Abstract
BackgroundModern 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.ResultsWe 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.ConclusionsOur 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.
Highlights
Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies
Network driver genes We applied our method for each functional network considered for 307 Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways (Fig. 2)
Our method is based on a well-validated approach, based on the hypothesis that if one gene is functionally connected in the pathway with more genes than those expected, its role is functionally central in that pathway
Summary
Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. An increasing number of disease biomarkers have been identified through high-throughput data, their reproducibility and overlap are poor This poor reproducibility is possibly due to the fact that individual biomarkers are often selected without considering their metabolic role in terms of their cellular function. [1, 2] Databases such as Gene Ontology [3], Reactome [4], the Kyoto Encyclopedia of Genes and Genomes (KEGG) [5], and Biocarta [6] describe the different cellular functions (pathways) as exploited by a list of genes. Some tools, such as GeneMania [7], describe the biological relationships among the cellular components, i.e. physical interactions, genetic interactions, shared-protein functional domains, or the co-localization of molecules These connections identify those gene regulatory networks that play crucial roles in
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