Department of Computer Engineering, Inha University, Incheon, 22212, South Korea.
Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
BMC Med Genomics. 2020 Aug 27;13(Suppl 6):81. doi: 10.1186/s12920-020-00736-7.
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.
癌症是一种复杂且异质的疾病，其发生有许多可能的遗传和环境因素。相同类型的癌症患者接受相同的治疗，其疗效和治疗副作用往往存在差异。因此，对个体癌症患者进行分子特征分析对于找到有效的治疗方法变得越来越重要。最近，已经开发出一些方法来构建基于癌症样本与参考样本之间 mRNA 表达水平差异的癌症样本特异性基因网络。
我们基于患者参考网络与扰动参考网络之间的差异，利用多组学数据构建了患者特异性网络。通过对组内患者特异性网络的相关系数和节点度的平均变化，得到了一组患者特异性的网络。
本文提出了一种利用多组学数据构建癌症患者特异性和组特异性基因网络的新方法。与之前的方法相比，我们的方法主要有以下几点不同：（1）利用多组学（mRNA 表达、拷贝数变异、DNA 甲基化和 microRNA 表达）数据构建网络，而不是仅利用 mRNA 表达数据；（2）构建背景网络时同时使用指定类型的正常样本和癌症样本，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组特异性网络。我们用几种类型的癌症数据对该方法进行了评估，结果表明，与之前的方法相比，该方法构建的基因网络更具信息量和准确性。
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，参考样本与患者样本之间基因相关性的差异比 mRNA 表达水平更具预测性，而且多组学数据构建的基因网络在预测大多数癌症类型的癌症方面比单组学数据构建的基因网络具有更好的性能。我们的方法将有助于找到针对个体特征的治疗方法的相关基因和基因对。
