Urinary metabolomics may improve prediction of overall survival beyond tumor stage in colorectal cancer: results from the ColoCare study.

Authors

Tengda Lin, Boyi Guo, Victoria M Bandera, David B Liesenfeld, Jincheng Shen, Benjamin Haaland, Paul A Stewart, Kenneth M Boucher, Patricia A Erickson, Sheetal Hardikar, Victoria Damerell, Doratha A Byrd, Jane C Figueiredo, Adetunji T Toriola, David Shibata, Erin M Siegel, Christopher I Li, Alexis B Ulrich, Christoph Kahlert, Daniel O Scharfstein, Biljana Gigic, Cornelia M Ulrich, Jennifer Ose

Published in

Metabolomics : Official journal of the Metabolomic Society. Volume 22. Issue 5. Sep 11, 2026. Epub Sep 11, 2026.

Abstract

Colorectal cancer (CRC) is a leading cause of cancer-related mortality. Prognosis is primarily guided by tumor stage despite substantial molecular heterogeneity. Urinary metabolomics may capture systemic and tumor-related biology beyond staging and could improve prognostic assessment. We hypothesized that incorporating urinary metabolomic profiles would improve overall survival (OS) prediction performance compared with a stage- and age-based reference model.
A total of n = 76 stage I-IV CRC patients recruited as part of the ColoCare Study in Heidelberg Germany with pre-surgery urinary metabolomics were included (23 deaths; median follow-up 3.03 years). Four metabolomics-based penalized Cox models adjusted for tumor stage and age at diagnosis were developed using LASSO, adaptive LASSO, spike-and-slab LASSO, and iterative sure independence screening (iSIS)-LASSO. Model discrimination was assessed using Harrell's C-index and time-dependent AUC based on the nested cross-validation.
Compared with the reference model (Cox model including only tumor stage and age at diagnosis), all metabolomics-based models provided better discrimination. The spike-and-slab LASSO Cox model demonstrated the best performance, achieving a C-index of 0.75 (vs. 0.68) and consistently higher time-dependent AUCs at 1-5 years of follow-up, with a peak AUC of 0.76 at year 3 (vs. 0.68). Three urinary metabolites were consistently selected across all metabolomics-based models: indolelactate, 2-hydroxyisobutyrate and a uridine-like metabolite.
Urinary metabolomics may improve CRC OS prediction beyond tumor stage and age at diagnosis, especially with the spike-and-slab LASSO Cox model. These results support urinary metabolomics as a promising noninvasive prognostic tool that merits external validation.

PMID:
42726173
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.

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