Prognosis prediction based on methionine metabolism genes signature in gliomas
Abstract Background 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 mod...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12920-023-01754-x |
| _version_ | 1827590306137112576 |
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| author | Sujin Zhou Xianan Zhao Shiwei Zhang Xue Tian Xuepeng Wang Yunping Mu Fanghong Li Allan Z. Zhao Zhenggang Zhao |
| author_facet | Sujin Zhou Xianan Zhao Shiwei Zhang Xue Tian Xuepeng Wang Yunping Mu Fanghong Li Allan Z. Zhao Zhenggang Zhao |
| author_sort | Sujin Zhou |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
| first_indexed | 2024-03-09T01:15:02Z |
| format | Article |
| id | doaj.art-c72a1c2fded34dc0a62f3bb4667ef9ad |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-03-09T01:15:02Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-c72a1c2fded34dc0a62f3bb4667ef9ad2023-12-10T12:35:06ZengBMCBMC Medical Genomics1755-87942023-12-0116111310.1186/s12920-023-01754-xPrognosis prediction based on methionine metabolism genes signature in gliomasSujin Zhou0Xianan Zhao1Shiwei Zhang2Xue Tian3Xuepeng Wang4Yunping Mu5Fanghong Li6Allan Z. Zhao7Zhenggang Zhao8School of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologyAbstract Background 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. Methods 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. Results 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. Conclusions 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.https://doi.org/10.1186/s12920-023-01754-xMethionine metabolismGliomaPrognostic modelOverall survivalGene signature |
| spellingShingle | Sujin Zhou Xianan Zhao Shiwei Zhang Xue Tian Xuepeng Wang Yunping Mu Fanghong Li Allan Z. Zhao Zhenggang Zhao Prognosis prediction based on methionine metabolism genes signature in gliomas BMC Medical Genomics Methionine metabolism Glioma Prognostic model Overall survival Gene signature |
| title | Prognosis prediction based on methionine metabolism genes signature in gliomas |
| title_full | Prognosis prediction based on methionine metabolism genes signature in gliomas |
| title_fullStr | Prognosis prediction based on methionine metabolism genes signature in gliomas |
| title_full_unstemmed | Prognosis prediction based on methionine metabolism genes signature in gliomas |
| title_short | Prognosis prediction based on methionine metabolism genes signature in gliomas |
| title_sort | prognosis prediction based on methionine metabolism genes signature in gliomas |
| topic | Methionine metabolism Glioma Prognostic model Overall survival Gene signature |
| url | https://doi.org/10.1186/s12920-023-01754-x |
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