{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T01:06:52Z","timestamp":1789175212366,"version":"build-2803163510"},"reference-count":44,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100018262","name":"Intercept Pharmaceuticals","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018262","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000264","name":"Canadian Liver Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000264","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100023363","name":"Praespero","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023363","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Prognostic biomarkers that link disease progression and\/or responses to therapeutic interventions in patients with primary biliary cholangitis (PBC) remain undefined. In this study, we used a machine learning (ML) approach with whole\u2010blood transcriptomic data to predict clinical outcomes in response to obeticholic acid (OCA) and disease progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an ML model that incorporated whole\u2010blood RNA\u2010seq analyses using longitudinal samples from the POISE study (discovery cohort) and a prospective group of PBC patients who remained unchanged or progressed to liver transplantation\/hepatic decompensation (independent validation cohort). The model identified 1200 candidate genes predictive of outcomes, which were investigated using pathway analysis. A genetic algorithm refined the panel to a 105\u2010gene list that was trained on the POISE end\u2010of\u2010treatment cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm's generalisability was tested by assessing the baseline POISE patients as responders or non\u2010responders to OCA (AUROC 0.93) and independently validated by differentiating liver disease\u2010related survival between progressors and non\u2010progressors (AUROC 0.94). Pathway analysis of the ML candidate genes identified enrichment of FXR regulated genes, autoimmune disease\u2010related inflammation, immune regulation, fibrosis and integrated stress response pathways as prognostically related to PBC. When we compared these ML\u2010derived prognostic pathways with RNA\u2010seq analysis of PBC versus healthy controls, the most relevant transcripts were those involved in the integrated stress response and metabolic remodelling in PBC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ML\u2010derived score links both prognostic and disease pathogenesis pathways and can be used in future studies to better understand the pathophysiology and management of PBC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.70764","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:48:18Z","timestamp":1784090898000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Predicts Treatment Response and Prognostic Pathways From Whole\u2010Blood Transcriptome in Primary Biliary Cholangitis"],"prefix":"10.1111","volume":"46","author":[{"given":"Hussain","family":"Syed","sequence":"first","affiliation":[{"name":"Division of Gastroenterology, CEGIIR University of Alberta  Edmonton Alberta Canada"},{"name":"Li Ka Shing Institute of Virology University of Alberta  Edmonton Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Sun","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, CEGIIR University of Alberta  Edmonton Alberta Canada"},{"name":"Li Ka Shing Institute of Virology University of Alberta  Edmonton Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doaa","family":"Waly","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, CEGIIR University of Alberta  Edmonton Alberta Canada"},{"name":"Li Ka Shing Institute of Virology University of Alberta  Edmonton Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varinder","family":"Verma","sequence":"additional","affiliation":[{"name":"Li Ka Shing Institute of Virology University of Alberta  Edmonton Alberta Canada"},{"name":"Department of Biological Sciences University of Alberta  Edmonton Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Smith","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, CEGIIR University of Alberta  Edmonton Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Shapiro","sequence":"additional","affiliation":[{"name":"Intercept Pharmaceuticals Inc.  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