Abstract
BackgroundCancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.ResultsConsidering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types.ConclusionsThe final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment.AvailabilityThe source code can be obtained at https://github.com/weiba/Subdyquency.
Highlights
Cancer as a worldwide problem is driven by genomic alterations
Datasets and resources In this research, we mainly focused on the somatic mutation and transcriptional expression data for three cancer types: lung adenocarcinoma, prostate adenocarcinoma, breast invasive carcinoma
To evaluate the performance of our method, we compared our method with six existing methods, DriverNet [16], Shi’s Diffusion algorithm [17], Muffinne-max [34], Muffinne-sum, Intdriver [21] and Dawn-Rank [18]
Summary
Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and throw a big challenge to those investigators. With the development of sequence technology, several large-scale cancer projects have generated a huge amount of cancer genomic data, such as The Cancer Genome Atlas (TCGA) [1], International Cancer Genome Consortium (ICGC) [2]. The successful of those projects help us to investigate the cancer generation and development from the gene level and provide a good opportunity and data support to the target therapies and diagnostics. Many computational methods have been proposed to identify driver genes based on cancer genomics data [4, 5]
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