Rheumatoid Arthritis Candidate Genes Identification by Investigating Core and Periphery Interaction Structures

Rheumatoid arthritis (RA) is a long-term systemic inflammatory disease that primarily attacks synovial joints and ultimately leads to their destruction. The disease is characterized by series of processes such as inflammation in the joints, synovial hyperplasia, and cartilage destruction leading to bone erosion. Since RA being a chronic inflammatory complex disease, there is a constant need to develop novel and dynamic treatment to cure the disease. In the present research, network biology and gene expression profiling technology are integrated to predict novel key regulatory molecules, biological pathways, and functional network associated with RA. The microarray datasets of synovial fibroblast (SF) (GSE7669) and macrophages (GSE10500 and GSE8286), which are the primary cells in the synovium and reported as the key players in the pathophysiology of RA, were considered for identification of signature molecules related to RA. The statistical analysis was performed using false discovery rate (FDR), t-test, one-way anova, and Pearson correlation with favorable p-value. The K-core analysis depicted the change in network topology which consisted of up- and downregulated genes network, resulted in six novel meaningful networks with seed genes OAS2, VCAN, CPB1, ZNF516, ACP2, and OLFML2B. Hence, we propose that, differential gene expression network studies will be a standard step to elucidate novel expressed gene(s) globally.