Brain Cancer Prediction Using Machine Learning Methods and High-Throughput Molecular Data

Abstract

Background Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with short survival time, poor prognosis and high mortality. Although research based on molecular data plays a crucial role in predicting cancer, few studies have been developed to combine high-throughput molecular data with clinical variables. Our goal is to establish machine learning based models to predict GBM survival. Methods The molecular data and clinical information of GBM were downloaded from The Cancer Genome Atlas (TCGA) database. The molecular data include somatic copy-number alteration (SCNA) and microRNA. Logistic regression and support vector machine (SVM) were used to establish the predictive models using molecular data, clinical variables and their combinations. Receiver operating characteristic (ROC) was conducted to estimate the sensitivity and specificity. Results The AUC scores are 0.72, 0.69 and 0.82, respectively using clinical variables, SCNA data and their combinations. The AUC scores are 0.97 and 0.98, respectively using microRNA data and the combinations of microRNA data and clinical variables. Conclusion Our study developed machine learning based models to predict GBM survival. The combination of molecular data and clinical variables can improve prediction accuracy. Moreover, it also brings us new insights into the molecular mechanisms underlying GBM cancer. Acknowledgements Supported by the National Natural Science Foundation of China (61471078, 31270903), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Program for Liaoning Excellent Talents in University (LJQ2015011) and the Fundamental Research Funds for the Central Universities (3132016330, 3132014306, 3132015213, and 3132017075).