A gene signature for breast cancer prognosis using support vector machine

Abstract

Breast cancer is a common disease in elderly women. With the development of microarray technique, discovering gene signature became a powerful approach in predicting survival of breast cancer. Previously, a 70-gene signature had been discovered for breast cancer prognosis prediction and received a good performance. In this study we adopted an efficient feature selection method: the support vector machine-based recursive feature elimination (SVM-RFE) approach for gene selection and prognosis prediction. Using the leave-one-out evaluation procedure on a gene expression dataset including 295 breast cancer patients, we discovered a 50-gene signature that by combing with SVM, achieved a superior prediction performance with 34%, 48% and 3% improvement in Accuracy, Sensitivity and Specificity, compared with the widely used 70-gene signature. Further analysis shows that the 50-gene signature is effective in predicting the prognoses of metastases and distinguishing patient who should receive adjuvant therapy.