Pathways identification in cancer survival analysis by network-based Cox models
Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. However, available datasets consist of a small number of samples (n patients) and a large number of gene expression data (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches have been proposed. In our analysis, we compare four network penalized Cox models for high-dimensional survival data aimed to determine pathway structures and biomarkers involved in cancer progression. Using these network-based models, it is possible to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to the cancer survival time. We evaluate cancer survival prediction to illustrate the benefits and drawbacks of the network techniques and to understand how patient features (i.e. age, gender and coexisting diseases-comorbidity) can influence cancer treatment, detection and outcome. In particular, we show results obtained in simulation and real cancer datasets using the Functional Linkage network, as network prior information.