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

Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, ON M5G 0A3, Canada.
Genome biology (Impact Factor: 10.81). 05/2010; 11(5):R53. DOI: 10.1186/gb-2010-11-5-r53
Source: PubMed


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.
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.
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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    • "Over twofold peptide absolute counts from LANA SIM than control samples with twice repeats were selected for analysis. Based on the proteomics band analysis and the whole genome protein–protein interaction network in humans [21] [22] [23], both core network (119 genes) and extended network (49 456 genes) were constructed for all gel slices analyzed. In the core network, two interaction partners are in the corresponding proteomics slice. "
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    ABSTRACT: The Kaposi's sarcoma-associated herpesvirus (KSHV)-encoded latent nuclear antigen LANA plays an essential role in viral episome maintenance. LANA also contributes to DNA replication and tumorigenesis during latency. Recent studies suggested that LANA was involved in regulation of SUMOylation which results in chromatin silencing. To examine the pleiotropic effects of LANA protein on host cell gene expression, we utilized MS analysis to identify cellular proteins associated with the SUMO-Interacting Motif of LANA (LANA(SIM) ). In addition to the 6 bands identified as substantially associated with LANA(SIM) , 151 proteins were positively identified by MS/MS analysis. Compared with previous proteomic analysis of the N- and C- truncated mutants of LANA (LANA(NC) ), our results revealed that a complex of specific proteins with relatively high SUMOylation and SIM motifs are associated with LANA(SIM) . Intriguingly, consistent with our previous report that identified KAP1 as a key component, the in-vitro SUMO-2 modified isoform has a substantially higher affinity with LANA(SIM) than the SUMO-1 modified isoform. Moreover, via cluster and pathway analysis, we proposed a hypothetical model for the LANA(SIM) regulatory circuit involving aberrant SUMOylation of cell cycle (particular mitotic), DNA unwinding and replication, and pre-mRNA/mRNA processing-related proteins. This study provides a SUMOylated and non-SUMOylated proteome profile of LANA(SIM) -associated complex, and facilitates our understanding that viral-mediated gene regulation through SUMOylation is important for KSHV persistence and pathogenesis. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Proteomics 04/2015; 15(12). DOI:10.1002/pmic.201400624 · 3.81 Impact Factor
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    • "In order to construct a more informative network, we added also two more functional gene networks taken from the literature and previously published in [26] [27]. "
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    ABSTRACT: The Human Phenotype Ontology (HPO) provides a conceptualization of phenotype information and a tool for the computational analysis of human diseases. It covers a wide range of phenotypic abnormalities encountered in human diseases and its terms (classes) are structured according to a directed acyclic graph. In this context the prediction of the phenotypic abnormalities associated to human genes is a key tool to stratify patients into disease subclasses that share a common biological or pathophisiological basis. Methods are being developed to predict the HPO terms that are associated for a given disease or disease gene, but most such methods adopt a simple ”flat” approach, that is they do not take into account the hierarchical relationships of the HPO, thus loosing important a priori information about HPO terms. In this contribution we propose a novel Hierarchical Top-Down (HTD) algorithm that associates a specific learner to each HPO term and then corrects the predictions according to the hierarchical structure of the underlying DAG. Genome-wide experimental results relative to a complex HPO DAG including more than 4000 HPO terms show that the proposed hierarchical-aware approach significantly improves predictions obtained with flat methods, especially in terms of precision/recall results.
    Third International Work-Conference on Bioinformatics and Biomedical Engineering - IWBBIO 2015, Granada, Spain; 04/2015
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    • "ReactomeFIViz implements multiple features for users to perform network-based data analysis, including FI sub-network construction 3, network module discovery 3, functional annotation 3, HotNet mutation analysis 5, 6, and network module-based gene signature discovery from microarray data sets 7. The HotNet algorithm 5, 6 was implemented by porting python and MatLab code of HotNet _v1.0.0 (downloaded from to Java and R. For details about other algorithms and their implementations, please refer to our previous work 3, 7. "
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    ABSTRACT: High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.
    F1000 Research 09/2014; 3:146. DOI:10.12688/f1000research.4431.2
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