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  3. Elevated serum levels of GPX4, NDUFS4, PRDX5, and TXNRD2 as predictive biomarkers for castration resistance in prostate cancer patients: an exploratory study

Elevated serum levels of GPX4, NDUFS4, PRDX5, and TXNRD2 as predictive biomarkers for castration resistance in prostate cancer patients: an exploratory study

  • Br J Cancer. 2025 Feb 3. doi: 10.1038/s41416-025-02947-0.
Rong Wang # 1 2,  Shaopeng Wang # 1,  Yuanyuan Mi 3,  Tianyi Huang 4,  Jun Wang 3,  Jiang Ni 3,  Jian Wang 3,  Jian Yin 5,  Menglu Li 1 6,  Xuebin Ran 7 8,  Shuangyi Fan 7,  Qiaoyang Sun 9,  Soo Yong Tan 7,  H Phillip Koeffler 8 10,  Lingwen Ding 11 12 13,  Yong Q Chen 14 15,  Ninghan Feng 16 17 18
Affiliations

Affiliations

  • 1 Jiangnan University Medical Center, Jiangnan University, Wuxi, China.
  • 2 Wuxi School of Medicine, Jiangnan University, Wuxi, China.
  • 3 Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, China.
  • 4 School of Computing, National University of Singapore, Singapore, Singapore.
  • 5 Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology & School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, China.
  • 6 Department of Urology, Wuxi No.2 People's Hospital, Nanjing Medical University, Wuxi, China.
  • 7 Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • 8 Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
  • 9 Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, Singapore.
  • 10 Division of Hematology/Oncology, Cedars-Sinai Medical Center, UCLA School of Medicine, California, Los Angeles, CA, USA.
  • 11 Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. patdl@nus.edu.sg.
  • 12 Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore. patdl@nus.edu.sg.
  • 13 Nanomedicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. patdl@nus.edu.sg.
  • 14 Jiangnan University Medical Center, Jiangnan University, Wuxi, China. yqchen@jiangnan.edu.cn.
  • 15 Wuxi School of Medicine, Jiangnan University, Wuxi, China. yqchen@jiangnan.edu.cn.
  • 16 Jiangnan University Medical Center, Jiangnan University, Wuxi, China. n.feng@njmu.edu.cn.
  • 17 Wuxi School of Medicine, Jiangnan University, Wuxi, China. n.feng@njmu.edu.cn.
  • 18 Department of Urology, Wuxi No.2 People's Hospital, Nanjing Medical University, Wuxi, China. n.feng@njmu.edu.cn.
  • # Contributed equally.
PMID: 39900986 DOI: 10.1038/s41416-025-02947-0
Abstract

Background: Prostate Cancer (PCa) is a heterogeneous disease affecting over 14% of the male population worldwide. Although patients often respond positively to initial treatments within the first 2-3 years, many eventually develop a more lethal form of the disease known as castration-resistant PCa (CRPC). At present, no biomarkers that predict the onset of CRPC are available. This study aims to provide insights into the diagnosis and prediction of CRPC emergence.

Methods: Protein expression dynamics were analysed in drug (Androgen Receptor Inhibitor)-tolerant persister (DTP) and drug withdrawal cells using proteomics to identify potential biomarkers. These biomarkers were subsequently validated using a mouse model, 180-paired carcinoma/benign tissues, and 482 serum samples. Five machine learning algorithms were employed to build clinical prediction models, wherein the SHapley Additive exPlanation (SHAP) framework was used to interpret the best-performing model. Moreover, three regression models were developed to determine the Time from initial PCa diagnosis to CRPC development (TPC) in patients.

Results: We identified that the protein expression levels of GPX4, NDUFS4, PRDX5, and TXNRD2 were significantly upregulated in PCa patients, particularly in those with CRPC. Among the tested machine learning models, the random forest and extreme gradient boosting models performed best on tissue and serum cohorts, achieving AUCs of 0.958 and 0.988, respectively. In addition, a significant inverse correlation was observed between TPC and serum levels of these four biomarkers. This correlation was formulated in three regression models, which achieved the smallest mean absolute error of 1.903 on independent datasets for predicting CRPC emergence.

Conclusion: Our study provides new insights into the role of DTP cells in CRPC development. The quad protein panel identified in our study, along with the post hoc and intrinsically explainable prediction models, may serve as a convenient and real-time prognostic tool, addressing the current lack of clinical biomarkers for CRPC.

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