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Glioma has one of the highest mortality rates of all cancer types; however, the prognosis cannot be predicted effectively using clinical indicators, due to the biological heterogeneity of the disease. A total of 31 gene expression-based signatures were identified using selected features in The Cancer Genome Atlas cohorts and machine learning methods. The signatures were assayed in the training dataset and were further validated in four completely independent datasets. Association analyses were implemented, and the results indicated that the signature was not significantly associated with age, radiation therapy or primary tumor size. A nomogram for the 1-year overall survival rate of patients with glioma following initial diagnosis was plotted to facilitate the clinical utilization of the signature. Gene Set Enrichment Analysis was performed based on the signature, in order to determine the potential altered pathways. Metabolic pathways were determined to be significantly enriched. In summary, the 31 gene expression-based signatures were effective and robust in predicting the clinical outcome of glioma in 1,016 glioma samples in five independent international cohorts.

31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma