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.

中文翻译：

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基于分子数据整合的潜在癌症驱动基因识别新方法。

癌症驱动基因的鉴定对于个性化治疗至关重要。大多数驱动基因的突变频率处于中间（2％至20％）甚至更低的范围，这使得很难找到具有低频突变的驱动基因。其他形式的基因组畸变，例如拷贝数变异（CNV）和表观遗传变化，也可能反映了癌症的进展。在这项工作中，提出了一种基于分子数据整合的潜在癌症驱动基因（iPDG）的识别方法。整合了匹配的癌症样本的DNA拷贝数变异，体细胞突变和基因表达数据。结合iKEEG的方法，鉴定出癌症的“关键基因”，并将其表达水平的变化用于辅助评估突变基因是否是潜在的驱动因素。对于突变基因，定义了突变效应的概念，其中考虑了拷贝数变异，突变基因本身及其邻近基因的影响。该方法主要包括两个步骤：第一步是数据预处理。首先，将DNA拷贝数变异和体细胞突变数据整合在一起。然后，将整合后的数据映射到给定的交互网络，并使用扩散核形成变异效应矩阵。第二步是通过iKGGE方法获得关键基因，并利用关键基因的基因表达数据和突变基因的突变影响矩阵构建连接矩阵。在TCGA乳腺癌和多形性胶质母细胞瘤数据集上的实验表明，iPDG不仅可以有效识别已知的癌症驱动基因，还可以发现稀有的潜在驱动基因。通过功能富集分析进行测量时，我们发现这些基因显然与这两种类型的癌症有关。