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An integrative multi-omics network-based approach identifies key regulators for breast cancer
Computational and Structural Biotechnology Journal ( IF 6 ) Pub Date : 2020-10-08 , DOI: 10.1016/j.csbj.2020.10.001
Yi-Xiao Chen , Hao Chen , Yu Rong , Feng Jiang , Jia-Bin Chen , Yuan-Yuan Duan , Dong-Li Zhu , Tie-Lin Yang , Zhijun Dai , Shan-Shan Dong , Yan Guo

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.



中文翻译:

基于综合多组学网络的方法可确定乳腺癌的关键调控因子

尽管全基因组关联研究(GWAS)已成功识别出数千种人类复杂疾病的风险变异体,但是了解复杂疾病中涉及的相关SNP的生物学功能和分子机制仍具有挑战性。在这里,我们开发了一个名为基于集成多组学网络的方法(IMNA)的框架,旨在通过整合跨多个生物学规模的分子相互作用(包括GWAS信号,基于基因表达的特征,染色质相互作用和蛋白质)来识别监管网络中的潜在关键基因。网络拓扑之间的交互。我们将这种方法应用于乳腺癌,并确定了参与调控网络的关键基因的优先级。我们还根据乳腺癌中排名前20位的基因的基因表达偏差开发了异常基因表达评分(AGES)签名。AGES值与遗传变异,肿瘤特性和患者生存结果相关。在前20个基因中,RNASEH2A被确定为乳腺癌的新候选基因。因此,我们基于网络的集成方法提供了一种遗传驱动的框架,以揭示来自多种生物学规模的组织特异性相互作用,并揭示乳腺癌的潜在关键调控基因。该方法还可用于其他复杂疾病,例如卵巢癌,以揭示潜在的机制并帮助制定治疗靶标。

更新日期:2020-10-08
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