Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data

Binhua Tang, Fei Gu, Victor X. Jin

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The primary goal of modeling gene regulatory networks in human cancer is to reveal pathways governing the cancer cellular to specific phenotypes. Knowledge of cancer-specific gene regulatory networks could potentially aid to design effective intervention strategies such as introduction of a factor or drug for altering the network to avoid undesirable cancerous cellular states. This chapter presents two computational approaches to infer the underlying regulatory architecture by integrating different high-throughput experiment data in human cancer. Our gene regulatory network analysis strongly suggested that a rewired estrogen receptor α (ERα) regulated network in breast cancer cells and a rewired SMAD4 regulated network in ovarian cancer cells.

Original languageEnglish (US)
Title of host publicationStatistical Diagnostics for Cancer
Subtitle of host publicationAnalyzing High-Dimensional Data
PublisherWiley-VCH
Pages153-171
Number of pages19
Volume3
ISBN (Print)9783527332625
DOIs
StatePublished - Apr 8 2013
Externally publishedYes

Keywords

  • Estrogen receptor α (ERα)
  • Estrogen response element (ERE)
  • Gene regulatory networks
  • Immortalized ovarian surface epithelial cell (IOSE)
  • Position weight matrices (PWM)
  • Transcription factors (TF)

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Fingerprint Dive into the research topics of 'Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data'. Together they form a unique fingerprint.

  • Cite this

    Tang, B., Gu, F., & Jin, V. X. (2013). Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data. In Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data (Vol. 3, pp. 153-171). Wiley-VCH. https://doi.org/10.1002/9783527665471.ch9