Decoding the cancer microbiome: multi-omics, AI, and translational opportunities

Abstract

Multi-omics technologies, coupled with AI technologies, have the potential to enable the systematic investigation of complex cancer microbiome biology by uncovering informative patterns and associations across complementary datasets. Here, we review existing and emerging cancer microbiome data, discuss the development, interpretation, and validation of AI models as key considerations for their integration and analysis, and provide practical suggestions for improving the reliability and biological relevance of AI-driven discoveries. We further highlight the opportunities and challenges of translating these discoveries into clinical practice, emphasizing strategies to bridge the gap between research-oriented models and clinical implementations.