Yanzhi Ge1, Li Zhou1, Zuxiang Chen1, Yingying Mao2, Ting Li3, Peijian Tong4, Letian Shan5. 1. The First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China. 2. Department of Epidemiology and Biostatistics, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China. 3. The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, PR China. 4. The First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China. tongpeijian@163.com. 5. The First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China. letian.shan@zcmu.edu.cn.
Abstract
BACKGROUND: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. MATERIALS AND METHODS: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. RESULTS: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. CONCLUSION: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.
BACKGROUND: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. MATERIALS AND METHODS: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. RESULTS: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. CONCLUSION: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.
Authors: Hennie G Raterman; Saskia Vosslamber; Sander de Ridder; Michael T Nurmohamed; Willem F Lems; Maarten Boers; Mark van de Wiel; Ben A C Dijkmans; Cornelis L Verweij; Alexandre E Voskuyl Journal: Arthritis Res Ther Date: 2012-04-27 Impact factor: 5.156
Authors: Aaron M Newman; Chloé B Steen; Chih Long Liu; Andrew J Gentles; Aadel A Chaudhuri; Florian Scherer; Michael S Khodadoust; Mohammad S Esfahani; Bogdan A Luca; David Steiner; Maximilian Diehn; Ash A Alizadeh Journal: Nat Biotechnol Date: 2019-05-06 Impact factor: 54.908