Network-Based Variable Selection for Survival Outcomes in Oncological Data

The accessibility to “big data” sets down an ambitious challenge in the medical field, especially in personalized medicine, where gene expression data are increasingly being used to establish a diagnosis and optimize treatment of oncological patients. However, the high-dimensionality nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. Additionally, the network structure of gene expression data has fostered the development of network-based regularization techniques to convey data into a low-dimensional and interpretable level. In this work, classical elastic net and two recently proposed network-based methods, HubCox and OrphanCox, are applied to high-dimensional gene expression data, to model survival data. An oncological transcriptomic dataset obtained from The Cancer Genome Atlas (TCGA) is used, with patients’ RNA-seq measurements as covariates. The application of sparsity-inducing techniques to the dataset enabled the selection of relevant genes over a range of parameters evaluated. Comparable results were obtained for the elastic net and the network-based OrphanCox regarding model performance and genes selected.