Abstract
BACKGROUND: Rheumatoid Arthritis (RA) is a chronic inflammatory joint disease. Current treatments often have limited efficacy and cause side effects due to their nonspecific action, while early diagnosis is challenging. This study combined bioinformatics and experimental methods to identify key genes and pathways involved in RA, aiming to discover novel therapeutic targets and diagnostic biomarkers. METHODS: RNA-seq data from immune cells of RA patients and healthy donors (GSE117769) were analyzed with DESeq to identify Differentially Expressed Genes (DEGs). Affected pathways were explored using EnrichR, and druggable genes were identified through DGIdb and a literature review. Expression of candidate genes was validated in additional RA blood and synovium microarray datasets (GSE45291, GSE82107, GSE77298) using the GEO2R tool. Finally, RT-qPCR was used to measure the expression of selected genes in Peripheral blood Mononuclear Cells (PBMCs) from newly-diagnosed and chronic RA patients and controls, with associations to clinical features and diagnostic accuracy assessed. Synovial fluid of RA patients were stained with Giemsa. RESULTS: Combined in-silico and experimental analysis demonstrated significant upregulation of CA1, OLAH, and ADAMTS2 in the PBMCs of RA patients. However, only ADAMTS2 showed high expression in the synovial tissue of these patients. While OLAH and ADAMTS2 were predominantly overexpressed in newly-diagnosed cases, CA1 levels were consistently elevated in both early and chronic stages of RA. CONCLUSION: This study identified CA1, OLAH, and ADAMTS2 as being upregulated in RA, with ADAMTS2 showing promise as a therapeutic target, suggesting it may also have potential as a candidate for diagnosis and treatment.