Qintao Ge, Zhenda Wang, Yangyun Wang, Yangyun Wang, Tonghui Chu, Zhongyuan Wang, Zhongyuan Wang, Wenkai Zhu, Youzhao Zhang, Yonghao Chen, Dingwei Ye, Wenhao Xu, Zhong Wang, Zhong Wang, Mierxiati Abudurexiti
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 concordance across independent cohorts. The C4-based risk score independently predicted recurrence-free survival after adjustment for age, Gleason score and T stage, and a nomogram combining this score with clinical variables showed good discrimination. SHAP interpretation highlighted MT1M, PCSK1N, and ACSL3 as major risk-driving features. PCSK1N was progressively upregulated from normal prostate to castration-resistant disease and promoted proliferation, clonogenicity, migration and enzalutamide resistance, while its inhibition sensitized organoids and xenografts to AR-targeted therapy. These findings define a C4-centered lethal tumor axis and provide an explainable, experimentally supported framework for prognostic stratification in PCa.