AI and Cancer Research ── Prostate ── 2026-10-11
Prostate cancer: treatment, trials and AI
- Abstract
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.
Journal IF-equivalent: 10.3 (OpenAlex 2-year mean citedness, value as of 2026-10-10, retrieved 2026-10-11; not the official Clarivate IF)Reference: Wang L, Wang L. A machine learning–derived E2F-G2M signature for prognostic stratification and therapeutic targeting in aggressive prostate cancer. Cell Commun Signal. 2026 Oct 9 [Epub ahead of print]. doi:10.1186/s12964-026-03276-2.Checked: Abstract only - Abstract
Objective: Biochemical recurrence (BCR) after laparoscopic radical prostatectomy (LRP) significantly affects the long-term survival of prostate cancer (PCa) patients. This study aimed to develop and validate an interpretable machine learning model integrating clinicopathological characteristics and preoperative inflammatory and nutritional indices to predict recurrence-free survival (RFS) and explore potential molecular therapeutic targets.
Methods: A retrospective cohort of 320 PCa patients undergoing LRP at Beijing Chaoyang Hospital, Capital Medical University was analyzed. We integrated demographic, clinicopathological variables, and seven composite inflammatory/nutritional indices before LRP. A Mime-based machine learning survival pipeline was employed, combining Cox-based feature selection with survival algorithms. Model performance was evaluated using the concordance index (C-index), time-dependent ROC, calibration curves, and decision curve analysis (DCA). SHAP (SHapley Additive exPlanations) was used for model interpretation and deriving clinical cutoffs. Furthermore, intersecting genes of BCR, lymphocyte-to-monocyte ratio (LMR), and prognostic nutritional index (PNI) were identified via GeneCards, followed by drug sensitivity screening and molecular docking.
Results: The StepCox[both] combined with Random Survival Forest model demonstrated optimal performance, achieving a validation C-index of 0.788. SHAP analysis identified the top five predictive factors for recurrence: preoperative LMR, PNI score, postoperative Gleason score ≥ 8, positive surgical margin, and lymphovascular invasion. Model-derived cutoffs were determined as LMR = 3.9 and PNI = 48.8. Intersection analysis revealed 1211 shared targets among BCR, LMR, and PNI, with TP53 and PTEN ranking highest. Drug sensitivity analysis and molecular docking suggested potential inhibitory effects of compounds like sabutoclax and dordaviprone on these targets.
Conclusions: The proposed model may serve as a robust and interpretable risk stratification for postoperative PCa patients. The integration of clinical phenotyping with exploratory molecular docking offers a translational pathway from prognostic indices to potential therapeutic hypotheses, warranting further prospective validation.
Journal IF-equivalent: 3.5 (OpenAlex 2-year mean citedness, value as of 2026-10-10, retrieved 2026-10-11; not the official Clarivate IF)Reference: Wang H, Zhang Y, Li Z, Wu J, Cao H, Gan L, et al. Interpretable Machine Learning Prognostic Model Integrating Inflammatory-Nutritional Biomarkers for Post-Prostatectomy Biochemical Recurrence: Development, Internal Validation, and Translational Target Exploration. Curr Oncol. 2026 Oct 9;33(10):608. doi:10.3390/curroncol33100608.Checked: Abstract only