Abstract
A 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.
Highlights
As cancer driver genes play significant roles in cancer development and progression, identifying cancer drivers and their regulatory mechanism is critical in the design of effective cancer treatments
The miRNA-Transcription Factors (TFs)-mRNA network obtained by Controllability based Biological Network Analysis (CBNA) consists of 7,726 nodes and 128,264 directed edges
We classify the nodes in the miRNA-TF-mRNA network as critical, ordinary, and redundant based on the change of ND upon their removal
Summary
As cancer driver genes (cancer drivers for short) play significant roles in cancer development and progression, identifying cancer drivers and their regulatory mechanism is critical in the design of effective cancer treatments. There has been evidence that cancer drivers are related to gene mutations. Mutations in the genome can be single-nucleotide variants (SNVs), insertions and deletions (indels), copy number aberrations (CNAs), or structural variants (SVs) [1]. These mutations might cause normal cells to transform to tumour cells, resulting in cancer initialisation and development. Genes that bear driver mutations are considered as cancer drivers [6]. Cancer drivers can be non-coding RNAs since non-coding regions account for around ninety eight percent of the human genome [7] and non-coding RNAs are proved to be related to cancer development [8, 9]
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