Oral microbiota and biliary tract cancers: unveiling hidden mechanistic links - Zhang, Yuhan; Zhang, Shu (Frontiers in Oncology, 2025)
Biliary tract cancers (BTCs), a group of rare aggressive malignancies, posed significant clinical challenges due to late diagnosis and limited therapies. While gut microbiota had been extensively studied in gastrointestinal cancers, the role of oral microbiota—a primary microbial reservoir entering the digestive system—remained poorly understood. Emerging evidence indicated that oral bacteria might affect biliary carcinogenesis through direct colonization, immune modulation, and metabolic interactions via the oral-gut-liver axis. This narrative review analyzed current research connecting oral microbial imbalance with BTCs. It explored how bacterial translocation, inflammatory metabolites, and immune alterations could promote cancer development. Established BTC risk factors—including gallstones, primary sclerosing cholangitis, cirrhosis, and H. pylori infection—were evaluated for their associations with oral microbiota changes. Epidemiological studies revealed that periodontal disease and poor oral hygiene elevated BTC risk. Sequencing analyses identified oral-origin bacteria (Prevotella, Fusobacterium, Streptococcus) in bile and tumor tissues, suggesting microbial migration through swallowing or bloodstream. Mechanistic investigations showed microbial components (e.g., lipopolysaccharides, membrane vesicles) activated inflammatory pathways (TLR4/NF-κB, STAT3) and modified immune checkpoints, while metabolites potentially altered biliary cell metabolism. Different studies have found variable changes in oral microbiota in the presence of BTCs, thus a novel “biphasic dysbiosis” hypothesis was proposed to explain differing oral microbial diversity patterns across BTC subtypes. Despite progress, critical knowledge gaps persisted regarding causality, spatial microbial variations, and functional impacts of metabolites in BTCs. Future research was recommended to employ multi-omics approaches, single-cell analysis, and AI tools to enhance early detection and prevention strategies.