Integrative single-cell and spatial transcriptomics with explainable AI reveal lethal prognostic axis in prostate cancer.

ABSTRACT: Prostate cancer (PCa) remains clinically heterogeneous. We integrated single-cell and spatial transcriptomics with explainable machine learning to define a lethal tumor axis and establish an interpretable prognostic model. From 141,986 high-quality single cells spanning localized, hormone-sensitive, and castration-resistant PCa, we identified a malignant C4 epithelial subpopulation characterized by high chromosomal instability, androgen receptor and cell-cycle activation, and stemness potential. Spatial mapping further revealed immune-enriched yet suppressive niches, where fibroblasts and myeloid cells coexisted with exhausted lymphocytes, reflecting functional immune imbalance. We benchmarked 101 machine learning pipelines, selecting a Lasso plus PLS-Cox model that achieved strong concord

SUBMITTER: Ge Q 

PROVIDER: S-EPMC12868606 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature