A human functional protein interaction network and its application to cancer data analysis

Wu, G. M., Feng, X., Stein, L. D. (2010) A human functional protein interaction network and its application to cancer data analysis. Genome Biology, 11 (5). ISSN 1474-760X

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URL: https://www.ncbi.nlm.nih.gov/pubmed/20482850
DOI: 10.1186/gb-2010-11-5-r53

Abstract

Background: One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results: We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions: We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases.

Item Type: Paper
Uncontrolled Keywords: GROWTH-FACTOR-BETA BREAST-CANCER COLORECTAL CANCERS TRANSCRIPTIONAL ACTIVATION BIOLOGICAL NETWORKS TUMOR-SUPPRESSOR GENE-EXPRESSION PROTEOMIC DATA SMALL-WORLD DATA SETS
Subjects: diseases & disorders > cancer
bioinformatics > genomics and proteomics > analysis and processing > NETBAG
diseases & disorders > cancer > cancer types > breast cancer
bioinformatics > genomics and proteomics > annotation > dataset annotation
organism description > animal > mammal > primates > hominids > human
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification > protein types > enzymes > kinase > tyrosine kinase
CSHL Authors:
Communities: CSHL labs > Stein lab
Depositing User: CSHL Librarian
Date Deposited: 19 Oct 2011 18:51
Last Modified: 13 Mar 2018 16:04
PMCID: PMC2898064
Related URLs:
URI: http://repository.cshl.edu/id/eprint/15588

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