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Published on: November 2, 2013
A Metabolic-Related Gene Signature for Predicting Biochemical Recurrence After Radical Prostatectomy: An Integrative
Wankun Wang1, Xiujuan Hong1, Xiaoqi Wang1
1School of Medicine, Zhejiang University, Hangzhou 310058, China.
International Journal of Molecular Sciences
|June 12, 2026
Summary
Researchers developed a machine learning model using four metabolic genes to predict biochemical recurrence (BCR) after prostate cancer surgery. The natural compound EGCG shows promise in suppressing cancer progression and PSA secretion.
Area of Science:
- Oncology
- Biochemistry
- Bioinformatics
Background:
- Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant clinical issue in prostate cancer (PCa).
- The role of metabolic reprogramming in PCa progression and its interaction with the tumor immune microenvironment (TIME) require further elucidation.
- Predictive biomarkers for BCR are crucial for personalized treatment strategies.
Purpose of the Study:
- To identify metabolic-related hub genes for predicting BCR after RP.
- To develop and validate a machine learning model for BCR risk stratification.
- To explore potential therapeutic strategies targeting identified biomarkers.
Main Methods:
- Weighted gene co-expression network analysis (WGCNA) and machine learning (XGBoost-Cox) were employed to identify metabolic hub genes.
- SHAP analysis was used for model interpretability, and immunohistochemistry (IHC) validated protein expression.
- In silico screening, molecular docking, molecular dynamics simulations, and in vitro assays were performed to evaluate EGCG as a therapeutic agent.
Main Results:
- Four metabolic hub genes (GDPD1, PLA2G7, PTGDS, SRD5A2) were identified, forming an accurate BCR risk prediction model (training 5-year AUC: 0.858; validation 5-year AUC: 0.745).
- These genes correlate with M2 macrophage-mediated immunosuppression and altered T-cell infiltration.
- (-)-epigallocatechin gallate (EGCG) was identified as a multi-target therapeutic candidate, showing stable binding to GDPD1, PTGDS, and SRD5A2 in vitro, suppressing PCa phenotypes and PSA secretion.
Conclusions:
- A robust and interpretable model for predicting BCR post-RP was established using metabolic gene signatures.
- The identified genes are linked to the tumor immune microenvironment, suggesting potential immunomodulatory roles.
- EGCG demonstrates therapeutic potential for delaying PCa progression by targeting key metabolic pathways.