基于糖酵解相关基因构建T细胞耗竭诊断模型并联合临床特征预测乳腺癌预后

唐美慧; 杨观虎; 安超

doi:10.14188/j.1671-8852.2024.0877

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基于糖酵解相关基因构建T细胞耗竭诊断模型并联合临床特征预测乳腺癌预后

肿瘤学研究 | 更新时间：2026-11-05

- 基于糖酵解相关基因构建T细胞耗竭诊断模型并联合临床特征预测乳腺癌预后
- T cell exhaustion diagnostic model based on glycolysis⁃related genes and combined with clinical features to predict the prognosis of breast cancer
- 武汉大学学报（医学版） 2025年46卷第9期页码：1137-1145
- 作者机构：
  
  1.北京中医药大学北京 100029
  2.俄亥俄大学美国俄亥俄州雅典 45701
- 作者简介：
  
  唐美慧，女，1999—，医学硕士生，主要从事中西医结合防治肿瘤的研究，E⁃mail:18801315110@163.com
  安超，女，1981—，医学博士，副主任医师，主要从事中西医结合防治肿瘤的研究，E⁃mail:annie_bucm@126.com
- 基金信息：
- DOI：10.14188/j.1671-8852.2024.0877
  中图分类号： R273
- 收稿：2024-11-13，
  
  纸质出版：2025-09-26
- 稿件说明：
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唐美慧,杨观虎,安超.基于糖酵解相关基因构建T细胞耗竭诊断模型并联合临床特征预测乳腺癌预后[J].武汉大学学报（医学版）,2025,46(09):1137-1145.

TANG Meihui,YANG Guanhu,AN Chao.T cell exhaustion diagnostic model based on glycolysis⁃related genes and combined with clinical features to predict the prognosis of breast cancer[J].Medical Journal of Wuhan University,2025,46(09):1137-1145.
唐美慧,杨观虎,安超.基于糖酵解相关基因构建T细胞耗竭诊断模型并联合临床特征预测乳腺癌预后[J].武汉大学学报（医学版）,2025,46(09):1137-1145. DOI： 10.14188/j.1671-8852.2024.0877.

TANG Meihui,YANG Guanhu,AN Chao.T cell exhaustion diagnostic model based on glycolysis⁃related genes and combined with clinical features to predict the prognosis of breast cancer[J].Medical Journal of Wuhan University,2025,46(09):1137-1145. DOI： 10.14188/j.1671-8852.2024.0877.

摘要

目的

基于糖酵解相关基因建立T细胞耗竭（TEX）诊断模型并验证其准确性，同时结合糖酵解相关基因、TEX相关基因和临床特征构建乳腺癌预后风险评估模型。

方法

从癌症基因组图谱（TCGA）下载乳腺癌患者的RNA⁃seq数据和临床信息，根据已知TEX相关基因，通过单样本基因集富集分析（ssGSEA）评估患者样本的TEX评分，利用9种机器学习方法训练并构建糖酵解相关基因的TEX诊断模型。结合糖酵解和TEX相关基因构成基因表达矩阵，以是否患癌作为分组信息，使用limma包筛选样本的差异基因，结合临床特征使用单因素Cox回归分析其生存时间，通过基因本体（GO）分析和京都基因和基因组百科全书（KEGG）分析探索这些基因参与的生物学过程和信号转导通路。采用多因素Cox回归分析建模，绘制Kaplan⁃Meier曲线对高低风险组进行生存分析并绘制第1年至第5年的受试者工作特征（ROC）曲线验证模型可靠性。

结果

糖酵解相关基因的TEX诊断模型有较高的识别准确度，差异基因筛选得到90个基因，单因素Cox分析得到8个（

ACSS2、ALDH3A1、CHPF、ALDH2、ELF3、CACNA1H、NFKBIE、LTB

）与细胞醛代谢、醇代谢、糖酵解、氨基酸代谢、丙酮酸代谢等过程相关的特征基因。多因素Cox分析发现，其中4个基因（

ACSS2

、

ALDH3A1

、

CHPF

、

ALDH2

）和年龄≥70岁是独立风险因素，Kaplan⁃Meier生存分析和ROC曲线表明该模型具有良好的预测性能。

结论

基于糖酵解相关基因的TEX诊断模型及由基因和临床特征构建的乳腺癌预后风险模型，对患者生存状态和生存时间的预测具有较好的参考价值。

Abstract

Objective

To establish a diagnostic model for T⁃cell exhaustion (TEX) based on glycolysis⁃related genes and validate its accuracy

and to construct a prognostic risk assessment model for breast cancer by integrating glycolysis⁃related genes

TEX⁃related genes

and clinical features.

Methods

The RNA⁃seq data and clinical information of breast cancer patients were downloaded from the Cancer Genome Atlas (TCGA). According to the known TEX⁃related genes

the TEX score of the patient sample was evaluated by single sample gene set enrichment analysis (ssGSEA). Nine machine learning methods were used to train and construct the TEX diagnosis model of glycolysis⁃related genes. The gene expression matrix was constructed by combining glycolysis and TEX⁃related genes

and the cancer was used as the grouping information. The limma package was used to screen the differential genes of the samples

and the single factor cox regression analysis of individual survival time was performed on the differential genes and clinical features. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to determine the biological processes and signal transduction pathways involved in these genes. Subsequently

multivariate Cox regression analysis modeling was performed

and Kaplan⁃Meier curves were drawn to perform survival analysis on high and low risk groups to verify the accuracy of the model. Receiver operating characteristic (ROC) curves from the first to fifth years were drawn to verify the reliability of the model.

Results

The TEX diagnostic model of glycolysis⁃related genes had high identification accuracy

and differential gene screening yielded 90 genes

and Cox analysis yielded eight genes (

ACSS2

ALDH3A1

CHPF

ALDH2

ELF3

CACNA1H

NFKBIE

and

LTB

) associated with cellular aldehyde metabolism

alcohol metabolism

glycolysis

amino acid metabolism

pyruvate metabolism and other processes. Multifactorial Cox analysis revealed that four of the genes (

ACSS2

ALDH3A1

CHPF

and

ALDH2

) and age ≥70 years were independent risk factors

and Kaplan⁃Meier su

rvival analysis and ROC curves demonstrated that the model had good predictive performance.

Conclusion

The TEX diagnostic model based on glycolysis⁃related genes and the prognostic risk model for breast cancer constructed from genes and clinical features are good references for the prediction of patients' survival time and status.

关键词

Keywords

references

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