Prognostic and therapeutic value of a 23-gene risk score tailored to the molecular characteristics of mucinous colorectal cancer

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Title
Prognostic and therapeutic value of a 23-gene risk score tailored to the molecular characteristics of mucinous colorectal cancer
Author(s)
Jee-Woo Seo; Jae-Yoon Kim; Y J Ha; K H Tak; Jeong Hwan Kim; Young-Bum Cho; Seong-Hwan Park; Y S Yoon; C W Kim; J L Lee; Seon-Young KimSeon-Kyu Kim
Bibliographic Citation
British Journal of Cancer, vol. 133, pp. 685-696
Publication Year
2025
Abstract
Background: Mucinous adenocarcinoma (MuC), a colorectal cancer (CRC) subtype, exhibits distinct molecular features and poorer response to chemoradiotherapy than non-mucinous adenocarcinoma (NMuC). Conventional treatments often fail to address CRC heterogeneity, particularly in stage II disease. Therefore, improved biomarkers for risk stratification and personalised treatment are needed. Methods: We analysed gene expression and mutation data from 259 CRC samples to identify characteristics of MuC. A 23-gene risk score (MuC-RS) was developed and validated across four independent cohorts (n?=?1157). Statistical analyses, including generalised linear model likelihood ratio tests, Kaplan-Meier curves, log-rank tests, and Cox regression models, evaluated the prognostic utility of the MuC-RS. Results: MuC showed significant upregulation of fibroblast-associated genes, pathways related to epithelial-mesenchymal transition, and mucin glycosylation. The MuC-RS effectively stratified patients into high-risk (MuC-H) and low-risk (MuC-L) groups, with multivariate analysis confirming its prognostic value (HR?=?1.72, 95% CI?=?1.31?2.25, P?<?0.001). Stage II MuC-L patients had poorer outcomes after conventional chemotherapy, but responded better to immune checkpoint inhibitors (ICIs), linked to higher tumour mutation burden and immune activation. Conclusions: The MuC-RS effectively predicts recurrence and guides personalised treatment in CRC, particularly benefiting stage II MuC patients through improved risk stratification and treatment selection.
ISSN
0007-0920
Publisher
Springer-Nature Pub Group
Full Text Link
http://dx.doi.org/10.1038/s41416-025-03104-3
Type
Article
Appears in Collections:
Division of A.I. & Biomedical Research > Genomic Medicine Research Center > 1. Journal Articles
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