CBNA: a control theory based method for identifying coding and non-coding cancer drivers

Simple item page
dc.contributor.authorPham, V.V.H.
dc.contributor.authorLiu, L.
dc.contributor.authorBracken, C.P.
dc.contributor.authorGoodall, G.J.
dc.contributor.authorLong, Q.
dc.contributor.authorLi, J.
dc.contributor.authorLe, T.D.
dc.contributor.editorIoshikhes, I.
dc.date.issued2019
dc.description.abstractA key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.
dc.description.statementofresponsibilityVu V. H. Pham, Lin Liu, Cameron P. Bracken, Gregory J. Goodall, Qi Long, Jiuyong Li, Thuc D. Le
dc.identifier.citationPLoS Computational Biology, 2019; 15(12):e1007538-e1007538
dc.identifier.doi10.1371/journal.pcbi.1007538
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.orcidGoodall, G.J. [0000-0003-1294-0692]
dc.identifier.urihttp://hdl.handle.net/2440/123166
dc.language.isoen
dc.publisherPLOS One
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1123042
dc.relation.granthttp://purl.org/au-research/grants/arc/DP170101306
dc.rights© 2019 Pham et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pcbi.1007538
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectBreast Neoplasms
dc.subjectTranscription Factors
dc.subjectMicroRNAs
dc.subjectRNA, Messenger
dc.subjectRNA, Neoplasm
dc.subjectRNA, Untranslated
dc.subjectGene Expression Profiling
dc.subjectComputational Biology
dc.subjectGene Expression Regulation, Neoplastic
dc.subjectMutation
dc.subjectOncogenes
dc.subjectModels, Genetic
dc.subjectDatabases, Genetic
dc.subjectFemale
dc.subjectGene Regulatory Networks
dc.subjectEpithelial-Mesenchymal Transition
dc.titleCBNA: a control theory based method for identifying coding and non-coding cancer drivers
dc.typeJournal article
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_123166.pdf
Size:
2.38 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Medicine publications