{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:13:02Z","timestamp":1785413582150,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2026R716"],"award-info":[{"award-number":["PNURSP2026R716"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PNURSP2026R716"],"award-info":[{"award-number":["PNURSP2026R716"]}],"id":[{"id":"https:\/\/ror.org\/05b0cyh02","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, highlighting the critical need for non-invasive, accurate, and interpretable diagnostic tools. Metabolomic profiling of fecal microbial extracellular vesicles (EVs) offers a promising yet underexplored avenue for biomarker discovery when integrated with explainable machine learning (ML) frameworks. This study aimed to identify stool-derived microbial EV metabolite biomarkers that discriminate CRC patients from healthy controls and to develop interpretable ML classifiers for non-invasive CRC detection. Metabolomic profiles of fecal microbial EVs from 76 age- and sex-comparable participants (36 CRC, 40 controls) were obtained using LC\/QTOFMS and GC\/TOFMS. Three ML classifiers (TPOT, LightGBM, XGBoost) were trained and evaluated through 100-repeat stratified hold-out and nested 5-fold cross-validation, with SHAP and LIME applied for global and local interpretability. Fourteen metabolites were significantly dysregulated between the CRC and control groups (adjusted p &lt; 0.05), with 13 upregulated and one (aminoisobutyric acid) downregulated. Furoic acid exhibited perfect diagnostic discrimination, followed by palmitic acid and tyramine. Nested cross-validation demonstrated robust performance: TPOT achieved AUC = 0.997 \u00b1 0.005, sensitivity = 0.973 \u00b1 0.022, and MCC = 0.957 \u00b1 0.033. Hold-out validation corroborated these findings (AUC = 0.998 \u00b1 0.008). SHAP analysis identified furoic acid, palmitic acid, and tyramine as the dominant predictive features, while aminoisobutyric acid exhibited a distinctive protective pattern. LIME analysis corroborated these findings at the individual prediction level. The identified fecal EV-derived metabolite panel\u2014particularly furoic acid, palmitic acid, and tyramine\u2014shows strong potential to predict CRC in a non-invasive, interpretable manner; however, given the modest sample size, these findings should be considered hypothesis-generating and require validation in larger, prospective, multi-center cohorts before clinical translation.<\/jats:p>","DOI":"10.3390\/ijms27125451","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T03:27:21Z","timestamp":1781666841000},"page":"5451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fecal Extracellular Vesicle Metabolomics as a Non-Invasive Biomarker Source in Colorectal Cancer: TPOT AutoML Superiority over Tree-Based Models with SHAP and LIME Clinical Interpretability"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9848-7958","authenticated-orcid":false,"given":"Fatma Hilal","family":"Yagin","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Faculty of Medicine, Malatya Turgut Ozal University, Malatya 44210, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1570-9402","authenticated-orcid":false,"given":"Yavuz","family":"Korkmaz","sequence":"additional","affiliation":[{"name":"Department of Family Medicine, Faculty of Medicine, Malatya Turgut Ozal University, Malatya 44210, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-098X","authenticated-orcid":false,"given":"Cemil","family":"Colak","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5795-9966","authenticated-orcid":false,"given":"Fahaid","family":"Al-Hashem","sequence":"additional","affiliation":[{"name":"Department of Physiology, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah A.","family":"Alzakari","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7273-4041","authenticated-orcid":false,"given":"Amal K.","family":"Alkhalifa","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5735-3825","authenticated-orcid":false,"given":"Mohammadreza","family":"Aghaei","sequence":"additional","affiliation":[{"name":"Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 \u00c5lesund, Norway"},{"name":"Department of Sustainable Systems Engineering (INATECH), Albert Ludwigs University of Freiburg, 79110 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","first-page":"233","article-title":"Colorectal cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1056\/NEJMoa1311194","article-title":"Multitarget stool DNA testing for colorectal-cancer screening","volume":"370","author":"Imperiale","year":"2014","journal-title":"N. Engl. J. Med."},{"key":"ref_5","unstructured":"Kelly, R.S., and Lasky-Su, J. (2020). Metabolomics and biomarker discovery in colorectal cancer. Cancers, 12."},{"key":"ref_6","first-page":"843","article-title":"Metabolomics and colorectal cancer: A systematic review","volume":"30","author":"Petrick","year":"2021","journal-title":"Cancer Epidemiol. Biomark. Prev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1152\/physrev.00035.2018","article-title":"Metabolomics for investigating physiological and pathophysiological processes","volume":"99","author":"Wishart","year":"2019","journal-title":"Physiol. Rev."},{"key":"ref_8","first-page":"333","article-title":"Metabolomics in cancer research and emerging applications in clinical oncology","volume":"71","author":"Schmidt","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: The next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1158\/2159-8290.CD-21-1059","article-title":"Hallmarks of cancer: New dimensions","volume":"12","author":"Hanahan","year":"2022","journal-title":"Cancer Discov."