A Bioinformatics System for Exploring Potential Targets and Therapies in Rheumatoid Arthritis

Rheumatoid arthritis (RA), one of the most common chronic inflammatory diseases, is an autoimmune disease that causes cartilage, bone damage, and disability. RA has become a concern, affecting more than 1.5% of the global population. Immunomodulatory or anti-inflammatory targets are the lead candidates in existing clinical trials to develop RA drugs. These targets were reported to be ineffective in reducing symptoms, slowing disease progression, and causing adverse side effects. Therefore, this study focuses on addressing the continuing need for safer, more selective, and more effective treatment options for RA by identifying novel targets and therapeutic approaches. The study involves six important steps: retrieving a dataset from the Gene Expression Omnibus (GEO) database, file conversion, gene differentiation (up-regulated and downregulated), Entrez gene ID retrieval for Search Tool for the Retrieval of Interacting Genes (STRING), protein–protein interaction (PPI) network delineation via STRING, drugs identification via Drug Bank, and relationship establishment between networks and drugs via The Drug–Gene​ Interaction Database (DGIdb). Nine protein hubs, three up-regulated and six down-regulated were identified as interacting with 8 and 18 drugs, respectively. Seven protein sites have never been directly associated with RA and could serve as potential therapeutic targets. Eleven drugs were shown to interact with these seven selected protein hubs as potential therapeutics for RA. These new targets and therapies will help researchers improve the RA treatment pipeline by adding these into the pipeline and bringing successful treatment options for RA patients.