Because of the variety of factors affecting glioma prognosis, prediction of patient survival is particularly difficult. Protein-protein interaction (PPI) networks have been considered with regard to how their spatial characteristics relate to glioma. However, the dynamic nature of PPIs in vivo makes them temporally and spatially complex events. Integration of prognosis-specific co-expression information adds further dynamic features to these networks. Although some biomarkers for glioma prognosis have been identified, none is sufficient for accurate prediction of either prognosis or improved survival. We have established co-expressed protein-interaction networks that integrate protein-protein interactions with glioma gene-expression profiles related to different survival times. Biomarkers related to glioma prognosis were identified by comparative analysis of the dynamic features of the glioma prognosis network, particularly subnetworks. Four significantly differently expressed genes (SDEGs) are upregulated and ten SDEGs downregulated as lifetime is extended. In addition, 97 enhanced differently co-expressed protein interactions (DCPIs) and 99 weakened DCPIs were associated with glioma patient lifetime extension. We propose a method for estimating glioma prognosis on the basis of the construction of a dynamic modular network. We have used this method to identify dynamic genes and interactions related to glioma prognosis. Among these, enhanced MYC expression was related to lifetime extension, as were interactions between E2F1 and RB1 and between EGFR and p38. This method is a novel means of studying the molecular mechanisms determining prognosis in glioma.