Digital intelligence-enabled tumor microbiome research: from mechanistic dissection to clinical translation

Tumor-associated microbiomes are integral components of the tumor microenvironment, modulating tumorigenesis and progression through immune and metabolic pathways. Microbiome data are high-dimensional, sparse, and compositional, which limits conventional analytical approaches. Digital intelligence-an umbrella term encompassing artificial intelligence (AI), machine learning (ML), big-data analytics, and multi-omics integration-has emerged as a powerful paradigm for tumor microbiome research. In this review, we delineate the applications of digital intelligence across the full pipeline: intelligent preprocessing of sequencing data, multi-modal data integration, inference of microbe-host interactions, construction of diagnostic and prognostic models, and discovery of immunotherapy biomarkers. We explicitly distinguish computational association from experimental mechanistic validation and clinical utility, and we critically appraise methodological limitations, including overfitting, data leakage, batch effects, and the scarcity of external prospective validation. Microbiome-related biomarkers are framed as prognostic, predictive, or dynamic, and we caution against equating retrospective performance with clinical value. Finally, we discuss current challenges-data standardization, model interpretability, and ethical privacy-and future directions, including spatial and single-cell microbiomics, FMT-based interventions, and prospectively validated clinical translation.