{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:20:23Z","timestamp":1792783223959,"version":"4.5.0"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Hochschule Hannover","id":[{"id":"https:\/\/ror.org\/03m2kj587","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Metabolomics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>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\u2019s C-index and time-dependent AUC based on the nested cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 (\n                      <jats:italic>vs.<\/jats:italic>\n                      0.68) and consistently higher time-dependent AUCs at 1\u20135 years of follow-up, with a peak AUC of 0.76 at year 3 (\n                      <jats:italic>vs.<\/jats:italic>\n                      0.68). Three urinary metabolites were consistently selected across all metabolomics-based models: indolelactate, 2-hydroxyisobutyrate and a uridine-like metabolite.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11306-026-02516-3","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:18:50Z","timestamp":1789103930000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Urinary metabolomics may improve prediction of overall survival beyond tumor stage in colorectal cancer: results from the ColoCare study"],"prefix":"10.1007","volume":"22","author":[{"given":"Tengda","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria M.","family":"Bandera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David B.","family":"Liesenfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincheng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Haaland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul A.","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth M.","family":"Boucher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia A.","family":"Erickson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheetal","family":"Hardikar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria","family":"Damerell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doratha A.","family":"Byrd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane C.","family":"Figueiredo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adetunji T.","family":"Toriola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Shibata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin M.","family":"Siegel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher I.","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexis B.","family":"Ulrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Kahlert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel O.","family":"Scharfstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biljana","family":"Gigic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cornelia M.","family":"Ulrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2030-9676","authenticated-orcid":false,"given":"Jennifer","family":"Ose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"issue":"24","key":"2516_CR1","doi-asserted-by":"publisher","first-page":"4817","DOI":"10.1002\/sim.3435","volume":"27","author":"E Atkinson","year":"2008","unstructured":"Atkinson, E., Crowson, C., Pedersen, R., & Therneau, T. (2008). Poisson models for person-years and expected rates. Statistics in Medicine, 27(24), 4817\u20134830. https:\/\/doi.org\/10.1002\/sim.3435","journal-title":"Statistics in Medicine"},{"issue":"1","key":"2516_CR2","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289\u2013300. https:\/\/doi.org\/10.1111\/j.2517-6161.1995.tb02031.x","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"1","key":"2516_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.2307\/2530817","volume":"39","author":"G Berry","year":"1983","unstructured":"Berry, G. (1983). The analysis of mortality by the subject-years method. Biometrics, 39(1), 173\u2013184. https:\/\/doi.org\/10.2307\/2530817","journal-title":"Biometrics"},{"issue":"1","key":"2516_CR4","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1158\/0008-5472.CAN-11-1543","volume":"72","author":"I Bertini","year":"2012","unstructured":"Bertini, I., Cacciatore, S., Jensen, B. V., Schou, J. V., Johansen, J. S., Kruh\u00f8ffer, M., Luchinat, C., Nielsen, D. L., & Turano, P. (2012). Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer. Cancer Research, 72(1), 356\u2013364. https:\/\/doi.org\/10.1158\/0008-5472.CAN-11-1543","journal-title":"Cancer Research"},{"issue":"9","key":"2516_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073076","volume":"8","author":"S Bouatra","year":"2013","unstructured":"Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., Liu, P., Dame, Z. T., Poelzer, J., Huynh, J., Yallou, F. S., Psychogios, N., Dong, E., Bogumil, R., Roehring, C., & Wishart, D. S. (2013). The human urine metabolome. PLOS ONE, 8(9), Article e73076. https:\/\/doi.org\/10.1371\/journal.pone.0073076","journal-title":"PLOS ONE"},{"issue":"1","key":"2516_CR6","doi-asserted-by":"publisher","first-page":"45","DOI":"10.2307\/1402659","volume":"43","author":"NE Breslow","year":"1975","unstructured":"Breslow, N. E. (1975). Analysis of survival data under the proportional hazards model. International Statistical Review\/Revue Internationale de Statistique, 43(1), 45\u201357. https:\/\/doi.org\/10.2307\/1402659","journal-title":"International Statistical Review\/Revue Internationale de Statistique"},{"issue":"19","key":"2516_CR7","doi-asserted-by":"publisher","first-page":"11171","DOI":"10.3390\/ijms231911171","volume":"23","author":"J