Graph-based Identification of Cancer Signaling Pathways from Published Gene Expression Signatures Using Publime.
From: The FIRC Institute of Molecular Oncology Foundation, Milan, Italy.
Nucleic acids research
- Publish Date: 2007
- ISSN: 1362-4962
- Volume: 35
- Issue: 7
- Pages: 2343-55
- Medium: Internet
- Language: English
- Citation (JAMA): Finocchiaro Giacomo, Mancuso Francesco Mattia, Cittaro Davide, et al. Graph-based Identification of Cancer Signaling Pathways from Published Gene Expression Signatures Using Publime.. Nucleic Acids Res. 2007;35:2343-55
Abstract
Gene expression technology has become a routine application in many laboratories and has provided large amounts of gene expression signatures that have been identified in a variety of cancer types. Interpretation of gene expression signatures would profit from the availability of a procedure capable of assigning differentially regulated genes or entire gene signatures to defined cancer signaling pathways. Here we describe a graph-based approach that identifies cancer signaling pathways from published gene expression signatures. Published gene expression signatures are collected in a database (PubLiME: Published Lists of Microarray Experiments) enabled for cross-platform gene annotation. Significant co-occurrence modules composed of up to 10 genes in different gene expression signatures are identified. Significantly co-occurring genes are linked by an edge in an undirected graph. Edge-betweenness and k-clique clustering combined with graph modularity as a quality measure are used to identify communities in the resulting graph. The identified communities consist of cell cycle, apoptosis, phosphorylation cascade, extra cellular matrix, interferon and immune response regulators as well as communities of unknown function. The genes constituting different communities are characterized by common genomic features and strongly enriched cis-regulatory modules in their upstream regulatory regions that are consistent with pathway assignment of those genes.
Mesh Headings (Keywords): Animals, Computer Graphics, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Mice, Neoplasms, Oligonucleotide Array Sequence Analysis, Promoter Regions (Genetics), Sequence Analysis, DNA, Signal Transduction
Check for Full Text / PubMed Unique Identifier (PMID): 17389643
This abstract is part of PubMed, a service of the U.S. National Library of Medicine. PubMed includes more than 17 million citations from MEDLINE and other life science journals for biomedical articles. See Copyright and Disclaimers.
Linked medical terms appearing on this page are added by Healia to help readers find more information and are not part of the original PubMed document.
The data herein was last updated on July 8th, 2008 and may not reflect the most current and accurate data available from NLM.