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首页> 外文期刊>BMC Medical Genomics >Constructing cancer patient-specific and group-specific gene networks with multi-omics data
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Constructing cancer patient-specific and group-specific gene networks with multi-omics data

机译：构建具有多OMICS数据的癌症患者特异性和群体特异性基因网络

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Cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. The same treatment for patients of the same cancer type often results in different outcomes in terms of efficacy and side effects of the treatment. Thus, the molecular characterization of individual cancer patients is increasingly important to find an effective treatment. Recently a few methods have been developed to construct cancer sample-specific gene networks based on the difference in the mRNA expression levels between the cancer sample and reference samples. We constructed a patient-specific network with multi-omics data based on the difference between a reference network and a perturbed reference network by the patient. A network specific to a group of patients was obtained using the average change in correlation coefficients and node degree of patient-specific networks of the group. In this paper, we present a new method for constructing cancer patient-specific and group-specific gene networks with multi-omics data. The main differences of our method from previous ones are as follows: (1) networks are constructed with multi-omics (mRNA expression, copy number variation, DNA methylation and microRNA expression) data rather than with mRNA expression data alone, (2) background networks are constructed with both normal samples and cancer samples of the specified type to extract cancer-specific gene correlations, and (3) both patient individual-specific networks and patient group-specific networks can be constructed. The results of evaluating our method with several types of cancer show that it constructs more informative and accurate gene networks than previous methods. The results of evaluating our method with extensive data of seven cancer types show that the difference of gene correlations between the reference samples and a patient sample is a more predictive feature than mRNA expression levels and that gene networks constructed with multi-omics data show a better performance than those with single omics data in predicting cancer for most cancer types. Our approach will be useful for finding genes and gene pairs to tailor treatments to individual characteristics.
机译：癌症是一种复杂和异质的疾病，具有许多可能的遗传和环境原因。同一癌症型患者的同样治疗通常在治疗的疗效和副作用方面导致不同的结果。因此，个体癌症患者的分子表征越来越重要，以找到有效的治疗。最近，已经开发了一些方法以基于癌症样品和参考样品之间mRNA表达水平的差异来构建癌症样本特异性基因网络。我们通过患者基于参考网络和患者的扰动参考网络之间的差异构建了一种具有多OMICS数据的患者特定于患者网络。利用该组的相关系数的平均变化和集团的患者特定网络的节点度，获得特异于一组患者的网络。在本文中，我们提出了一种用多OMICS数据构建癌症患者特异性和群体特异性基因网络的新方法。我们从先前的方法的主要差异如下：（1）网络由多OMICS（mRNA表达，拷贝数变异，DNA甲基化和MicroRNA表达）构建，而不是单独使用mRNA表达数据，（2）背景网络由指定类型的正常样本和癌症样本构成，以提取癌症特异性基因相关性，并且（3）可以构建患者个人特定网络和患者组特定网络。评估我们具有若干类型癌症的方法的结果表明它构成了比以前的方法更具信息丰富和准确的基因网络。评估我们具有七种癌症类型的广泛数据的方法的结果表明，参考样品和患者样品之间的基因相关性差异是比mRNA表达水平更高的预测特征，并且由多OMICS数据构建的基因网络显示更好表现比具有单一OMIC数据的性能预测大多数癌症类型。我们的方法对于寻找基因和基因对来定制对个体特​​征的基因对。

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