Crucially, we introduced in silico cytokine perturbation experiments to simulate the effect of modulating IL-6, TNF-α, and IL-1β responsive gene modules on RA risk prediction.<h4>Results</h4>GeneCytNet achieved superior classification performance, with a test AUC of 0.962 ± 0.005, accuracy of 0.914 ± 0.007, and an F1-score of 0.915 ± 0.006, outperforming all baseline models. This evidence concerns the gene IL1B and rheumatoid arthritis.