Brezmes","year":"2022","unstructured":"Brezmes, J., Llambrich, M., Cumeras, R., & Gum\u00e0, J. (2022). Urine NMR metabolomics for precision oncology in colorectal cancer. International Journal of Molecular Sciences, 23(19), 11171. https:\/\/doi.org\/10.3390\/ijms231911171","journal-title":"International Journal of Molecular Sciences"},{"issue":"1","key":"2516_CR8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.trecan.2025.10.004","volume":"12","author":"ML Brown","year":"2026","unstructured":"Brown, M. L., Cai, X., & Simon, M. C. (2026). Branched chain amino acids and their aberrant metabolism in cancer. Trends in Cancer, 12(1), 34\u201347. https:\/\/doi.org\/10.1016\/j.trecan.2025.10.004","journal-title":"Trends in Cancer"},{"issue":"1","key":"2516_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/nutraceuticals5010004","volume":"5","author":"M Cannon","year":"2025","unstructured":"Cannon, M., Cosantino, A., Tran, L., Chandel, N. S., & Ghoreishi, N. (2025). Effects of xylitol on tumor progression in syngeneic mice cancer models. Nutraceuticals, 5(1), 4. https:\/\/doi.org\/10.3390\/nutraceuticals5010004","journal-title":"Nutraceuticals"},{"key":"2516_CR10","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley, G. C., & Talbot, N. L. C. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11, 2079\u20132107.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"2516_CR11","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1021\/pr201001a","volume":"11","author":"Y Cheng","year":"2012","unstructured":"Cheng, Y., Xie, G. X., Chen, T. L., Qiu, Y. P., Zou, X., Zheng, M. H., Tan, B. B., Feng, B., Dong, T. T., He, P. A., Zhao, L. J., Zhao, A. H., Xu, L. X., Zhang, Y., & Jia, W. (2012). Distinct urinary metabolic profile of human colorectal cancer. Journal of Proteome Research, 11(2), 1354\u20131363. https:\/\/doi.org\/10.1021\/pr201001a","journal-title":"Journal of Proteome Research"},{"issue":"8","key":"2516_CR12","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1038\/s12276-025-01402-7","volume":"57","author":"KM Choi","year":"2025","unstructured":"Choi, K. M., Berard, B. A., Yoon, J. H., & Kim, D. (2025). Uridine as a hub in cancer metabolism and RNA biology. Experimental and Molecular Medicine, 57(8), 1651\u20131662. https:\/\/doi.org\/10.1038\/s12276-025-01402-7","journal-title":"Experimental and Molecular Medicine"},{"issue":"4","key":"2516_CR13","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1177\/0962280213497434","volume":"25","author":"CS Crowson","year":"2016","unstructured":"Crowson, C. S., Atkinson, E. J., & Therneau, T. M. (2016). Assessing calibration of prognostic risk scores. Statistical Methods in Medical Research, 25(4), 1692\u20131706. https:\/\/doi.org\/10.1177\/0962280213497434","journal-title":"Statistical Methods in Medical Research"},{"issue":"6","key":"2516_CR14","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1093\/bib\/bbz136","volume":"21","author":"R De Bin","year":"2020","unstructured":"De Bin, R., Boulesteix, A.-L., Benner, A., Becker, N., & Sauerbrei, W. (2020). Combining clinical and molecular data in regression prediction models: Insights from a simulation study. Briefings in Bioinformatics, 21(6), 1904\u20131919. https:\/\/doi.org\/10.1093\/bib\/bbz136","journal-title":"Briefings in Bioinformatics"},{"issue":"7","key":"2516_CR15","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1038\/nprot.2011.335","volume":"6","author":"WB Dunn","year":"2011","unstructured":"Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., Haselden, J. N., Nicholls, A. W., Wilson, I. D., Kell, D. B., & Goodacre, R. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7), 1060\u20131083. https:\/\/doi.org\/10.1038\/nprot.2011.335","journal-title":"Nature Protocols"},{"key":"2516_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4541-9","volume-title":"An introduction to the bootstrap","author":"B Efron","year":"1993","unstructured":"Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall\/CRC."},{"key":"2516_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/cancers10080246","author":"V Erben","year":"2018","unstructured":"Erben, V., Bhardwaj, M., Schrotz-King, P., & Brenner, H. (2018). Metabolomics biomarkers for detection of colorectal neoplasms: A systematic review. Cancers. https:\/\/doi.org\/10.3390\/cancers10080246","journal-title":"Cancers"},{"issue":"456","key":"2516_CR19","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1198\/016214501753382273","volume":"96","author":"J Fan","year":"2001","unstructured":"Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348\u20131360. https:\/\/doi.org\/10.1198\/016214501753382273","journal-title":"Journal of the American Statistical Association"},{"issue":"5","key":"2516_CR20","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","volume":"70","author":"J Fan","year":"2008","unstructured":"Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B, 70(5), 849\u2013911. https:\/\/doi.org\/10.1111\/j.1467-9868.2008.00674.x","journal-title":"Journal of the Royal Statistical Society: Series B"},{"issue":"4","key":"2516_CR18","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1214\/10-AOS798","volume":"38","author":"J Fan","year":"2010","unstructured":"Fan, J., Feng, Y., & Wu, Y. (2010). High-dimensional variable selection for cox\u2019s proportional hazards model. Annals of Statistics, 38(4), 2307\u20132334. https:\/\/doi.org\/10.1214\/10-AOS798","journal-title":"Annals of Statistics"},{"key":"2516_CR21","first-page":"2013","volume":"10","author":"J