The clinical adenoma - carcinoma progression represents a well-established framework for understanding colorectal cancer (CRC) development, although the molecular mechanisms underlying this transition remain only partially understood. Increasing evidence suggests the gut microbiome (GM) as a key modulator of colorectal carcinogenesis, positioning microbial profiling as a promising avenue for noninvasive risk stratification and early detection. In this study, Machine Learning (ML) classifiers integrated with eXplainable Artificial Intelligence (XAI) techniques were employed to identify microbiome-derived biomarkers predictive of CRC and adenomatous lesions. The models were trained on 16S rRNA sequencing data from 453 patients and evaluated through cross-validation, achieving AU-ROC and AU-PRC scores of 0.71 and 0.67, respectively. External validation on an independent Italian cohort (n=43) yielded AU-ROC and AU-PRC scores of 0.70 and 0.89, respectively. XAI-based interpretation revealed consistent microbial signatures across datasets. In detail, taxa belonging to the Fusobacterium and Peptostreptococcus genera were associated with increased CRC risk, whereas the Eubacterium eligens group was identified as a robust negative predictor. Beyond classification, patient-level explanations enabled by XAI facilitated the identification of adenoma subgroups exhibiting microbiome profiles converging toward those of CRC, suggesting the presence of transitional microbial states. Moreover, SHAP-based interaction networks uncovered microbial hubs and inter-species dependencies characterizing high-risk configurations, providing insights into the ecological dynamics of colorectal tumorigenesis. These findings demonstrate the added XAI value in elucidating microbiome interactions, enhancing model interpretability, and supporting biologically informed hypotheses. This integrative, explainable framework highlights the potential of AI-driven microbiome analysis in precision oncology and advances the development of interpretable, noninvasive tools for CRC risk prediction and management.
Novielli et al. (Mon,) studied this question.