ArticleLiver international : official journal of the International Association for the Study of the Liver2026
Machine Learning Predicts Treatment Response and Prognostic Pathways From Whole-Blood Transcriptome in Primary Biliary Cholangitis.
Article in Liver international : official journal of the International Association for the Study of the Liver, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine Learning Predicts Treatment Response and Prognostic Pathways From Whole-Blood Transcriptome in Primary Biliary Cholangitis.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
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11 authors.
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Abstract
BACKGROUND AND
aimsPrognostic 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-blood transcriptomic data to predict clinical outcomes in response to obeticholic acid (OCA) and disease progression.
methodsWe developed an ML model that incorporated whole-blood RNA-seq 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-gene list that was trained on the POISE end-of-treatment cohort.
resultsThe algorithm's generalisability was tested by assessing the baseline POISE patients as responders or non-responders to OCA (AUROC 0.93) and independently validated by differentiating liver disease-related survival between progressors and non-progressors (AUROC 0.94). Pathway analysis of the ML candidate genes identified enrichment of FXR regulated genes, autoimmune disease-related inflammation, immune regulation, fibrosis and integrated stress response pathways as prognostically related to PBC. When we compared these ML-derived prognostic pathways with RNA-seq analysis of PBC versus healthy controls, the most relevant transcripts were those involved in the integrated stress response and metabolic remodelling in PBC.
conclusionThe ML-derived score links both prognostic and disease pathogenesis pathways and can be used in future studies to better understand the pathophysiology and management of PBC.
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