Identification and Analysis of Novel Synovial Tissue-based Biomarkers and Interacting Pathways for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an inflammatory disorder with autoimmune pathogenesis characterized by the immune system attacking the synovium. It is a clinically heterogeneous disease that affects approximately 1.2 million Americans and 20 million people worldwide. It is advantageous to diagnose RA before extensive erosion as treatments are more effective at early stages. RA treatments have made notable progress, yet a significant number of patients still fail to respond to current medication and most of these come with harmful side effects. While the mechanistic reason for such failure rates remains unknown, the cellular and molecular signatures in the synovial tissues of patients with RA are likely to play a role in the variable treatment response and heterogeneous clinical evolution. While blood-based criteria are currently employed for diagnostics and treatments. such serologic parameters do not necessarily reflect biological actions in the target tissue of the patient and are relatively nonspecific to RA. Synovial tissue-based biomarkers are especially attractive as they can provide a confirmed diagnosis for RA. The shortage of accurate synovial tissue-based identifiers for RA diagnosis encouraged this research. This study analyzed data from several gene expression studies for differentially expressed genes in donor synovial tissue. Bioinformatics tools were used to construct and analyse protein interaction networks. Analysis deduced that regulating hematopoietic stem cell migration could serve as a potential RA diagnostic. VAV1, CD3G, LCK, PTPN6, ITGB2, CXCL13, CD4, and IL7R are found to be previously unclassified, potential biomarkers.