Identifying suutype specific network-Uiomarkers of breast cancer survivauility

Abstract

Current studies of breast cancer find small subsets of gene biomarkers able to accurately predict the survivability of patients. In these studies, the selected genes are not necessarily functionally related, and hence, they may not correctly indicate the molecular mechanism behind breast cancer survivability. Also, several studies have shown there is a very low overlap between the different respective biomarkers subsets for the same cancer disease. To improve the robustness of classification performance and stability of detected biomarkers, recent methods take existing knowledge on relations between genes into account in the classifier, by aggregating functionality related genes to produce discriminative gene subnetworks called network-biomarkers. In this paper, given a breast cancer dataset of patients with different subtypes, we devise a novel network-based approach by integrating protein-protein interaction network (PPI) with gene expression data (1) to identify the network-biomarkers (metagene) of breast cancer survivability and (2) to predict the survivability of breast cancer patients based on subtypes. Our method uses the concept of seed gene for identification of network- biomarkers, ADASYN to solve class imbalance and random forest to predict survivability of patients. We obtained best classification performance with distance 3 from seed gene protein where the gmean, fl-measure and accuracy are respectively 0.900, 0.800 and 90.34%. The maximum size of a network biomarkers with distance 3 is 9. Maximum 34 genes are needed to predict survivability of patients.