基于RNA结合蛋白基因表达特征的乳腺癌预后预测模型构建及评价分析

doi:10.3877/cma.j.issn.1674-0807.2026.03.002

中华乳腺病杂志(电子版) ›› 2026, Vol. 20 ›› Issue (03) : 138 -147. doi: 10.3877/cma.j.issn.1674-0807.2026.03.002

论著

基于RNA结合蛋白基因表达特征的乳腺癌预后预测模型构建及评价分析

丁彩霞¹, 曲景辉¹, 裴英宏¹, 李静娜¹, 郑晓瑜², 徐岭植³^,^†(

), 李思思¹^,^†(

)

¹　150081　哈尔滨，哈尔滨医科大学附属肿瘤医院病理科
²　150081　哈尔滨，哈尔滨医科大学附属肿瘤医院麻醉科
³　116023　大连，大连医科大学附属第二医院乳腺肿瘤科

收稿日期:2025-12-14 出版日期:2026-06-01
通信作者: 徐岭植, 李思思
基金资助:
国家自然科学基金面上项目(82573309)

Construction and evaluation of a prognostic prediction model for breast cancer based on RNA-binding protein genes expression signatures

Caixia Ding¹, Jinghui Qu¹, Yinghong Pei¹, Jingna Li¹, Xiaoyu Zheng², Lingzhi Xu³^,^†(), Sisi Li¹^,^†()

¹ Department of Pathology, Harbin Medical University Cancer Hospital, Harbin 150081, China
² Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
³ Department of Breast Oncology, Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China

Received:2025-12-14 Published:2026-06-01
Corresponding author: Lingzhi Xu, Sisi Li

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引用本文：

丁彩霞, 曲景辉, 裴英宏, 李静娜, 郑晓瑜, 徐岭植, 李思思. 基于RNA结合蛋白基因表达特征的乳腺癌预后预测模型构建及评价分析[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(03): 138-147.

Caixia Ding, Jinghui Qu, Yinghong Pei, Jingna Li, Xiaoyu Zheng, Lingzhi Xu, Sisi Li. Construction and evaluation of a prognostic prediction model for breast cancer based on RNA-binding protein genes expression signatures[J/OL]. Chinese Journal of Breast Disease(Electronic Edition), 2026, 20(03): 138-147.

目的

筛选差异表达的RNA结合蛋白基因（RBPs），根据患者风险评分与临床病理特征联合构建预后预测模型并进行验证，分析不同风险患者免疫表型评分（IPS）、药物敏感性情况。

方法

收集癌症基因组图谱（TCGA）乳腺癌队列的转录组与临床数据（1 106例乳腺癌肿瘤样本和 137例癌旁正常样本）作为训练集，以GSE86166数据集330例乳腺癌样本作为验证集。通过训练集筛选肿瘤样本与癌旁正常样本差异表达的RBPs。采用单因素Cox比例风险回归与最小绝对收缩和选择算子（LASSO）回归分析，筛选核心RBPs构建预后风险评分模型。根据风险评分截断值将乳腺癌患者分为高风险组（649例）和低风险组（457例），利用 Kaplan-Meier生存分析和受试者操作特征（ROC）曲线评估模型效能。采用与训练集相同的风险评分公式和截断值在验证集（高风险组161例，低风险组169例）中进行外部验证。在 TCGA训练集中结合临床病理特征，采用单因素和多因素 Cox比例风险回归分析评估风险评分的独立预后价值。基于患者临床病理特征和风险评分构建预后预测模型，采用校准曲线评价其准确性，采用临床决策曲线分析（DCA）评估预后预测模型的临床应用价值。采用IPS评估高、低风险组肿瘤免疫表型特征。采用半数抑制浓度（IC₅₀）评估高、低风险组对296种临床常用化疗药物及靶向治疗药物的药物敏感性。采用便利抽样法选取 2023年 1月至 2025年12月哈尔滨医科大学附属肿瘤医院10例乳腺癌组织样本及对应癌旁正常组织样本，采用组织化学评分（H-score）从蛋白水平验证模型 5个核心基因的表达差异。