Fan","year":"2009","unstructured":"Fan, J., Samworth, R., & Wu, Y. (2009). Ultrahigh dimensional feature selection: Beyond the linear model. Journal of Machine Learning Research, 10, 2013\u20132038.","journal-title":"Journal of Machine Learning Research"},{"issue":"7","key":"2516_CR22","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1038\/bjc.2016.243","volume":"115","author":"F Farshidfar","year":"2016","unstructured":"Farshidfar, F., Weljie, A. M., Kopciuk, K. A., Hilsden, R., McGregor, S. E., Buie, W. D., MacLean, A., Vogel, H. J., & Bathe, O. F. (2016). A validated metabolomic signature for colorectal cancer: Exploration of the clinical value of metabolomics. British Journal of Cancer, 115(7), 848\u2013857. https:\/\/doi.org\/10.1038\/bjc.2016.243","journal-title":"British Journal of Cancer"},{"issue":"1\u20132","key":"2516_CR23","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1023\/A:1013713905833","volume":"48","author":"O Fiehn","year":"2002","unstructured":"Fiehn, O. (2002). Metabolomics-the link between genotypes and phenotypes. Plant Molecular Biology, 48(1\u20132), 155\u2013171. https:\/\/doi.org\/10.1002\/0471142727.mb3004s114","journal-title":"Plant Molecular Biology"},{"issue":"3","key":"2516_CR24","doi-asserted-by":"publisher","first-page":"725","DOI":"10.3390\/cancers14030725","volume":"14","author":"A Gold","year":"2022","unstructured":"Gold, A., Choueiry, F., Jin, N., Mo, X., & Zhu, J. (2022). The application of metabolomics in recent colorectal cancer studies: A state-of-the-art review. Cancers, 14(3), 725. https:\/\/doi.org\/10.3390\/cancers14030725","journal-title":"Cancers"},{"issue":"7","key":"2516_CR25","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1038\/nrc1390","volume":"4","author":"JL Griffin","year":"2004","unstructured":"Griffin, J. L., & Shockcor, J. P. (2004). Metabolic profiles of cancer cells. Nature Reviews Cancer, 4(7), 551\u2013561. https:\/\/doi.org\/10.1038\/nrc1390","journal-title":"Nature Reviews Cancer"},{"key":"2516_CR26","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.aca.2018.06.081","volume":"1034","author":"C Guo","year":"2018","unstructured":"Guo, C., Xie, C., Chen, Q., Cao, X., Guo, M., Zheng, S., & Wang, Y. (2018). A novel malic acid-enhanced method for the analysis of 5-methyl-2\u2019-deoxycytidine, 5-hydroxymethyl-2\u2019-deoxycytidine, 5-methylcytidine and 5-hydroxymethylcytidine in human urine using hydrophilic interaction liquid chromatography-tandem mass spectrometry. Analytica Chimica Acta, 1034, 110\u2013118. https:\/\/doi.org\/10.1016\/j.aca.2018.06.081","journal-title":"Analytica Chimica Acta"},{"issue":"4","key":"2516_CR27","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","volume":"15","author":"FE Harrell","year":"1996","unstructured":"Harrell, F. E., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15(4), 361\u2013387.","journal-title":"Statistics in Medicine"},{"key":"2516_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: Data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https:\/\/doi.org\/10.1007\/978-0-387-84858-7","edition":"2"},{"issue":"2","key":"2516_CR29","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1111\/j.0006-341X.2000.00337.x","volume":"56","author":"PJ Heagerty","year":"2000","unstructured":"Heagerty, P. J., Lumley, T., & Pepe, M. S. (2000). Time-dependent roc curves for censored survival data and a diagnostic marker. Biometrics, 56(2), 337\u2013344. https:\/\/doi.org\/10.1111\/j.0006-341X.2000.00337.x","journal-title":"Biometrics"},{"issue":"1","key":"2516_CR30","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1111\/j.0006-341X.2005.030814.x","volume":"61","author":"PJ Heagerty","year":"2005","unstructured":"Heagerty, P. J., & Zheng, Y. (2005). Survival model predictive accuracy and roc curves. Biometrics, 61(1), 92\u2013105. https:\/\/doi.org\/10.1111\/j.0006-341X.2005.030814.x","journal-title":"Biometrics"},{"issue":"1","key":"2516_CR31","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/cr.2017.149","volume":"28","author":"H Huang","year":"2018","unstructured":"Huang, H., Luo, Z., Qi, S., Huang, J., Xu, P., Wang, X., Gao, L., Li, F., Wang, J., Zhao, W., Gu, W., Chen, Z., Dai, L., Dai, J., & Zhao, Y. (2018). Landscape of the regulatory elements for lysine 2-hydroxyisobutyrylation pathway. Cell Research, 28(1), 111\u2013125. https:\/\/doi.org\/10.1038\/cr.2017.149","journal-title":"Cell Research"},{"key":"2516_CR32","unstructured":"International Agency for Research on Cancer. (2024). Colorectal cancer. Retrieved April 07, 2026, from https:\/\/www.iarc.who.int\/cancer-type\/colorectal-cancer\/"},{"issue":"1","key":"2516_CR33","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1186\/s12885-022-10117-1","volume":"22","author":"R Jardillier","year":"2022","unstructured":"Jardillier, R., Koca, D., Chatelain, F., & Guyon, L. (2022). Prognosis of lasso-like penalized cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening. BMC Cancer, 22(1), 1045. https:\/\/doi.org\/10.1186\/s12885-022-10117-1","journal-title":"BMC Cancer"},{"issue":"2","key":"2516_CR34","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1021\/pr3010106","volume":"12","author":"B Jim\u00e9nez","year":"2013","unstructured":"Jim\u00e9nez, B., Mirnezami, R., Kinross, J., Cloarec, O., Keun, H. C., Holmes, E., Goldin, R. D., Ziprin, P., Darzi, A., & Nicholson, J. K. (2013). 