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乳腺癌复发相关基因预测疾病的特异生存率研究

叶云, 黄临凌, 钟英英, 孙宇飞, 张倩

叶云, 黄临凌, 钟英英, 孙宇飞, 张倩. 乳腺癌复发相关基因预测疾病的特异生存率研究[J]. 肿瘤防治研究, 2016, 43(9): 762-766. DOI: 10.3971/j.issn.1000-8578.2016.09.007
引用本文: 叶云, 黄临凌, 钟英英, 孙宇飞, 张倩. 乳腺癌复发相关基因预测疾病的特异生存率研究[J]. 肿瘤防治研究, 2016, 43(9): 762-766. DOI: 10.3971/j.issn.1000-8578.2016.09.007
YE Yun, HUANG Linling, ZHONG Yingying, SUN Yufei, ZHANG Qian. Prediction of Disease Specific Survival Rates by Genes Associated with Relapse of Breast Cancer[J]. Cancer Research on Prevention and Treatment, 2016, 43(9): 762-766. DOI: 10.3971/j.issn.1000-8578.2016.09.007
Citation: YE Yun, HUANG Linling, ZHONG Yingying, SUN Yufei, ZHANG Qian. Prediction of Disease Specific Survival Rates by Genes Associated with Relapse of Breast Cancer[J]. Cancer Research on Prevention and Treatment, 2016, 43(9): 762-766. DOI: 10.3971/j.issn.1000-8578.2016.09.007

乳腺癌复发相关基因预测疾病的特异生存率研究

基金项目: 

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详细信息
    作者简介:

    叶云(1978-),男,博士,副教授,主要从事基因芯片应用的研究;黄临凌(1980-),女,本科,主治医师,主要从事肿瘤的临床诊断和治疗

    *:并列第一作者

  • 中图分类号: R737.9

Prediction of Disease Specific Survival Rates by Genes Associated with Relapse of Breast Cancer

    *: Contributed Equally as the First Author

  • 摘要:
    目的 

    复发是导致乳腺癌患者死亡的主要原因,通过研究与乳腺癌复发相关的分子标记有助于预测乳腺癌的预后。

    方法 

    本研究采用BRB-ArrayTools分析了两组乳腺癌基因芯片(GSE1456和GSE2034),筛选与复发相关的差异基因,并用Cox比例风险模型进行基因表达的单因素分析得到与生存显著相关的基因,用于GSE1456中肿瘤特异生存率的预测,通过留一法交叉验证计算错误分类率,用受试者工作特征(ROC)曲线评估预测结果。

    结果 

    用于预测的29个基因中,交叉验证准确率均超过96%,ROC曲线下面积为0.803,分类预测结果良好。通过基因功能注释,发现这些基因与细胞周期、细胞增殖、细胞运动与黏着及DNA修复等生物学功能相关,具有较强的肿瘤细胞特征。

    结论 

    基因表达谱分析为研究乳腺癌的发病机制提供了新思路, 也为转移性乳腺癌的分子诊断和个体化治疗奠定基础。

     

    Abstract:
    Objective 

    Relapse is responsible for the majority of deaths in breast cancer. Molecular marker related to relapse is helpful for the diagnosis and treatment of breast cancer.

    Methods 

    Two microarray datasets of breast cancer, GSE1456 and GSE2034, from GEO database were analyzed by software BRB-ArrayTools. Genes significantly associated with survival were obtained by univariate analysis and Cox proportional hazards model from differential genes related to relapse. These genes were used as candidate genes to predict specific survival rates in GSE1456. Leave-one-out cross-validation method was used to compute mis-classification rate. The result of prediction was assessed with receiver operating characteristic (ROC) curve.

    Results 

    Twenty-nine genes were used as the signature to predict the disease specific survival of breast cancer. Area under ROC curve was 0.803. Cross validation of 29 genes were all higher than 96%. The methods showed satisfactory classification result. Gene annotation analysis showed that these genes were associated with cell cycle, cell proliferation, DNA repair, cell motility and adhesion.

    Conclusion 

    The analysis of gene expression profiles may provide a new thought for understanding the pathogenesis of breast cancer, and is helpful for molecular diagnosis and individualized therapy.

     

  • 图  1   基因分类器预测乳腺癌样本的生存曲线

    Figure  1   Survival curves of breast cancer by gene classifier

    图  2   分类预测的ROC曲线

    Figure  2   ROC curve of classification

    图  3   STRING分析的蛋白相互作用

    Figure  3   Protein-protein interactions analyzed by STRING

    表  1   复发相关基因的预测分类器

    Table  1   Classifier of differential genes related to relapse

    下载: 导出CSV

    表  2   分类器基因的GATHER分析

    Table  2   Pathway analysis of genes in classifier by GATHER

    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-10-11
  • 修回日期:  2016-01-04
  • 网络出版日期:  2024-02-04
  • 刊出日期:  2016-08-31

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