A gene signature for breast cancer prognosis using support vector machine

• Published in: 2012 5th International Conference on Biomedical Engineering and Informatics pp. 928 - 931
• Main Authors: Xu, Xiaoyi, Zhang, Ya, Zou, Liang, Wang, Minghui, Li, Ao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Accuracy; Breast cancer; Feature extraction; feature selection; Gene expression; gene signature; Machine learning algorithms; Nearest neighbor methods; Prediction algorithms; Prognostics and health management; support vector machine; Support vector machines; Vectors
• ISBN: 9781467311830; 1467311839
• DOI: 10.1109/BMEI.2012.6513032

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.