基于子宫内膜癌中可变剪接事件的全基因组分析和预后模型构建。
Whole Genome Analysis and Prognostic Model Construction Based on Alternative Splicing Events in Endometrial Cancer.
机构信息
Department of Obstetrics and Gynaecology, Shengjing Hospital Affiliated to China Medical University, Liaoning, China.
Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Liaoning, China.
出版信息
Biomed Res Int. 2019 Jul 2;2019:2686875. doi: 10.1155/2019/2686875. eCollection 2019.
OBJECTIVES
A growing body of evidence has shown that aberrant alternative splicing (AS) is closely related to the occurrence and development of cancer. However, prior studies mainly have concentrated on a few genes that exhibit aberrant AS. This study aimed to determine AS events through whole genome analysis and construct a prognostic model of endometrial cancer (EC).
METHODS
We downloaded gene expression RNAseq data from UCSC Xena, and seven types of AS events from TCGA SpliceSeq. Univariate Cox regression was employed to analyze the prognostic-related alternative splicing events (PASEs) and splicing factors; multivariate Cox regression was conducted to analyze the effect of risk score (All) and clinicopathological parameters on EC prognosis. An underlying interaction network of PASEs of EC was constructed by Cytoscape Reactome FI, GO, and KEGG pathway enrichment was performed by DAVID. ROC curves and Kaplan-Meir analysis were used to assess the diagnostic value of prognostic model. The correlation between PASEs and splicing factors was analyzed by GraphPad Prism; then a network was constructed using Cytoscape.
RESULTS
In total, 28,281 AS events in EC were identified, which consisted of 1166 PASEs. RNPS1, NEK2, and CTNNB1 were the hub genes in the network of the top 600 PASEs. The area under the curve (AUC) of risk score (All) reached 0.819. Risk score (All) together with FIGO stage, cancer status, and primary therapy outcome success was risk factors of the prognosis of EC patients. Splicing factors YBX1, HNRNPDL, and HNRNPA1 were significantly related to the overall survival (OS). The splicing network indicated that the expression of splicing factors was significantly correlated with percent-splice-in (PSI) value of PASEs.
CONCLUSION
We constructed a model for predicting the prognosis of EC patients based on PASEs using whole genome analysis of AS events and thereby provided a reliable theoretical basis for EC clinical prognosis evaluation.
目的
越来越多的证据表明，异常的选择性剪接（AS）与癌症的发生和发展密切相关。然而，先前的研究主要集中在少数表现出异常 AS 的基因上。本研究旨在通过全基因组分析确定 AS 事件，并构建子宫内膜癌（EC）的预后模型。
方法
我们从 UCSC Xena 下载基因表达 RNAseq 数据，从 TCGA SpliceSeq 下载七种类型的 AS 事件。采用单因素 Cox 回归分析与预后相关的选择性剪接事件（PASEs）和剪接因子；采用多因素 Cox 回归分析风险评分（All）和临床病理参数对 EC 预后的影响。通过 Cytoscape Reactome FI 构建 EC 的 PASE 潜在相互作用网络，通过 DAVID 进行 GO 和 KEGG 通路富集分析。ROC 曲线和 Kaplan-Meier 分析用于评估预后模型的诊断价值。通过 GraphPad Prism 分析 PASEs 与剪接因子的相关性；然后使用 Cytoscape 构建网络。
结果
共鉴定出 28281 个 EC 的 AS 事件，包括 1166 个 PASEs。RNPS1、NEK2 和 CTNNB1 是 top600PASEs 网络中的核心基因。风险评分（All）的曲线下面积（AUC）达到 0.819。风险评分（All）与 FIGO 分期、癌症状态和主要治疗效果成功一起是 EC 患者预后的危险因素。剪接因子 YBX1、HNRNPDL 和 HNRNPA1 与总生存期（OS）显著相关。剪接网络表明，剪接因子的表达与 PASEs 的百分插补率（PSI）值显著相关。
结论
我们使用 AS 事件的全基因组分析构建了一个基于 PASEs 预测 EC 患者预后的模型，为 EC 临床预后评估提供了可靠的理论依据。
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