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首页> 外文期刊>BMC Medical Genomics >Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network
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Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network

机译：识别涉及基因失调表达，组织特异性表达和基因基因网络的司机基因

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Cancer 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. Considering 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). This 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. The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.
机译：癌症作为一种基因组改变疾病，每年剥夺了许多人的生命。克服癌症的最大挑战是鉴定促进促进癌症发展的司机基因，从大量的乘客突变促进癌症的大量乘客突变对癌症的选择性生长优势没有影响。为了解决这些问题，一些研究人员已经开始通过与其他生物信息集成网络来专注于识别驾驶员基因。但是，应该需要更多的努力来改善预测性能。考虑到驾驶员基因对其下游基因的表达影响的事实，它们可能彼此相互作用以形成功能模块，并且这些模块应倾向于在同一组织中类似地表达。我们提出了一种由DytiDriver命名的新型模型，以识别驾驶基因，通过涉及基因对所述的基因对所述的基因表达，组织特异性表达和变异频率进入人类功能相互作用网络（例如人鳍）。该方法适用于974例乳腺癌，316例前列腺癌和230例肺癌患者。结果表明，在FScore，精度和召回值方面，我们的方法表现出其他五种现有方法。富集和携带者分析说明Dytidriver不仅可以识别富含一些重要途径的驾驶基因，而且还具有弄清楚一些未知的驾驶基因的能力。最终结果意味着驾驶员基因是那些影响更令人讨发的基因的那些，并且在同一组织中类似地表达。

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