Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events
Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events
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利用选择性剪接事件构建预测胆管癌患者预后的模型
DOI：
10.3892/ol.2019.10838
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发表时间：
2019-09
期刊：
影响因子：
2.9
通讯作者：
Pan Shang-Ling
中科院分区：
文献类型：
--
作者：
Wu Hua-Yu;Wei Yi;Liu Li-Min;Chen Zhong-Biao;Hu Qi-Ping;Pan Shang-Ling
胆管癌（CCA）是一种起源于胆管系统粘膜上皮细胞的恶性肿瘤。它是一种高度侵袭性的癌症，进展迅速，手术切除率低，复发率高。目前，尚未确定CCA的预后分子生物标志物。然而，CCA进展受到mRNA前体的影响，mRNA前体通过选择性剪接（AS）事件改变基因表达水平和蛋白质结构，从而产生可能用于预测CCA结果的分子指标。本研究的目的是建立一个模型来预测CCA预后的基础上AS事件。使用从癌症基因组图谱中获得的预后数据，包括从32例CCA病例的TCGASpliceSeq中获得的AS事件的剪接指数百分比，进行单变量和多变量考克斯回归分析，以评估AS事件与CCA患者的总生存率（OS）之间的相关性。使用额外的多变量考克斯回归分析来识别与预后显著相关的AS事件，其用于构建具有预后指数（PI）的预测模型。受试者工作特征（ROC）曲线用于确定PI的预测值，Pearson相关分析用于确定OS相关AS事件与剪接因子之间的关联。在9，673个CCA基因中共发现38，804例AS事件，其中单因素考克斯回归分析发现1，639例AS事件与OS相关（P<0.05）;多因素考克斯回归分析将此列表缩小到23例CCA AS事件（P<0.001）。最终构建的PI模型用于预测CCA患者的生存期; ROC曲线表明其对CCA预后具有较高的预测能力，最高曲线下面积为0.986。23个OS相关的AS事件和剪接因子之间的相关性也被注意到，因此，这些AS事件可用于改善OS的预测。总之，AS事件表现出潜在的预测CCA患者的预后，因此，AS事件在CCA的影响需要进一步检查。
Cholangiocarcinoma (CCA) is a type of malignant tumor that originates in the mucosal epithelial cells of the biliary system. It is a highly aggressive cancer that progresses rapidly, has low surgical resection rates and a high recurrence. At present, no prognostic molecular biomarker for CCA has been identified. However, CCA progression is affected by mRNA precursors that modify gene expression levels and protein structures through alternative splicing (AS) events, which create molecular indicators that may potentially be used to predict CCA outcomes. The present study aimed to construct a model to predict CCA prognosis based on AS events. Using prognostic data available from The Cancer Genome Atlas, including the percent spliced index of AS events obtained from TCGASpliceSeq in 32 CCA cases, univariate and multivariate Cox regression analyses were performed to assess the associations between AS events and the overall survival (OS) rates of patients with CCA. Additional multivariate Cox regression analyses were used to identify AS events that were significantly associated with prognosis, which were used to construct a prediction model with a prognostic index (PI). A receiver operating characteristic (ROC) curve was used to determine the predictive value of the PI, and Pearson's correlation analysis was used to determine the association between OS-related AS events and splicing factors. A total of 38,804 AS events were identified in 9,673 CCA genes, among which univariate Cox regression analysis identified 1,639 AS events associated with OS (P<0.05); multivariate Cox regression analysis narrowed this list to 23 CCA AS events (P<0.001). The final PI model was constructed to predict the survival of patients with CCA; the ROC curve demonstrated that it had a high predictive power for CCA prognosis, with a highest area under the curve of 0.986. Correlations between 23 OS-related AS events and splicing factors were also noted, and may thus, these AS events may be used to improve predictions of OS. In conclusion, AS events exhibited potential for predicting the prognosis of patients with CCA, and thus, the effects of AS events in CCA required further examination.
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