Development of a prognostic model based on necroptosis‑related lncRNAs in endometrial cancer using TCGA database

CAI Ting; ZHAO Lijuan; CHEN Huang

doi:10.14188/j.1671-8852.2025.0058

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Development of a prognostic model based on necroptosis‑related lncRNAs in endometrial cancer using TCGA database

更新时间：2026-11-05

- Development of a prognostic model based on necroptosis‑related lncRNAs in endometrial cancer using TCGA database
- Medical Journal of Wuhan University Vol. 46, Issue 5, Pages: 630-636(2025)
- 作者机构：
  
  1.湖北省襄阳市南漳县人民医院妇科湖北襄阳 441514
  2.武汉市第八医院妇产科湖北武汉 430012
- 作者简介：
- 基金信息：
- DOI：10.14188/j.1671-8852.2025.0058
  CLC： R737.6
- Received：15 January 2025，
  
  Published：25 May 2025
- 稿件说明：
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蔡婷,赵丽娟,陈煌.基于TCGA数据库构建坏死性凋亡相关lncRNA模型评估子宫内膜癌预后[J].武汉大学学报（医学版）,2025,46(05):630-636.

CAI Ting,ZHAO Lijuan,CHEN Huang.Development of a prognostic model based on necroptosis‑related lncRNAs in endometrial cancer using TCGA database[J].Medical Journal of Wuhan University,2025,46(05):630-636.
蔡婷,赵丽娟,陈煌.基于TCGA数据库构建坏死性凋亡相关lncRNA模型评估子宫内膜癌预后[J].武汉大学学报（医学版）,2025,46(05):630-636. DOI： 10.14188/j.1671-8852.2025.0058.

CAI Ting,ZHAO Lijuan,CHEN Huang.Development of a prognostic model based on necroptosis‑related lncRNAs in endometrial cancer using TCGA database[J].Medical Journal of Wuhan University,2025,46(05):630-636. DOI： 10.14188/j.1671-8852.2025.0058.

摘要

目的

构建基于坏死性凋亡相关长链非编码RNA（NRlncRNA）的预后模型，并评估其在子宫内膜癌（EC）患者中的应用，为患者的个性化治疗提供理论依据。

方法

从TCGA数据库中获取EC患者的转录组数据和临床信息，筛选出与坏死性凋亡相关的lncRNA。采用单因素Cox回归、多因素Cox回归及LASSO回归分析构建多基因风险评分模型。通过Kaplan‑Meier生存分析、ROC曲线分析等方法评估模型的预测能力，并在不同临床亚组中进行验证。

结果

筛选出与坏死性凋亡相关的1 020个lncRNA，并通过回归分析构建了7个lncRNA的预后模型。模型在训练集、测试集及整个数据集中显示出较高的预测准确性。Kaplan‑Meier生存分析显示高风险组与低风险组患者生存率显著不同（

0.001）。ROC曲线分析结果表明，模型在1年、3年和5年的

AUC

值分别为0.731、0.732和0.739，表明该模型具有良好的预后预测性能。

结论

本研究成功构建了基于坏死性凋亡相关lncRNA的预后模型，具有较高的预测准确性和临床适用性，为EC患者的个性化预后评估和靶向治疗提供了新的理论支持。

Abstract

Objective

To construct a prognostic model based on necroptosis‑related long noncoding RNA (NRlncRNA) and evaluate its application in patients with endometrial cancer (EC) and provide a theoretical basis for the personalized treatment of patients.

Methods

EC patients' transcriptome data and clinical information were obtained from the TCGA database

and lncRNAs related to necroptosis were screened. Univariate Cox regression

multivariate Cox regression

and LASSO regression analysis were used to construct a polygenic risk scoring model. The model's predictive ability was evaluated using Kaplan‑Meier survival analysis and ROC curve analysis

and was validated across different clinical subgroups.

Results

A total of 1 020 lncRNAs related to necroptosis were screened

and prognostic models of 7 lncRNAs were constructed by regression analysis. The model demonstrated high prediction accuracy across the training set

test set

and the entire dataset. Kaplan⁃Meier survival analysis revealed a significant difference in survival between the high⁃risk and low⁃risk patient groups (

0.001). Results from ROC curve analysis indicated that the model's

AUC

values at 1

and 5 years were 0.731

0.732

and 0.739

respectively

attesting to its robust prognostic prediction performance.

Conclusion

This study successfully constructed a prognostic model based on necroptosis‑related lncRNA

which has high predictive accuracy and clinical applicability and provides new theoretical support for personalized prognostic evaluation and targeted treatment of EC patients.

关键词

Keywords

references

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