1h hr-mas nmr spectroscopy of tumor-induced local metabolic field-effects enables colorectal cancer staging and prognostication. Journal of Proteome Research, 12(2), 959\u2013968. https:\/\/doi.org\/10.1021\/pr3010106","journal-title":"Journal of Proteome Research"},{"issue":"1","key":"2516_CR35","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s12874-017-0332-6","volume":"17","author":"AN Kamarudin","year":"2017","unstructured":"Kamarudin, A. N., Cox, T., & Kolamunnage-Dona, R. (2017). Time-dependent roc curve analysis in medical research: Current methods and applications. BMC Medical Research Methodology, 17(1), 53. https:\/\/doi.org\/10.1186\/s12874-017-0332-6","journal-title":"BMC Medical Research Methodology"},{"issue":"5","key":"2516_CR36","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1001\/jamasurg.2014.3572","volume":"150","author":"PJ Kneuertz","year":"2015","unstructured":"Kneuertz, P. J., Chang, G. J., Hu, C.-Y., Rodriguez-Bigas, M. A., Eng, C., Vilar, E., Skibber, J. M., Feig, B. W., Cormier, J. N., & You, Y. N. (2015). Overtreatment of young adults with colon cancer: More intense treatments with unmatched survival gains. JAMA Surgery, 150(5), 402\u2013409. https:\/\/doi.org\/10.1001\/jamasurg.2014.3572","journal-title":"JAMA Surgery"},{"key":"2516_CR37","doi-asserted-by":"publisher","unstructured":"Koncina, E., Haan, S., Rauh, S., & Letellier, E. (2020). Prognostic and predictive molecular biomarkers for colorectal cancer: Updates and challenges. Cancers, 12(2), 319. https:\/\/doi.org\/10.3390\/cancers12020319","DOI":"10.3390\/cancers12020319"},{"issue":"1","key":"2516_CR38","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1136\/gutjnl-2017-315333","volume":"67","author":"M Lawler","year":"2018","unstructured":"Lawler, M., Alsina, D., Adams, R. A., Anderson, A. S., Brown, G., Fearnhead, N. S., Fenwick, S. W., Halloran, S. P., Hochhauser, D., Hull, M. A., Koelzer, V. H., McNair, A. G. K., Monahan, K. J., N\u00e4thke, I., Norton, C., Novelli, M. R., Steele, R. J. C., Thomas, A. L., Wilde, L. M., & Tomlinson, I. (2018). Critical research gaps and recommendations to inform research prioritisation for more effective prevention and improved outcomes in colorectal cancer. Gut, 67(1), 179\u2013193. https:\/\/doi.org\/10.1136\/gutjnl-2017-315333","journal-title":"Gut"},{"issue":"4","key":"2516_CR39","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1007\/s11306-014-0758-3","volume":"11","author":"DB Liesenfeld","year":"2015","unstructured":"Liesenfeld, D. B., Habermann, N., Toth, R., Owen, R. W., Frei, E., Staffa, J., Schrotz-King, P., Klika, K. D., & Ulrich, C. M. (2015). Changes in urinary metabolic profiles of colorectal cancer patients enrolled in a prospective cohort study (colocare). Metabolomics, 11(4), 998\u20131012. https:\/\/doi.org\/10.1007\/s11306-014-0758-3","journal-title":"Metabolomics"},{"key":"2516_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.micres.2023.127392","volume":"272","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Pei, Z., Pan, T., Wang, H., Chen, W., & Lu, W. (2023). Indole metabolites and colorectal cancer: Gut microbial tryptophan metabolism, host gut microbiome biomarkers, and potential intervention mechanisms. Microbiological Research, 272, Article 127392. https:\/\/doi.org\/10.1016\/j.micres.2023.127392","journal-title":"Microbiological Research"},{"issue":"7","key":"2516_CR41","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.trecan.2025.03.008","volume":"11","author":"ZQ Liu","year":"2025","unstructured":"Liu, Z. Q., Ciudad, M. T., & McGaha, T. L. (2025). New insights into tryptophan metabolism in cancer. Trends in Cancer, 11(7), 629\u2013641. https:\/\/doi.org\/10.1016\/j.trecan.2025.03.008","journal-title":"Trends in Cancer"},{"key":"2516_CR42","doi-asserted-by":"publisher","DOI":"10.3389\/fams.2021.693126","volume":"7","author":"J Magnanensi","year":"2021","unstructured":"Magnanensi, J., Maumy-Bertrand, M., Meyer, N., & Bertrand, F. (2021). New developments in sparse pls regression. Frontiers in Applied Mathematics and Statistics, 7, Article 693126. https:\/\/doi.org\/10.3389\/fams.2021.693126","journal-title":"Frontiers in Applied Mathematics and Statistics"},{"key":"2516_CR43","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.chemolab.2012.07.010","volume":"118","author":"T Mehmood","year":"2012","unstructured":"Mehmood, T., Liland, K. H., Snipen, L., & S\u00e6b\u00f8, S. (2012). A review of variable selection methods in partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 118, 62\u201369. https:\/\/doi.org\/10.1016\/j.chemolab.2012.07.010","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"issue":"6","key":"2516_CR44","doi-asserted-by":"publisher","DOI":"10.1002\/cem.3226","volume":"34","author":"T Mehmood","year":"2020","unstructured":"Mehmood, T., S\u00e6b\u00f8, S., & Liland, K. H. (2020). Comparison of variable selection methods in partial least squares regression. Journal of Chemometrics, 34(6), Article e3226. https:\/\/doi.org\/10.1002\/cem.3226","journal-title":"Journal of Chemometrics"},{"issue":"3","key":"2516_CR45","doi-asserted-by":"publisher","first-page":"756","DOI":"10.3390\/ijms19030756","volume":"19","author":"T Nakajima","year":"2018","unstructured":"Nakajima, T., Katsumata, K., Kuwabara, H., Soya, R., Enomoto, M., Ishizaki, T., Tsuchida, A., Mori, M., Hiwatari, K., Soga, T., Tomita, M., & Sugimoto, M. (2018). Urinary polyamine biomarker panels with machine-learning differentiated colorectal cancers, benign disease, and healthy controls. International Journal of Molecular Sciences, 19(3), 756. https:\/\/doi.org\/10.3390\/ijms19030756","journal-title":"International Journal of Molecular