This study seeks to unravel the complex pathogenesis of digestive system disorders by investigating the dynamic interplay between genetic predisposition, metabolic dysregulation, gut microbiome alterations, and immune dysfunction. Using an integrated multi-omics approach, we will systematically characterize genomic variants (APC, KRAS, HLA loci), metabolomic profiles (bile acids, short-chain fatty acids), and gut microbiome composition to identify dysregulated pathways in digestive cancers and inflammatory conditions. We will particularly focus on how microbial metabolites like butyrate and LPS modulate immune responses and epithelial barrier function, employing both clinical samples and experimental models. The generated multi-dimensional data will then be analyzed through advanced machine learning algorithms to develop predictive models for disease subtyping, risk stratification, and treatment response. By bridging molecular signatures with clinical phenotypes, this research aims to uncover novel biomarkers and therapeutic targets, ultimately paving the way for precision medicine approaches in digestive diseases. The findings are expected to transform our understanding of disease heterogeneity while providing clinically actionable tools for early detection and personalized intervention strategies.
