A machine learning–derived E2F-G2M signature for prognostic stratification and therapeutic targeting in aggressive prostate cancer

Prostate cancer (PCa) exhibits profound clinical heterogeneity, and current prognostic tools fail to capture the molecular drivers of aggressive progression. The functional interplay between E2F-driven proliferation and G2/M checkpoint regulation forms a core axis connecting cell cycle dysregulation with dependence on DNA damage repair, yet integrated signatures capturing this crosstalk remain lacking. We developed a customized AutoML R package to systematically construct and benchmark prognostic models across multiple independent PCa cohorts. An integrated E2F-G2M signature was established and validated across five independent cohorts comprising 1,128 patients. Genomic characterization, drug sensitivity prediction, and experimental validation were performed to investigate the biological features and therapeutic implications of the E2F-G2M signature. Both E2F and G2/M pathways were consistently activated during PCa progression and independently predicted poor outcomes. The integrated E2F-G2M signature achieved a mean C‑index of 0.78 across four external PCa cohorts, outperforming single‑pathway models, conventional clinical variables, and previously reported signatures. E2F-G2M–high tumors were associated with recurrent tumor suppressor gene alterations, attenuated AR signaling, and neuroendocrine-like features. Additionally, E2F-G2M-high tumors showed sensitivity to PARP inhibitors and selected replication stress-targeting agents, including ATR, WEE1, and CHK1/2 inhibitors, while exhibiting reduced sensitivity to CDK4/6 and ATM inhibitors. Combination strategies targeting E2F-associated cell-cycle programs and G2/M checkpoint regulation exhibited context-dependent synergy related to RB1 status. The integrated E2F-G2M signature provides a robust prognostic framework that captures aggressive PCa phenotypes and identifies potential therapeutic vulnerabilities.