Recently, cancer immunotherapies have been life-savers, however, only a fraction of treated patients have durable responses. Consequently, statistical methods that enable the discovery of target genes for developing new treatments and predicting patient survival are of importance. This paper introduced a network-based survival analysis method and applied it to identify candidate genes as possible targets for developing new treatments. RNA-seq data from a mouse study was used to select differentially expressed genes, which were then translated to those in humans. We constructed a gene network and identified gene clusters using a training set of 310 human gliomas. Then we conducted gene set enrichment analysis to select the gene clusters with significant biological function. A penalized Cox model was built to identify a small set of candidate genes to predict survival. An independent set of 690 human glioma samples was used to evaluate predictive accuracy of the survival model. The areas under time-dependent ROC curves in both the training and validation sets are more than 90%, indicating strong association between selected genes and patient survival. Consequently, potential biomedical interventions targeting these genes might be able to alter their expressions and prolong patient survival.