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Updated: Jun 30, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Prognostic significance of prostate cancer originating from the transition zone
Christopher R King1, Michelle Ferrari, James D Brooks
1Department of Radiation Oncology, Division of Urologic Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA. crking@stanford.edu
Purpose:
Transition zone (TZ) cancers are reported to have better biochemical relapse-free survival (bRFS) after radical prostatectomy (RP) than cancers from the peripheral zone (PZ). To understand the influence of tumor location, we compared bRFS for TZ and PZ cancers stratified for risk using known clinical and pathological prognostic factors.
Patients And Methods:
The surgical pathology and outcomes of 494 patients were reviewed. Cancers originating from the TZ and PZ were identified from step sectioning of surgical specimens and tumor mapping. Univariate and multivariate analyses of bRFS after RP were compared.
Results:
TZ cancers were present in 89 (18%) patients. On univariate analysis, most factors predicted bRFS, although cancer location did not: 5-year bRFS was 85% for TZ vs. 77% for PZ (P = 0.12). However, on multivariate analysis, all factors except SV involvement were significant, including TZ cancer location (P = 0.04, HR = 1.88 [1.02-3.47]). Interestingly, TZ location was correlated with improved 5-year bRFS for cancers > 2 cc (81% for TZ vs. 65% for PZ, P = 0.017), for preop PSA >10 (80% for TZ vs. 59% for PZ, P = 0.027), and for PSAV > 2 (85% for TZ vs. 66% for PZ, P = 0.08). However, TZ cancers showed no difference in outcome for small volumes, low preop PSA, low PSAV, or high Gleason grade.
Conclusions:
TZ cancers that are large, with high preop PSA, low Gleason scores, and high PSAV show better outcomes than their PZ counterparts. However, high-grade cancer tumor location had no apparent influence on outcome. Tumor location could be considered in subsets for optimal prognostication.
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