Chaudhary Mandar S, Pham Vu V H, Le Thuc D
Infinia ML, Durham, NC 27560, USA.
UniSA STEM University of South Australia, Mawson Lakes, SA 5095, Australia.
Bioinformatics. 2021 Sep 9;37(17):2521-2528. doi: 10.1093/bioinformatics/btab145.
Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance-based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process: first, a condition-specific network is built by incorporating gene expression data and gene networks; second, the community structures in the network are estimated; and third, a centrality-based metric is applied to compute node importance.
We apply NIBNA to the BRCA dataset, and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers, and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers, and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA's performance in detecting epithelial-mesenchymal transition drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes.
The source code can be accessed at https://github.com/mandarsc/NIBNA.
Supplementary data are available at Bioinformatics online.
在一组肿瘤中识别有意义的癌症驱动基因是癌症基因组学中的一项具有挑战性的任务。尽管现有研究已经识别出已知的癌症驱动基因，但其中大多数都集中于检测具有突变的编码驱动基因。人们认识到非编码驱动基因可以调节驱动突变以促进癌症生长。在这项工作中，我们提出了一种基于节点重要性的新型网络分析（NIBNA）框架来检测编码和非编码癌症驱动基因。我们假设癌症驱动基因对于癌症网络中社区结构的形成至关重要，并且将它们从网络中移除会极大地扰乱网络结构，从而严重影响网络的功能。NIBNA通过一个三步过程来检测癌症驱动基因：首先，通过整合基因表达数据和基因网络构建一个条件特异性网络；其次，估计网络中的社区结构；第三，应用基于中心性的指标来计算节点重要性。
我们将NIBNA应用于BRCA数据集，在检测编码癌症驱动基因方面它优于现有的先进方法。NIBNA还预测了265个miRNA驱动基因，并且这些驱动基因中的大多数已在文献中得到验证。此外，我们应用NIBNA来检测癌症亚型特异性驱动基因，并且一些预测的驱动基因已被验证与癌症亚型相关。最后，我们评估了NIBNA在检测上皮-间质转化驱动基因方面的性能，并且在已知基因列表中确认了8个编码驱动基因和13个miRNA驱动基因。
源代码可在https://github.com/mandarsc/NIBNA获取。
补充数据可在《生物信息学》在线获取。
