IEEE/ACM Trans Comput Biol Bioinform. 2023 Jan-Feb;20(1):266-276. doi: 10.1109/TCBB.2022.3145796. Epub 2023 Feb 3.
Typically patient-specific gene networks are constructed with gene expression data only. Such networks cannot distinguish direct gene interactions from indirect interactions via others such as the effect of epigenetic events to gene activity. There is an increasing evidence of inter-individual variations not only in gene expression but also in epigenetic events such as DNA methylation. In this paper we propose a new method for constructing a cancer patient-specific gene correlation network using both gene expression and DNA methylation data. We derive a patient-specific network from differential second-order partial correlations of gene expression and DNA methylation between normal samples and the patient sample. The network represents direct interactions between genes by controlling the effect of DNA methylation. Using this method, we constructed 4,000 patient-specific networks for 10 types of cancer. The networks are highly effective in classifying different types of cancer and in deriving potential prognostic gene pairs. In particular, potential prognostic gene pairs derived from the networks were powerful in predicting the survival time of cancer patients. This approach will help identify patient-specific gene correlations and predict prognosis of cancer patients.
通常，患者特异性基因网络是仅使用基因表达数据构建的。这样的网络无法区分直接基因相互作用与间接相互作用，例如通过表观遗传事件对基因活性的影响。越来越多的证据表明，不仅在基因表达方面，而且在表观遗传事件（如 DNA 甲基化）方面，个体之间存在差异。在本文中，我们提出了一种使用基因表达和 DNA 甲基化数据构建癌症患者特异性基因相关网络的新方法。我们从正常样本和患者样本之间的基因表达和 DNA 甲基化的差异二阶部分相关中推导出患者特异性网络。该网络通过控制 DNA 甲基化的影响来表示基因之间的直接相互作用。使用这种方法，我们为 10 种癌症构建了 4000 个患者特异性网络。这些网络在对不同类型的癌症进行分类和提取潜在预后基因对方面非常有效。特别是，从网络中提取的潜在预后基因对在预测癌症患者的生存时间方面非常有效。这种方法将有助于识别患者特异性基因相关性并预测癌症患者的预后。
