Bioinformatic Profiling Identifies a Glucose-Related Risk Signature for the Malignancy of Glioma and the Survival of Patients.

The aim of this study is to investigate the glucose metabolic status and its prognostic value in glioma. The Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and GSE16011 datasets were used to develop the glucose-related signature. A cohort of 305 glioma samples with whole genome microarray expression data from the Chinese Glioma Genome Atlas database was included for discovery. TCGA and GSE16011 datasets were used for validation. Gene Set Enrichment Analysis (GSEA) and Cytoscape were used to explore the bioinformatic implication. GSEA revealed the biological process associated with the glucose-related signature. Cytoscape visualized the correlation analysis among the genes. We also collected the blood glucose information of patients with gliomas to analyze the association with tumor malignancy and patients' survival. In this study, we identified that glucose-related gene sets could distinguish the clinical and molecular features of gliomas, involved in the malignancy of gliomas. And then, we developed a glucose-related prognostic signature for patients with glioblastoma in the CGGA dataset, validated in other additional public datasets. GSEA illustrated that tumor with higher risk score of glucose-related signature could correlate with cell cycle phase. In addition, blood glucose concentration was associated with the malignancy of glioma and the survival of patients. These results might provide new view for the research of glioma malignancy and individual treatment. Our research provided important resources for future dissection of glucose metabolic role in glioma.