Identification and Integrated Analysis of Oxidative Stress-Related Genes as Potential Biomarkers for Diagnosis and Treatment of Rheumatoid Arthritis using Bioinformatics Methods

Background Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disorder predominantly characterized by joint inflammation and subsequent joint damage. Accumulating evidence underscores the pivotal role of oxidative stress in the pathogenesis of RA; however, the precise functions and underlying mechanisms of oxidative stress in RA remain inadequately elucidated. Methods Within the datasets GSE39340, GSE55457, and GSE55584, differentially expressed genes (DEGs) associated with RA were identified. By intersecting these with oxidative stress-related genes, oxidative stress-related differentially expressed genes (ORDEGs) were delineated. Employing the STRING database and Cytoscape software, a protein-protein interaction (PPI) network was constructed, facilitating the identification of oxidative stress-related hub genes (ORHGs). The miRNet and miRTarbase databases were utilized to construct an mRNA-miRNA-lncRNA regulatory network pertinent to oxidative stress, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the ORHGs. Potential therapeutic agents were predicted using the DsigDB database, and their medicinal value was validated through molecular docking. Results Ultimately, 12 upregulated and 5 downregulated ORHGs were identified, among which 10 upregulated and 3 downregulated pivotal targets exhibited miRNA pairing. GO and KEGG enrichment analyses of the 13 key targets indicated a significant association with neuron death, regulation of the mitotic cell cycle, and cell development regulation. The signaling pathways, including Rap1, Ras, HIF, MAPK, and PI3K-Akt, emerged as principal conduits in oxidative stress-associated RA. Furthermore, potential therapeutic agents targeting the 13 ORHGs were predicted, and molecular docking outcomes substantiated their robust binding affinity. Receiver Operating Characteristic (ROC) curve analysis revealed that CCL2, BDNF, and MYC possess high predictive accuracy within the GSE39340 dataset, suggesting their potential as biomarkers for RA. Conclusion This study identifies thirteen potential drug targets associated with oxidative stress in RA (RA). Drugs engineered to target these genes are anticipated to have a greater likelihood of success in clinical trials, potentially prioritizing RA drug development and significantly reducing associated costs. By focusing on these validated targets, the pharmaceutical development process can become more efficient and economically sustainable, ultimately accelerating the delivery of effective therapies to patients.