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pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers
journal contribution
posted on 2021-09-15, 00:00 authored by Vu V H Pham, Lin Liu, Cameron P Bracken, Thin NguyenThin Nguyen, Gregory J Goodall, Jiuyong Li, T D LeAbstract
Motivation
Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.
Results
We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.
Availability and implementation
pDriver is available at https://github.com/pvvhoang/pDriver.
Supplementary information
Supplementary data are available at Bioinformatics online.
Motivation
Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.
Results
We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.
Availability and implementation
pDriver is available at https://github.com/pvvhoang/pDriver.
Supplementary information
Supplementary data are available at Bioinformatics online.
History
Journal
BIOINFORMATICSVolume
37Issue
19Pagination
3285 - 3292Publisher
OXFORD UNIV PRESSLocation
EnglandPublisher DOI
ISSN
1367-4803eISSN
1460-2059Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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No categories selectedKeywords
Science & TechnologyLife Sciences & BiomedicineTechnologyPhysical SciencesBiochemical Research MethodsBiotechnology & Applied MicrobiologyComputer Science, Interdisciplinary ApplicationsMathematical & Computational BiologyStatistics & ProbabilityBiochemistry & Molecular BiologyComputer ScienceMathematicsCELL-PROLIFERATIONMUTATIONSHETEROGENEITYEXPRESSIONBIOMARKERSRESOURCESEARCHATLASRATIO
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