Blood-based gene signatures associated with therapeutic response to anti-TNF therapy in rheumatoid arthritis: a combined meta-analytical and machine learning approach.

Objectives: Tumour necrosis factor alpha (TNF-α) inhibitors have significantly improved outcomes in rheumatoid arthritis (RA); however, up to 30% to 40% of patients show inadequate response. This highlights the need for predictive biomarkers to guide personalised therapy. Blood transcriptome profiling is a promising strategy for identifying gene signatures linked to treatment response, though prior studies have shown inconsistent results due to technical and biological heterogeneity. Methods: We performed a comprehensive meta-analysis of baseline blood transcriptome datasets from 8 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 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. Conclusions: 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 personalised treatment strategies in clinical practice.