Bacteria-related signals in brain metastases: evidence boundaries, tumor-microenvironment remodeling, and...

Brain metastases (BrM) develop within a highly specialized central nervous system niche shaped by the blood-brain barrier/blood-tumor barrier, brain-resident stromal cells, myeloid populations, and distinct metabolic constraints. Emerging studies suggest that bacteria-related signals can be detected in primary and metastatic brain tumors; however, their biological meaning remains incompletely defined. In particular, low-biomass brain tissues are highly vulnerable to reagent contamination, environmental carry-over, batch effects, and bioinformatic misclassification, making it essential to distinguish molecular bacterial traces from viable intratumoral bacteria or a bona fide tumor microbiome. In this review, we propose a graded conceptual framework that separates bacterial signals/elements, intratumoral bacteria, and intratumoral microbiota/microbiome according to evidentiary strength. We summarize current evidence for the spatial and cellular localization of bacteria-related signals in BrM and discuss potential source models, including primary-tumor carry-over, hematogenous dissemination, gut microbiota-derived metabolites, oral microbial input, and bacterial extracellular vesicles. We further examine how these signals may interact with the BrM tumor microenvironment by influencing tumor-cell stress adaptation, myeloid inflammatory niches, antigen-presentation pathways, vascular-barrier remodeling, and metabolic reprogramming. Particular attention is given to the emerging gut-brain-metastasis axis and to cancer-type-specific contexts in breast cancer, lung cancer, and melanoma brain metastases. From a translational perspective, bacteria-related signals in BrM may eventually contribute to biomarker development, patient stratification, and therapeutic modulation of the microbe-host axis. Nevertheless, current evidence remains insufficient to conclude that BrM broadly harbor stable, active, and clinically actionable microbial communities. Future progress will require multi-source matched cohorts, longitudinal sampling, stringent low-biomass contamination control, absolute quantification, spatial validation, functional models, and explicit separation of microbial presence, viability, and causality. A rigorous evidence-based approach will be essential for moving this field from intriguing associations toward biologically interpretable and clinically meaningful applications.