Zhang Di, Bin Yannan
College of Information Engineering, Shaoguan University, Shaoguan, China.
Institutes of Physical Science and Information Technology, Anhui University, Hefei, China.
Front Genet. 2021 Feb 19;11:607798. doi: 10.3389/fgene.2020.607798. eCollection 2020.
Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.
从癌症中大量无功能的乘客基因中识别驱动基因仍然是一项严峻挑战。在此，提出了一种名为DriverSubNet的有效且无参数的算法，通过基于子网富集分析有效挖掘突变和基因表达信息来检测驱动基因。如对四个癌症数据集的分析所示，与现有的经典方法相比，DriverSubNet在精确率、召回率和F1分数方面能够更有效地对驱动基因进行排名并过滤掉乘客基因。在检测到的前100个基因中，该方法比其他算法多找回了约50%的已知癌症驱动基因。有趣的是，DriverSubNet能够找到这些未知的癌症驱动基因，它们可作为癌症患者潜在的治疗靶点和有用的预后生物标志物。因此，DriverSubNet可能是一种通过子网富集分析识别驱动基因的有用工具。
