. The relationships between these pathways were also analysed. Genes such as NRAS, SPHK2, FOS, CXCR4, PLD1, GNAI2, and PLA2G4F were strongly implicated in synovitis of OA. In summary, this study indicated that several molecular mechanisms were implicated in the development and progression of synovitis in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic advances.
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spelling pubmed-60301562018-07-11 Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses Huang, Hui Zheng, Jiaxuan Shen, Ningjiang Wang, Guangji Zhou, Gang Fang, Yehan Lin, Jianping Zhao, Jianning Sci Rep Article Synovitis in osteoarthritis (OA) is a very common condition. However, its underlying mechanism is still not well understood. This study aimed to explore the molecular mechanisms of synovitis in OA. The gene expression profile GSE82107 (downloaded from the Gene Expression Omnibus database) included 10 synovial tissues of the OA patients and 7 synovial tissues of healthy people. Subsequently, differentially expressed gene (DEG) analysis, GO (gene ontology) enrichment analysis, pathway analysis, pathway network analysis, and gene signal network analysis were performed using Gene-Cloud of Biotechnology Information (GCBI). A total of 1,941 DEGs consisting of 1,471 upregulated genes and 470 downregulated genes were determined. Genes such as PSMG3, LRP12 MIA-RAB4B, ETHE1, SFXN1, DAZAP1, RABEP2, and C9orf16 were significantly regulated in synovitis of OA. In particular, the MAPK signalling pathway, apoptosis, and pathways in cancer played the most important roles in the pathway network. The relationships between these pathways were also analysed. Genes such as NRAS, SPHK2, FOS, CXCR4, PLD1, GNAI2, and PLA2G4F were strongly implicated in synovitis of OA. In summary, this study indicated that several molecular mechanisms were implicated in the development and progression of synovitis in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic advances. Nature Publishing Group UK 2018-07-03 /pmc/articles/PMC6030156/ /pubmed/29968759 http://dx.doi.org/10.1038/s41598-018-28280-6 Text en © The Author(s) 2018 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Huang, Hui
Zheng, Jiaxuan
Shen, Ningjiang
Wang, Guangji
Zhou, Gang
Fang, Yehan
Lin, Jianping
Zhao, Jianning
Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
title Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
title_full Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
title_fullStr Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
title_full_unstemmed Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
title_short Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
title_sort identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030156/
https://www.ncbi.nlm.nih.gov/pubmed/29968759
http://dx.doi.org/10.1038/s41598-018-28280-6
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