乳腺癌复发相关基因预测疾病的特异生存率研究

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

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。

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 BRBArrayTools. 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.