结果

从1 106例乳腺癌肿瘤样本及 137例癌旁正常样本中筛选出126个差异表达的RBPs，经单因素Cox比例风险回归和LASSO回归分析最终筛选出 5个核心RBPs（NUAK2、ACSL1、MAP1LC3C、WT1和MYOCD），并据此建立预后风险评分模型。Kaplan-Meier生存分析显示，训练集中高、低风险组患者中位总生存（OS）分别为 97.5个月（95%CI：90.2~104.8）、216.6个月（95%CI：198.3~234.9），两组比较，差异有统计学意义（χ²=13.20，P<0.001）；验证集中高、低风险组患者OS分别为76.8个月（95%CI：70.5~83.1）、182.4个月（95%CI：165.7~199.2），两组比较，差异有统计学意义（χ²=4.14，P=0.042）。ROC曲线评估结果显示，训练集和验证集模型 3、5、7年OS的曲线下面积分别为 0.60（95%CI：0.54~0.66）、0.60（95%CI：0.53~0.67）、0.65（95%CI：0.59~0.71）和0.64（95%CI：0.58~0.70）、0.60（95%CI：0.54~0.66）、0.62（95%CI：0.56~0.68），该模型在训练集和验证集中均具有预后预测价值。多因素 Cox比例风险回归分析显示，风险评分是预测患者OS的独立影响因素（HR=6.807，95%CI：3.940~11.715，P<0.001）。校准曲线显示，预后预测模型乳腺癌患者3、5、7年OS的一致性指数（c-index）分别为 0.782、0.765、0.748 (χ²=8.62、9.15、7.89，P均>0. 05)，证实该模型预测效能稳定。DCA 结果显示，在 0.153~0.604的临床决策阈值区间内，预后预测模型的临床净获益优于全部干预和零干预方案。肿瘤免疫原性及免疫治疗响应分析显示，低风险组的 IPS 显著高于高风险组（P均<0.05）。药物敏感性分析显示，146种药物在低风险组中 IC₅₀ 低于高风险组（P均<0.05），而 20种药物在高风险组中 IC₅₀ 低于低风险组（P均<0.05）。低风险组 7种经典化疗药物（紫杉醇、多柔比星、卡铂、奥沙利铂、环磷酰胺、多西他赛及拓扑替康）的 IC₅₀ 显著低于高风险组（P均<0.001）。蛋白验证结果显示，NUAK2（152.00±17.51比16.00±13.08，t=16.60，P<0.001）、WT1［35.00（15.00，72.50）比7.50（1.75，30.00），Z=−2.80，P=0.005］在肿瘤组织中的表达高于癌旁正常组织；MAP1LC3C（49.20±44.90比128.00±37.06，t=-4.61，P=0.001）、ACSL1 ［145.00（75.00，187.50）比270.00（247.50，273.75），Z=−2.81，P=0.005］和MYOCD ［100.00（47.50，140.00）比160.00（150.00，165.00），Z=−2.82，P=0.005］在肿瘤组织中的表达低于癌旁正常组织。

结论

本研究基于5个核心RBPs构建的乳腺癌预后预测模型效能较好，不同风险患者的IPS和药物敏感性存在差异。

关键词: 乳腺肿瘤, RNA结合蛋白基因, 预后预测模型, 免疫表型评分, 药物敏感性

Objective

To screen for differentially expressed RNA-binding protein genes (RBPs), construct a prognostic prediction model combined with risk score and clinicopathological characteristics of patients, validate it, and analyze the immunophenoscore (IPS) and drug sensitivity in different risk groups.

Methods

Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) breast cancer cohort (1 106 breast cancer tumor samples and 137 adjacent normal samples) were collected as the training set, and the GSE86166 dataset (containing 330 breast cancer samples) was used as the validation set. Differentially expressed RBPs between tumor samples and adjacent normal samples were screened in the training set. Univariate Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select core RBPs and construct a prognostic risk score model. Breast cancer patients were divided into high-risk group (649 cases) and low-risk group (457 cases) based on the risk score cut-off value. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were used to evaluate model performance. External validation was conducted in the validation set samples (high-risk group 161 cases and low-risk group 169 cases) using the same risk score formula and cut-off value. In the TCGA training set, univariate and multivariate Cox proportional hazards regression analyses combined with patients clinicopathological characteristics were used to evaluate the independent prognostic value of the risk score. A prognostic model was constructed based on clinicopathological characteristics and the risk score, with calibration curves used to assess its accuracy and decision curve analysis (DCA) used to evaluate its clinical utility. IPS was used to assess the tumor immunophenotype characteristics of the high and low risk groups. The half maximal inhibitory concentration (IC₅₀) was used to evaluate the drug sensitivity of 296 commonly used clinical chemotherapeutic and targeted therapeutic drugs in the high and low risk groups. Using convenience sampling, 10 pairs of breast cancer tissue samples and corresponding adjacent normal tissue samples from Harbin Medical University Cancer Hospital collected between January 2023 and December 2025 were used to validate the expression differences of the 5 core genes at the protein level using histochemistry score(H-score).

