Developing predictive models for tocilizumab response in rheumatoid arthritis: a gene expression and machine learning approaches

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease treated with tocilizumab in patients unresponsive to methotrexate. This study aimed to identify gene expression profiles and predictive models for tocilizumab treatment response in RA patients.Methods: Using the GSE78068 dataset from 38 RA patients, we identified differentially expressed genes between remission and non-remission groups. Predictive models were created using CART, random forest, and SVM techniques, with model genes selected through LASSO regression. Gene set enrichment and immune cell landscape analyses were performed to understand biological pathways and immune cell composition.Results: Analysis revealed 40 differentially expressed genes, with LASSO regression identifying 8 model genes significantly associated with remission. The SVM-based model achieved the highest performance (AUC=1.0, Brier score=0.025).Conclusions: This study developed an effective 8-gene model for predicting tocilizumab treatment response, potentially supporting personalized therapy in RA patients.