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Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

BACKGROUND: Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological chan...

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Autores principales: Cai, Weisong, Li, Haohuan, Zhang, Yubiao, Han, Guangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6970550/
https://www.ncbi.nlm.nih.gov/pubmed/31988808
http://dx.doi.org/10.7717/peerj.8390
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author Cai, Weisong
Li, Haohuan
Zhang, Yubiao
Han, Guangtao
author_facet Cai, Weisong
Li, Haohuan
Zhang, Yubiao
Han, Guangtao
author_sort Cai, Weisong
collection PubMed
description BACKGROUND: Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. OBJECTIVE: This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. MATERIALS AND METHODS: The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. RESULTS: A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. CONCLUSION: The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.
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spelling pubmed-69705502020-01-27 Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis Cai, Weisong Li, Haohuan Zhang, Yubiao Han, Guangtao PeerJ Bioinformatics BACKGROUND: Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. OBJECTIVE: This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. MATERIALS AND METHODS: The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. RESULTS: A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. CONCLUSION: The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development. PeerJ Inc. 2020-01-17 /pmc/articles/PMC6970550/ /pubmed/31988808 http://dx.doi.org/10.7717/peerj.8390 Text en ©2020 Cai et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Cai, Weisong
Li, Haohuan
Zhang, Yubiao
Han, Guangtao
Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
title Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
title_full Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
title_fullStr Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
title_full_unstemmed Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
title_short Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
title_sort identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6970550/
https://www.ncbi.nlm.nih.gov/pubmed/31988808
http://dx.doi.org/10.7717/peerj.8390
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