Gut Microbiota and Extraintestinal Cancers: Mechanistic Insights and Microbiome-Targeted Interventions.

Abstract

The gut microbiota is a dynamic community of bacteria, viruses, fungi, and archaea that plays a pivotal role in regulating host immunity, metabolism, and systemic homeostasis. Dysbiosis, characterized by an imbalance in the microbial composition, is being increasingly recognized as a contributor not only to gastrointestinal cancers but also to extraintestinal malignancies. Mechanistic studies highlight the gut-microbiota-cancer axis, where microbial metabolites such as bile acids, short-chain fatty acids (SCFAs), and tryptophan derivatives influence genetic, epigenetic, and immune pathways, influencing carcinogenesis. Germ-free models demonstrate that commensal signals are essential for CD4⁺ and CD8⁺ T-cell differentiation, IgA production, and anti-tumor immunity. Dysbiosis-induced immune dysregulation is believed to impair immune checkpoint inhibitor (ICI) efficacy, while specific taxa such as Bifidobacterium and Akkermansia have been shown to enhance therapeutic responses. Emerging evidence links gut microbiota to breast cancer via estrogen metabolism "estrobolome" to lung cancer through the gut-lung axis and modulation of ICI responses, to melanoma by shaping systemic T-cell function and immunotherapy outcomes, and to prostate cancer through androgen receptor signaling and microbial metabolite interactions. These findings underscore the systemic oncogenic and tumor-suppressive potential of microbial communities. Microbiome-targeted interventions, including fecal microbiota transplantation (FMT), defined live biotherapeutics, probiotics, prebiotics, dietary modulation, and postbiotic delivery, are being actively investigated to optimize cancer treatment. While early trials have demonstrated feasibility, variability between individuals and methodological challenges remain significant hurdles. Hence, understanding how gut microbes influence extraintestinal cancers could revolutionize diagnostics, risk prediction, and treatment strategies.