IODNE：一种用于识别癌症精准医学中失调子网的综合优化方法。
IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer.
作者信息
Mounika Inavolu S, Renbarger J, Radovich M, Vasudevaraja V, Kinnebrew G H, Zhang S, Cheng L
机构信息
Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
出版信息
CPT Pharmacometrics Syst Pharmacol. 2017 Mar;6(3):168-176. doi: 10.1002/psp4.12167. Epub 2017 Mar 7.
Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein-protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal-A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer-related genes and the well-known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple-negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.
子网分析可以探索整个分子通路的复杂模式，以识别药物靶点。在本文中，乳腺癌患者队列的基因表达谱与蛋白质-蛋白质相互作用（PPI）网络同时使用边评分和节点评分进行整合。开发了一种新颖的优化算法——识别失调子网的整合优化方法（IODNE），以搜索合并后的基因和蛋白质网络的最佳失调子网。IODNE应用于从癌症基因组图谱（TCGA）数据中选择管腔A型乳腺癌的子网。IODNE发现了很大一部分癌症相关基因以及著名的临床靶点ER1/PR和HER2。这验证了IODNE的实用性。当将IODNE应用于三阴性乳腺癌（TNBC）亚型数据时，我们识别出了包含ERBB2、HRAS、PGR、CAD、POLE和SLC2A1等基因的子网。
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