基于紫杉烷类治疗反应构建泛素化相关基因的乳腺癌预后模型

罗慧敏; 罗密; 王素威; 李玲; 朱江华; 汤卓红

doi:10.11817/j.issn.1672-7347.2026.250584

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基于紫杉烷类治疗反应构建泛素化相关基因的乳腺癌预后模型
Construction of a ubiquitination-related gene prognostic model for breast cancer based on taxane treatment response
中南大学学报(医学版) 中南大学学报(医学版) 2026年51卷第2期页码：240-255
作者机构：

广东医科大学附属佛山妇女儿童医院药学部，佛山 528000
作者简介：

罗慧敏，Email: luohm6@alumni.sysu.edu.cn, ORCID: 0009-0000-1962-1905
汤卓红，副主任药师，Email: tzh0811@smu.edu.cn, ORCID: 0000-0001-9134-5319
基金信息：

佛山市自筹经费类科技创新项目(2320001006317);广东医科大学附属佛山市妇幼保健院妇女儿童医学研究中心院内科研资助项目(FEYJZX-2020-005┫。This work was supported by the Foshan Self-financed Science and Technology Innovation Project ┣2320001006317);the Scientific Research Fund of Women and Children Medical Research Center, Foshan Women and Children Hospital Affiliated to Guangdong Medical University(FEYJZX-2020-005)
DOI：10.11817/j.issn.1672-7347.2026.250584
中图分类号：
CSTR：
收稿：2025-10-21，

纸质出版：2026-02-28
稿件说明：

罗慧敏, 罗密, 王素威, 李玲, 朱江华, 汤卓红. 基于紫杉烷类治疗反应构建泛素化相关基因的乳腺癌预后模型[J]. 中南大学学报(医学版), 2026, 51(2): 240-255.

LUO Huimin, LUO Mi, WANG Suwei, LI Ling, ZHU Jianghua, TANG Zhuohong. Construction of a ubiquitination-related gene prognostic model for breast cancer based on taxane treatment response[J]. Journal of Central South University. Medical Science, 2026, 51(2): 240-255.
罗慧敏, 罗密, 王素威, 李玲, 朱江华, 汤卓红. 基于紫杉烷类治疗反应构建泛素化相关基因的乳腺癌预后模型[J]. 中南大学学报(医学版), 2026, 51(2): 240-255. DOI：10.11817/j.issn.1672-7347.2026.250584.

LUO Huimin, LUO Mi, WANG Suwei, LI Ling, ZHU Jianghua, TANG Zhuohong. Construction of a ubiquitination-related gene prognostic model for breast cancer based on taxane treatment response[J]. Journal of Central South University. Medical Science, 2026, 51(2): 240-255. DOI：10.11817/j.issn.1672-7347.2026.250584.

摘要

目的

紫杉烷类药物是乳腺癌一线化疗药物，其耐药常导致临床治疗失败。泛素化修饰在肿瘤进展及耐药中发挥关键调控作用。本研究旨在基于紫杉烷类药物的治疗反应，构建泛素化相关基因(ubiquitin-related genes，URGs)的乳腺癌预后模型，以预测患者对药物的敏感性与预后，挖掘潜在治疗靶点，为乳腺癌个体化治疗提供依据。

方法

从癌症基因组图谱(The Cancer Genome Atlas，TCGA)数据库中获得接受紫杉烷类药物治疗的乳腺癌样本的相关信息，根据治疗反应将样本分为敏感组和耐药组，并作为训练集；采用基因表达综合(Gene Expression Omnibus，GEO)数据库中的GSE25055数据集作为验证集。从泛素和泛素样缀合综合注释数据库(integrated Annotations for Ubiquitin and Ubiquitin-like Conjugation Database，iUUCD)中获得URGs全集后，筛选敏感组与耐药组差异表达的URGs。采用单因素Cox回归分析筛选与预后相关的差异表达基因，结合最小绝对收缩与选择算子(least absolute shrinkage and selection operator，LASSO)回归分析及10倍交叉验证法确定模型的核心基因并构建风险评分公式。根据风险评分将患者分为高风险组和低风险组，通过Kaplan-Meier生存分析、受试者操作特征(receiver operating characteristic，ROC)曲线评估模型的预后预测效能，整合风险评分与预后相关临床因素构建列线图并验证其准确性。此外，还基于癌症药物敏感性基因组学(Genomics of Drug Sensitivity in Cancer，GDSC)数据库及“pRRophetic”包分析高风险组与低风险组的药物敏感性差异，并计算药物半抑制浓度(half maximal inhibitory concentration，IC

)。通过组织微阵列芯片分析和细胞实验对关键基因

E4F1

进行验证。

结果

筛选出59个敏感组与耐药组间差异表达的URGs，经单因素Cox和LASSO-Cox回归分析，确定

E4F1、CBLL1、RNF13、TRIM59

为核心基因并构建预后模型。在训练集(TCGA队列)中，预测1、3、5年总生存期(overall survival，OS)的曲线下面积(areas under the curve，AUC)分别为0.896(95%