Sciences"},{"issue":"13","key":"2516_CR46","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.3390\/cancers15133391","volume":"15","author":"J Ose","year":"2023","unstructured":"Ose, J., Gigic, B., Brezina, S., Lin, T., Peoples, A. R., Schobert, P. P., Baierl, A., van Roekel, E., Robinot, N., Gicquiau, A., Achaintre, D., Scalbert, A., van Duijnhoven, F. J. B., Holowatyj, A. N., Gumpenberger, T., Schrotz-King, P., Ulrich, A. B., Ulvik, A., Ueland, P. M., & Ulrich, C. M. (2023). Higher plasma creatinine is associated with an increased risk of death in patients with non-metastatic rectal but not colon cancer. Cancers, 15(13), 3391. https:\/\/doi.org\/10.3390\/cancers15133391","journal-title":"Cancers"},{"issue":"8","key":"2516_CR47","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1158\/1055-9965.EPI-21-1357","volume":"31","author":"J Ose","year":"2022","unstructured":"Ose, J., Gigic, B., Hardikar, S., Lin, T., Himbert, C., Warby, C., Peoples, A., Lindley, C., Boehm, J., Schrotz-King, P., & Figueiredo, J. (2022). Presurgery adhesion molecules and angiogenesis biomarkers are differently associated with outcomes in colon and rectal cancer: Results from the colocare study. Cancer Epidemiology, Biomarkers & Prevention, 31(8), 1650\u20131660. https:\/\/doi.org\/10.1158\/1055-9965.EPI-21-1357","journal-title":"Cancer Epidemiology, Biomarkers & Prevention"},{"issue":"W1","key":"2516_CR48","doi-asserted-by":"publisher","first-page":"W398","DOI":"10.1093\/nar\/gkae253","volume":"52","author":"Z Pang","year":"2024","unstructured":"Pang, Z., Lu, Y., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E., Wishart, D. S., Li, S., & Xia, J. (2024). Metaboanalyst 6.0: Towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Research, 52(W1), W398\u2013W406. https:\/\/doi.org\/10.1093\/nar\/gkae253","journal-title":"Nucleic Acids Research"},{"issue":"2","key":"2516_CR49","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1001\/jamaoncol.2016.4227","volume":"3","author":"F Petrelli","year":"2017","unstructured":"Petrelli, F., Tomasello, G., Borgonovo, K., Ghidini, M., Turati, L., Dallera, P., Sgroi, G., & Barni, S. (2017). Prognostic survival associated with left-sided vs right-sided colon cancer: A systematic review and meta-analysis. JAMA Oncology, 3(2), 211\u2013219. https:\/\/doi.org\/10.1001\/jamaoncol.2016.4227","journal-title":"JAMA Oncology"},{"issue":"4","key":"2516_CR50","doi-asserted-by":"publisher","first-page":"389","DOI":"10.4161\/cbt.27625","volume":"15","author":"LC Phua","year":"2014","unstructured":"Phua, L. C., Chue, X. P., Koh, P. K., Cheah, P. Y., Ho, H. K., & Chan, E. C. (2014). Non-invasive fecal metabonomic detection of colorectal cancer. Cancer Biology & Therapy, 15(4), 389\u2013397. https:\/\/doi.org\/10.4161\/cbt.27625","journal-title":"Cancer Biology & Therapy"},{"key":"2516_CR51","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-319-47656-8_7","volume":"965","author":"RC Pinto","year":"2017","unstructured":"Pinto, R. C. (2017). Chemometrics methods and strategies in metabolomics. Advances in Experimental Medicine and Biology, 965, 163\u2013190. https:\/\/doi.org\/10.1007\/978-3-319-47656-8_7","journal-title":"Advances in Experimental Medicine and Biology"},{"key":"2516_CR52","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1186\/1471-2105-11-395","volume":"11","author":"T Pluskal","year":"2010","unstructured":"Pluskal, T., Castillo, S., Villar-Briones, A., & Oresic, M. (2010). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11, 395. https:\/\/doi.org\/10.1186\/1471-2105-11-395","journal-title":"BMC Bioinformatics"},{"issue":"8","key":"2516_CR53","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.1158\/1078-0432.CCR-13-1939","volume":"20","author":"Y Qiu","year":"2014","unstructured":"Qiu, Y., Cai, G., Zhou, B., Li, D., Zhao, A., Xie, G., Li, H., Cai, S., Xie, D., Huang, C., Ge, W., Zhou, Z., Xu, L. X., Jia, W., Zheng, S., & Yen, Y. (2014). A distinct metabolic signature of human colorectal cancer with prognostic potential. Clinical Cancer Research, 20(8), 2136\u20132146. https:\/\/doi.org\/10.1158\/1078-0432.CCR-13-1939","journal-title":"Clinical Cancer Research"},{"key":"2516_CR54","doi-asserted-by":"publisher","first-page":"1296765","DOI":"10.3389\/fonc.2024.1296765","volume":"14","author":"T Rais","year":"2024","unstructured":"Rais, T., Riaz, R., Siddiqui, T., Shakeel, A., Khan, A., & Zafar, H. (2024). Innovations in colorectal cancer treatment: Trifluridine and tipiracil with bevacizumab for improved outcomes\u2014a review. Frontiers in Oncology, 14, 1296765. https:\/\/doi.org\/10.3389\/fonc.2024.1296765","journal-title":"Frontiers in Oncology"},{"issue":"506","key":"2516_CR55","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1080\/01621459.2013.869223","volume":"109","author":"V Rockov\u00e1","year":"2014","unstructured":"Rockov\u00e1, V., & George, E. I. (2014). EMVS: The EM approach to Bayesian variable selection. Journal of the American Statistical Association, 109(506), 828\u2013846. https:\/\/doi.org\/10.1080\/01621459.2013.869223","journal-title":"Journal of the American Statistical Association"},{"issue":"11","key":"2516_CR56","doi-asserted-by":"publisher","first-page":"2194","DOI":"10.1136\/gutjnl-2021-325373","volume":"71","author":"E Rosati","year":"2022","unstructured":"Rosati, E., Rios Martini, G., Pogorelyy, M. V., Minervina, A. A., Degenhardt, F., Wendorff, M., Sari, S., Mayr, G., Fazio, A., Dowds, C. M., Hauser, C., Tran, F., von Sch\u00f6nfels, W., Pochhammer, J., Salnikova, M. A., Jaeckel, C., Gigla, J. B., Sabet, S. S., H\u00fcbenthal, M., & Franke, A. (2022). A