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Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

BACKGROUND: Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated....

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Autores principales: Wu, Zheng-yuan, Du, Gang, Lin, Yi-cai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527722/
https://www.ncbi.nlm.nih.gov/pubmed/34670585
http://dx.doi.org/10.1186/s13018-021-02796-6
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author Wu, Zheng-yuan
Du, Gang
Lin, Yi-cai
author_facet Wu, Zheng-yuan
Du, Gang
Lin, Yi-cai
author_sort Wu, Zheng-yuan
collection PubMed
description BACKGROUND: Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated. METHODS: In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis. RESULTS: Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA. CONCLUSION: Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA.
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spelling pubmed-85277222021-10-25 Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis Wu, Zheng-yuan Du, Gang Lin, Yi-cai J Orthop Surg Res Research Article BACKGROUND: Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated. METHODS: In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis. RESULTS: Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA. CONCLUSION: Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA. BioMed Central 2021-10-20 /pmc/articles/PMC8527722/ /pubmed/34670585 http://dx.doi.org/10.1186/s13018-021-02796-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Wu, Zheng-yuan
Du, Gang
Lin, Yi-cai
Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
title Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
title_full Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
title_fullStr Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
title_full_unstemmed Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
title_short Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
title_sort identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527722/
https://www.ncbi.nlm.nih.gov/pubmed/34670585
http://dx.doi.org/10.1186/s13018-021-02796-6
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