In Journal of Crohn's & colitis
BACKGROUND AND AIMS : Ulcerative colitis [UC] is a complex heterogeneous disease. This study aims to reveal the underlying molecular features of UC using genome-scale transcriptomes of patients with UC and develop and validate a novel stratification scheme.
METHODS : A normalized compendium was created using colon tissue samples [455 patients with UC and 147 healthy controls [HCs]], covering genes from 10 microarray datasets. Up-regulated differentially expressed genes [DEGs] were subjected to functional network analysis, wherein samples were grouped using unsupervised clustering. Additionally, the robustness of subclustering was further assessed by two RNA sequencing datasets [100 patients with UC and 16 HCs]. Finally, the Xgboost classifier was applied to the independent datasets to evaluate the efficacy of different biologics in patients with UC.
RESULTS : Based on 267 up-regulated DEGs of the transcript profiles, UC patients were classified into three subtypes [subtype A-C] with distinct molecular and cellular signatures. Epithelial activation-related pathways were significantly enriched in subtype A [named epithelial proliferation], whereas subtype C was characterized as the immune activation subtype with prominent immune cells and proinflammatory signatures. Subtype B [named mixed] was modestly activated in all the signalling pathways. Notably, subtype A showed a stronger association with the superior response of biologics such as golimumab, infliximab, vedolizumab and ustekinumab compared to subtype C.
CONCLUSIONS : We conducted a deep stratification of mucosal tissue using the most comprehensive microarray and RNA sequencing data, providing critical insights into pathophysiological features of UC, which could serve as a template for stratified treatment approaches.
Chang Min-Jing, Hao Jia-Wei, Qiao Jun, Chen Miao-Ran, Wang Qian, Wang Qi, Zhang Sheng-Xiao, Yu Qi, He Pei-Feng
2023-Jan-22
Machine Learning, Ulcerative Colitis, Unsupervised clustering
