Mining biomarkers for the occurrence, development and therapeutic response to infliximab of rheumatoid arthritis based on weighted gene co-expression network analysis

Abstract

Rheumatoid arthritis (RA) is a chronic systemic autoimmune and recurrent disease characterized by joint lesion with high disability rate and poor prognosis. Although anti-TNF therapy (infliximab) has been validated to be effective in controlling the symptoms of RA, more than one-third of patients cannot benefit from anti-TNF therapy. Therefore, it is critical to explore novel biomarkers associated with the occurrence and development of the disease, and therapeutic response of infliximab treatment. In this study, we first analyzed differentially expressed genes (DEGs) between RA patients and healthy controls. Subsequently, WGCNA was used to analyze the co-expression module (tan module) associated with the therapeutic response of infliximab treatment in RA, which further found that the genes (a total of 102) in the module tan were also the DEGs between RA patients and healthy controls (confirming that the close correlation of these genes with the occurrence and development of RA). Subsequently, enrichment analysis revealed that these 102 genes were mainly related to the body’s inflammatory immune response and the functions of NOD-like receptor signaling pathway as well as miR-146a-5p. Moreover, protein-protein interaction (PPI) was constructed on the 102 genes, followed by mining the key genes in the network (a total of 14 genes) by topological analysis. Finally, we confirmed that 10 genes (OAS1, STAT1, IFIT3, IFIT2, IFI6, RSAD2, OASL, GBP2, IRF7 and IFIT1) were potential biomarkers for the occurrence and development, as well as the therapeutic response against infliximab treatment in RA by analyzing the differences in the expressions of these genes between RA and healthy control samples, as well as drug response and non-response samples. Therefore, this study can provide guidance on medication selection and prognostic prediction of RA patients for clinicians, which can also identify relevant genes to provide therapeutic targets for precise medicine.