Results

A total of 126 differentially expressed RBPs were identified from 1 106 breast cancer tumor samples and 137 adjacent normal samples. Univariate Cox proportional hazards regression analysis and LASSO regression analysis ultimately identified 5 core RBPs (NUAK2, ACSL1, MAP1LC3C, WT1, and MYOCD), based on which a prognostic risk score model was established. Kaplan-Meier survival analysis showed that the median overall survival (OS) of patients in the high-risk group and low-risk group in the training set was 97.5 months (95%CI: 90.2-104.8) and 216.6 months (95%CI: 198.3-234.9), indicating a statistically significant difference (χ²=13.20, P<0.001) ; The median OS of patients in the high-risk group and low-risk group in the validation set was 76.8 months (95%CI: 70.5-83.1) and 182.4 months (95%CI: 165.7-199.2), indicating a statistically significant difference (χ²=4.14, P=0.042). ROC curve analysis showed that the area under the curve at 3, 5, and 7 years OS for the training and validation sets were 0.60 (95% CI: 0.54-0.66), 0.60 (95%CI: 0.53-0.67), 0.65 (95%CI: 0.59-0.71), and 0.64 (95%CI: 0.58-0.70), 0.60 (95%CI: 0.54-0.66), 0.62 (95%CI: 0.56-0.68), respectively, indicating that the model has prognostic predictive value in both the training and external validation sets. Multivariate Cox proportional hazards regression analysis showed that the risk score was an independent factor predicting overall survival (HR=6.807, 95%CI: 3.940-11.715, P<0.001). Calibration curves showed that the concordance index (c-index) of predicting prognostic model at 3, 5, and 7 years OS in breast cancer patients were 0.782, 0.765, and 0.748, respectively (χ²=8.62, 9.15, 7.89, all P>0.05), confirming the stable predictive performance of the model. DCA results showed that, within the clinical decision threshold interval of 0.153-0.604, the prognostic model provided a better net clinical benefit than both the treat-all and treat-none strategies.Tumor immunogenicity and immunotherapy response analysis showed that the IPS of the low-risk group was significantly higher than that of the high-risk group (all P<0.05). Drug sensitivity analysis showed that 146 drugs had lower IC₅₀ values in the low-risk group than in the high-risk group (all P<0.05), while 20 drugs had lower IC₅₀ values in the high-risk group than in the low-risk group (all P<0.05). The IC₅₀ values of seven classical chemotherapeutic drugs (paclitaxel, doxorubicin, carboplatin, oxaliplatin, cyclophosphamide, docetaxel and topotecan) were significantly lower in the low-risk group than in the high-risk group (all P<0.001). Protein validation results showed that the expression of NUAK2 (152.00±17.51 vs 16.00±13.08, t=16.60, P<0.001) and WT1 [35.00 (15.00, 72.50) vs 7.50 (1.75, 30.00), Z=−2.80, P=0.005] were higher in tumor tissues than in adjacent normal tissues, whereas the expression of MAP1LC3C (49.20±44.90 vs 128.00±37.06, t=-4.61, P=0.001), ACSL1 [145.00 (75.00, 187.50) vs 270.00 (247.50, 273.75), Z=−2.81, P=0.005], and MYOCD [100.00 (47.50, 140.00) vs 160.00 (150.00, 165.00), Z=−2.82, P=0.005] were lower in tumor tissues than in adjacent normal tissues.

Conclusion

In this study, the prognostic prediction model for breast cancer constructed based on 5 core RBPs has good predictive efficacy, and accordingly different risk groups show significant difference in IPS and drug sensitivity.

