Identification of Diagnostic Biomarkers Related to Oxidative Stress in Rheumatoid Arthritis.

BACKGROUND

This study aimed to identify oxidative stress-related genes as potential diagnostic biomarkers for rheumatoid arthritis (RA).

METHODS

We first obtained RA transcriptomic data from the Gene Expression Omnibus (GEO) database and calculated oxidative stress scores using single-sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) were identified by limma (R). Gene Ontology (GO) enrichment was performed using the clusterProfiler package, and weighted gene co-expression network analysis (WGCNA) was used to classify oxidative stress modules. Candidate biomarker genes for RA were screened by support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). We then constructed a logistic regression-based diagnostic model. Immune infiltration analysis, pathway enrichment analysis, single-cell RNA sequencing, and in vitro experiments were employed to comprehensively elucidate the expression profiles and potential mechanisms of action of the identified genes in rheumatoid arthritis (RA).

RESULTS

RA samples had higher oxidative stress scores than normal samples. WGCNA showed that the cyan module was linked to oxidative stress, from which 88 potential genes were screened. Five signature genes (CARS2, VCP, FCGR2A, ITGB2, SETD2) were selected. RT-qPCR showed higher expression of the five genes in MH7A than in HFLS cells. The logistic model developed based on these genes demonstrated robust performance, with an area under the curve (AUC) greater than 0.9 in both training and validation sets. The model was positively associated with ImmuneScore and the infiltration of specific immune cells. Notably, the model genes showed distinct expression patterns in plasmacytoid dendritic cells (pDCs) and monocytes.

DISCUSSION: The five oxidative stress-related genes identified in this study exhibited elevated expression in RA. Immune infiltration and pathway enrichment analysis revealed their potential contribution to immune dysregulation via IL6-JAK-STAT3, ROS, and hypoxia pathways in RA.

CONCLUSION

This study established a five-oxidative-stress-gene diagnostic model, offering a potential molecular tool for early RA detection and immune-based patient stratification.