Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies <i>ERG</i> as the strongest independent predictor of recurrence.


As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression.

We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes.

After controlling for false discovery rates, we identified a panel of eight genes (<i>ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1</i> and <i>CACNAID</i>) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays.

Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.

  • Chen Y
  • Cullen J
  • Dobi A
  • Jamal M
  • Kagan J
  • Katta S
  • Kohaar I
  • McLeod DG
  • Petrovics G
  • Ravindranath L
  • Rosner IL
  • Sesterhenn IA
  • Song Y
  • Srinivasan A
  • Srivastava S
  • Srivastava S
  • Tan SH
  • Woodle T
  • Yan W
  • Ying K
  • Young D
PubMed ID
Appears In
Oncotarget, 2019, 10 (60)