Prognosis prediction based on methionine metabolism genes signature in gliomas

• Published in: BMC medical genomics 2023-12, Vol.16 (1), p.317-317, Article 317
• Main Authors: Zhou, Sujin, Zhao, Xianan, Zhang, Shiwei, Tian, Xue, Wang, Xuepeng, Mu, Yunping, Li, Fanghong, Zhao, Allan Z, Zhao, Zhenggang
• Format: Article
• Language: English
• Subjects: Amino acid metabolism; Amino acids; Analysis; Biomarkers; Brain cancer; Cancer; Gene expression; Gene signature; Genes; Genetic aspects; Genomes; Genomics; Glioma; Glioma - genetics; Glioma cells; Gliomas; Health aspects; Humans; Immune response; Immunology; Medical prognosis; Metabolism; Methionine; Methionine metabolism; Mutation; Oncology, Experimental; Overall survival; Patients; Physiological aspects; Prediction models; Prognosis; Prognostic model; Racemethionine; Regression analysis; Reproducibility of Results; Risk assessment; Software; Stem cells; Survival analysis; Tumors
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-023-01754-x

Glioma 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. We 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. Our 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. The 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.