Objective: Rheumatoid arthritis (RA), as an autoimmune disease, poses a huge social and economic burden worldwide. Although the diagnosis of RA has been gradually improved, there is still a need to discover accurate and rapid biomarkers for diagnosis and therapy with a precise understanding of the disease. This study aimed to screen diagnostic biomarkers and analyze immune infiltration in RA based on weighted gene co-expression network analysis (WGCNA).
Materials and methods: Firstly, we screened the experimental and validation sets associated with RA from the GEO database. Crossover genes were obtained using differential genes (DEGs) and key modules in WGCNA. Subsequently, the crossover genes were constructed into protein-protein interaction (PPI) networks and screened to obtain hub genes. The receiver operating characteristic (ROC) curve assessment was performed to identify diagnostic biomarkers. In addition, we used the Cibersort algorithm for immuno-infiltration analysis and the DGidb database to search for drugs associated with diagnostic biomarkers.
Results: In the end, 377 DEGs were identified, and the enrichment analysis revealed significant associations with the immune system. Blue modules in the WGCNA analysis were positively associated with the disease and were identified as key modules. ROC curves evaluated the four hub genes, which significantly differentiated RA from healthy controls and could be used as diagnostic biomarkers. In further analysis, we found that RA is closely related to immunity, and the search identified multiple drugs that hold promise for treating RA.
Conclusions: BCL2A1, PTGS2, FAS, and LY96 may be used as diagnostic biomarkers, which is significant for diagnosing and treating RA.