Key words: Breast neoplasms, RNA-binding protein genes, Prognostic prediction model, Immunophenoscore, Drug sensitivity

图1 乳腺癌预后相关基因的筛选及预后风险评分模型的建立　A图为LASSO分析中调整参数的十折交叉验证；B图为LASSO分析中调整参数的十折交叉验证及系数谱图；C图为训练集不同风险患者的 Kaplan-Meier生存曲线；D图为训练集患者3、5、7年生存预测的ROC曲线；E图为验证集不同风险患者的 Kaplan-Meier生存曲线；F图为验证集患者3、5、7年生存预测的ROC曲线注：ROC为受试者操作特征；C图，χ²=13.20，P<0.001；E图，χ²=4.14，P=0.042

表1 TCGA队列中1 106例乳腺癌患者总生存的影响因素分析

因素	单因素分析			多因素分析
因素	风险比	95%置信区间	P值	风险比	95%置信区间	P值
风险评分	1.496	1.260~1.775	<0.001	6.807	3.940~11.715	<0.001
年龄（≤59岁比>59岁）	1.727	1.300~2.295	<0.001	1.925	1.428~2.591	<0.001
性别（男比女）	0.580	0.101~3.334	0.542	-	-	-
T分期（T_1-2比T_3-4）	1.638	1.178~2.276	0.003	0.996	0.586~1.694	0.989
N分期（N_0-1比N_2-3）	1.960	1.427~2.693	<0.001	1.075	0.587~1.967	0.814
临床分期（Ⅰ-Ⅱ比Ⅲ-Ⅳ）	2.334	1.718~3.167	<0.001	2.478	1.322~4.645	<0.010

表1 TCGA队列中1 106例乳腺癌患者总生存的影响因素分析

因素	单因素分析			多因素分析
因素	风险比	95%置信区间	P值	风险比	95%置信区间	P值
风险评分	1.496	1.260~1.775	<0.001	6.807	3.940~11.715	<0.001
年龄（≤59岁比>59岁）	1.727	1.300~2.295	<0.001	1.925	1.428~2.591	<0.001
性别（男比女）	0.580	0.101~3.334	0.542	-	-	-
T分期（T_1-2比T_3-4）	1.638	1.178~2.276	0.003	0.996	0.586~1.694	0.989
N分期（N_0-1比N_2-3）	1.960	1.427~2.693	<0.001	1.075	0.587~1.967	0.814
临床分期（Ⅰ-Ⅱ比Ⅲ-Ⅳ）	2.334	1.718~3.167	<0.001	2.478	1.322~4.645	<0.010

图2 乳腺癌患者3、5、7年预后预测模型注：OS为总生存

图3 1 106例乳腺癌患者预后预测模型校准曲线

图4 1 106例乳腺癌患者预后预测模型临床决策曲线

图5 5个核心RBPs在乳腺癌肿瘤组织样本与癌旁正常组织样本中蛋白表达的差异　A图为NUAK2（EnVision ×4）；B图为A图黑框部分放大（EnVision ×10）；C图为A图红框部分放大（EnVision ×10）；D图为WT1（EnVision ×4）；E图为D图黑框部分放大（EnVision ×10）；F图为D图红框部分放大（EnVision ×10）；G图为MAP1LC3C（EnVision ×4）；H图为G图黑框部分放大（EnVision ×10）；I图为G图红框部分放大（EnVision ×10）；J图为ACSL1（EnVision ×4）；K图为J图黑框部分放大（EnVision ×10）；L图为J图红框部分放大（EnVision ×10）；M图为MYOCD（EnVision ×4）；N图为M图黑框部分放大（EnVision ×10）；O图为M图红框部分放大（EnVision ×10）注：RBPs为RNA结合蛋白基因；NUAK2为家族激酶 2；WT1为肾母细胞瘤基因1；MAP1LC3C为微管相关蛋白1轻链3γ；ACSL1为长链脂酰辅酶A合成酶1；MYOCD为心肌素；黑色箭头示肿瘤组织；红色箭头示癌旁正常组织

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Construction and evaluation of a prognostic prediction model for breast cancer based on RNA-binding protein genes expression signatures

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Construction and evaluation of a prognostic prediction model for breast cancer based on RNA-binding protein genes expression signatures