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A novel alternative splicing-based prediction model for uteri corpus endometrial carcinoma

机译:子宫体子宫内膜癌的新型基于剪接的新型预测模型

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摘要

Alternative splicing (AS) is crucial a mechanism by which the complexity of mammalian and viral proteom increased overwhelmingly. There lacks systematic and comprehensive analysis of the prognostic significance of AS profiling landscape for uteri corpus endometrial carcinoma (UCEC). In this study, univariate and multivariate Cox regression analyses were conducted to identify candidate survival-associated AS events curated from SpliceSeq for the construction of prognostic index (PI) models. A correlation network between splicing factor-related AS events and significant survival-associated AS events were constructed using Cytoscape 3.5. As consequence, 28281 AS events from 8137 genes were detected from 506 UCEC patients, including 2630 survival-associated AS events. Kaplan Meier survival analysis revealed that six of the seven PI models (AD, AP, AT, ME, RI and ALL) exhibited good performance in stratifying the prognosis of low risk and high risk group (P<0.05). Among the six PI models, PI-AT performed best with an area under curves (AUC) value of 0.758 from time-dependent receiver operating characteristic. Correlation network implicated potential regulatory mechanism of AS events in UCEC. PI models based on survival-associated AS events for UCEC in this study showed preferable prognosis-predicting ability and may be promising prognostic indicators for UCEC patients.Summary: This is the first study to systematically investigate the prognostic value of AS in UCEC. Findings in the presents study supported the clinical potential of AS for UCEC and shed light on the potential AS-associated molecular basis of UCEC.
机译:选择性剪接(AS)是至关重要的一种机制,通过该机制,哺乳动物和病毒蛋白质组的复杂性急剧增加。缺乏对AS轮廓分析对子宫内膜癌(UCEC)的预后意义的系统和综合分析。在这项研究中,进行了单因素和多因素Cox回归分析,以鉴定从SpliceSeq策划的与生存相关的候选AS事件,以构建预后指数(PI)模型。使用Cytoscape 3.5构建了剪接因子相关的AS事件与显着的生存相关的AS事件之间的相关网络。结果,从506例UCEC患者中检测到来自8137个基因的28281个AS事件,包括2630个与生存相关的AS事件。 Kaplan Meier生存分析显示,七个PI模型(AD,AP,AT,ME,RI和ALL)中的六个在对低风险和高风险组的预后进行分层中表现出良好的表现(P <0.05)。在六个PI模型中,PI-AT在与时间相关的接收机工作特性方面的曲线下面积(AUC)值为0.758,效果最佳。相关网络暗示了UCEC中AS事件的潜在调节机制。本研究基于UCEC生存相关AS事件的PI模型显示出较好的预后预测能力,可能是UCEC患者的有希望的预后指标。总结:这是首次系统研究AS在UCEC中的预后价值的研究。本研究的发现支持了UCEC的AS的临床潜力,并阐明了UCEC的与AS相关的潜在分子基础。

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