Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway
BackgroundRecent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironments are crucial for improving treatment strategies a...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1512491/full |
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| author | YaoFeng Hu Sen Qin RuCui Deng |
| author_facet | YaoFeng Hu Sen Qin RuCui Deng |
| author_sort | YaoFeng Hu |
| collection | DOAJ |
| description | BackgroundRecent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironments are crucial for improving treatment strategies and patient outcomes.MethodsWe integrated glioma datasets from multiple sources, employing Non-negative Matrix Factorization (NMF) to cluster samples and filter for differentially expressed metabolic genes. Additionally, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify key genes. A predictive model was developed utilizing the optimal consistency index derived from a combination of 101 machine learning techniques, and its effectiveness was confirmed through multiple datasets employing different methodologies. In-depth analyses were conducted on immune cell infiltration and tumor microenvironmental aspects. Single-cell sequencing data were employed for clustering and differential expression analysis of genes associated with glioma. Finally, the immune relevance of the model gene ALPK1 in the context of pan-cancer was explored, including its relationship with immune checkpoints.ResultsThe application of NMF, coupled with differential analysis of metabolic-related genes, led to the identification of two clusters exhibiting significant differences in survival, age, and metabolic gene expression among patients. Core genes were identified through WGCNA, and a total of 101 machine learning models were constructed, with LASSO+GBM selected as the optimal model, demonstrating robust validation performance. Comprehensive analyses revealed that high-risk groups exhibited greater expression of specific genes, with ALPK1 showing significant correlations with immune regulation.ConclusionThis research employed a multi-dataset strategy and various methods to clarify the differences in metabolic traits and immune conditions in glioma patients, while creating an innovative prognostic risk evaluation framework. These results offer fresh perspectives on the intricate biological processes that define gliomas. |
| format | Article |
| id | doaj-art-bc8e33176cde4887b454ddfc0d495cff |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-bc8e33176cde4887b454ddfc0d495cff2025-08-20T02:45:30ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15124911512491Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathwayYaoFeng Hu0Sen Qin1RuCui Deng2Department of Neurological Care Unit, The First Affiliated Hospital of YangTze University, Jingzhou, Hubei, ChinaDepartment of Orthopedics, The First Affiliated Hospital of YangTze University, Jingzhou, Hubei, ChinaDepartment of Neurological Care Unit, The First Affiliated Hospital of YangTze University, Jingzhou, Hubei, ChinaBackgroundRecent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironments are crucial for improving treatment strategies and patient outcomes.MethodsWe integrated glioma datasets from multiple sources, employing Non-negative Matrix Factorization (NMF) to cluster samples and filter for differentially expressed metabolic genes. Additionally, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify key genes. A predictive model was developed utilizing the optimal consistency index derived from a combination of 101 machine learning techniques, and its effectiveness was confirmed through multiple datasets employing different methodologies. In-depth analyses were conducted on immune cell infiltration and tumor microenvironmental aspects. Single-cell sequencing data were employed for clustering and differential expression analysis of genes associated with glioma. Finally, the immune relevance of the model gene ALPK1 in the context of pan-cancer was explored, including its relationship with immune checkpoints.ResultsThe application of NMF, coupled with differential analysis of metabolic-related genes, led to the identification of two clusters exhibiting significant differences in survival, age, and metabolic gene expression among patients. Core genes were identified through WGCNA, and a total of 101 machine learning models were constructed, with LASSO+GBM selected as the optimal model, demonstrating robust validation performance. Comprehensive analyses revealed that high-risk groups exhibited greater expression of specific genes, with ALPK1 showing significant correlations with immune regulation.ConclusionThis research employed a multi-dataset strategy and various methods to clarify the differences in metabolic traits and immune conditions in glioma patients, while creating an innovative prognostic risk evaluation framework. These results offer fresh perspectives on the intricate biological processes that define gliomas.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1512491/fullgliomametabolic genesimmune microenvironmentALPK1prognostic biomarkers |
| spellingShingle | YaoFeng Hu Sen Qin RuCui Deng Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway Frontiers in Immunology glioma metabolic genes immune microenvironment ALPK1 prognostic biomarkers |
| title | Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway |
| title_full | Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway |
| title_fullStr | Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway |
| title_full_unstemmed | Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway |
| title_short | Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway |
| title_sort | impact of glioma metabolism related gene alpk1 on tumor immune heterogeneity and the regulation of the tgf β pathway |
| topic | glioma metabolic genes immune microenvironment ALPK1 prognostic biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1512491/full |
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