novel unconventional T cell population enriched in Crohn\u2019s disease. Gut, 71(11), 2194\u20132204. https:\/\/doi.org\/10.1136\/gutjnl-2021-325373","journal-title":"Gut"},{"issue":"1","key":"2516_CR57","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/1471-2288-13-33","volume":"13","author":"P Royston","year":"2013","unstructured":"Royston, P., & Altman, D. G. (2013). External validation of a Cox prognostic model: Principles and methods. BMC Medical Research Methodology, 13(1), 33. https:\/\/doi.org\/10.1186\/1471-2288-13-33","journal-title":"BMC Medical Research Methodology"},{"issue":"10","key":"2516_CR58","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.3390\/nu14102023","volume":"14","author":"Y Sahasakul","year":"2022","unstructured":"Sahasakul, Y., Angkhasirisap, W., Lam-Ubol, A., Aursalung, A., Sano, D., Takada, K., & Trachootham, D. (2022). Partial substitution of glucose with xylitol prolongs survival and suppresses cell proliferation and glycolysis of mice bearing orthotopic xenograft of oral cancer. Nutrients, 14(10), 2023. https:\/\/doi.org\/10.3390\/nu14102023","journal-title":"Nutrients"},{"issue":"4","key":"2516_CR59","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1159\/000488652","volume":"34","author":"J Scheele","year":"2018","unstructured":"Scheele, J., Schmidt, S. A., Tenzer, S., Henne-Bruns, D., & Kornmann, M. (2018). Overstaging: A challenge in rectal cancer treatment. Visceral Medicine, 34(4), 301\u2013306. https:\/\/doi.org\/10.1159\/000488652","journal-title":"Visceral Medicine"},{"issue":"4","key":"2516_CR60","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3322\/caac.21670","volume":"71","author":"DR Schmidt","year":"2021","unstructured":"Schmidt, D. R., Patel, R., Kirsch, D. G., Lewis, C. A., Vander Heiden, M. G., & Locasale, J. W. (2021). Metabolomics in cancer research and emerging applications in clinical oncology. CA: A Cancer Journal for Clinicians, 71(4), 333\u2013358. https:\/\/doi.org\/10.3322\/caac.21670","journal-title":"CA: A Cancer Journal for Clinicians"},{"issue":"1","key":"2516_CR61","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1186\/s12916-025-04107-w","volume":"23","author":"T Seum","year":"2025","unstructured":"Seum, T., Cardoso, R., Stevenson-Hoare, J., Holleczek, B., Sch\u00f6ttker, B., Hoffmeister, M., & Brenner, H. (2025). Exploring metabolomics for colorectal cancer risk prediction: Evidence from the UK biobank and Esther cohorts. BMC Medicine, 23(1), 283. https:\/\/doi.org\/10.1186\/s12916-025-04107-w","journal-title":"BMC Medicine"},{"key":"2516_CR62","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1038\/s41419-019-1832-6","volume":"10","author":"YL Shan","year":"2019","unstructured":"Shan, Y. L., Gao, Y., Jin, W., Fan, M. M., Wang, Y., Gu, Y. H., Shan, C. X., Sun, L. J., Li, X., Yu, B., Luo, Q., & Xu, Q. (2019). Targeting HIBCH to reprogram valine metabolism for the treatment of colorectal cancer. Cell Death & Disease, 10, 618. https:\/\/doi.org\/10.1038\/s41419-019-1832-6","journal-title":"Cell Death & Disease"},{"issue":"10","key":"2516_CR63","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1007\/s00535-020-01706-7","volume":"55","author":"D Shida","year":"2020","unstructured":"Shida, D., Inoue, M., Tanabe, T., Moritani, K., Tsukamoto, S., Yamauchi, S., Sugihara, K., & Kanemitsu, Y. (2020). Prognostic impact of primary tumor location in stage iii colorectal cancer-right-sided colon versus left-sided colon versus rectum: A nationwide multicenter retrospective study. Journal of Gastroenterology, 55(10), 958\u2013968. https:\/\/doi.org\/10.1007\/s00535-020-01706-7","journal-title":"Journal of Gastroenterology"},{"issue":"1","key":"2516_CR64","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.70043.CA:A","volume":"76","author":"RL Siegel","year":"2026","unstructured":"Siegel, R. L., Giaquinto, A. N., & Jemal, A. (2026). Cancer statistics. Cancer Journal for Clinicians, 76(1), 7\u201333. https:\/\/doi.org\/10.3322\/caac.70043.CA:A","journal-title":"Cancer Journal for Clinicians"},{"issue":"3","key":"2516_CR65","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3322\/caac.70067","volume":"76","author":"RL Siegel","year":"2026","unstructured":"Siegel, R. L., Miller, K. D., Wagle, N. S., & Jemal, A. (2026). CA: A Colorectal cancer statistics. Cancer Journal for Clinicians, 76(3), 154\u2013172. https:\/\/doi.org\/10.3322\/caac.70067","journal-title":"Cancer Journal for Clinicians"},{"issue":"5","key":"2516_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v039.i05","volume":"39","author":"N Simon","year":"2011","unstructured":"Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2011). Regularization paths for Cox\u2019s proportional hazards model via coordinate descent. Journal of Statistical Software, 39(5), 1\u201313. https:\/\/doi.org\/10.18637\/jss.v039.i05","journal-title":"Journal of Statistical Software"},{"key":"2516_CR67","unstructured":"Society, A.\u00a0C. (2024). Cancer facts & figures 2024. https:\/\/www.cancer.org\/research\/cancer-facts-statistics.html"},{"issue":"2","key":"2516_CR68","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","volume":"36","author":"M Stone","year":"1974","unstructured":"Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111\u2013133. https:\/\/doi.org\/10.1111\/j.2517-6161.1974.tb00994.x","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"3","key":"2516_CR69","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11306-007-0082-2","volume":"3","author":"LW Sumner","year":"2007","unstructured":"Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W., Fiehn, O., Goodacre, R., Griffin, J. L., Hankemeier, T., Hardy, N., Harnly, J., Higashi, R., Kopka, J., Lane, A. N., Lindon, J. C., Marriott, P., Nicholls, A. W., & Viant, M. R. (2007). Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics, 3(3), 211\u2013221. https:\/\/doi.org\/10.1007\/s11306-007-0082-2","journal-title":"Metabolomics"},{"issue":"3","key":"2516_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.gendis.2023.04.018","volume":"11","author":"J Sun","year":"2024","unstructured":"Sun, J., & Xia, Y. (2024). Pretreating and normalizing metabolomics data for statistical analysis. Genes & Diseases, 11(3), Article 100979. https:\/\/doi.org\/10.1016\/j.gendis.2023.04.018","journal-title":"Genes & Diseases"},{"issue":"18","key":"2516_CR71","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1093\/bioinformatics\/btx300","volume":"33","author":"Z Tang","year":"2017","unstructured":"Tang, Z., Shen, Y., Zhang, X., & Yi, N. (2017). The spike-and-slab lasso cox model for survival prediction and associated genes detection. Bioinformatics, 33(18), 2799\u20132807. https:\/\/doi.org\/10.1093\/bioinformatics\/btx300","journal-title":"Bioinformatics"},{"issue":"1","key":"2516_CR72","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267\u2013288. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","journal-title":"Journal of the Royal Statistical Society: Series B"},{"issue":"2","key":"2516_CR73","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1021\/pr060594q","volume":"6","author":"J Trygg","year":"2007","unstructured":"Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabonomics. Journal of Proteome Research, 6(2), 469\u2013479. https:\/\/doi.org\/10.1021\/pr060594q","journal-title":"Journal of Proteome Research"},{"issue":"3","key":"2516_CR74","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1002\/cem.695","volume":"16","author":"J Trygg","year":"2002","unstructured":"Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (o-pls). Journal of Chemometrics, 16(3), 119\u2013128. https:\/\/doi.org\/10.1002\/cem.695","journal-title":"Journal of Chemometrics"},{"issue":"3","key":"2516_CR75","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1158\/1055-9965.EPI-18-0773","volume":"28","author":"CM Ulrich","year":"2019","unstructured":"Ulrich, C. M., Gigic, B., B\u00f6hm, J., Ose, J., Viskochil, R., Schneider, M., Colditz, G. A., Figueiredo, J. C., Grady, W. M., Li, C. I., Shibata, D., Siegel, E. M., Toriola, A. T., & Ulrich, A. (2019). The colocare study: A paradigm of transdisciplinary science in colorectal cancer outcomes. Cancer Epidemiology, Biomarkers & Prevention, 28(3), 591\u2013601. https:\/\/doi.org\/10.1158\/1055-9965.EPI-18-0773","journal-title":"Cancer Epidemiology, Biomarkers & Prevention"},{"issue":"478","key":"2516_CR76","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1198\/016214507000000149","volume":"102","author":"H Uno","year":"2007","unstructured":"Uno, H., Cai, T., Tian, L., & Wei, L. J. (2007). Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association, 102(478), 527\u2013537. https:\/\/doi.org\/10.1198\/016214507000000149","journal-title":"Journal of the American Statistical Association"},{"key":"2516_CR77","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/1471-2105-7-91","volume":"7","author":"S Varma","year":"2006","unstructured":"Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7, 91. https:\/\/doi.org\/10.1186\/1471-2105-7-91","journal-title":"BMC Bioinformatics"},{"issue":"6","key":"2516_CR78","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.21873\/anticanres.12634","volume":"38","author":"MK Ven\u00e4l\u00e4inen","year":"2018","unstructured":"Ven\u00e4l\u00e4inen, M. K., Roine, A. N., H\u00e4kkinen, M. R., Veps\u00e4l\u00e4inen, J. J., Kumpulainen, P. S., Kiviniemi, M. S., Lehtim\u00e4ki, T., Oksala, N. K., & Rantanen, T. K. (2018). Altered polyamine profiles in colorectal cancer. Anticancer Research, 38(6), 3601\u20133607. https:\/\/doi.org\/10.21873\/anticanres.12634","journal-title":"Anticancer Research"},{"key":"2516_CR79","doi-asserted-by":"publisher","first-page":"270","DOI":"10.3389\/fgene.2013.00270","volume":"4","author":"P Waldmann","year":"2013","unstructured":"Waldmann, P., Meszaros, G., Gredler, B., Fuerst, C., & S\u00f6lkner, J. (2013). Evaluation of the LASSO and the elastic net in genome-wide association studies. Frontiers in Genetics, 4, 270. https:\/\/doi.org\/10.3389\/fgene.2013.00270","journal-title":"Frontiers in Genetics"},{"issue":"12","key":"2516_CR80","doi-asserted-by":"publisher","first-page":"O335","DOI":"10.1111\/codi.14431","volume":"20","author":"MM Widlak","year":"2018","unstructured":"Widlak, M. M., Neal, M., Daulton, E., Thomas, C. L., Tomkins, C., Singh, B., Harmston, C., Wicaksono, A., Evans, C., Smith, S., Savage, R. S., Covington, J. A., & Arasaradnam, R. P. (2018). Risk stratification of symptomatic patients suspected of colorectal cancer using Faecal and urinary markers. Colorectal Disease, 20(12), O335\u2013O342. https:\/\/doi.org\/10.1111\/codi.14431","journal-title":"Colorectal Disease"},{"key":"2516_CR81","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1017\/S0021900200047604","volume":"12","author":"HOA Wold","year":"1975","unstructured":"Wold, H. O. A. (1975). Soft modelling by latent variables: The non-linear iterative partial least squares (NIPALS) approach. Journal of Applied Probability, 12, 117\u2013142. https:\/\/doi.org\/10.1017\/S0021900200047604","journal-title":"Journal