ArticleBioMed research international2021
A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A novel cuproptosis-related prognostic gene signature is identified by machine learning and integrative analyses in gliomas.Molecular and cellular biochemistry · 2026Article
- Interpretable machine learning-driven multi-omics risk stratification and drug repurposing nominates Treg/Th17 with gluconeogenesis/lactylation integration as a prognostic and druggable biomarker for glioblastoma patients.Frontiers in oncology · 2026Article
- Glioblastoma insights: Focus on crosstalk with the suppressive tumor microenvironment and advanced therapeutic strategies.Medicine · 2025Review
- Crosstalk between the aryl hydrocarbon receptor and hypoxia-inducible factor 1α pathways in human islet models.Islets · 2025Article
- Transcriptomic-Driven Drug Repurposing Reveals SP600125 as a Promising Drug Candidate for the Treatment of Glial-Mesenchymal Transition in Glioblastoma.International journal of molecular sciences · 2025Article
- Differential expression of glucose metabolism and circadian rhythm-related genes in patients with acute myocardial infarction and their clinical significance.Scientific reports · 2025Article
- A novel prognostic risk score associated with resistance to docetaxel chemotherapy for predicting biochemical recurrence-free survival in patients with prostate cancer.Discover oncology · 2025Article
- Transcriptomic analysis of identical twins with different onset ages of adrenoleukodystrophy.Frontiers in neuroscience · 2025Article
- ZFP64 drives glycolysis-mediated stem cell-like properties and tumorigenesis in breast cancer.Biology direct · 2024Article
- T cell-mediated tumor killing based signature to predict the prognosis and immunotherapy for glioblastoma.Heliyon · 2024Article
- Metabolic Contrasts: Fatty Acid Oxidation and Ketone Bodies in Healthy Brains vs. Glioblastoma Multiforme.International journal of molecular sciences · 2024Review
- Identifying prognostic characteristics of m6A-related glycolysis gene and predicting the immune infiltration landscape in bladder cancer.Cancer cell international · 2023Article
- The Role of Ketone Bodies in Treatment Individualization of Glioblastoma Patients.Brain sciences · 2023Article
- Cancer-associated fibroblasts facilitate breast cancer progression through exosomal circTBPL1-mediated intercellular communication.Cell death & disease · 2023Article
- Development and validation of a two glycolysis-related LncRNAs prognostic signature for glioma and in vitro analyses.Cell division · 2023Article
- Article
- Identification and validation of potential hypoxia-related genes associated with coronary artery disease.Frontiers in physiology · 2023Article
- TGF-β based risk model to predict the prognosis and immune features in glioblastoma.Frontiers in neurology · 2023Article
- A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data.Medicine · 2022Article
- Natural killer cell awakening: unleash cancer-immunity cycle against glioblastoma.Cell death & disease · 2022Review
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Authors and funding
8 authors.
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Abstract
introductionGlioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients.
methodsIn this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM.
resultsDepending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, 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. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups.
conclusionThe 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.
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