Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.
Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.
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通过在个性化基因相互作用网络中引入边缘加权策略，利用获奖斯坦纳树对癌症驱动基因进行优先排序
DOI：
10.1186/s12859-022-04802-y
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发表时间：
2022-08-16
影响因子：
3
通讯作者：
Guo, Wei-Feng
中科院分区：
文献类型：
--
作者：
Zhang, Shao-Wu;Wang, Zhen-Nan;Li, Yan;Guo, Wei-Feng
癌症是一种异质性疾病，在这种疾病中，肿瘤基因相互协作，并适应和进化为个体患者不断变化的条件。发现能够为个体患者提供诊断和靶向药物的个性化肿瘤驱动基因是一项有意义的任务。然而，现有的大多数方法主要是通过考虑基因/蛋白质相互作用网络上患者特定的节点信息来对潜在的个性化癌症驱动基因进行排序。这些方法忽略了基因相互作用网络中的个性化边缘权重信息，导致了假阳性结果。在这项工作中，我们提出了一种新的算法(称为PDGPCS)，该算法基于获奖收集的Steiner树模型，通过考虑个性化的边缘权重信息来预测个性化的癌症驱动基因。PDGPCS首先将个性化的基因表达数据和已知的基因/蛋白质相互作用网络知识相结合，构建个性化加权基因相互作用网络。然后，结合基因突变数据和路径数据，利用获奖Steiner树模型量化每个突变基因对每条失调路径的影响。最后，根据突变基因在所有失调通路上的聚合影响分数，对突变基因进行排序，以确定个性化癌症驱动基因的优先顺序。在四个TCGA癌症数据集上的实验结果表明，PDGPCS具有比其他个性化驾驶员基因预测方法更好的性能。此外，我们还验证了基因交互网络的个性化边权重可以提高预测性能。PDGPCS可以更准确地识别个性化的司机基因，向个性化的用药和治疗又迈进了一步。PDGPCS的源代码可以从https://github.com/NWPU-903PR/PDGPCS.免费下载网上版载有补充材料，可在10.1186/s12859-022-04802-y查阅。
Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene’s aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS. The online version contains supplementary material available at 10.1186/s12859-022-04802-y.
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影响因子：
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作者：
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DOI：
10.1038/nrc1299
发表时间：
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期刊：
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