Abstract Background Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract characterised by recurrent, transmural inflammation that progresses to irreversible intestinal fibrosis in up to 70% of patients. Despite therapeutic advances, nearly 40% still require surgery, as current anti-fibrotic treatments are effective only in experimental models. Early detection of fibrosis is therefore critical for improving outcomes and guiding intervention. By comparing healthy, inflamed, and fibrotic tissue, this study identifies molecular mechanisms and pathways involved in the progression from inflammation to fibrosis in CD. Methods Bulk RNA-sequencing of colonic tissue from baseline CD (N = 239) and fibrotic CD (N = 58) identified overlapping differentially expressed genes (DEGs), forming a shared transcriptional signature. Genes were annotated (GO, KEGG, Reactome), z-score normalised, and pathway enrichment identified biological processes linked to fibrosis progression. Machine-learning models, Extreme Gradient Boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO), were trained on CD data to identify predictive genes, which were then evaluated in the fibrotic CD dataset. A co-expression network was constructed using Spearman correlation-based clustering to identify gene modules and hub genes, with log2 fold changes quantifying expression changes across disease stages. Results Transcriptomic analysis identified 94 DEGs, with hierarchical clustering distinguishing CD from fibrotic CD (Figure1). Pathway enrichment highlighted immune regulation and epithelial remodelling. Machine-learning models on CD samples with feature selection identified 43 genes across all models (Figure2). These models showed strong predictive performance in CD (LASSO:0.960.94-0.99, Random Forest:0.960.94-0.98, XGBoost:0.950.93-0.98) and moderate generalisation to fibrosis (LASSO:0.650.49-0.79, Random Forest:0.650.49-0.79, XGBoost:0.690.54-0.77). Network analysis identified 20 hub genes and three gene modules with predominant pathways. Six genes displayed significant log2 fold-change trends across controls, CD, and fibrotic CD, indicating progressive dysregulation along the inflammation–fibrosis trajectory. Conclusion This study identified six genes, FCGR1CP, SMLR1, FCGR1BP, PROK2, CHI3L1, and C17orf78, showing progressive upregulation from inflammation to fibrosis, implicating roles in immune activation, ERK1/ERK2 signalling, and angiogenesis. Age and sex had minimal influence, with only modest age-related effects in PROK2 and CHI3L1, indicating fibrosis-associated expression is disease driven. These findings highlight key mediators of fibrotic progression and their potential as transcriptomic biomarkers. References: 1. Wang Y, Huang B, Jin T, Ocansey DKW, Jiang J, Mao F. Intestinal Fibrosis in Inflammatory Bowel Disease and the Prospects of Mesenchymal Stem Cell Therapy. Front Immunol. 2022;13. doi:10.3389/FIMMU.2022.835005 2. Fousekis FS, Mpakogiannis K, Mastorogianni IN, Lianos GD, Christodoulou DK, Katsanos KH. Intestinal Fibrosis in Crohn’s Disease: Pathophysiology, Diagnosis, and New Therapeutic Targets. Journal of Clinical Medicine 2025, Vol 14, Page 4060. 2025;14(12):4060. doi:10.3390/JCM14124060 3. Lin SN, Mao R, Qian C, et al. Development of antifibrotic therapy for stricturing Crohn’s disease: lessons from randomized trials in other fibrotic diseases. Physiol Rev. 2022;102(2):605-652. doi:10.1152/PHYSREV.00005.2021 4. Solitano V, Dal Buono A, Gabbiadini R, et al. Fibro-Stenosing Crohn’s Disease: What Is New and What Is Next? Journal of Clinical Medicine 2023, Vol 12, Page 3052. 2023;12(9):3052. doi:10.3390/JCM12093052 Conflict of interest: Ms. Philip, Daryll: No Conflict of Interest. Santos, Daniela: The authors declare no conflicts of interest. Mondal, Sudip: No Conflicts of Interest. Alomar, Haneen: No Conflicts of Interest Acharjee, Animesh: No conflicts
Philip et al. (Thu,) studied this question.