Insights into adenoma-CRC sequence: microbiome biomarker identification through

The adenoma-carcinoma sequence serves as a foundational framework for understanding colorectal cancer (CRC), a disease with sig- nificant global morbidity and mortality. The intricate molecular mech- anisms driving the adenoma-carcinoma sequence in CRC are not fully un- derstood, presenting a significant global health challenge. While colonoscopy stands as the gold standard for CRC diagnosis, its invasiveness and pa- tient discomfort necessitate exploration of non-invasive approaches for early detection and risk assessment. Recent investigations highlight the influential role of the gut microbiota in colorectal carcinogenesis, of- fering potential microbial biomarkers for disease progression. Machine Learning (ML) techniques, coupled with eXplainable Artificial Intelli- gence (XAI) methodologies, are increasingly applied to unravel complex microbiome relationships and identify biomarkers associated with the adenoma-carcinoma transition. This integration of computational ap- proaches holds promise in advancing CRC pathogenesis understanding, enhancing diagnostic accuracy, and improving prognostic capabilities.