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gold, A., Choueiry, F., Jin, N., Mo, X., and Zhu, J. (2022). The application of metabolomics in recent colorectal cancer studies: A state-of-the-art review. Cancers, 14.","DOI":"10.3390\/cancers14030725"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.1038\/s41598-020-59529-8","article-title":"Colorectal cancer diagnostic model utilizing metagenomic and metabolomic data of stool microbial extracellular vesicles","volume":"10","author":"Kim","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Galal, A., Talal, M., and Moustafa, A. (2022). Applications of machine learning in metabolomics: Disease modeling and classification. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.1017340"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_15","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., and Moore, J.H. (2016). Automating biomedical data science through tree-based pipeline optimization. Applications of Evolutionary Computation, Springer.","DOI":"10.1007\/978-3-319-31204-0_9"},{"key":"ref_17","unstructured":"Doshi-Velez, F., and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., and Madai, V.I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01332-6"},{"key":"ref_19","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). Why should I trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ccr.2004.11.022","article-title":"Succinate links TCA cycle dysfunction to oncogenesis by inhibiting HIF-alpha prolyl hydroxylase","volume":"7","author":"Selak","year":"2005","journal-title":"Cancer Cell"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.molcel.2019.10.023","article-title":"Cancer-derived succinate promotes macrophage polarization and cancer metastasis via succinate receptor","volume":"77","author":"Wu","year":"2020","journal-title":"Mol. Cell"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ccell.2019.12.011","article-title":"Emerging roles for branched-chain amino acid metabolism in cancer","volume":"37","author":"Sivanand","year":"2020","journal-title":"Cancer Cell"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1038\/s41419-021-04076-x","article-title":"Toll-like receptor 4 is a master regulator for colorectal cancer growth under high-fat diet by programming cancer metabolism","volume":"12","author":"Hu","year":"2021","journal-title":"Cell Death Dis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.cmet.2013.12.003","article-title":"\u03b2-Aminoisobutyric acid induces browning of white fat and hepatic \u03b2-oxidation and is inversely correlated with cardiometabolic risk factors","volume":"19","author":"Roberts","year":"2014","journal-title":"Cell Metab."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.7326\/M13-1484","article-title":"Accuracy of fecal immunochemical tests for colorectal cancer: Systematic review and meta-analysis","volume":"160","author":"Lee","year":"2014","journal-title":"Ann. Intern. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18189","DOI":"10.1002\/cam4.6399","article-title":"Diagnostic accuracy of risk assessment and fecal immunochemical test in colorectal cancer screening: Results from a population-based program and meta-analysis","volume":"12","author":"Wang","year":"2023","journal-title":"Cancer Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1038\/s41591-019-0405-7","article-title":"Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation","volume":"25","author":"Thomas","year":"2019","journal-title":"Nat. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/s41591-019-0406-6","article-title":"Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer","volume":"25","author":"Wirbel","year":"2019","journal-title":"Nat. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6818","DOI":"10.1038\/s41467-022-34405-3","article-title":"Faecal microbiome-based machine learning for multi-class disease diagnosis","volume":"13","author":"Su","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s40168-021-01208-5","article-title":"Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers","volume":"10","author":"Coker","year":"2022","journal-title":"Microbiome"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1016\/j.ccell.2024.07.005","article-title":"Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers","volume":"42","author":"Sun","year":"2024","journal-title":"Cancer Cell"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1002\/(SICI)1097-0258(19970715)16:13<1529::AID-SIM565>3.0.CO;2-H","article-title":"Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices","volume":"16","author":"Obuchowski","year":"1997","journal-title":"Stat. Med."},{"key":"ref_35","first-page":"211","article-title":"Proposed minimum reporting standards for chemical analysis","volume":"3","author":"Sumner","year":"2007","journal-title":"Metabolomics Off. J. Metabolomic Soc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/12\/5451\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T03:14:25Z","timestamp":1782270865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/12\/5451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["ijms27125451"],"URL":"https:\/\/doi.org\/10.3390\/ijms27125451","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]}}}