Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis
Rheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify criti...
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doaj-5e54275c386e427b8d275334c70c073a2020-11-25T03:19:51ZengHindawi LimitedBioMed Research International2314-61332314-61412020-01-01202010.1155/2020/69431036943103Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid ArthritisConglin Ren0Mingshuang Li1Weibin Du2Jianlan Lü3Yang Zheng4Haipeng Xu5Renfu Quan6The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310051, ChinaThe First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310002, ChinaDepartment of Orthopedics, Xiaoshan Traditional Chinese Medicine Hospital, Hangzhou, Zhejiang 311200, ChinaThe Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310051, ChinaThe Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310051, ChinaThe Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310051, ChinaThe Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310051, ChinaRheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify critical biomarkers and possible pathogenesis of RA. Three gene expression datasets profiled by microarray were obtained from GEO database. Dataset GSE55235 and GSE55457 were merged for subsequent analyses. We identified differentially expressed genes (DEGs) in RStudio with limma package, performing functional enrichment analysis based on GSEA software and clusterProfiler package. Next, protein-protein interaction (PPI) network was set up through STRING database and Cytoscape. Moreover, CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE77298. As a result, we identified 106 DEGs (72 upregulated and 34 downregulated genes). Through GO, KEGG, and GSEA analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway. With the help of Cytoscape software and MCODE plug-in, the most prominent subnetwork was screened out, containing 14 genes and 45 edges. For ROC curve analysis, eight genes with AUC >0.80 were considered as hub genes of RA. In conclusion, compared with healthy controls, the DEGs and their closely related biological functions were analyzed, and we held that chemokines and immune cells infiltration promote the progression of rheumatoid arthritis. Targeting the eight biomarkers we identified may be useful for the diagnosis and treatment of rheumatoid arthritis.http://dx.doi.org/10.1155/2020/6943103 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Conglin Ren Mingshuang Li Weibin Du Jianlan Lü Yang Zheng Haipeng Xu Renfu Quan |
spellingShingle |
Conglin Ren Mingshuang Li Weibin Du Jianlan Lü Yang Zheng Haipeng Xu Renfu Quan Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis BioMed Research International |
author_facet |
Conglin Ren Mingshuang Li Weibin Du Jianlan Lü Yang Zheng Haipeng Xu Renfu Quan |
author_sort |
Conglin Ren |
title |
Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis |
title_short |
Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis |
title_full |
Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis |
title_fullStr |
Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis |
title_full_unstemmed |
Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis |
title_sort |
comprehensive bioinformatics analysis reveals hub genes and inflammation state of rheumatoid arthritis |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2020-01-01 |
description |
Rheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify critical biomarkers and possible pathogenesis of RA. Three gene expression datasets profiled by microarray were obtained from GEO database. Dataset GSE55235 and GSE55457 were merged for subsequent analyses. We identified differentially expressed genes (DEGs) in RStudio with limma package, performing functional enrichment analysis based on GSEA software and clusterProfiler package. Next, protein-protein interaction (PPI) network was set up through STRING database and Cytoscape. Moreover, CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE77298. As a result, we identified 106 DEGs (72 upregulated and 34 downregulated genes). Through GO, KEGG, and GSEA analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway. With the help of Cytoscape software and MCODE plug-in, the most prominent subnetwork was screened out, containing 14 genes and 45 edges. For ROC curve analysis, eight genes with AUC >0.80 were considered as hub genes of RA. In conclusion, compared with healthy controls, the DEGs and their closely related biological functions were analyzed, and we held that chemokines and immune cells infiltration promote the progression of rheumatoid arthritis. Targeting the eight biomarkers we identified may be useful for the diagnosis and treatment of rheumatoid arthritis. |
url |
http://dx.doi.org/10.1155/2020/6943103 |
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