多网络中基因社区的检测揭示了癌症驱动因素
Detection of gene communities in multi-networks reveals cancer drivers
摘要：
我们提出了一种新的基于多网络的策略来整合不同层次的基因组信息，并以协调的方式使用它们来识别驱动癌症的基因。我们考虑的多网络结合了转录因子共靶向、microRNA共靶向、蛋白质相互作用和基因共表达网络。这种选择背后的基本原理是基因共表达和蛋白质相互作用需要紧密的配偶体共调节，并且只有结合转录和转录后调节层才能获得这种微调调节。为了从多网络中提取相关的生物信息，我们研究了它的社区划分。为此，我们采用了一种共识聚类算法的基础上，最先进的社区检测方法。即使我们的程序原则上对本工作中的任何病理学都有效，我们也集中在胃癌、肺癌、胰腺癌和结直肠癌上，并从多网络社区的富集分析中确定了一组候选驱动癌症基因。其中一些是已知的癌基因，而一些是新的。不同信息层的组合使我们能够从多网络指示中提取已知和新候选驱动基因的调控模式和功能作用。
英文摘要：
We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.
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