Deciphering post-surgery gut microbial dynamics in colorectal cancer through multi- cohort machine learning

Surgical resection remains the primary treatment for colorectal cancer (CRC), yet its influence on the postoperative gut microbiota remains incompletely understood. In this study, we analyzed the gut microbial communities before and after surgery from our study cohort and integrated findings from four independent CRC datasets to enhance robustness. Our results revealed that post-surgery samples had a reduced microbial diversity but were enriched with commensal taxa, suggesting a potential re-establishment of beneficial microbiota following tumor removal. Leveraging machine learning and Explainable Artificial Intelligence (XAI) through SHapley Additive exPlanations (SHAP), we identified potential postoperative microbial biomarkers, notably Akkermansia , among the dominant commensal bacteria enriched in post-surgery. Collectively, these findings highlight suggest that surgical resection may promote a favorable shift in gut microbial composition and this could guide targeted microbial modulation to improve postoperative recovery. Our study lays the groundwork for microbiota-informed strategies aimed at improving clinical outcomes in CRC patients after surgery.