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

By Guanming Wu, Xin Feng, Lincoln D. Stein. Genome Biology, 2010

706 citations recorded by the source corpus.

BackgroundOne 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.ResultsWe 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.ConclusionsWe 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.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a1f2f49e54e112b2be322074dd78bba24e54bf59

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Citations in the corpus

Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “The different data types are integrated using an influence graph (Wu et al., 2010).”
Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, background): “Recent evidence for the case of human height shows that such minor contributions are common from polymorphisms falling below the significance threshold for association, but nonetheless contributing to total variation (Yang et al. 2010).”
Reactome: a database of reactions, pathways and biological processes (2010, methodology): “org/) and PID (7,21–23) with pair-wise interactions gleaned from physical protein–protein interactions in human and model organisms, gene co-expression data, protein domain–domain interactions, protein interactions generated from text mining and GO annotations (24).”
Mutual exclusivity analysis identifies oncogenic network modules. (2012, background): “…plus additional inferred interactions derived from non-curated sources of information, including high-throughput derived protein–protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations, and text-mined protein interactions (Wu et al. 2010) (see Methods).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Inclusion of additional data in the interactome and gene expression datasets, such as protein domain interactions, gene ontology annotations, cancer-related mutations, or cancer prognosis information refined predictions further (Franke et al., 2006; Pujana et al., 2007; Chang et al., 2009; Lee et al”