ArticleRheumatology (Oxford, England)2022
Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis.
Article in Rheumatology (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Machine learning for predicting treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in rheumatoid arthritis: a scoping review.BMC rheumatology · 2025Pooled it
- Trial
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Proteomic and glycosylation biomarkers in rheumatoid arthritis: advancing early diagnosis and precision therapy.Clinical proteomics · 2026Review
- Multiomics Analyses Reveal an Essential Role of Tryptophan in Treatment of csDMARDs in Rheumatoid Arthritis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advanced immunotherapy across diseases and the role of artificial intelligence: A review.Biomolecules & biomedicine · 2026Review
- The lipid-podocyte axis: emerging clues in membranous nephropathy pathogenesis.Frontiers in medicine · 2026Review
- GeneCytNet: an interpretable deep learning framework for rheumatoid arthritis classification andFrontiers in immunology · 2026Article
- Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.Health data science · 2026Article
- Extracting TNFi switching reasons and trajectories from real-world data using large language models.JAMIA open · 2025Article
- Rheumatoid Arthritis: Biomarkers and the Latest Breakthroughs.International journal of molecular sciences · 2025Review
- Serum metabolomics identifies unique inflammatory signatures to distinguish rheumatoid arthritis responders and non-responders to TNF inhibitor therapy.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs.Scientific reports · 2025Article
- Current state and future directions of basic research in rheumatoid arthritis.Journal of rheumatic diseases · 2025Review
- AI-Assisted Plasmonic Diagnostics Platform for Osteoarthritis and Rheumatoid Arthritis With Biomarker Quantification Using Mathematical Models.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Systems Human Immunology and AI: Immune Setpoint and Immune Health.Annual review of immunology · 2025Review
- Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI.European journal of immunology · 2025Review
- Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape.Frontiers in big data · 2025Review
- Pharmacogenomics of TNF inhibitors.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAdvances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition.
methodsPeripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment.
resultsThe gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA.
conclusionsOur integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.