Abstract
BackgroundCancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance.MethodsConsidering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN).ResultsThis method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes.ConclusionThe final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.
Highlights
Cancer as a kind of genomic alteration disease each year deprives many people’s life
In order to testify the effectiveness of our method, we applied our method and other four models: DriverNet [29], DawnRank [31] and Diffusion algorithm [30], Muffinn [28] on the breast cancer, prostate cancer and lung cancer to identify their driver genes
Compared with other methods, our method integrates the features of dysregulated expression information, variation frequency and human human functional interaction network (FIN) and considers the modularity of mutated genes and their co-expression in the same tissue
Summary
Cancer as a kind of genomic alteration disease each year deprives many people’s life. From 11 cancer types, there are only 2 to 6 mutations have been regarded as the driver mutations among 200 somatic mutations which including missense, nonsense, silent, non-coding, splice-site, nonstop mutations, frameshift insertions and deletions (indels) and inframe indels [9,10,11,12]. Those important alterations are not uniformly distributed across the genome and target to some specific genes associated with important cellular functions such as cell survival, cell fate etc. This act is essential to understand the tumor biology and designing precision therapies [4, 19]
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