of Applied Probability"},{"key":"2516_CR82","unstructured":"World Health Organization. (2024). Colorectal cancer. Retrieved 07 April, 2026, from https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/colorectal-cancer"},{"key":"2516_CR83","doi-asserted-by":"publisher","first-page":"1220638","DOI":"10.3389\/fonc.2023.1220638","volume":"13","author":"E Xu","year":"2023","unstructured":"Xu, E., Ji, B., Jin, K., & Chen, Y. (2023). Branched-chain amino acids catabolism and cancer progression: Focus on therapeutic interventions. Frontiers in Oncology, 13, 1220638. https:\/\/doi.org\/10.3389\/fonc.2023.1220638","journal-title":"Frontiers in Oncology"},{"issue":"8","key":"2516_CR84","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.21037\/tcr-20-1860","volume":"11","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Wang, X., Huang, Y., Ye, D., & Chi, P. (2022). A lasso-based survival prediction model for patients with synchronous colorectal carcinomas based on SEER. Translational Cancer Research, 11(8), 2795\u20132809. https:\/\/doi.org\/10.21037\/tcr-20-1860","journal-title":"Translational Cancer Research"},{"issue":"8","key":"2516_CR85","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1093\/bioinformatics\/bty803","volume":"35","author":"N Yi","year":"2019","unstructured":"Yi, N., Tang, Z., Zhang, X., & Guo, B. (2019). BHGLM: Bayesian hierarchical GLMS and survival models, with applications to genomics and epidemiology. Bioinformatics, 35(8), 1419\u20131421. https:\/\/doi.org\/10.1093\/bioinformatics\/bty803","journal-title":"Bioinformatics"},{"issue":"1","key":"2516_CR86","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1002\/ijc.32076","volume":"145","author":"I Zaimenko","year":"2019","unstructured":"Zaimenko, I., Jaeger, C., Brenner, H., Chang-Claude, J., Hoffmeister, M., Gr\u00f6tzinger, C., Detjen, K., Burock, S., Schmitt, C. A., Stein, U., & Lisec, J. (2019). Non-invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study. International Journal of Cancer, 145(1), 221\u2013231. https:\/\/doi.org\/10.1002\/ijc.32076","journal-title":"International Journal of Cancer"},{"issue":"21","key":"2516_CR87","doi-asserted-by":"publisher","first-page":"35460","DOI":"10.18632\/oncotarget.16727","volume":"8","author":"F Zhang","year":"2017","unstructured":"Zhang, F., Zhang, Y., Zhao, W., Deng, K., Wang, Z., Yang, C., Ma, L., Openkova, M. S., Hou, Y., & Li, K. (2017). Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: A systematic review. Oncotarget, 8(21), 35460\u201335472. https:\/\/doi.org\/10.18632\/oncotarget.16727","journal-title":"Oncotarget"},{"issue":"1","key":"2516_CR88","doi-asserted-by":"publisher","first-page":"2508949","DOI":"10.1080\/19490976.2025.2508949","volume":"17","author":"SZ Zhou","year":"2025","unstructured":"Zhou, S. Z., Wang, K., Huang, J. D., Xu, Z., Yuan, Q. G., Liu, L. X., Wang, Z. F., Miao, J., Wang, H., Wang, T. T., Guan, W. X., & Ding, C. (2025). Indole-3-lactic acid suppresses colorectal cancer via metabolic reprogramming. Gut Microbes, 17(1), 2508949. https:\/\/doi.org\/10.1080\/19490976.2025.2508949","journal-title":"Gut Microbes"},{"key":"2516_CR89","doi-asserted-by":"publisher","first-page":"1595176","DOI":"10.3389\/fphar.2025.1595176","volume":"16","author":"Y Zhou","year":"2025","unstructured":"Zhou, Y., Kou, J., Li, W., Wang, Y., Su, X., & Zhang, H. (2025). BCAA metabolism in cancer progression and therapy resistance: The balance between fuel and cell signaling. Frontiers in Pharmacology, 16, 1595176. https:\/\/doi.org\/10.3389\/fphar.2025.1595176","journal-title":"Frontiers in Pharmacology"},{"issue":"476","key":"2516_CR90","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1198\/016214506000000735","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418\u20131429. https:\/\/doi.org\/10.1198\/016214506000000735","journal-title":"Journal of the American Statistical Association"}],"container-title":["Metabolomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11306-026-02516-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11306-026-02516-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11306-026-02516-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T01:02:17Z","timestamp":1791853337000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11306-026-02516-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":90,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["2516"],"URL":"https:\/\/doi.org\/10.1007\/s11306-026-02516-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-9737689\/v1","asserted-by":"object"}]},"ISSN":["1573-3890"],"issn-type":[{"value":"1573-3890","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]},"assertion":[{"value":"17 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no financial or other competing interests that could have influenced the objectivity of this research or the preparation of this manuscript, except as noted below.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no financial or other competing interests that could have influenced the objectivity of this research or the preparation of this manuscript, except as noted below.AG, Emil-Barell-Str. 1, 79639 Grenzach-Wyhlen, Germany). This work was conducted while D.L. was affiliated with NCT\/DKFZ and is unrelated to his current employment. C.M.U., as cancer center director, oversees research funded by several pharmaceutical companies but has not received direct personal funding.","order":2,"name":"Ethics","label":"Disclosure","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"150"}}