[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128243-en":3,"doc-seo-128243-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128243,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab","Tumor necrosis factor inhibitors have improved rheumatoid arthritis management, yet patient response remains highly variable, with many individuals discontinuing therapy due to nonresponse or adverse effects. This study uses whole-blood transcriptomics and machine learning to discover biomarkers predicting adalimumab response. RNA sequencing from baseline and 3-month follow-up samples supports differential expression, network analysis, and survival confirmation. Results highlight gene signatures and nominate MZB1 as a treatment-relevant biomarker.","Arthritis & Rheumatology Vol. 0, No. 0, Month 2025, pp 1–10 DOI 10.1002/art.43255  \n© 2025 The Author(s). Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nIdentifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole-Blood Transcriptomics Data  \nChuan Fu Yap,1  Nisha Nair,2 Ann W. Morgan,3 John D. Isaacs,4 Anthony G. Wilson,5  Kimme Hyrich,6 Guillermo Barturen,7  María Riva-Torrubia,8 Marta Gut,9 Ivo Gut,9 Marta E. Alarcn Riquelme,10 Anne Barton,2 and Darren Plant2  \nObjective. Tumornecrosis 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.  \nMethods. A 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.  \nResults. Differential 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.  \nConclusion. This study advances the understanding of transcriptomic alterations in RA treatment and enhancesour 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.  \nINTRODUCTION  \nTumor necrosis factor inhibitors (TNFi) have significantly improved the management of rheumatoid arthritis (RA),  \nThe funders had no input in relation to the study design, collection, analysis and interpretation of data, writing of the manuscript, or decision to submit the article for publication.  \nSupported by the Innovative Medicines Initiative 2 Joint Undertaking (JU) (grant agreement 831434 Taxonomy, Treatment, Targets and Remission Identification of the Molecular Mechanisms of Non-response to Treatments, Relapses and Remission in Autoimmune Inflammatory Conditions [3TR]) . The JU receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. This research was also supported by the NIHR Manchester Biomedical Research Centre, NIHR Leeds Biomedical Research Centre (grant reference IS-BRC-1215-20015), NIHR Newcastle Biomedical Research Centre and Versus Arthritis (grants 21173, 21754, and 21755) and an NIHR Senior Investigator Award (grant reference NIHR202395 awarded to  \noffering relief from joint inflammation and the associated risk of cartilage and bone damage. However, ","cbCailWjqwCBsg3f","https://ap.wps.com/l/cbCailWjqwCBsg3f","pdf",6455695,1,10,"English","en",105,"# Objective\n## Background and clinical need\n# Methods\n## Cohort and study design\n## Transcriptomics and machine learning\n## Network and survival analyses\n# Results\n## Differential gene expression\n## Predictive performance\n## Biomarker discovery and biological interpretation\n# Conclusion","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets the variability of response in rheumatoid arthritis patients treated with TNF inhibitors, including adalimumab, where a substantial portion stop therapy due to nonresponse or side effects.\"},{\"question\":\"How were predictive biomarkers identified?\",\"answer\":\"Patients receiving TNFi therapy were assessed at 6 months, with RNA sequencing from baseline and 3-month samples. Machine learning classifiers were trained on transcriptomics, followed by network analysis and survival confirmation.\"},{\"question\":\"Which biomarker is highlighted as a key finding and why?\",\"answer\":\"MZB1 is identified as a novel biomarker, linked to B cell development and antibody production, and associated with anti-drug antibody formation affecting treatment efficacy.\"}]","Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab | PDF",1785946057,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"identifying-predictive-biomarkers-of-response-in-patients-with-rheumatoid-arthritis-treated-with-adalimumab","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-predictive-biomarkers-of-response-in-patients-with-rheumatoid-arthritis-treated-with-adalimumab/128243/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical problem does the study address?","Question",{"text":76,"@type":77},"The study targets the variability of response in rheumatoid arthritis patients treated with TNF inhibitors, including adalimumab, where a substantial portion stop therapy due to nonresponse or side effects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were predictive biomarkers identified?",{"text":81,"@type":77},"Patients receiving TNFi therapy were assessed at 6 months, with RNA sequencing from baseline and 3-month samples. Machine learning classifiers were trained on transcriptomics, followed by network analysis and survival confirmation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which biomarker is highlighted as a key finding and why?",{"text":85,"@type":77},"MZB1 is identified as a novel biomarker, linked to B cell development and antibody production, and associated with anti-drug antibody formation affecting treatment efficacy.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]