Integrating metabolomics, clinical factors, and genetic risk for colorectal cancer risk prediction: Model development and internal-external cross-validation

doi:10.3748/wjg.124207

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Dec 21, 2026; 32(47): 124207
Published online Dec 21, 2026. doi: 10.3748/wjg.124207

Integrating metabolomics, clinical factors, and genetic risk for colorectal cancer risk prediction: Model development and internal-external cross-validation

Liang-Tang Zeng, Jun-Nan Hu, Ming-Hui Pang, Qin-Xing Cao, Neng-Yi Hou, Feng Liu

Liang-Tang Zeng, Jun-Nan Hu, Feng Liu, Digestive Endoscopy Center, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai 200072, China

Ming-Hui Pang, Qin-Xing Cao, Neng-Yi Hou, Department of Geriatric General Surgery, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, Sichuan Province, China

Co-first authors: Liang-Tang Zeng and Jun-Nan Hu.

Co-corresponding authors: Neng-Yi Hou and Feng Liu.

Author contributions: Zeng LT and Hu JN contributed equally to this article, they are the co-first authors of this manuscript; Zeng LT drafted the manuscript; Zeng LT, Hu JN, Cao QX, and Hou NY performed data processing, statistical analyses and result interpretation; Zeng LT and Liu F designed the research study; Hou NY and Liu F supervised the study and critically revised the manuscript, study supervision, methodological oversight and critical revision, they contributed equally to this article, they are the co-corresponding authors of this manuscript; and all authors read and approved the final manuscript.

AI contribution statement: Not applicable.

Supported by the Special Project for Clinical Research in Health Industry of Shanghai Municipal Health Commission, No. 202540026; and the Cultivation Grant for Clinical and Basic Integration Research of Shanghai Tenth People’s Hospital, No. SYYYRH2025006.

Institutional review board statement: The UK Biobank received ethical approval from the North West–Haydock Research Ethics Committee and operates as a Research Tissue Bank, approval No. 21/NW/0157.

Clinical trial registration statement: This was a prospective observational cohort study, and no health-related intervention was assigned.

Informed consent statement: All UK Biobank participants provided written informed consent before enrollment and data collection.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.

Data sharing statement: UK Biobank participant-level data are available to approved researchers through the UK Biobank data-access process and cannot be redistributed by the authors. Analytic code is available from the corresponding author upon reasonable request.

Corresponding author: Feng Liu, Chief Physician, Full Professor, Digestive Endoscopy Center, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, No. 301 Yanchang Road, Shanghai 200072, China. 1905041@tongji.edu.cn

Received: June 9, 2026
Revised: July 14, 2026
Accepted: July 27, 2026
Published online: December 21, 2026
Processing time: 140 Days and 15.7 Hours

Abstract

BACKGROUND

Metabolomics may provide complementary information for colorectal cancer (CRC) risk prediction beyond clinical factors and polygenic risk scores (PRS), but its incremental value remains uncertain. We hypothesized that integrating plasma nuclear magnetic resonance metabolomics with clinical and genetic factors would improve CRC risk stratification and prediction.

AIM

To evaluate the incremental predictive value of plasma metabolomics beyond clinical factors and PRS for CRC.

METHODS

This study included 468095 UK Biobank participants. Cox models examined associations between 325 baseline nuclear magnetic resonance-based plasma metabolic markers and incident CRC. A metabolomic risk score (MetRS) was developed using Light Gradient Boosting Machine-based feature selection. We evaluated its risk stratification ability and compared the predictive performance of a clinical model adapted from QCancer (hereafter referred to as QCancer), QCancer + MetRS, QCancer + PRS, and QCancer + PRS + MetRS. Performance was assessed by internal-external cross-validation using Harrell’s C-index, calibration, time-dependent areas under the curve, decision-curve analysis, and reclassification metrics.

RESULTS

During a median follow-up of 14.7 years, 7150 participants developed CRC. Among 325 metabolic markers, 115 were significantly associated with CRC risk after false discovery rate correction. The MetRS stratified long-term CRC risk. Compared with QCancer + PRS, adding MetRS modestly improved discrimination, with Harrell’s C-index increasing from 0.705 (95% confidence interval: 0.696-0.714) to 0.711 (95% confidence interval: 0.703-0.719). The integrated model showed generally acceptable calibration, the greatest net benefit across most examined thresholds, and modest improvement in reclassification.

CONCLUSION

Plasma metabolomics complements clinical and genetic factors for CRC risk assessment, providing modest improvements in risk stratification and predictive performance.

Keywords: Plasma metabolomics; Colorectal cancer; Risk prediction; Risk stratification; Polygenic risk score

Core Tip: Using data from 468095 UK Biobank participants, we evaluated 325 nuclear magnetic resonance-based plasma metabolic markers and developed a seven-feature metabolomic risk score for incident colorectal cancer. The score stratified long-term risk and modestly improved prediction when added to an adapted QCancer clinical model and a polygenic risk score. Internal-external cross-validation across 10 geographical clusters supported consistency and geographical transportability within the UK Biobank. These findings suggest that metabolomics may complement, rather than replace, established clinical and genetic risk assessment. Independent external validation and cost-effectiveness evaluation are required before clinical implementation.