ArticleMolecular neurobiology2017
Bioinformatic Profiling Identifies a Glucose-Related Risk Signature for the Malignancy of Glioma and the Survival of Patients.
Article in Molecular neurobiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review.Frontiers in oncology · 2021Pooled it
- Targeting Metabolic Vulnerabilities in Glioblastoma: a Framework for Multi-node Combination Therapy.Current oncology reports · 2026Review
- Development of a fatty acid metabolism (FAM)-related gene signature for prognosis prediction and personalized therapy in lower grade gliomas.Translational cancer research · 2025Article
- Natural killer cell-related gene signature predicts malignancy of glioma and the survival of patients.BMC cancer · 2022Article
- Identification of an IL-4-Related Gene Risk Signature for Malignancy, Prognosis and Immune Phenotype Prediction in Glioma.Brain sciences · 2022Article
- A Novel Glutamine Metabolism-Related Gene Signature in Prognostic Prediction of Osteosarcoma.International journal of general medicine · 2022Article
- Metabolic management of microenvironment acidity in glioblastoma.Frontiers in oncology · 2022Review
- Ketogenic Metabolic Therapy, Without Chemo or Radiation, for the Long-Term Management ofFrontiers in nutrition · 2021Article
- A Multi-Center Validated Subtyping Model of Esophageal Cancer Based on Three Metabolism-Related Genes.Frontiers in oncology · 2021Article
- Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma.Cell communication and signaling : CCS · 2020Article
- Development and validation of metabolism-related gene signature in prognostic prediction of gastric cancer.Computational and structural biotechnology journal · 2020Article
- Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma.BioMed research international · 2020Article
- Provocative Question: Should Ketogenic Metabolic Therapy Become the Standard of Care for Glioblastoma?Neurochemical research · 2019Article
- Bioinformatic Profiling Identifies a Fatty Acid Metabolism-Related Gene Risk Signature for Malignancy, Prognosis, and Immune Phenotype of Glioma.Disease markers · 2019Article
- The clinical significance of collagen family gene expression in esophageal squamous cell carcinoma.PeerJ · 2019Article
- A comprehensive review of available omics data resources and molecular profiling for precision glioma studies.Biomedical reports · 2019Review
- Amino acid metabolism-related gene expression-based risk signature can better predict overall survival for glioma.Cancer science · 2019Article
- Mesenchymal glioblastoma constitutes a major ceRNA signature in the TGF-β pathway.Theranostics · 2018Article
- Management of Glioblastoma Multiforme in a Patient Treated With Ketogenic Metabolic Therapy and Modified Standard of Care: A 24-Month Follow-Up.Frontiers in nutrition · 2018Article
- Effects of acute exposure to low-dose radiation on the characteristics of human bone marrow mesenchymal stromal/stem cells.Inflammation and regeneration · 2017Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
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.
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