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A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells

Atsushi Niida1, Seiya Imoto, Masao Nagasaki

  • 1Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aniida@ims.u-tokyo.ac.jp

Genome Informatics. International Conference on Genome Informatics
|March 19, 2010
PubMed

Insights

We developed a computational method, Expression Exploration Modules (EEM), to predict cancer transcriptional networks. This approach reveals a common cancer network involving cell-cycle and immune functions across diverse tumor types.

Area of Science:

  • Computational Biology
  • Cancer Genomics
  • Systems Biology

Background:

  • Microarray technology has identified transcriptomic variations in cancer.
  • Transcriptional programs driving these variations remain poorly understood.
  • Existing methods lack comprehensive analysis of cancer transcriptomes.

Purpose of the Study:

  • To develop and apply a computational method for predicting cancer transcriptional networks.
  • To elucidate the regulatory mechanisms underlying cancer phenotypes.
  • To identify common transcriptional programs across various cancer types.

Main Methods:

  • Extended the Expression Exploration Modules (EEM) computational method.
  • EEM identifies expression modules from gene sets based on biological knowledge (e.g., TF binding motifs).
  • Applied EEM to 122 public microarray datasets (approx. 15,000 experiments) from diverse cancer tissues.

Main Results:

  • Successfully predicted cancer transcriptional networks.
  • Identified a prevailing cancer transcriptional network present in a large fraction of transcriptomes.
  • Discovered cell-cycle and immune-related sub-networks within the prevailing network.

Conclusions:

  • The EEM approach is broadly applicable for predicting cancer transcriptional networks.
  • This study provides a comprehensive understanding of transcriptional regulation in cancer.
  • Opens new avenues for investigating cancer biology through network analysis.

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