ArticleBMC medical genomics2023
Prognosis prediction based on methionine metabolism genes signature in gliomas.
Article in BMC medical genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Hp1bp3 loss links chromatin reorganization to metabolic vulnerability in glioma.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Amino acid metabolism in glioblastoma pathogenesis, immune evasion, and treatment resistance.Cancer cell international · 2025Review
- A novel golgi related genes based correlation prognostic index can better predict the prognosis of glioma and responses to immunotherapy.Discover oncology · 2025Article
- The Nuclear-Encoded Cytochrome c Oxidase Subunit COX4-1 Enhances Hypoxia Tolerance in Glioblastoma Cells.Journal of oncology research and therapy · 2025Article
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9 authors.
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Abstract
backgroundGlioma cells have increased intake and metabolism of methionine, which can be monitored with 11 C-L-methionine. However, a short half-life of 11 C (~ 20 min) limits its application in clinical practice. It is necessary to develop a methionine metabolism genes-based prediction model for a more convenient prediction of glioma survival.
methodsWe evaluated the patterns of 29 methionine metabolism genes in glioma from the Cancer Genome Atlas (TCGA). A risk model was established using Lasso regression analysis and Cox regression. The reliability of the prognostic model was validated in derivation and validation cohorts (Chinese Glioma Genome Atlas; CGGA). GO, KEGG, GSEA and ESTIMATE analyses were performed for biological functions and immune characterization.
resultsOur results showed that a majority of the methionine metabolism genes (25 genes) were involved in the overall survival of glioma (logrank p and Cox p < 0.05). A 7-methionine metabolism prognostic signature was significantly related to a poor clinical prognosis and overall survival of glioma patients (C-index = 0.83). Functional analysis revealed that the risk model was correlated with immune responses and with epithelial-mesenchymal transition. Furthermore, the nomogram integrating the signature of methionine metabolism genes manifested a strong prognostic ability in the training and validation groups.
conclusionsThe current model had the potential to improve the understanding of methionine metabolism in gliomas and contributed to the development of precise treatment for glioma patients, showing a promising application in clinical practice.
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