Systematic profiling of alternative splicing signature reveals prognostic predictor for ovarian cancer.
Systematic profiling of alternative splicing signature reveals prognostic predictor for ovarian cancer.
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DOI：
10.1016/j.ygyno.2017.11.028
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发表时间：
2017-11
影响因子：
4.7
通讯作者：
J. Zhu;Zuhua Chen;Lei Yong
中科院分区：
文献类型：
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作者：
J. Zhu;Zuhua Chen;Lei Yong
目的大多数基因是选择性剪接的，越来越多的证据表明，选择性剪接在癌症中是被修饰的，并且与癌症进展有关。卵巢癌选择性剪接信号的系统分析是缺乏和迫切需要的。方法我们在TCGA中描述了408例卵巢浆液性囊腺癌(OV)患者的全基因组选择性剪接事件。结果在10,582个基因的48,049个mRNA剪接事件中，我们检测到2,036个基因的2,611个选择性剪接事件，它们与OV患者的总体生存显著相关。在7种类型中，外显子跳跃事件是最有力的预后因素。在总生存2,000天时，用最显著的选择性剪接事件建立的预测预后的接收者-操作者特征曲线的曲线下面积为0.937，表明在区分患者预后方面具有强大的效率。有趣的是，剪接相关网络提示剪接因子在卵巢癌中的作用有明显的趋势。结论综上所述，我们建立了强大的OV患者预后预测因子，并发现了有趣的剪接网络，这可能是潜在的机制。
ObjectiveThe majority of genes are alternatively spliced and growing evidence suggests that alternative splicing is modified in cancer and is associated with cancer progression. Systematic analysis of alternative splicing signature in ovarian cancer is lacking and greatly needed.MethodsWe profiled genome-wide alternative splicing events in 408 ovarian serous cystadenocarcinoma (OV) patients in TCGA. Seven types of alternative splicing events were curated and prognostic analyses were performed with predictive models and splicing network built for OV patients.ResultsAmong 48,049 mRNA splicing events in 10,582 genes, we detected 2,611 alternative splicing events in 2,036 genes which were significant associated with overall survival of OV patients. Exon skip events were the most powerful prognostic factors among the seven types. The area under the curve of the receiver-operator characteristic curve for prognostic predictor, which was built with top significant alternative splicing events, was 0.937 at 2,000 days of overall survival, indicating powerful efficiency in distinguishing patient outcome. Interestingly, splicing correlation network suggested obvious trends in the role of splicing factors in OV.ConclusionsIn summary, we built powerful prognostic predictors for OV patients and uncovered interesting splicing networks which could be underlying mechanisms.