gms | German Medical Science

27th German Cancer Congress Berlin 2006

German Cancer Society (Frankfurt/M.)

22. - 26.03.2006, Berlin

Reverse engineering of extended regulatory networks in human gliomas

Meeting Abstract

  • corresponding author presenting/speaker Markus Bredel - Stanford University School of Medicine, Stanford, U.S.A.
  • Claudia Bredel - Stanford University School of Medicine, Stanford
  • Dejan Juric - Stanford University School of Medicine, Stanford
  • Griffith R. Harsh - Stanford University School of Medicine, Stanford
  • Hannes Vogel - Stanford University School of Medicine, Stanford
  • Lawrence D. Recht - Stanford University School of Medicine, Stanford
  • Branimir I. Sikic - Stanford University School of Medicine, Stanford

27. Deutscher Krebskongress. Berlin, 22.-26.03.2006. Düsseldorf, Köln: German Medical Science; 2006. DocPO261

The electronic version of this article is the complete one and can be found online at:

Published: March 20, 2006

© 2006 Bredel et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Introduction: A sizable body of work has demonstrated the utility of global gene expression profiling in sub-classification and outcome prognostication in human glial brain tumors. The assignment of biological impact to most often extensive numbers of implicated genes has remained an unrelenting obstacle of such large-scale analysis, even when inferential statistics has been used to allocate confidence to the discovery of regulated genes. Based on an increasing appreciation that genes do not act as individual units but collaborate in overlapping networks, the deregulation of which is a hallmark of cancer, we have here applied refined network knowledge to the in silico analysis of key functions and pathways associated with gliomagenesis.

Materials and Methods: Genome-wide gene expression profiles were generated in a set of 50 human gliomas of various histogenesis using a 43,000-element cDNA microarray platform. The profiles were analyzed by inferential statistics and glioma-linked gene expression subsets dynamically mapped into a functional network knowledge database. Expression datasets were also explored without any a priori assumptions using relevance network analysis.

Results: This integrated approach has revealed extended and refined pathway maps of gliomagenesis. Highest-significance networks were assembled around the MYC oncogene in gliomagenesis and around the integrin signaling pathway in the glioblastoma subtype, which is paradigmatic for its strong migratory and invasive behavior. Three novel MYC interacting genes (UBE2C, EMP1, and FBXW7) with cancer-related functions were identified as network constituents differentially expressed in gliomas, as was CD151 as a new component of a network that mediates glioblastoma cell invasion. Complementary unsupervised relevance network analysis demonstrated a conserved high-level self-organization of modules of interconnected genes with functions in cell cycle regulation in human gliomas.

Conclusion: Our network analysis has extended existing knowledge about the organizational pattern of gene expression in human gliomas, which may serve as target-rich environment for future therapeutic manipulation.