基于生物信息学方法构建接受新辅助治疗HER2阳性Ⅲ期乳腺癌预后预测模型  

Construction of a prognostic prediction model for HER2-positive stageⅢbreast cancer with neoadjuvant therapy based on bioinformatics methods

作　　者：冯伟霞 罗飞[2] Feng Weixia;Luo Fei(The Second Clinical School of Shanxi Medical University,Taiyuan 030001,China;Department of Breast Surgery,Shanxi Province Cancer Hospital,Shanxi Hospital Affiliated to Cancer Hospital,Chinese Academy of Medical Sciences,Cancer Hospital Affiliated to Shanxi Medical University,Taiyuan 030013,China)

机构地区：[1]山西医科大学第二临床医学院,太原030001 [2]山西省肿瘤医院、中国医学科学院肿瘤医院山西医院、山西医科大学附属肿瘤医院乳腺外科,太原030013

出　　处：《肿瘤研究与临床》2023年第11期815-821,共7页Cancer Research and Clinic

基　　金：山西省应用基础研究计划(201901D111396)。

摘　　要：目的基于生物信息学方法探讨影响接受新辅助治疗(NAT)的人表皮生长因子受体2(HER2)阳性Ⅲ期乳腺癌患者预后的因素并构建预后预测模型。方法用美国国家癌症研究所监测、流行病学和最终结果(SEER)数据库*Stat 8.3.9软件收集2010年1月至2019年12月登记的接受新辅助治疗的HER2阳性Ⅲ期乳腺癌患者临床资料。获得患者一般资料和临床病理特征资料。使用R 4.2.1软件createDataPartition工具以7∶3将患者分为建模组和验证组。采用Cox比例风险模型分析乳腺癌患者特异性生存(BCSS)的影响因素,并采用R 4.2.1软件survival包和rms包构建BCSS预测列线图模型。采用一致性指数(C指数)、受试者工作特征(ROC)曲线以及曲线下面积(AUC)、生存率校准曲线评估列线图模型的预测效能;采用X-tile软件计算与预后相关得分的最佳临界值,采用Kaplan-Meier法绘制生存曲线。采用决策曲线分析(DCA)观察列线图模型的净收益。结果共纳入接受新辅助治疗的HER2阳性Ⅲ期乳腺癌患者1790例,按7∶3的比例随机分为建模组(1250例)和验证组(540例),两组患者一般资料比较,差异均无统计学意义(均P>0.05)。多因素Cox回归分析结果显示,未放疗、组织学分级Ⅲ~Ⅳ级、雌激素受体(ER)阴性、孕激素受体(PR)阴性、T4期、淋巴结转移率1%~30%、淋巴结转移率31%~60%、淋巴结转移率>61%是患者BCSS的独立危险因素(均P<0.05)。根据以上因素构建NAT的HER2阳性Ⅲ期乳腺癌预后列线图模型。经内部验证,建模组C指数为0.732(95%CI 0.724~0.739),验证组C指数为0.728(95%CI 0.721~0.737);建模组列线图模型预测3、6、9年BCSS率的AUC分别为0.724、0.719、0.775,验证组分别为0.773、0.699、0.758;生存率校准曲线显示,建模组和验证组3、6、9年BCSS率与实际观测的结果高度拟合。计算SEER数据库纳入患者列线图模型总得分,预测预后的最佳临界值分别为80.0分和142.0分,依此划�Objective To explore the factors affecting the prognosis of human epidermal growth factor receptor 2(HER2)-positive stageⅢbreast cancer patients receiving neoadjuvant therapy(NAT)based on bioinformatics methods,and to construct a prognostic prediction model.Methods Clinical data of HER2-positive stageⅢbreast cancer patients with NAT registered from January 2010 to December 2019 were collected using National Cancer Institute Surveillance,Epidemiology,and End Results(SEER)database*Stat 8.3.9 software.Data on general information and clinicopathological characteristics of patients were obtained.Patients were divided into modeling and validation groups using R 4.2.1 software createDataPartition tool in 7∶3.Cox proportional hazards model was used to analyze the influencing factors of breast cancer patient-specific survival(BCSS),and the BCSS prediction nomogram model was constructed using R 4.2.1 software survival package and rms package.The predictive efficacy of the nomogram model was assessed using consistency index(C-index),receiver operating characteristic(ROC)curve and area under the curve(AUC),and survival calibration curve;the optimal cut-off value of the prognostic-related scores was calculated using the X-tile software,and the survival curves were plotted using Kaplan-Meier method.Decision curve analysis(DCA)was used to observe the net benefit of the nomogram model.Results A total of 1790 HER2-positive stageⅢbreast cancer patients receiving NAT were enrolled,and they were randomly divided into a modeling group(1250 patients)and a validation group(540 patients)according to the ratio of 7∶3,and the differences in the general information of patients between the two groups were not statistically significant(all P>0.05).The result of multivariate Cox regression analysis showed that no radiotherapy,histologic gradeⅢ-Ⅳ,estrogen receptor(ER)negative,progesterone receptor(PR)negative,stage T4,lymph node metastasis rate of 1%-30%,lymph node metastasis rate of 31%-60%,and lymph node metastasis rate of>61%w

关 键 词：乳腺肿瘤 受体 erbB-2 新辅助疗法 预后 

分 类 号：Q811.4[生物学—生物工程] R737.9[医药卫生—肿瘤]