| Yi Cheng,Long Li,Chen Gong,Kai Qin. Construction and validation of a prognostic risk model for uterine corpus endometrial carcinoma based on alternative splicing events. Oncol Transl Med, 2022, 8: 276-284. |
| 构建及验证基于可变剪切事件的子宫内膜癌预后风险模型 |
| Construction and validation of a prognostic risk model for uterine corpus endometrial carcinoma based on alternative splicing events |
| Received:August 07, 2022 Revised:November 28, 2022 |
| DOI：10.1007/s10330-022-0593-3 |
| 中文关键词: 子宫内膜癌；可变剪切事件；预后模型；TCGA数据库; SpliceSeq数据库 |
| 英文关键词: TCGA; SpliceSeq; uterine corpus endometrial carcinoma; alternative splicing event; prognostic model |
| 基金项目:湖北省自然科学基金面上项目（NO.2020CFB592） |
| Author Name | Affiliation | E-mail | | Yi Cheng | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | yi_chengtj@163.com | | Long Li | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | | | Chen Gong | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | | | Kai Qin* | Department of Oncology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | qinkaitj@126.com |
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| 中文摘要: |
| 目的：通过分析TCGA数据库中子宫内膜癌的可变剪切事件与预后的相关性，构建及验证子宫内膜癌的预后风险模型。
方法：从癌症基因组图谱(TCGA) 数据库和SpliceSep数据库下载子宫内膜癌的临床数据及患者相应的可变剪切事件。应用生物信息学筛选差异表达的可变剪切事件，通过Cox和Lasso回归分析构建基于可变剪切事件的预后风险评分模型。运用Kaplan-Meier生存分析、受试者特征曲线和独立预后分析评价模型的有效性。此外，建立潜在的可变剪切因子与可变剪切事件的调控网络。
结果：从TCGA和TCGA SpliceSeq数据库下载得到527例子宫内膜癌患者的临床资料和相应的可变剪切事件。通过单因素COX回归分析筛选18779个与子宫内膜癌的预后相关可变剪切事件，随后采用Lasso回归分析获得487个可变剪切事件，进一步多因素COX回归分析建立了基于13个基因可变剪切事件（MAST1|47879|AT、CCZ1B|78768|ES、ZNF706|84749|ES、MAGED1|89145|AP、ECD|12132|ES、NSUN5|79934|AA、SULT1A3|94136|AP、ARHGEF11|8336|AP、CYB561|42921|AP、SCRIB|85500|ES、STK32C|13483|AP、NGFRAP1|89733|ES and FOLH1|15817|ES）PSI值的子宫内膜癌预后风险模型。单变量/多变量Cox回归揭示该模型对子宫内膜癌风险具有独立预后作用，且P＜0.001。风险评分的ROC曲线下面积为0.827。病理分期和风险评分是子宫内膜癌的独立预后因子。同时，我们建立了子宫内膜癌相关可变剪切事件与剪接因子之间的调控网络。
结论：通过TCGA数据库和SpliceSep数据库构建了子宫内膜癌的可变剪切事件预后模型，预测准确性高。病理分期和风险评分是预后风险模型的独立预后因子。 |
| 英文摘要: |
| Objective To establish a prognostic risk model for uterine corpus endometrial carcinoma (UCEC) based
on alternative splicing (AS) event data from The Cancer Genome Atlas (TCGA) and assess the accuracy
of the model.
Methods TCGA and SpliceSeq databases were used to acquire a summary of AS events and clinical
data related to UCEC. Bioinformatic analysis was performed to identify differentially expressed AS
events in UCEC. Least absolute shrinkage and selection operator (LASSO) regression and multivariate
Cox regression analyses were used for constructing a prognostic risk model. Next, using the receiver
operating characteristic (ROC) curve, Kaplan-Meier survival analysis, and independent prognostic analysis,
we assessed the accuracy of the model. In addition, a splicing network was established based on the
association between potential splicing factors and AS events.
Results We downloaded clinical data and AS events of 527 UCEC cases from TCGA and SpliceSeq
databases, respectively. We obtained 18,779 survival-associated AS events in UCEC using univariate Cox
regression analysis and 487 AS events using LASSO regression analysis. Multivariate Cox regression
analysis established a prognostic risk model for UCEC based on the percentage splicing value of 13 AS
events. Independent prognostic effect on UCEC risk was then assessed using multivariate and univariate
Cox regression analyses (P < 0.001). The area under the curve was 0.827. The pathological stage and risk
score were independent prognostic factors for UCEC. Herein, we established a regulatory network between
alternative endometrial cancer-related splicing events and splicing factors.
Conclusion We constructed a prognostic model of UCEC based on 13 AS events by analyzing datasets
from TCGA and SpliceSeq databases with medium accuracy. The pathological stage and risk score were
independent prognostic factors in the prognostic risk model. |
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