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Updated: Aug 14, 2026
Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
Published on: November 17, 2023
Establishment of a prognostic risk model for prostate cancer based on Gleason grading and cuprotosis related genes
Haicheng Wang1,2, Meiyi Xie2, Yuming Zhao2
1Department of Urology, Hebei Medical University, Shijiazhuang, China.
Introduction:
Prostate cancer (PCa) is common in aging males, diagnosed via the Gleason grading system. The study explores the unexamined prognostic value of cuprotosis, a distinct cell death type, alongside Gleason grades in PCa.
Methods:
We explored Cuprotosis-related genes (CRGs) in prostate cancer (PCa), using NMF on TCGA-PRAD data for patient classification and WGCNA to link genes with Gleason scores and prognosis. A risk model was crafted via LASSO Cox regression. STX3 knockdown in PC-3 cells, analyzed for effects on cell behaviors and tumor growth in mice, highlighted its potential therapeutic impact.
Results:
We identified five genes crucial for a prognostic risk model, with higher risk scores indicating worse prognosis. Survival analysis and ROC curves confirmed the model's predictive accuracy in TCGA-PRAD and GSE70769 datasets. STX3 was a key adverse prognostic factor, with its knockdown significantly reducing mRNA and protein levels, impairing PC-3 cell functions. In vivo, STX3 knockdown in PC-3 cells led to significantly smaller tumors in nude mice, underscoring its potential therapeutic value.
Conclusion:
Our prognostic model, using five genes linked to Gleason scores, effectively predicts prostate cancer outcomes, offering a novel treatment strategy angle.
