Xu Xin-Jian, Gao Hong-Xiang, Zhu Liu-Cun, Zhu Rui
Department of Mathematics, Shanghai University, Shanghai 200444, China.
School of Life Sciences, Shanghai University, Shanghai 200444, China.
Life (Basel). 2022 Dec 27;13(1):76. doi: 10.3390/life13010076.
Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.
网络理论因其在识别肿瘤相关基因方面的高效性而备受生物界关注。然而，大多数研究人员专注于单一组学的单一网络，其预测能力较弱。随着多组学数据的可得性，多层网络现在可用于分子研究。在本研究中，我们通过构建DNA甲基化位点和RNA的双层网络实现了这一点。我们将该网络模型应用于五种类型的肿瘤数据，以识别与肿瘤相关的关键基因。与单一网络相比，所提出的双层网络产生了更多与肿瘤相关的DNA甲基化位点和基因，我们通过预后和KEGG富集分析对其进行了验证。
