A map of human cancer signaling

DOIResolve DOI: http://doi.org/10.1038/msb4100200
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Pages152164; # of pages: 13
Subjectanalysis; Biotechnology; chemistry; Databases,Factual; Dna; DNA Methylation; DNA,Neoplasm; Gene Expression Regulation,Neoplastic; Gene Regulatory Networks; Genes; genetics; genome; Human; Humans; Kaplan-Meiers Estimate; Lung; Models,Biological; mortality; Mutation; Neoplasm Proteins; Neoplasms; Oncogene Proteins; pha; physiology; physiopathology; Proportional Hazards Models; Protein; Proteins; Signal Transduction
AbstractWe conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancer-signaling architectural and functional organization. From the network, we extracted an oncogene-signaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogene-signaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancer-associated genes and biomarkers
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AffiliationNRC Biotechnology Research Institute; National Research Council Canada; NRC Genomics and Health Initiative
Peer reviewedNo
NRC number49546
NPARC number3540012
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Record identifier11ae1cca-2134-44df-854b-d5276f677cc1
Record created2009-03-01
Record modified2016-05-09
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