Background: Glioblastoma (GBM) is one of the most common primary intracranial malignancies, with limited treatment options and poor overall survival (OS). Metabolic changes in GBM have attracted wide attention in recent years, and one of the main metabolic features of cancer cells is the high level of glycolysis. Therefore, it is necessary to identify novel biomarkers associated with glycolysis in GBM. Methods: In this study, we performed gene set enrichment analysis and profiled four glycolysis-related gene sets, which revealed 327 genes associated with biological processes. Univariate and multivariate Cox regression analyses were performed to identify genes for constructing a risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI and TPBG) in the Cox proportional hazards regression model for GBM. Results: Based on this gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with better outcomes) subgroups. Multivariate Cox regression analysis showed that the prognostic power of this ten-gene signature is independent of clinical variables. Furthermore, we validated this model in two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT). In the functional analysis, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance and even an immune-suppressed microenvironment. Conclusion: The 10 glycolysis-related gene risk signature could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients.