Identification and validation of the PARP inhibitor-related gene KANK3 for predicting prognosis and immunotherapeutic response in prostate cancer
- Yan Zhao 1, Qinghua Wang 1, Xin Qin 1, Wei Jiang 1, Haopeng Li 1, Mingming Xu 1, Xilei Li 1, Hanchu Ye 1, Juan Zhou 2, Xi Chen 1, Gang Wu 1
- Yan Zhao 1, Qinghua Wang 1, Xin Qin 1
- 1Department of Urology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
- 2ICU, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
- 0Department of Urology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies PARP inhibitor-related genes (PIRGs) impacting prostate cancer (PCa) recurrence. A novel risk score model accurately predicts biochemical recurrence-free survival (BCRFS) and aids in risk stratification.
Area Of Science
- Oncology
- Genetics
- Immunology
Background
- Prostate cancer (PCa) is a common male malignancy where biochemical recurrence-free survival (BCRFS) is key for prognosis.
- PARP inhibitors show therapeutic promise for PCa, but their associated genes' impact on BCRFS is unclear.
Purpose Of The Study
- To identify PARP inhibitor-related genes (PIRGs) and assess their association with PCa prognosis and immune microenvironment.
- To develop a machine learning-based model for predicting BCRFS in PCa patients.
Main Methods
- Differentially expressed genes after olaparib treatment were identified as PIRGs.
- Consensus clustering analyzed PIRG relationships with prognosis and immune infiltration.
- Random forest models were used to build a BCRFS prediction model and a prognostic nomogram.
Main Results
- Distinct prognostic and immune microenvironment clusters were identified based on PIRGs.
- The developed random forest model achieved a high C-index for BCRFS prediction.
- A nomogram integrating risk score and clinical data accurately predicted PCa BCRFS, with high-risk patients showing poorer immunotherapy response.
Conclusions
- PARP inhibitor-related genes correlate with the immune landscape, recurrence risk, and clinical features in PCa.
- The developed risk score model enhances existing PCa risk stratification systems.
- KANK3 was identified as a potential tumor suppressor in PCa, downregulated in cancer and upregulated by olaparib.
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