AI-Driven Insights into Microbial Biomarkers for Colorectal Cancer Progression

Colorectal cancer (CRC) is a leading cause of cancer-related deaths, and early detection is key to improving patient outcomes. While non-invasive stool-based tests show promise, their sensitivity remains lower than colonoscopy, highlighting the need for more effective screening methods. The gut microbiome, with its complex and dynamic composition, has emerged as a promising source of biomarkers for CRC. This study aims to develop a machine learning model to identify microbial signatures associated with the transition from adenoma to CRC, focusing on capturing subtle microbial changes that occur during this progression. The model analyzes microbiome data from individuals with adenomas and CRC to uncover patterns that may serve as early indicators of malignant transformation. To improve interpretability, explainable AI is employed to provide insight into the specific microbial factors driving the model predictions, thus enabling a deeper understanding of the microbial dynamics involved in CRC progression. This approach has the potential to identify specific microbial biomarkers for non-invasive CRC screening, facilitating earlier detection, personalized prevention strategies, and advancing the field of precision medicine.