ArticleArthritis & rheumatology (Hoboken, N.J.)2025
Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole-Blood Transcriptomics Data.
Article in Arthritis & rheumatology (Hoboken, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Unveiling an ALS Blood Transcriptomic Signature: A Machine Learning Classifier Distinct from Neurodegenerative Controls.Neuroinformatics · 2026Article
- Role of MZB1 in disease pathogenesis: current insights and future directions.Clinical and experimental medicine · 2026Review
- Immunological heterogeneity in rheumatoid arthritis: challenges in early-stage stratification, non-response to targeted therapy, and the restoration of immune tolerance.Frontiers in immunology · 2026Review
- Research progress on the role of MZB1 in immune regulation.Frontiers in immunology · 2026Review
- MZB1 at the ER-immunity interface: from antibody folding to disease vulnerability in autoimmunity, inflammation, and cancer.Journal of Cancer · 2026Review
- Transcriptomics and AI-driven approaches to the diagnosis and treatment of rheumatoid arthritis.Frontiers in immunology · 2026Review
- A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistantMicrobiology spectrum · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
objectiveTumornecrosis factor inhibitors (TNFi) have significantly improved rheumatoid arthritis (RA) management, yet variability in patient response remains a substantial challenge, with approximately 40% of patients discontinuing TNFi due to nonresponse or adverse effects. This study aimed to identify biomarkers predictive of adalimumab treatment response using whole-blood transcriptomics, leveraging machine learning models for data mining observed by targeted statistical analysis.
methodsA cohort of patients with RA starting TNFi therapy (n = 100) was assessed for treatment response at 6 months, with RNA sequencing performed on baseline (pretreatment) and 3-month follow-up samples. Machine learning classifiers were built to identify predictive biomarkers for treatment outcomes. This was observed by a network analysis on the biomarkers to elucidate the most influential biomarker, which was subsequently confirmed through survival analysis.
resultsDifferential gene expression analysis in 97 samples passing quality control identified 84 genes associated with treatment response. Random forest classifiers achieved high predictive accuracy with area under the receiver operating characteristic curves up to 0.86, identifying genes contributing to treatment outcomes. Network analysis further elucidated gene interactions, highlighting marginal zone B And B1 cell-specific protein 1 (MZB1) as a novel biomarker not captured by machine learning alone. MZB1's role in B cell development and antibody production was associated with antidrug antibody formation, impacting treatment efficacy.
conclusionThis study advances the understanding of transcriptomic alterations in RA treatment and enhances our understanding of treatment response mechanisms. Although the gene signatures identified require independent replication, the study serves as a starting point to pave the way for personalized therapeutic strategies in patients commencing TNFi therapy in RA.
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