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Characterization of Transcriptional Changes in ERG Rearrangement-Positive Prostate Cancer Identifies the Regulation of Metabolic Sensors Such as Neuropeptide Y

Author

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  • Petra Massoner
  • Karl G Kugler
  • Karin Unterberger
  • Ruprecht Kuner
  • Laurin A J Mueller
  • Maria Fälth
  • Georg Schäfer
  • Christof Seifarth
  • Simone Ecker
  • Irmgard Verdorfer
  • Armin Graber
  • Holger Sültmann
  • Helmut Klocker

Abstract

ERG gene rearrangements are found in about one half of all prostate cancers. Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. To identify transcriptional changes in prostate cancer, including tumors with ERG gene rearrangements, we performed a meta-analysis on published gene expression data followed by validations on mRNA and protein levels as well as first functional investigations. Eight expression studies (n = 561) on human prostate tissues were included in the meta-analysis. Transcriptional changes between prostate cancer and non-cancerous prostate, as well as ERG rearrangement-positive (ERG+) and ERG rearrangement-negative (ERG−) prostate cancer, were analyzed. Detailed results can be accessed through an online database. We validated our meta-analysis using data from our own independent microarray study (n = 57). 84% and 49% (fold-change>2 and >1.5, respectively) of all transcriptional changes between ERG+ and ERG− prostate cancer determined by meta-analysis were verified in the validation study. Selected targets were confirmed by immunohistochemistry: NPY and PLA2G7 (up-regulated in ERG+ cancers), and AZGP1 and TFF3 (down-regulated in ERG+ cancers). First functional investigations for one of the most prominent ERG rearrangement-associated genes - neuropeptide Y (NPY) - revealed increased glucose uptake in vitro indicating the potential role of NPY in regulating cellular metabolism. In summary, we found robust population-independent transcriptional changes in prostate cancer and first signs of ERG rearrangements inducing metabolic changes in cancer cells by activating major metabolic signaling molecules like NPY. Our study indicates that metabolic changes possibly contribute to the selective pressure favoring ERG rearrangements in prostate cancer.

Suggested Citation

  • Petra Massoner & Karl G Kugler & Karin Unterberger & Ruprecht Kuner & Laurin A J Mueller & Maria Fälth & Georg Schäfer & Christof Seifarth & Simone Ecker & Irmgard Verdorfer & Armin Graber & Holger Sü, 2013. "Characterization of Transcriptional Changes in ERG Rearrangement-Positive Prostate Cancer Identifies the Regulation of Metabolic Sensors Such as Neuropeptide Y," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0055207
    DOI: 10.1371/journal.pone.0055207
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    1. Scott A. Tomlins & Bharathi Laxman & Saravana M. Dhanasekaran & Beth E. Helgeson & Xuhong Cao & David S. Morris & Anjana Menon & Xiaojun Jing & Qi Cao & Bo Han & Jindan Yu & Lei Wang & James E. Montie, 2007. "Distinct classes of chromosomal rearrangements create oncogenic ETS gene fusions in prostate cancer," Nature, Nature, vol. 448(7153), pages 595-599, August.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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