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.

中文翻译：

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利用多组学数据构建癌症患者特异性和群体特异性基因网络。

癌症是一种复杂的异质性疾病，可能有许多遗传和环境原因。对于相同癌症类型的患者，相同的治疗方法通常会在疗效和副作用方面带来不同的结果。因此，个体癌症患者的分子表征对于寻找有效的治疗方法越来越重要。最近，基于癌症样品和参考样品之间的mRNA表达水平的差异，已经开发了一些方法来构建癌症样品特异性基因网络。我们基于参考网络和患者干扰的参考网络之间的差异，使用多组学数据构建了针对患者的网络。使用该组患者专用网络的相关系数和节点度的平均变化，获得了一组患者专用的网络。在本文中，我们提出了一种利用多组学数据构建癌症患者特异性基因组和群体特异性基因网络的新方法。我们的方法与以前的方法的主要区别如下：（1）网络是用多组学（mRNA表达，拷贝数变异，DNA甲基化和microRNA表达）数据而不是仅用mRNA表达数据构建的；（2）背景利用正常样本和指定类型的癌症样本构建网络，以提取癌症特异性基因相关性；（3）可以构建患者个体特异性网络和患者群体特异性网络。对几种类型的癌症进行评估的结果表明，与以前的方法相比，该方法可构建更多信息和准确的基因网络。用七种癌症类型的大量数据对我们的方法进行评估的结果表明，与mRNA表达水平相比，参考样品和患者样品之间的基因相关性差异具有更可预测的特征，并且由多组学数据构建的基因网络显示出更好的预测性。在大多数癌症类型的癌症预测中，其性能优于单一组学数据。我们的方法将有助于发现基因和基因对，以根据个体特征调整治疗方案。用七种癌症类型的大量数据对我们的方法进行评估的结果表明，与mRNA表达水平相比，参考样品和患者样品之间的基因相关性差异具有更可预测的特征，并且由多组学数据构建的基因网络显示出更好的预测性。在大多数癌症类型的癌症预测中，其性能优于单一组学数据。我们的方法将有助于发现基因和基因对，以根据个体特征调整治疗方案。用七种癌症类型的大量数据对我们的方法进行评估的结果表明，与mRNA表达水平相比，参考样品和患者样品之间的基因相关性差异具有更可预测的特征，并且由多组学数据构建的基因网络显示出更好的预测性。在大多数癌症类型的癌症预测中，其性能优于单一组学数据。我们的方法将有助于发现基因和基因对，以根据个体特征调整治疗方案。