Predicting the Risk of Breast Cancer Recurrence and Metastasis based on miRNA Expression

Abstract

Background: Even after surgery, breast cancer patients still suffer from recurrence and metastasis. Thus, it is critical to predict accurately the risk of recurrence and metastasis for individual patients, which can help determine the appropriate adjuvant therapy. Methods: The purpose of this study is to investigate and compare the performance of several categories of molecular biomarkers, i.e., microRNA (miRNA), long non-coding RNA (lncRNA), messenger RNA (mRNA), and copy number variation (CNV), in predicting the risk of breast cancer recurrence and metastasis. First, the molecular data (miRNA, lncRNA, mRNA, and CNV) of 483 breast cancer patients were downloaded from the Cancer Genome Atlas, which were then randomly divided into the training and test sets with a ratio of 7:3. Second, the feature selection process was applied by univariate Cox and multivariate Cox variance analysis on the training set (e.g., 15 miRNAs). According to the selected features (e.g., 15 miRNAs), a random forest classifier and several other classification methods were established according to the label of recurrence and metastasis. Finally, the performances of the classification models were compared and evaluated on the test set. Results: The area under the ROC curve was 0.70 for miRNA, better than those using other biomarkers. Conclusion: These results indicated that miRNA has important guiding significance in predicting recurrence and metastasis of breast cancer.