3-lncRNAs预后模型在HER2阳性乳腺癌预后评价中的意义

doi:10.6040/j.issn.1671-7554.0.2020.117

摘要/Abstract

摘要： 目的利用癌症基因组图谱(TCGA)数据库,构建预测人表皮生长因子受体2(HER2)阳性乳腺癌预后的长链非编码RNA(lncRNA)风险评分模型。方法获取TCGA数据库中HER2阳性乳腺癌和正常乳腺组织的RNA表达谱及临床病理数据。基于R语言的“DESeq2”软件包、单因素Cox回归分析和Kaplan-Meier生存曲线,筛选HER2阳性乳腺癌中差异表达并与预后有统计学意义关联性的lncRNAs。利用多因素Cox逐步回归模型在训练集中构建lncRNA风险评分模型,Kaplan-Meier生存曲线评估该模型在训练集、验证集和总样本集以及不同亚组中的预测效能。结果从TCGA数据库中共获得161例HER2阳性乳腺癌和113例正常乳腺组织的RNA表达谱,“DESeq2”差异分析得到1 332个HER2阳性乳腺癌中差异表达的lncRNAs,单因素Cox回归分析和Kaplan-Meier生存曲线发现其中25个lncRNAs与预后有统计学意义的关联性,利用多因素Cox逐步回归模型建立了基于3-lncRNAs的风险评分模型:风险评分=0.710×表达量_LINC01833+1.869×表达量_LINC00536+2.992×表达量_LINC02725。该模型可有效区分HER2阳性乳腺癌人群中预后高风险组和低风险组,Kaplan-Meier生存曲线提示高风险组生存率低于低风险组,差异有统计学意义。同时,该模型在总样本集中的时间依赖性受试者工作特征(ROC)曲线下面积(AUC)高达0.825,高于TNM分期(AUC=0.605)。此外,在TNM Ⅰ-Ⅱ期亚组、ER阳性亚组、PR阴性亚组和PR阳性亚组中均可有效鉴别预后较差的高风险患者。结论基于LINC01833、LINC00536和LINC02725的风险评分模型可有效预测HER2阳性乳腺癌患者的预后,为HER2阳性乳腺癌的临床管理提供参考依据。

关键词: 人表皮生长因子受体2, 乳腺癌, 长链非编码RNA, 预后, Cox回归模型

Abstract: Objective To construct a long non-coding RNA(lncRNA)-based signature for predicting the survival of human epidermal growth factor receptor-2(HER2)positive breast cancer patients by using The Cancer Genome Atlas(TCGA)database. Methods RNA expression profiles and clinical data were downloaded from TCGA database. Differentially expression lncRNAs(DELs)were identified by the “DESeq2” package in R. Univariate Cox proportional hazards regression(CPHR)analysis and Kaplan-Meier curve were then used to identify prognostic DELs. Stepwise multivariate CPHR model was performed for constructing a lncRNAs-based prognostic signature in the training set. Kaplan-Meier curve was used to assess the predictive performance of the signature in the training set, validation set, total set and other subgroups. Results Based on TCGA database, 1 332 DELs were identified between 161 HER2 positive breast cancer and 113 normal cases by using the “DESeq2” package. Out of these DELs, 25 DELs were found to be associated with overall survival(OS)according to the univariate CPHR analysis and Kaplan-Meier curve. In the training set, a 3-lncRNAs signature was constructed: risk score=0.710 × Expression_LINC01833+1.869×Expression_LINC00536+2.992×Expression_LINC02725. This signature could effectively pick out high-risk group and low-risk group. Kaplan-Meier curve revealed that high-risk group tended to shorten survival. The area under the time-dependent receiver operating characteristic(ROC)curve of the molecular signature was 0.825, which was superior to TNM stage(AUC=0.605). Furthermore, the 3-lncRNAs signature could still distinguish the high-risk patients who had a shorter OS from the low-risk patients even in the TNM Ⅰ-Ⅱ subgroup, ER positive subgroup, PR negative subgroup and PR positive subgroup. Conclusion The proposed 3-lncRNAs signature based on LINC01833, LINC00536, and LINC02725 exhibits satisfactory individual prediction of survival for HER2 positive breast cancer, which may improve clinical management of HER2 positive breast cancer.

Key words: Human epidermal growth factor receptor-2, Breast cancer, Long non-coding RNA, Prognosis, Cox regression model

中图分类号:

R737.9

杨雪梅,李娟,王一凡,李培龙,王允山,杜鲁涛,王传新. 3-lncRNAs预后模型在HER2阳性乳腺癌预后评价中的意义[J]. 山东大学学报 (医学版), 2020, 58(5): 69-76.

YANG Xuemei, LI Juan, WANG Yifan, LI Peilong, WANG Yunshan, DU Lutao, WANG Chuanxin. Significance of a three-lncRNAs signature on the prediction of survival for HER2 positive breast cancer patients[J]. Journal of Shandong University (Health Sciences), 2020, 58(5): 69-76.

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