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A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration

机译：一种基于分子数据集成识别潜在癌症驾驶基因的新方法

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摘要

The identification of the cancer driver genes is essential for personalized therapy. The mutation frequency of most driver genes is in the middle (2-20%) or even lower range, which makes it difficult to find the driver genes with low-frequency mutations. Other forms of genomic aberrations, such as copy number variations (CNVs) and epigenetic changes, may also reflect cancer progression. In this work, a method for identifying the potential cancer driver genes (iPDG) based on molecular data integration is proposed. DNA copy number variation, somatic mutation, and gene expression data of matched cancer samples are integrated. In combination with the method of iKEEG, the "key genes" of cancer are identified, and the change in their expression levels is used for auxiliary evaluation of whether the mutated genes are potential drivers. For a mutated gene, the concept of mutational effect is defined, which takes into account the effects of copy number variation, mutation gene itself, and its neighbor genes. The method mainly includes two steps: the first step is data preprocessing. First, DNA copy number variation and somatic mutation data are integrated. Then, the integrated data are mapped to a given interaction network, and the diffusion kernel is used to form the mutation effect matrix. The second step is to obtain the key genes by using the iKGGE method, and construct the connection matrix by means of the gene expression data of the key genes and mutation impact matrix of the mutated genes. Experiments on TCGA breast cancer and Glioblastoma multiforme datasets demonstrate that iPDG is effective not only to identify the known cancer driver genes but also to discover the rare potential driver genes. When measured by functional enrichment analysis, we find that these genes are clearly associated with these two types of cancers.
机译：癌症驾驶基因的鉴定对于个性化治疗至关重要。大多数驾驶员基因的突变频率在中间（2-20％）或甚至较低的范围内，这使得难以找到具有低频突变的驾驶员基因。其他形式的基因组像差，例如拷贝数变异（CNV）和表观遗传变化也可能反映癌症进展。在这项工作中，提出了一种基于分子数据集成识别潜在癌症驾驶基因（IPDG）的方法。 DNA拷贝数变异，体细胞突变和匹配癌样品的基因表达数据是集成的。结合IKEEG的方法，鉴定了癌症的“关键基因”，并且其表达水平的变化用于辅助评估是否是突变的基因是潜在的司机。对于突变的基因，定义突变效应的概念，其考虑了拷贝数变异，突变基因本身及其邻居基因的影响。该方法主要包括两个步骤：第一步是数据预处理。首先，集成了DNA拷贝数变化和体细胞突变数据。然后，将集成数据映射到给定的交互网络，并且扩散内核用于形成突变效果矩阵。第二步是通过使用IKGGE方法获得键基因，并通过突变基因的键基因的基因表达数据构建连接基质和突变基因的突变抗冲基质。 TCGA乳腺癌和胶质母细胞瘤的实验，多形态数据集表明，IPDG不仅有效鉴定已知的癌症驾驶员基因，而且还有效地发现罕见的潜在驾驶员基因。当通过功能性富集分析测量时，我们发现这些基因明显与这两种类型的癌症有关。

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