Systems analysis utilising pathway interactions identifies sonic hedgehog pathway as a primary biomarker and oncogenic target in hepatocellular carcinoma

Sol Efroni, Daoud Meerzaman, Carl F. Schaefer, Sharon Greenblum, Myung Soo-Lyu, Ying Hu, Constance Cultraro, Eran Meshorer, Kenneth Buetow

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The development and progression of cancer is associated with disruption of biological networks. Historically studies have identified sets of signature genes involved in events ultimately leading to the development of cancer. Identification of such sets does not indicate which biologic processes are oncogenic drivers and makes it difficult to identify key networks to target for interventions. Using a comprehensive, integrated computational approach, the authors identify the sonic hedgehog (SHH) pathway as the gene network that most significantly distinguishes tumour and tumour-adjacent samples in human hepatocellular carcinoma (HCC). The analysis reveals that the SHH pathway is commonly activated in the tumour samples and its activity most significantly differentiates tumour from the nontumour samples. The authors experimentally validate these in silico findings in the same biologic material using Western blot analysis. This analysis reveals that the expression levels of SHH, phosphorylated cyclin B1, and CDK7 levels are much higher in most tumour tissues as compared to normal tissue. It is also shown that siRNA-mediated silencing of SHH gene expression resulted in a significant reduction of cell proliferation in a liver cancer cell line, SNU449 indicating that SHH plays a major role in promoting cell proliferation in liver cancer. The SHH pathway is a key network underpinning HCC aetiology which may guide the development of interventions for this most common form of human liver cancer.

Original languageEnglish (US)
Pages (from-to)243-251
Number of pages9
JournalIET Systems Biology
Volume7
Issue number6
DOIs
StatePublished - Dec 1 2013

ASJC Scopus subject areas

  • Biotechnology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cell Biology

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