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Updated: Jun 17, 2026
10:13
A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells
Published on: July 3, 2013
Revealing global regulatory perturbations across human cancers
Hani Goodarzi1, Olivier Elemento, Saeed Tavazoie
1Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
Molecular Cell
|December 17, 2009
Summary
This study identifies cancer-driving pathway perturbations and regulatory elements from gene expression data. It reveals novel regulatory interactions crucial for cancer therapy and drug discovery.
Area of Science:
- Genomics
- Systems Biology
- Cancer Biology
Background:
- Identifying cancer-related pathways and regulatory networks is crucial for understanding neoplastic transformation.
- Global gene expression profiles offer a rich source of information for dissecting these complex biological systems.
Purpose of the Study:
- To develop an efficient method for extracting pathway perturbations and cis-regulatory elements from gene expression data.
- To identify novel regulatory interactions and potential therapeutic targets in human cancers.
Main Methods:
- Utilized information-theoretic analysis of gene expression levels, pathways, and genomic sequences.
- Employed de novo motif discovery to associate pathways with transcription-factor binding sites and miRNA targets.
- Conducted follow-up experiments to validate predicted in vivo regulatory interactions.
Main Results:
- Successfully extracted known cancer pathways and identified associated cis-regulatory elements from diverse human cancers.
- Associated pathways with key transcription factors (E2F, NF-Y, p53) and miRNA targets (let-7).
- Discovered that a significant number of regulatory perturbations and cis-elements fall outside previously defined cancer pathways.
Conclusions:
- The study provides a systems-level understanding of regulatory perturbations in cancer.
- The findings are essential for developing rational therapeutic interventions and discovering new drug targets.
- This approach enables efficient extraction of regulatory information from gene expression data for cancer research.
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