0.754~1.000)、0.866(95%

0.723~1.000)和0.893(95%

0.768~1.000)；在独立验证集(GSE25055队列)中，相应的AUC分别为0.620(95%

0.467~0.685)、0.624(95%

0.508~0.692)和0.618(95%

0.497~0.719)，表明该模型能有效区分高风险组与低风险组患者。整合风险评分与TNM分期的列线图预测1、3、5年总生存期的AUC分别为0.992(95%

0.976~1.000)、0.890(95%

0.754~1.000)、0.897(95%

0.786~1.000)，校准曲线显示预测结果与实际生存情况吻合度较高。药物敏感性分析发现，高风险组与低风险组相比，有73种药物的IC

存在显著差异，其中高风险组对Bryostatin-1、PHA-665752、Salubrinal的敏感性更高(均

0.05)。高风险组患者与更高的T分期及更晚的综合临床分期均显著相关(均

0.05)。实验结果发现，

E4F1

在乳腺癌细胞和组织中均显著高表达，且与患者较短的OS显著相关(均

0.05)。敲减

E4F1

的表达能有效抑制乳腺癌细胞的侵袭与迁移，且与紫杉醇联用后该抑制作用被进一步增强(均

0.05)。

结论

由

E4F1

、

CBLL1

、

RNF13

、

TRIM59

构建的预后模型是预测乳腺癌患者预后和药物敏感性的有效工具，有助于指导乳腺癌患者的个体化治疗。其中，

E4F1

为潜在的致癌基因，可能是增强紫杉醇敏感性的干预靶点。

Abstract

Objective

Taxanes are first-line cchemotherapeutic agents for breast cancer; however

the development of resistance often leads to treatment failure. Ubiquitination plays a critical regulatory role in tumor progression and drug resistance. This study aims to construct a prognostic model based on ubiquitination-related genes (URGs) according to taxane treatment response

to predict patient prognosis and drug sensitivity

identify potential therapeutic targets

and provide a basis for individualized treatment of breast cancer.

Methods

Breast cancer samples treated with taxanes were obtained from The Cancer Genome Atlas (TCGA) and classified into taxane-sensitive and taxane-resistant groups as the training cohort. The GSE25055 dataset from the Gene Expression Omnibus (GEO) was used as the validation cohort. A comprehensive list of URGs was retrieved from the Integrated Annotations for Ubiquitin and Ubiquitin-like Conjugation Database (iUUCD)

and differentially expressed URGs between sensitive and resistant groups were identified. Univariate Cox regression analysis was performed to screen prognostic genes

followed by least absolute shrinkage and selection operator (LASSO

) Cox regression with 10-fold cross-validation to identify core genes and construct a risk score model. Patients were stratified into high- and low-risk groups based on the risk score. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were used to evaluate predictive performance. A nomogram integrating risk score and clinical factors was developed and validated. Drug sensitivity differences between risk groups were analyzed using the Genomics of Drug Sensitivity in Cancer (GDSC) and the pRRophetic R package

with estimation of half-maximal inhibitory concentration (IC

). Tissue microarray analysis and cellular experiments were conducted to validate the key gene E4F1.

Results

A total of 59 differentially expressed URGs were identified between taxane-sensitive and resistant groups. Through univariate Cox and LASSO-Cox analyses

4 core genes (

E4F1

CBLL1

RNF13

and

TRIM59

) were selected to construct the prognostic model. In the training cohort (TCGA)

the areas under the ROC curve (AUCs) for predicting 1-

and 5-year overall survival (OS) were 0.896 (95%

0.754 to 1.000)

0.866 (95%

0.723 to 1.000)

and 0.893 (95%

0.768 to 1.000)

respectively. In the validation cohort (GSE25055)

the corresponding AUCs were 0.620 (95%

0.467 to 0.685)

0.624 (95%

0.508 to 0.692)

and 0.618 (95%

0.497 to 0.719)

indicating that the model could effectively distinguish high- and low-risk patients. The nomogram integrating risk score and TNM stage showed improved predictive performance

with AUCs of 0.992 (95%

0.976 to 1.000)

0.890 (95%

0.754 to 1.000)

and 0.897 (95%

0.786 to 1.000) for 1-

and 5-year OS

respectively

and good calibration. Drug sensitivity analysis identified 73 agents with significant IC

differences between the two groups. The high-risk group exhibited increased sensitivity to Bryostatin-1

PHA-665752

and Salubrinal (all

0.05). High-risk patients were significantly associated with both higher T stage and more advanced overall clinical stage (both

0.05). Experimental validation demonstrated that

E4F1

was significantly overexpressed in breast cancer cells and tissues

and correlated with shorter OS (all

0.05). Knockdown of

E4F1

significantly inhibited invasion and migration of breast cancer cells

and this inhibitory effect was further enhanced when combined with paclitaxel (all

0.05).

Conclusion

The prognostic model based on

E4F1

CBLL1

RNF13

and

TRIM59

is an effective tool for predicting prognosis and drug sensitivity in breast cancer patients and may facilitate individualized treatment strategies. Among these

E4F1

may function as a potential oncogene and serve as a therapeutic target for enhancing taxane sensitivit.

关键词

Keywords

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