Blood-Based Gene Signatures Associated with Therapeutic Response to Anti-TNF Therapy in Rheumatoid Arthritis: A Combined Meta-Analytical and Machine Learning Approach

We performed a comprehensive meta-analysis of baseline blood transcriptome datasets from eight independent RA cohorts to identify genes consistently upregulated in patients who responded to TNF-α inhibitors. This analysis identified a core set of 39 recurrent genes (the Recurrent gene set). To evaluate its predictive relevance, we trained machine learning models using four gene subsets: the Recurrent set, the Patent gene set derived from a biomarker patent, the Top-ranked gene set based on Cohen’s d effect size, and the full gene set.

External validation was performed using an independent dataset. Results The Recurrent gene set showed strong and consistent predictive performance across machine learning models. Notably, several genes overlapping with the Patent and Top-ranked sets were among the top contributors, supporting their potential value for future diagnostic applications.

Conclusion Our study demonstrates the feasibility of transcriptome-guided prediction of response to TNF-α inhibitors in RA. The integration of meta-analysis and machine learning provides a strong foundation for the development of precision diagnostic tools to support personalized treatment strategies in clinical practice.