Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis
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...
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doaj-5b6c72cc98ca425a8180f8c1d2c8475b2020-11-24T21:07:52ZengBMCBMC Genomics1471-21642018-01-0119111610.1186/s12864-017-4423-xIntegration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysisClaudia Cava0Gloria Bertoli1Antonio Colaprico2Catharina Olsen3Gianluca Bontempi4Isabella Castiglioni5Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)Interuniversity Institute of Bioinformatics in Brussels (IB)2Interuniversity Institute of Bioinformatics in Brussels (IB)2Interuniversity Institute of Bioinformatics in Brussels (IB)2Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)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.http://link.springer.com/article/10.1186/s12864-017-4423-xGenesPathwaysMulti-networksPan-cancer |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Claudia Cava Gloria Bertoli Antonio Colaprico Catharina Olsen Gianluca Bontempi Isabella Castiglioni |
spellingShingle |
Claudia Cava Gloria Bertoli Antonio Colaprico Catharina Olsen Gianluca Bontempi Isabella Castiglioni Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis BMC Genomics Genes Pathways Multi-networks Pan-cancer |
author_facet |
Claudia Cava Gloria Bertoli Antonio Colaprico Catharina Olsen Gianluca Bontempi Isabella Castiglioni |
author_sort |
Claudia Cava |
title |
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis |
title_short |
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis |
title_full |
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis |
title_fullStr |
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis |
title_full_unstemmed |
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis |
title_sort |
integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2018-01-01 |
description |
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. |
topic |
Genes Pathways Multi-networks Pan-cancer |
url |
http://link.springer.com/article/10.1186/s12864-017-4423-x |
work_keys_str_mv |
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