DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer
DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer
复制标题
DNA 甲基化模块基于网络的癌症预后和分子分型
DOI：
10.3390/genes10080571
复制
发表时间：
2019-08-01
期刊：
影响因子：
3.5
通讯作者：
Zhang, Hong-Yu
中科院分区：
文献类型：
--
作者：
Cui, Ze-Jia;Zhou, Xiong-Hui;Zhang, Hong-Yu
实现癌症预后和分子分型对于癌症治疗至关重要。先前的研究已经基于基因表达数据确定了一些用于癌症预后和分型的基因特征。一些研究表明，DNA甲基化与癌症的发生、进展和转移有关。此外，在癌症预后中，DNA甲基化数据比基因表达数据更稳定。因此，在这项工作中，我们专注于DNA甲基化数据。一些先前的研究表明，基因模块在癌症预后中比基因标签更可靠，并且基因模块不是孤立的。然而，很少有研究考虑到基因模块之间的串扰，这可能会使一些重要的癌症基因模块被忽视。因此，我们基于癌症患者的DNA甲基化数据构建了一个基因共甲基化网络，并检测了共甲基化网络中的基因模块。然后，通过排列测试，识别每两个模块之间的串扰，从而生成模块网络。接下来，使用K-shell方法识别癌症模块网络中的核心基因模块，并将这些核心基因模块用作研究癌症预后和分子分型的特征。我们的方法应用于三种类型的癌症（乳腺浸润癌，皮肤黑色素瘤，子宫体子宫内膜癌）。基于构建的DNA甲基化模块网络识别的核心基因模块，我们不仅可以区分癌症患者的预后，而且可以将其用于癌症的分子分型。这些结果表明，我们的方法对癌症的诊断有重要的应用价值，并可能揭示潜在的致癌机制。
Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.