Hu Wei-Lin, Zhou Xiong-Hui
College of Science, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
BMC Med Genomics. 2017 Dec 21;10(Suppl 4):63. doi: 10.1186/s12920-017-0307-9.
The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.
In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.
We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.
A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.
识别癌症患者的预后生物标志物对癌症研究至关重要。近年来，DNA甲基化已被证明与癌症预后相关。然而，很少有方法能基于DNA甲基化数据系统地识别预后标志物，尤其是考虑到DNA甲基化位点之间的相互作用。
在本文中，我们首先评估了微小RNA、信使核糖核酸和DNA甲基化数据在癌症预后中的稳定性。之后，应用一种基于排序的方法构建DNA甲基化相互作用网络。在这个网络中，选择度数最大的节点（占所有节点的10%）作为中心节点。应用Cox回归将这些中心节点选为预后特征。在这个预后特征中，每个DNA甲基化位点的DNA甲基化水平与癌症患者的预后相关。获得这些预后基因后，我们在训练组和测试组中进行生存分析，以验证这些基因的可靠性。
我们将我们的方法应用于三种癌症（卵巢癌、乳腺癌和多形性胶质母细胞瘤）。在所有这三种癌症中，与基因表达数据和微小RNA表达数据相比，从DNA甲基化数据的不同样本中选择的预后基因有更多的共同之处，这表明DNA甲基化数据在癌症预后中可能更稳定。幂律分布拟合表明DNA甲基化相互作用网络是无标度的。从这三个网络中选择的中心节点都富集了与癌症相关的通路。分别为这三种癌症获得了基因特征，生存分析表明它们可以在训练数据集和测试数据集中区分肿瘤患者的预后，其表现优于对照特征。
提出了一种计算方法来构建DNA甲基化相互作用网络，该网络可用于选择癌症的预后特征。
