Classification of glioma based on prognostic alternative splicing
Classification of glioma based on prognostic alternative splicing
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基于预后选择性剪接的神经胶质瘤分类
DOI：
10.1186/s12920-019-0603-7
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发表时间：
2019-11-15
影响因子：
2.7
通讯作者：
Liu, Yawei
中科院分区：
文献类型：
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作者：
Li, Yaomin;Ren, Zhonglu;Liu, Yawei
背景胶质瘤的早期分类为胶质瘤的诊断和治疗提供了巨大的优势。虽然选择性剪接（AS）在癌症，特别是在胶质瘤中的作用已被证实，但尚未对AS在胶质瘤中的作用进行全面分析。在这项研究中，我们的目的是分类胶质瘤的基础上预后AS。方法使用TCGA胶质母细胞瘤（GBM）和低级别胶质瘤（LGG）数据集，我们分析了预后剪接事件。对胶质瘤样本进行一致性聚类分析，并进行相关性分析以表征剪接因子和剪接事件的调节网络。结果我们分析了预后剪接事件，并提出了新的剪接分类跨泛胶质瘤样本（标记为pST 1 -7）和跨GBM样本（标记为ST 1 -3）。GBM和LGG之间存在明显的剪接模式，并且泛胶质瘤剪接分类的主要依据是肿瘤分级。确定了亚型特异性剪接事件;一个例子是锌指蛋白的AS，其参与胶质瘤预后。此外，剪接因子和剪接事件的相关性分析确定SNRPB和CELF 2作为枢纽剪接因子，分别上调和下调致癌AS。结论本研究对胶质瘤AS进行了全面分析，为胶质瘤异质性的研究提供了新的视角，为胶质瘤的诊断和治疗提供了新的思路。
Background Previously developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma. Although the role of alternative splicing (AS) in cancer, especially in glioma, has been validated, a comprehensive analysis of AS in glioma has not yet been conducted. In this study, we aimed at classifying glioma based on prognostic AS. Methods Using the TCGA glioblastoma (GBM) and low-grade glioma (LGG) datasets, we analyzed prognostic splicing events. Consensus clustering analysis was conducted to classified glioma samples and correlation analysis was conducted to characterize regulatory network of splicing factors and splicing events. Results We analyzed prognostic splicing events and proposed novel splicing classifications across pan-glioma samples (labeled pST1-7) and across GBM samples (labeled ST1-3). Distinct splicing profiles between GBM and LGG were observed, and the primary discriminator for the pan-glioma splicing classification was tumor grade. Subtype-specific splicing events were identified; one example is AS of zinc finger proteins, which is involved in glioma prognosis. Furthermore, correlation analysis of splicing factors and splicing events identified SNRPB and CELF2 as hub splicing factors that upregulated and downregulated oncogenic AS, respectively. Conclusion A comprehensive analysis of AS in glioma was conducted in this study, shedding new light on glioma heterogeneity and providing new insights into glioma diagnosis and treatment.