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Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
Published on: November 17, 2023
Prognostic Significance of PTEN Loss in Prostate Cancer: A Meta-Analysis of Gleason Grade and Clinical Outcomes
Filip Kisiel1, Dougal Ferguson1,2, Claire Hart3
1Department of Chemical Engineering, School of Engineering, University of Manchester, Oxford Road, Manchester M13 9PL, UK.
Aims:
Prostate cancer (PCa) presents ongoing challenges in differentiating aggressive from indolent disease using traditional biomarkers such as prostate-specific antigen (PSA). The Phosphatase and Tensin Homolog (PTEN), a key tumour suppressor involved in cellular growth regulation, is emerging as a promising biomarker for risk stratification. This meta-analysis aims to evaluate the prognostic significance of PTEN loss in PCa, particularly its relationship with Gleason grade groups (GG), as defined by the ISUP system, and clinical outcomes.
Methods:
A systematic review and meta-analysis of 16 studies encompassing 11,375 patients was conducted in accordance with PRISMA guidance. Studies included evaluated PTEN loss, stratified by hemizygous and homozygous deletions, and its association with GG and clinical endpoints such as biochemical recurrence and lethal progression. Pooled odds ratios (ORs) and hazard ratios (HRs) were calculated using a random-effects model.
Results:
PTEN loss was significantly associated with tumour aggressiveness. Compared to GG1 tumours, the odds of PTEN loss were markedly increased in Gleason GG 2 and 3(OR: 2.78, 95% CI: 1.95-3.61) and GG ≥ 4 (OR: 6.35, 95% CI: 5.37-7.33). Homozygous PTEN deletions were more strongly associated with high-grade tumours than hemizygous deletions. Clinically, PTEN loss was predictive of adverse outcomes, including increased risk of biochemical recurrence (HR: 1.78, 95% CI: 1.31-2.25) and lethal progression (HR: 2.57, 95% CI: 1.12-3.95).
Conclusion:
PTEN loss correlates with higher GG and poorer clinical outcomes in PCa. Incorporating PTEN assessment into clinical decision making could improve risk stratification, guiding early intervention strategies and identifying patients suitable for active surveillance.

