{
  "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 personalized and effective treatment. This study aimed to identify biomarkers predictive of MTX response at 6 months through whole-blood transcriptomic signatures using machine learning.\n\nMethods: RNA sequencing data were generated in whole-blood samples taken from 100 MTX-naive 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 SHapley Additive exPlanations (SHAP) values to identify predictive genes.\n\nResults: 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 generalizable. Key predictive genes at baseline, which were downregulated in responders, included CAV1, LCN12 and GLB1L.\n\nConclusion: 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 55,
  "authors": [
    {
      "name": "Chuan Fu Yap"
    },
    {
      "name": "Nisha Nair",
      "orcid": "0000-0001-9829-792X"
    },
    {
      "name": "Suzanne M M Verstappen"
    },
    {
      "name": "Kimme Hyrich",
      "orcid": "0000-0001-8242-9262"
    },
    {
      "name": "Guillermo Barturen"
    },
    {
      "name": "María Rivas-Torrubia"
    },
    {
      "name": "Marta Gut"
    },
    {
      "name": "Ivo Gut"
    },
    {
      "name": "Marta E Alarcón Riquelme",
      "orcid": "0000-0002-7632-4154"
    },
    {
      "name": "Anne Barton",
      "orcid": "0000-0003-3316-2527"
    },
    {
      "name": "Darren Plant",
      "orcid": "0000-0003-1395-9344"
    },
    {
      "name": "Ade Adebajo"
    },
    {
      "name": "Ade Adebajo"
    },
    {
      "name": "Khalid Ahmed"
    },
    {
      "name": "Atheer Al-Ansari"
    },
    {
      "name": "Roshan Amarasena"
    },
    {
      "name": "Marwan Bukhari"
    },
    {
      "name": "Margaret Callan"
    },
    {
      "name": "Easwaradhas G Chelliah"
    },
    {
      "name": "Hector Chinoy"
    },
    {
      "name": "Annie Cooper"
    },
    {
      "name": "Bhaskar Dasgupta"
    },
    {
      "name": "Martin Davis"
    },
    {
      "name": "James Galloway"
    },
    {
      "name": "Andrew Gough"
    },
    {
      "name": "Michael Green"
    },
    {
      "name": "Nicola Gullick"
    },
    {
      "name": "Jennifer Hamilton"
    },
    {
      "name": "Waji Hassan"
    },
    {
      "name": "Samantha Hider"
    },
    {
      "name": "Kimme Hyrich"
    },
    {
      "name": "Sanjeet Kamath"
    },
    {
      "name": "Susan Knight"
    },
    {
      "name": "Suzanne Lane"
    },
    {
      "name": "Martin Lee"
    },
    {
      "name": "Sarah Levy"
    },
    {
      "name": "Lizzy Macphie"
    },
    {
      "name": "Christopher Marguerie"
    },
    {
      "name": "Tarnya Marshall"
    },
    {
      "name": "Catherine Mathews"
    },
    {
      "name": "Frank McKenna"
    },
    {
      "name": "Sophia Naz"
    },
    {
      "name": "Mark Perry"
    },
    {
      "name": "Louise Pollard"
    },
    {
      "name": "Brian Quilty"
    },
    {
      "name": "Lindsay Robertson"
    },
    {
      "name": "Dipak Roy"
    },
    {
      "name": "Paul Sanders"
    },
    {
      "name": "Vadivelu Saravanan"
    },
    {
      "name": "David Scott"
    },
    {
      "name": "Gillian Smith"
    },
    {
      "name": "Richard Smith"
    },
    {
      "name": "Deborah Symmons"
    },
    {
      "name": "Lee-Suan Teh"
    },
    {
      "name": "Nick Viner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ekzxscw8z5tmd.bib",
  "cited_by": 0,
  "doi": "10.1093/rheumatology/keag560",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13743429/",
  "id": "ekzxscw8z5tmd",
  "issns": [
    "1462-0324",
    "1462-0332"
  ],
  "landing_url": "https://doi.org/10.1093/rheumatology/keag560",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/MED00360"
    ],
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "2745|Rheumatoid Arthritis Research and Therapies"
    ]
  ],
  "openalex": "W7220976762",
  "pdf_url": "https://academic.oup.com/rheumatology/advance-article-pdf/doi/10.1093/rheumatology/keag560/71730893/keag560.pdf",
  "pmcid": "PMC13743429",
  "pmid": "42850566",
  "published": "2026-10-08",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/ekzxscw8z5tmd.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T12:24:10Z",
      "native_id": "PMC13743429",
      "source": "pmc",
      "url": "https://doi.org/10.1093/rheumatology/keag560"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2745|Rheumatoid Arthritis Research and Therapies",
      "subfield": "general"
    }
  ],
  "title": "Identifying biomarkers for methotrexate efficacy in rheumatoid arthritis: a machine learning approach to whole-blood transcriptomic analysis",
  "title_text": "Identifying biomarkers for methotrexate efficacy in rheumatoid arthritis: a machine learning approach to whole-blood transcriptomic analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-11T12:21:08Z",
  "url": "https://research.draconitic.com/paper/ekzxscw8z5tmd",
  "venue": "Rheumatology (Oxford, England)",
  "versions": []
}