Identification and validation of the PARP inhibitor-related gene KANK3 for predicting prognosis and immunotherapeutic response in prostate cancer

  • 0Department of Urology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China.

|

|

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.