Defining aggressive prostate cancer using a 12-gene model

Tarek A. Bismar, Francesca Demichelis, Alberto Riva, Robert Kim, Sooryanarayana Varambally, Le He, Jeff Kutok, Jonathan C. Aster, Jeffery Tang, Rainer Kuefer, Matthias D. Hofer, Phillip G. Febbo, Arul M. Chinnaiyan, Mark A. Rubin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


The critical clinical question in prostate cancer research is: How do we develop means of distinguishing aggressive disease from indolent disease? Using a combination of proteomic and expression array data, we identified a set of 36 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Another five prostate cancer biomarkers were included using linear discriminant analysis, we determined that the optimal model used to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, transcriptional levels of the 12 genes encoding for these proteins predicted prostate-specific coding antigen failure in 79 men following surgery for clinically localized prostate cancer (P = .0015). This study demonstrates that cross-platform models can lead to predictive models with the possible advantage of being more robust through this selection process.

Original languageEnglish (US)
Pages (from-to)59-68
Number of pages10
Issue number1
StatePublished - Jan 2006


  • Bioinformatics
  • Cancer
  • Metastasis
  • Prostate cancer
  • Proteomics

ASJC Scopus subject areas

  • Cancer Research


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