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中华普外科手术学杂志(电子版) ›› 2026, Vol. 20 ›› Issue (04) : 366 -373. doi: 10.3877/cma.j.issn.1674-3946.2026.04.016

论著

MDSCs相关基因在乳腺癌中表达特征分析与风险预测模型构建
李若隐, 罗义(), 张雪琳, 雷李凤, 李思丽   
  1. 637000 四川南充,川北医学院第二临床医学院/附属南充市中心医院乳腺甲状腺血管外科
  • 收稿日期:2026-01-15 出版日期:2026-08-26
  • 通信作者: 罗义

Analysis of expression characteristics of MDSCs-Related genes and construction of risk prediction model in breast cancer

Ruoyin Li, Yi Luo(), Xuelin Zhang, Lifeng Lei, Sili Li   

  1. Department of Surgery of mammary gland, thyroid, and blood vessels, The Second Clinical Medical College of North Sichuan Medical University / Affiliated Nanchong Central Hospital, Nanchong, Sichuan Province 637000, China
  • Received:2026-01-15 Published:2026-08-26
  • Corresponding author: Yi Luo
  • Supported by:
    Sichuan Provincial Department of Science and Technology Project(2023YFS0473)
引用本文:

李若隐, 罗义, 张雪琳, 雷李凤, 李思丽. MDSCs相关基因在乳腺癌中表达特征分析与风险预测模型构建[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 366-373.

Ruoyin Li, Yi Luo, Xuelin Zhang, Lifeng Lei, Sili Li. Analysis of expression characteristics of MDSCs-Related genes and construction of risk prediction model in breast cancer[J/OL]. Chinese Journal of Operative Procedures of General Surgery(Electronic Edition), 2026, 20(04): 366-373.

目的

探讨髓源性抑制细胞(MDSCs)相关基因在乳腺癌中表达特征,构建风险预测模型并评估其风险分层和预测效能。

方法

从TCGA数据库下载1216例乳腺癌患者的RNA-seq数据及临床资料,按7:3比例随机划分为训练集(n=851)和验证集(n=365)。从GeneCards数据库获取494个MDSCs相关基因。在训练集中,通过单因素COX回归(P<0.05)初步筛选预后相关基因,采用LASSO-COX回归压缩变量,最终构建一个多基因风险评分(Riskscore)模型。通过Kaplan-Meier曲线、时间依赖性ROC曲线评估模型的风险分层和预测效能。将独立预测因子整合,构建预测2、3、5年生存率的列线图,使用校准曲线、决策曲线(DCA)和C指数对其进行全面验证。

结果

LASSO回归最终筛选出49个关键基因,构建Riskscore模型。该模型在训练集和验证集中均显示出良好的预测能力(训练集3年、5年AUC分别为0.785、0.787;验证集分别为0.691、0.765)。多因素COX分析证实,Riskscore是独立于年龄、临床分期等传统因素的预后预测因子(HR=3.552, 95% CI: 2.933~4.301, P<0.001)。基于Riskscore、年龄和临床分期构建的列线图模型表现出优异的预测准确性(训练集5年AUC=0.814,验证集5年AUC=0.810)和良好的校准度,且决策曲线分析表明其具有显著的临床实用价值。

结论

构建并验证了一个基于MDSCs相关基因的预后预测模型。该模型能有效区分乳腺癌患者的生存风险,所构建的列线图作为一种可视化工具,可为临床医生提供个体化的预后评估,辅助治疗决策。

Objective

To investigate the expression characteristics of myeloid-derived suppressor cell (MDSC)-related genes in breast cancer, construct a risk prediction model, and evaluate its risk stratification and predictive performance.

Methods

RNA-seq data and clinical information of 1216 breast cancer patients were downloaded from the TCGA database, and randomly divided into a training set (n=851) and a validation set (n=365) at a ratio of 7∶3. A total of 494 MDSC-related genes were retrieved from the GeneCards database. In the training set, univariate Cox regression (P<0.05) was used to preliminarily screen prognosis-related genes, followed by LASSO-Cox regression for variable compression, and a multi-gene risk score (Riskscore) model was finally established. Kaplan-Meier curves and time-dependent ROC curves were adopted to assess the risk stratification ability and predictive efficacy of the model. Independent prognostic factors were integrated to construct a nomogram for predicting 2-, 3- and 5-year survival rates, which was comprehensively validated via calibration curves, decision curve analysis (DCA) and the C-index.

Results

Forty-nine hub genes were screened out by LASSO regression to establish the Riskscore model. The model exhibited favorable predictive power in both the training and validation sets (3-year and 5-year AUC values of 0.785 and 0.787 in the training set, versus 0.691 and 0.765 in the validation set). Multivariate Cox regression analysis verified that Riskscore was an independent prognostic factor beyond conventional clinical indicators including age and tumor stage (HR=3.552, 95% CI: 2.933~4.301, P<0.001). The nomogram built on Riskscore, age and tumor stage achieved excellent predictive accuracy (5-year AUC=0.814 in the training set and 0.810 in the validation set) and satisfactory calibration. Decision curve analysis further confirmed its prominent clinical application value.

Conclusion

A prognostic prediction model based on MDSC-related genes was constructed and validated. This model can effectively stratify survival risks among breast cancer patients. As a visual assessment tool, the established nomogram enables clinicians to deliver individualized prognostic evaluation and facilitate clinical treatment decision-making.

图1 乳腺癌中髓源性抑制细胞相关基因Lasso筛选过程
表1 乳腺癌中髓源性抑制细胞相关基因Lasso筛选结果
图2 乳腺癌中髓源性抑制细胞相关基因预后模型预测效能时间依赖ROC曲线 注:AUC为曲线下面积;ROC为受试者工作特征
图3 乳腺癌中髓源性抑制细胞相关基因预后模型风险评分风险分层K-M曲线
表2 乳腺癌预后风险单因素COX回归结果
图4 乳腺癌预后风险多因素COX回归结果
图5 乳腺癌预后风险因素LASSO筛选结果
表3 乳腺癌预后风险因素LASSO筛选结果
图6 乳腺癌预后生存列线图模型校准曲线
图7 乳腺癌预后生存列线图模型
图8 乳腺癌预后生存时间依赖ROC曲线分析 注:AUC为曲线下面积;ROC为受试者工作特征
图9 乳腺癌预后生存列线图模型风险分层K-M曲线
图10 乳腺癌预后生存列线图模型DCA曲线
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