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      Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

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          Abstract

          Background

          Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.

          Results

          We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data.

          A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer.

          Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools.

          Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.

          Conclusions

          Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-017-4423-x) contains supplementary material, which is available to authorized users.

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          Most cited references42

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
          Article 0 comments Cited 15302 times – based on 0 reviews     Review now
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            Cancer genes and the pathways they control.

            Bert Vogelstein, Kenneth W. Kinzler (2004)
            The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized. The purposes of this review are to highlight examples of progress in these areas, indicate where knowledge is scarce and point out fertile grounds for future investigation.
            Article 0 comments Cited 951 times – based on 0 reviews     Review now
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              Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma.

              Rameen Beroukhim, Gad Getz, Leia Nghiemphu … (2007)
              Comprehensive knowledge of the genomic alterations that underlie cancer is a critical foundation for diagnostics, prognostics, and targeted therapeutics. Systematic efforts to analyze cancer genomes are underway, but the analysis is hampered by the lack of a statistical framework to distinguish meaningful events from random background aberrations. Here we describe a systematic method, called Genomic Identification of Significant Targets in Cancer (GISTIC), designed for analyzing chromosomal aberrations in cancer. We use it to study chromosomal aberrations in 141 gliomas and compare the results with two prior studies. Traditional methods highlight hundreds of altered regions with little concordance between studies. The new approach reveals a highly concordant picture involving approximately 35 significant events, including 16-18 broad events near chromosome-arm size and 16-21 focal events. Approximately half of these events correspond to known cancer-related genes, only some of which have been previously tied to glioma. We also show that superimposed broad and focal events may have different biological consequences. Specifically, gliomas with broad amplification of chromosome 7 have properties different from those with overlapping focalEGFR amplification: the broad events act in part through effects on MET and its ligand HGF and correlate with MET dependence in vitro. Our results support the feasibility and utility of systematic characterization of the cancer genome.
              Article 0 comments Cited 394 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Claudia Cava:
                ORCID: http://orcid.org/0000-0002-5540-4104
                claudia.cava@ibfm.cnr.it
                Gloria Bertoli: gloria.bertoli@ibfm.cnr.it
                Antonio Colaprico: antoniocolaprico@ulb.ac.be
                Catharina Olsen: colsen@ulb.ac.be
                Gianluca Bontempi: gbonte@ulb.ac.be
                Isabella Castiglioni: isabella.castiglioni@ibfm.cnr.it
                Journal
                Journal ID (nlm-ta): BMC Genomics
                Journal ID (iso-abbrev): BMC Genomics
                Title: BMC Genomics
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2164
                Publication date (Electronic): 6 January 2018
                Publication date PMC-release: 6 January 2018
                Publication date Collection: 2018
                Volume: 19
                Electronic Location Identifier: 25
                Affiliations
                [1 ]ISNI 0000 0001 1940 4177, GRID grid.5326.2, Institute of Molecular Bioimaging and Physiology, , National Research Council (IBFM-CNR), ; Via F.Cervi 93, 20090 Milan, Segrate-Milan Italy
                [2 ]Interuniversity Institute of Bioinformatics in Brussels (IB)2, 1050 Brussels, Belgium
                [3 ]ISNI 0000 0001 2348 0746, GRID grid.4989.c, Machine Learning Group (MLG), Department d’Informatique, , Universite libre de Bruxelles (ULB), ; 1050 Brussels, Belgium
                Author information
                http://orcid.org/0000-0002-5540-4104
                Article
                Publisher ID: 4423
                DOI: 10.1186/s12864-017-4423-x
                PMC ID: 5756345
                PubMed ID: 29304754
                SO-VID: ffa669a9-1bd9-4a32-bd29-86ed19578434
                Copyright © © The Author(s) 2018
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 18 May 2017
                Date accepted : 27 December 2017
                Funding
                Funded by: INTEROMICS flagship project
                Award ID: CUP Grant B91J12000190001
                Funded by: SysBioNet, Italian Roadmap Research Infrastructures 2012
                Categories
                Subject: Methodology Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

                ScienceOpen disciplines: Genetics
                Keywords: genes,pathways,multi-networks,pan-cancer
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: genes, pathways, multi-networks, pan-cancer

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