Identifying inflammatory bowel disease subtypes: a comprehensive exploration of transcriptomic data and machine learning-based approaches.

Background: Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), is a heterogeneous condition characterised by chronic gastrointestinal inflammation and dysregulated immune responses. Despite advances in transcriptomic analysis and machine learning (ML), consistent molecular subtyping across datasets remains a challenge. There is a critical need for robust subtypes that reflect disease heterogeneity and correlate with clinical outcomes. Objectives: Unlike prior studies focused on either UC or CD or based on small datasets, this study analyses a large-scale RNA sequencing (RNA-seq) dataset to identify transcriptomic subtypes in both UC and CD. Design: We analysed RNA-seq data from four prospective cross-sectional cohorts from Gene Expression Omnibus: GSE193677, GSE186507, GSE137344 and GSE235236. Methods: Analysed RNA-sequenced data from inflamed and non-inflamed intestinal biopsies of 2490 adult IBD patients. Results: Conclusion: We report three transcriptomic subtypes in UC and CD, each with distinct molecular signatures and clinical relevance. These findings support a stratified approach to IBD diagnosis and therapy, enabling more personalised disease management strategies.