Gut microbial and functional signatures in breast cancer: an integrated metagenomic and machine learning approach to non-invasive detection.

[INTRODUCTION] Breast cancer is associated with significant restructuring of the gut ecosystem. Gut microbial composition and function may influence cancer development and progression through immune modulation, metabolic regulation, and inflammation-related pathways.

[METHODS] Using shotgun metagenomic sequencing of fecal samples from 38 stage I-III breast cancer patients and 36 age- and body mass index-matched healthy controls. Machine learning models were constructed to evaluate the diagnostic potential of integrated microbial and metabolic features.

[RESULTS] Significant alterations were observed in gut microbiota composition, including depletion of beneficial taxa ( sp.) and enrichment of . Pathways involved in short-chain fatty acid and purine metabolism were reduced. The gut phageome exhibited structural changes and altered correlations with bacterial hosts. Predictive analysis revealed depletion of short-chain fatty acids (butyrate, propionate), purine intermediates (hypoxanthine, xanthine), and nicotinate in patients. A machine learning model integrating microbial and predicted metabolic features achieved an area under the curve values of 0.78 in the discovery cohort and 0.73 (recall = 0.74) in an independent validation cohort.

[DISCUSSION] Coordinated gut microbiome, phageome, and metabolome alterations characterize breast cancer, offering potential non-invasive biomarkers and mechanistic insights for disease detection and intervention.