PDF(Pubmed)
Abstract：
Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of ~5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.
摘要：
激酶融合基因是人类癌症融合基因中最活跃的融合基因群。帮助选择具有临床意义的激酶，以便具有融合基因的癌症患者可以更好地诊断，我们需要一个度量来推断泛癌融合基因中激酶的评估，而不是依赖于样本频率表达的融合基因。最重要的是,多项研究使用多种类型的基因组和临床信息评估人类激酶作为药物靶标，但是在他们的研究中没有人使用激酶融合基因。对无激酶融合基因事件的激酶的评估研究可能错过了增强激酶在癌症中的功能的机制之一的作用。为了填补这个空白，在这项研究中，我们提出了一种使用网络传播方法评估基因的新方法，以推断单个激酶影响由〜5K激酶融合基因对组成的激酶融合基因网络的可能性。为了选择更好的繁殖种子，我们通过降维来选择顶级基因，例如泛癌融合基因中单个基因的六个特征的主成分或潜在层信息。我们的方法可能提供一种新的方法来评估癌症中的人类激酶。
