Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma
Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma
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DOI：
10.1371/journal.pone.0190001
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发表时间：
2018-05-22
期刊：
影响因子：
3.7
通讯作者：
Hardin, Johanna
中科院分区：
文献类型：
--
作者：
Pan, Yuan;Duron, Christina;Hardin, Johanna
传统的差异表达分析已成功地用于鉴定其水平在实验条件下变化的基因。这种方法的一个局限性是无法发现控制基因表达网络的中央调节器。此外，虽然用于识别网络中的中心节点的方法被广泛实施，但很少考虑生物信息学验证过程和反映分析每个步骤中的不确定性的理论误差估计。使用介数中心性测量，我们确定Etv5作为一个潜在的组织水平调节因子在小鼠神经纤维瘤病1型（Nf1）低级别脑肿瘤（视神经胶质瘤）。因此，Etv5和Etv5靶基因的表达在多个独立产生的小鼠视神经胶质瘤模型中相对于非肿瘤性（正常健康）视神经增加，以及在同源人肿瘤（毛细胞性星形细胞瘤）中相对于正常人脑增加。重要的是，Etv5和Etv5网络的差异表达不是特定细胞类型中Nf1基因功能障碍的直接结果，而是反映了肿瘤作为聚集组织的性质。此外，这种差异Etv5表达在RNA和蛋白质水平上得到独立验证。两者合计，网络分析，差异RNA表达结果和实验验证的结合使用突出了计算网络方法提供肿瘤生物学新见解的潜力。
Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated mouse optic glioma models relative to non-neoplastic (normal healthy) optic nerves, as well as in the cognate human tumors (pilocytic astrocytoma) relative to normal human brain. Importantly, differential Etv5 and Etv5 network expression was not directly the result of Nf1 gene dysfunction in specific cell types, but rather reflects a property of the tumor as an aggregate tissue. Moreover, this differential Etv5 expression was independently validated at the RNA and protein levels. Taken together, the combined use of network analysis, differential RNA expression findings, and experimental validation highlights the potential of the computational network approach to provide new insights into tumor biology.