Fecal Extracellular Vesicle Metabolomics as a Non-Invasive Biomarker Source in Colorectal Cancer: TPOT AutoML...

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 < 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 ± 0.005, sensitivity = 0.973 ± 0.022, and MCC = 0.957 ± 0.033. Hold-out validation corroborated these findings (AUC = 0.998 ± 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-particularly furoic acid, palmitic acid, and tyramine-shows 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.