{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T09:28:07Z","timestamp":1789464487655,"version":"build-2803163510"},"reference-count":73,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ACR Open Rheumatology"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Patients with rheumatoid arthritis (RA) often fail to respond to therapies, including JAK inhibitors (JAKi), and treatment allocation is made via a trial\u2010and\u2010error strategy. A comprehensive analysis of responses to JAKi, including tofacitinib, by RNA sequencing (RNAseq) would allow the discovery of transcriptomic markers with a two\u2010fold meaning: (1) an improved knowledge about the mechanisms of response to treatment (inference modeling) and (2) the definition of features that may be useful in treatment optimization and assignment (predictive modeling).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Thirty\u2010three patients with active RA were treated with a tofacitinib dose of 5 mg twice a day for 24 weeks and evaluated for EULAR Disease Activity Score in 28 joints using the C\u2010reactive protein level response. Whole\u2010blood RNA was collected before and after treatment to perform RNAseq transcriptome analysis. Linear models were used to determine differentially expressed genes (DEGs) (1) at baseline according to clinical responses and (2) in the pre\u2010post comparison after tofacitinib treatment and in relation to EULAR responses. The capability of DEGs to predict a successful treatment was tested via machine learning modeling after extensive internal validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 26 patients who completed the study (per\u2010protocol analysis), 15 (57.7%) achieved good responses, and 7 (26.9%) and 4 (15.3%) had moderate and no responses, respectively. Overall, 273 baseline genes were significantly associated with the attainment of good responses, contributing to several pathways linked to the immune system or RA pathogenesis (eg, citrullination processes and the negative regulation of natural killer function). The expression of several molecules was reverted by tofacitinib when good responses were reached, including <jats:italic>AKT3<\/jats:italic>, <jats:italic>GK5<\/jats:italic>, <jats:italic>KLF12<\/jats:italic>, <jats:italic>FCRL3<\/jats:italic>, <jats:italic>BIRC3<\/jats:italic>, <jats:italic>TSPOAP1<\/jats:italic>, and <jats:italic>P2RY10<\/jats:italic>. Finally, we isolated 14 markers that singularly were capable of predicting the attainment of good responses, including, <jats:italic>NKG2D<\/jats:italic>, <jats:italic>CD226<\/jats:italic>, <jats:italic>CLEC2D<\/jats:italic>, and <jats:italic>CD52<\/jats:italic>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Whole\u2010blood transcriptome analysis of patients with RA treated with tofacitinib identified genes whose expression may be relevant in prognostication and understanding the mechanisms of responses to therapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acr2.11761","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T11:51:08Z","timestamp":1747655468000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Whole\u2010Blood <scp>RNA<\/scp> Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With 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