Differential Diagnosis of Osteoarthritis and Rheumatoid Arthritis by Bioinformatics Analysis

Abstract Background: Osteoarthritis (OA) and rheumatoid arthritis (RA) is the most common joint disease. The aim of my current academic work is to identify important genes associated with OA and RA, clarify their underlying mechanisms, and define differences between OA and RA.Methods: Gene expression profiles of GSE55235 were available from Gene Expression Omnibus database. Differentially expressed genes between 1) OA tissues and normal tissues, 2) RA tissues and normal tissues, were picked out by GEO2R tool, Venn diagram software, and Volcanic map. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze Kyoto Encyclopedia of Gene and Genome pathway and gene ontology. The protein-protein interaction of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes. Of entire PPI network analyzed by Molecular Complex Detection plug-in and obtain central nodes. Then the central nodes were re-analysis via DAVID. We next obtained the intersection of the analysis results of GSE55457 and GSE55235 to verify the results. Finally, potential biomarkers were evaluated by receiver operating characteristic.Results: Twelve genes were found to be significantly enriched in the TNF signaling pathway in OA, and eleven genes were found to be significantly enriched in the Chemokine signaling pathway in RA. Receiver operating characteristic curve suggested that the detection of JUN in OA and CCL5, CXCL9, CXCL10, CXCL11, CXCL13 in RA exhibited a high diagnostic performance.Conclusions: We have identified six significantly DEGs on the basis of integrated bioinformatical methods, which could be potential targets for differential diagnosis for OA and RA.