TY - JOUR
T1 - A human functional protein interaction network and its application to cancer data analysis
AU - Wu, Guanming
AU - Feng, Xin
AU - Stein, Lincoln
N1 - Funding Information:
The authors wish to thank the whole Reactome team: Ewan Birney, Michael Caudy, David Croft, Bernard de Bono, Peter D'Eustachio, Phani Garapati, Marc Gillespie, Gopal Gopinath, Jill Hemish, Henning Hermjakob, Bijay Jassal, Alex Kanapin, Suzanna Lewis, Shahana Mahajan, Lisa Matthews, Bruce May, Esther Schmidt, and Imre Vastrik. The data and data model from the Reactome project form the basis of this work. We thank Ethan Cerami of MSKCC for discussions on network module discovery that inspired our choice of the edge-betweenness algorithm [78]. We also wish to thank Paul Boutros, Irina Kalatskaya and Shannon McWeeney for their comments on the draft manuscript. We are also indebted to Rzhetsky's group, who provided us with the text-mined PPI data set, and to Zhang's group, who provided us with the TRED database. This work was supported by a NIH grant (P41 HG003751).
PY - 2010/5/19
Y1 - 2010/5/19
N2 - 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.
AB - 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.
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U2 - 10.1186/gb-2010-11-5-r53
DO - 10.1186/gb-2010-11-5-r53
M3 - Article
C2 - 20482850
AN - SCOPUS:77952310528
SN - 1474-7596
VL - 11
JO - Genome biology
JF - Genome biology
IS - 5
M1 - R53
ER -