To identify prospective diagnostic biomarkers for RA, we used 6 different cytoHubba algorithms on the aforementioned 24 overlapping genes and discovered 13 genes as probable candidate genes: MMP9, IFNG, CDK1, CHEK1, BTK, PIK3CG, PIM2, CSF1R, AURKA, TLR8, PBK, AURKB, and TOP2A. Then, a prediction model for the diagnosis of RA was created using the LASSO method to identify RA patients from healthy controls in the testing cohorts (MMP9 was not significant with a zero coefficient in the GSE100191 dataset). Here, AURKB is linked to rheumatoid arthritis.