Wei Ting, Fa Botao, Luo Chengwen, Johnston Luke, Zhang Yue, Yu Zhangsheng
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Front Genet. 2021 Jan 8;11:613033. doi: 10.3389/fgene.2020.613033. eCollection 2020.
Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.
识别个性化驱动基因对于发现关键生物标志物和开发有效的癌症个性化治疗方法至关重要。然而，很少有方法考虑不同类型突变的权重，并且难以在大量乘客基因中有效区分驱动基因。我们提出了MinNetRank（基于网络排名的最小值法），这是一种对癌症基因进行优先级排序的新方法，该方法为不同类型的突变设置权重，同时考虑相互作用网络的入度和出度，并使用最小化策略整合多组学数据。MinNetRank在每个样本的多组学数据中对癌症基因进行优先级排序。然后将基因的样本特异性排名整合到群体水平的排名中。在评估对驱动基因进行优先级排序的准确性和稳健性时，在六个癌症数据集上，我们的方法在精度、F1分数和曲线下部分面积（AUC）方面几乎总是显著优于其他方法。重要的是，MinNetRank在发现新的驱动基因方面效率很高。只有我们的方法将SP1选为候选驱动基因（排名前三），并且在肝细胞癌中，肿瘤样本和正常样本之间的SP1 RNA和蛋白质差异表达具有统计学意义。在前五种癌症类型中，排名前七的基因将患者分为两个亚型，其生存差异具有统计学意义。如先前研究人员所示，这排名前七的基因与总生存期相关。MinNetRank对于识别癌症驱动基因可能非常有用，并且这些具有生物学相关性的标记基因与临床结果相关。MinNetRank的R包可在https://github.com/weitinging/MinNetRank获取。
