Identifying biomarkers for methotrexate efficacy in rheumatoid arthritis: a machine learning approach to whole-blood transcriptomic analysis

Clicks: 7
ID: 331772
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #278 of 296 articles by views in Lara D. Veeken

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 296 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Objectives Methotrexate (MTX) is the first-line treatment for rheumatoid arthritis (RA), yet inadequate response is reported in 30-40% of patients. Predicting MTX response early could enable more personalised and effective treatment. This study aimed to identify biomarkers predictive of MTX response at 6 months through whole-blood transcriptomic signatures using machine learning. Methods RNA-sequencing data were generated in whole-blood samples taken from 100 MTX-naïve RA patients at baseline (pre-treatment) and following 4-weeks post treatment with MTX. Machine learning models were trained to classify MTX response following 6-months of treatment using gradient boosted trees and interpreted using SHAP values to identify predictive genes. Results Machine learning models trained on baseline and 4-weeks data achieved AUCs of 0.89 and 0.90 respectively. Stability of SHAP values showed that the baseline model was generally more stable, and therefore potentially more generalisable. Key predictive genes at baseline, which were downregulated in responders, included CAV1, LCN12, and GLB1L. Conclusion Blood-based gene expression profiling at baseline and after 4-weeks of MTX treatment can predict treatment response with high confidence revealing relevant gene pathways and candidate gene targets but requires independent validation. These findings highlight the potential for transcriptomic biomarkers to inform early treatment decision in RA, supporting precision medicine approaches.
Reference Key
openalex_W7220976762 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chuan Fu Yap, Nisha Nair, Suzanne M. M. Verstappen, Kimme Hyrich, Guillermo Barturen, María Rivas-Torrubia, Marta Ortova Gut, Ivo Gut, Marta E Alarcón Riquelme, Anne C Barton, Darren Plant, RAMS co-investigators, Adebajo Ade, Ahmed Khalid, Atheer Al-Ansari, Amarasena Roshan, Marwan Bukhari, Callan Margaret, G Chelliah Easwaradhas, Chinoy Hector, Cooper Annie, DasGupta Bhaskar, Davis Martin, Galloway James, Gough Andrew, Green Michael, Gullick Nicola, Hamilton Jennifer, Hassan Waji, Hider Samantha, Hyrich Kimme, Sanjeet Kamath, Knight Susan, Lane Suzanne, Lee Martin, Levy Sarah, Macphie Lizzy, Marguerie Christopher, Marshall Tarnya, Mathews Catherine, McKenna Frank, Naz Sophia, Perry Mark, Louise Pollard, Brian Quilty, Robertson Lindsay, Roy Dipak, Sanders Paul, Saravanan Vadivelu, Scott David, Smith Gillian, Richard Smith, Symmons Deborah, Teh Lee-Suan, Viner Nick
Journal Lara D. Veeken
Year 2026
DOI
10.1093/rheumatology/keag560
URL
https://doi.org/10.1093/rheumatology/keag560
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.