Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses

Zhaoyan Li, Meng Xu, Ronghang Li, Zhengqing Zhu, Yuzhe Liu, Zhenwu Du, Guizhen Zhang, Yang Song
2020 Bioscience Reports  
Objectives. RA is the most common inflammatory arthritis in the world, but its underlying mechanism is still unclear. This study aims to screen and verify the potential biomarkers of RA. Methods: We searched the GEO database for synovial expression profiling from different RA microarray studies to perform a systematic analysis. Functional annotation of DEGs was conducted, including GO enrichment analysis and KEGG pathway enrichment analysis. The PPI networks of the DEGs were constructed based
more » ... data from the STRING database. The expression levels of the hub genes in normal membranes and RA synovium were detected by qRT-PCR and western blot system. Results: A total of 444 differential expression genes were identified, including 172 upregulated genes and 272 downregulated genes in RA synovium compared with normal controls. The top 10 hub genes PTPRC, LCK, CDC20, JUN, CDK1, KIF11, EGFR, VEGFA, MAD2L1, and STAT1 were identified from the PPI network, and the expression level of VEGFA and EGFR was significantly increased in RA membranes (p < 0.05). Conclusion: Our results indicate that the hub genes VEGFA and EGFR may have essential effects during the development of RA and can be used as potential biomarkers of RA.
doi:10.1042/bsr20201713 pmid:32840301 pmcid:PMC7502692 fatcat:rffcn5i6yvd4nbtwwdifij3oty