Interpretable Machine Learning Prognostic Model Integrating Inflammatory-Nutritional Biomarkers for Post-Prostatectomy Biochemical Recurrence: Development, Internal Validation, and Translational Target Exploration.
- 1Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
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.
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BibTeX
@article{wang2026interpretable,
title = {{Interpretable Machine Learning Prognostic Model Integrating Inflammatory-Nutritional Biomarkers for Post-Prostatectomy Biochemical Recurrence: Development, Internal Validation, and Translational Target Exploration.}},
author = {Hao Wang and Yifei Zhang and Zhen Li and Jiyue Wu and Huawei Cao and Lijian Gan and Xuemeng Qiu and Wei Wang},
journal = {Current oncology (Toronto, Ont.)},
year = {2026},
month = oct,
doi = {10.3390/curroncol33100608},
url = {https://europepmc.org/article/MED/42930175},
note = {CC BY},
}
RIS
TY - JOUR
TI - Interpretable Machine Learning Prognostic Model Integrating Inflammatory-Nutritional Biomarkers for Post-Prostatectomy Biochemical Recurrence: Development, Internal Validation, and Translational Target Exploration.
AU - Hao Wang
AU - Yifei Zhang
AU - Zhen Li
AU - Jiyue Wu
AU - Huawei Cao
AU - Lijian Gan
AU - Xuemeng Qiu
AU - Wei Wang
PY - 2026
DA - 2026/10/09
JO - Current oncology (Toronto, Ont.)
DO - 10.3390/curroncol33100608
UR - https://europepmc.org/article/MED/42930175
AB - 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.
SN - 1198-0052
SN - 1718-7729
ER -