乳腺癌外周血中分子检测标记的筛选

Identification of Gene Signature in Peripheral Blood of Breast Cancer

目的对乳腺癌患者和健康者血液样本的基因表达谱进行分析,从中发现检测乳腺癌的分子标记。方法以公共数据库GEO中表达谱数据GSE11545作为训练集,利用BRB-Array Tools软件提取乳腺癌/正常血液样本的差异表达基因作为候选基因,选取两组间差异水平小于0.001的基因,通过复合变量预测、对角线线性判别分析、最邻近算法和支持向量机四种不同的方法对验证集GSE27562中的样本进行分类预测,留一法交叉验证计算错误分类率,ROC曲线评估预测结果。结果训练集中乳腺癌与正常血液样本的显著差异基因为61个,从中筛选出39个基因作为分类器,四种不同的方法对验证集进行的分类预测准确率都基本达到甚至超过80%,ROC曲线下面积达到0.925,表明分类预测效果良好。结论基因芯片分析可以筛选出外周血中乳腺癌的分子标记,有望为乳腺癌的早期临床检测提供一种新的方法。

Objective To find out molecular signature in breast cancer(BC) for early detection by analyzing the gene expression profile in the peripheral blood of BC and healthy samples. Methods GSE11545 from GEO database was taken as training cohort in this paper. Differentially expressed genes between BC and healthy samples were obtained by BRB-Array Tools software. And these genes were used as candidate genes to predict classification in validation cohort GSE27562 by four methods including compound covariate predictor, diagonal linear discriminant analysis, 3-nearest neighbors and support vector machine. Only genes signif icantly differed between the classes at 0.001 signif icance level were used for class prediction. Leave-oneout cross-validation method was used to compute mis-classification rate. Result of prediction was assessed with receiver operating characteristic(ROC) curve. Results Sixty-one differential genes were obtained from the training cohort. 39-gene classifier was used to predict validation cohort. The accuracy rate of classification reached or exceeded 80% with four methods. Areas under ROC curve were 0.925. The methods showed satisfactory classification result. Conclusion Microarray analysis is an effective method in screening gene signature in the peripheral blood of BC. It may provide a new method for diagnosing breast cancer in early stage.