Metabolomics : Official journal of the Metabolomic Society · 2026
Urinary metabolomics may improve prediction of overall survival beyond tumor stage in colorectal cancer: results from the ColoCare study.
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Lin Tengda, Guo Boyi, Bandera Victoria M, Liesenfeld David B, Shen Jincheng, Haaland Benjamin, Stewart Paul A, Boucher Kenneth M, Erickson Patricia A, Hardikar Sheetal, Damerell Victoria, Byrd Doratha A, Figueiredo Jane C, Toriola Adetunji T, Shibata David, Siegel Erin M, Li Christopher I, Ulrich Alexis B, Kahlert Christoph, Scharfstein Daniel O, Gigic Biljana, Ulrich Cornelia M, Ose Jennifer
Аннотация
BACKGROUND: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSIONS: 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.