Screening and identification of key biomarkers in Rheumatoid arthritis: Evidence from bioinformatic analysis

Abstract Objectives: Our objective was to dissect the biomarkers and the inflammation state of rheumatoid arthritis(RA) by comprehensively applying multiple bioinformatics analysis tools. Methods: To identify candidate genes in the carcinogenesis and progression of RA, the microarray datasets GSE153015, GSE197057, and GSE206848 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEG) were identified and function enrichment analyzes were performed. Next, the protein-protein interaction (PPI) network was established through the STRING database and Cytoscape. Furthermore, the CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE93776. Results: A total of 73 DEGs were identified, consisting of 50 down-regulated genes and 23 up-regulated genes. Through GO and KEGG analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway.With the help of Cytoscape software and the MCODE plug-in, the most prominent subnetwork was selected, containing 8 genes. For the analysis of the ROC curve, three genes with AUC >0.70 were considered core genes of RA. Conclusions: Compared to healthy controls, DEG and its closely related biological functions were analyzed, and we were of the opinion that chemokines and immune cell infiltration promote the progression of rheumatoid arthritis. The three biomarkers identified may be useful for the diagnosis and treatment of rheumatoid arthritis.