摘要
目的筛选转移性乳腺癌患者预后相关关键基因,构建并验证转移性乳腺癌患者的预后预测模型。方法从GEO数据库获取转移性乳腺癌组织基因表达数据集GSE124648,筛选转移性乳腺癌组织与乳腺癌原发灶组织、转移性乳腺癌组织与正常乳腺组织之间的差异表达基因(DEGs),并进行基因本体(GO)功能富集、京都基因和基因组百科全书(KEGG)信号通路分析。将140例转移性乳腺癌患者数据集随机分为训练集(72例)和测试集(68例)。利用LASSO&COX回归模型对训练集进行转移性乳腺癌患者预后相关关键基因的筛选,构建出训练集转移性乳腺癌患者预后预测模型。根据风险值中位数将训练集患者分为高、低风险组,绘制Kaplan-Meier生存曲线分析中位生存时间,绘制ROC曲线对模型进行预测效能评价。将预后预测模型应用于测试集患者,检验预后预测模型的预测效能。结果筛选出287个转移性乳腺癌组织DEGs,包括29个高表达基因和258个低表达基因,DEGs的功能主要涉及乳腺癌细胞增殖和迁移、细胞外基质调节降解、血管生成、免疫炎症反应等,其中EGFR、GEM、PTPRB、RARRES1、LAMA4、NFAT5、LHFP等7个基因为转移性乳腺癌患者预后相关关键基因。构建训练集转移性乳腺癌患者预后预测模型:风险值=(0.279×EGFR)+(0.704×GEM)+(0.326×PTPRB)+(0.138×RARRES1)+(-0.570×LAMA4)+(0.262×NFAT5)+(-0.555×LHFP)。训练集中高风险组患者的中位生存时间明显低于低风险组患者(P<0.001),转移性乳腺癌患者3年生存率的曲线下面积为0.787。测试集中高风险组患者的中位生存时间亦明显低于低风险组患者(P<0.05),转移性乳腺癌患者3年生存率的曲线下面积为0.785。结论成功构建了由EGFR、GEM、PTPRB、RARRES1、LAMA4、NFAT5、LHFP等7个基因组成的转移性乳腺癌患者预后预测模型。
Objective To screen out the key genes-related to the prognosis of patients with metastatic breast cancer,and to construct and verify the prognosis-prediction model of patients with metastatic breast cancer.Methods The gene expression dataset GSE124648 of metastatic breast cancer tissue was obtained from GEO database to screen out the differentially expressed genes(DEGs)between metastatic breast cancer tissues and primary breast cancer tissues,and between metastatic breast cancer tissues and normal breast tissues.The functional enrichment of Gene Ontology(GO)and the analysis of Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway were carried out.Data sets of 140 patients with metastatic breast cancer were randomly divided into the training set(72 cases)and test set(68 cases).We used LASSO&COX regression model to screen out the key genes-related to the prognosis of patients with metastatic breast cancer in the training set,and constructed the prognosis prediction model of patients with metastatic breast cancer in the training set.According to the median risk value,the patients in the training set were divided into the high-risk group and low-risk group.The Kaplan-Meier survival curve was drawn to analyze the median survival time,and the ROC curve was drawn to evaluate the predictive effectiveness of the risk model.The prediction model was applied to the patients in the test set to determine the prediction efficiency of the prediction model.Results A total of 287 DEGs of metastatic breast cancer tissues were screened out,including 29 high expression genes and 258 low expression genes.The functions of DEGs were mainly related to the proliferation and migration of breast cancer cells,the regulation and degradation of extracellular matrix,angiogenesis,and immune inflammatory reaction.Among them,7 key genes-related to prognosis were EGFR,GEM,PTPRB,RARRES1,LAMA4,NFAT5 and LHFP.We constructed a prediction model for prognosis of metastatic breast cancer patients with training set:risk value=(0.279×EGFR)+(0.704×GEM)+(0.326×PTPRB)+(0.138×RARRES1)+(-0.570×LAMA4)+(0.262×NFAT5)+(-0.555×LHFP).In the training set,the median survival time of the high-risk group was significantly shorter than that of low-risk group(P<0.001),and the area under the curve of 3-year survival rate of patients with metastatic breast cancer was 0.787.In the test set,the median survival time of the high-risk group was significantly shorter than that of low-risk group(P<0.05),and the area under the curve of 3-year survival rate of patients with metastatic breast cancer was 0.785.Conclusion The prognosis-prediction model including seven genes EGFR,GEM,PTPRB,RARRES1,LAMA4,NFAT5 and LHFP is successfully constructed to predict the prognosis of patients with metastatic breast cancer.
作者
毛昀
蔡亚芳
谢飞宇
薛鹏
朱世杰
MAO Yun;CAI Yafang;XIE Feiyu;XUE Peng;ZHU Shijie(Wangjing Hospital of China Academy of Chinese Medical Sciences,Beijing 100102,China)
出处
《山东医药》
CAS
2020年第21期1-5,共5页
Shandong Medical Journal
基金
国家自然科学基金资助项目(81973640)。
关键词
乳腺肿瘤
乳腺癌
转移性乳腺癌
差异表达基因
预后预测模型
breast neoplasms
breast carcinoma
metastatic breast cancer
differentially expressed genes
prognosis-prediction model
