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Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis

OBJECTIVE: Rheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related...

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Autores principales: Xu, Jia, Zhang, Ming-Ying, Jiao, Wei, Hu, Cong-Qi, Wu, Dan-Bin, Yu, Jia-Hui, Chen, Guang-Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575484/
https://www.ncbi.nlm.nih.gov/pubmed/34764682
http://dx.doi.org/10.2147/IJGM.S333512
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author Xu, Jia
Zhang, Ming-Ying
Jiao, Wei
Hu, Cong-Qi
Wu, Dan-Bin
Yu, Jia-Hui
Chen, Guang-Xing
author_facet Xu, Jia
Zhang, Ming-Ying
Jiao, Wei
Hu, Cong-Qi
Wu, Dan-Bin
Yu, Jia-Hui
Chen, Guang-Xing
author_sort Xu, Jia
collection PubMed
description OBJECTIVE: Rheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis. METHODS: We searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein–protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network. RESULTS: There were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network. CONCLUSION: This study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.
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spelling pubmed-85754842021-11-10 Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis Xu, Jia Zhang, Ming-Ying Jiao, Wei Hu, Cong-Qi Wu, Dan-Bin Yu, Jia-Hui Chen, Guang-Xing Int J Gen Med Original Research OBJECTIVE: Rheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis. METHODS: We searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein–protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network. RESULTS: There were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network. CONCLUSION: This study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen. Dove 2021-11-04 /pmc/articles/PMC8575484/ /pubmed/34764682 http://dx.doi.org/10.2147/IJGM.S333512 Text en © 2021 Xu et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Xu, Jia
Zhang, Ming-Ying
Jiao, Wei
Hu, Cong-Qi
Wu, Dan-Bin
Yu, Jia-Hui
Chen, Guang-Xing
Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis
title Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis
title_full Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis
title_fullStr Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis
title_full_unstemmed Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis
title_short Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis
title_sort identification of candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575484/
https://www.ncbi.nlm.nih.gov/pubmed/34764682
http://dx.doi.org/10.2147/IJGM.S333512
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