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Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma

BACKGROUND: Although isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDH(WT)) and lack reliable signatures to identify distinct entities. METHODS: To develop...

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Autores principales: Wang, Shiqun, Li, Lu, Zuo, Shuguang, Kong, Lingkai, Wei, Jiwu, Dong, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561761/
https://www.ncbi.nlm.nih.gov/pubmed/36248907
http://dx.doi.org/10.3389/fimmu.2022.869061
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author Wang, Shiqun
Li, Lu
Zuo, Shuguang
Kong, Lingkai
Wei, Jiwu
Dong, Jie
author_facet Wang, Shiqun
Li, Lu
Zuo, Shuguang
Kong, Lingkai
Wei, Jiwu
Dong, Jie
author_sort Wang, Shiqun
collection PubMed
description BACKGROUND: Although isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDH(WT)) and lack reliable signatures to identify distinct entities. METHODS: To develop a robust prognostic signature for IDH(WT) GBM patients, we retrospectively analyzed 4 public datasets of 377 primary frozen tumor tissue transcriptome profiling and clinical follow-up data. Samples were divided into a training dataset (204 samples) and a validation (173 samples) dataset. A prognostic signature consisting of 21 metabolism-related gene pairs (MRGPs) was developed based on the relative ranking of single-sample gene expression levels. GSEA and immune subtype analyses were performed to reveal differences in biological processes between MRGP risk groups. The single-cell RNA-seq dataset was used to examine the expression distribution of each MRG constituting the signature in tumor tissue subsets. Finally, the association of MRGs with tumor progression was biologically validated in orthotopic GBM models. RESULTS: The metabolic signature remained an independent prognostic factor (hazard ratio, 5.71 [3.542-9.218], P < 0.001) for stratifying patients into high- and low-risk levels in terms of overall survival across subgroups with MGMTp methylation statuses, expression subtypes, and chemo/ratio therapies. Immune-related biological processes were significantly different between MRGP risk groups. Compared with the low-risk group, the high-risk group was significantly enriched in humoral immune responses and phagocytosis processes, and had more monocyte infiltration and less activated DC, NK, and γδ T cell infiltration. scRNA-seq dataset analysis identified that the expression levels of 5 MRGs (ABCA1, HMOX1, MTHFD2, PIM1, and PTPRE) in TAMs increased with metabolic risk. With tumor progression, the expression level of ABCA1 in TAMs was positively correlated with the population of TAMs in tumor tissue. Downregulation of ABCA1 levels can promote TAM polarization towards an inflammatory phenotype and control tumor growth. CONCLUSIONS: The metabolic signature is expected to be used in the individualized management of primary IDH(WT) GBM patients.
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spelling pubmed-95617612022-10-15 Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma Wang, Shiqun Li, Lu Zuo, Shuguang Kong, Lingkai Wei, Jiwu Dong, Jie Front Immunol Immunology BACKGROUND: Although isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDH(WT)) and lack reliable signatures to identify distinct entities. METHODS: To develop a robust prognostic signature for IDH(WT) GBM patients, we retrospectively analyzed 4 public datasets of 377 primary frozen tumor tissue transcriptome profiling and clinical follow-up data. Samples were divided into a training dataset (204 samples) and a validation (173 samples) dataset. A prognostic signature consisting of 21 metabolism-related gene pairs (MRGPs) was developed based on the relative ranking of single-sample gene expression levels. GSEA and immune subtype analyses were performed to reveal differences in biological processes between MRGP risk groups. The single-cell RNA-seq dataset was used to examine the expression distribution of each MRG constituting the signature in tumor tissue subsets. Finally, the association of MRGs with tumor progression was biologically validated in orthotopic GBM models. RESULTS: The metabolic signature remained an independent prognostic factor (hazard ratio, 5.71 [3.542-9.218], P < 0.001) for stratifying patients into high- and low-risk levels in terms of overall survival across subgroups with MGMTp methylation statuses, expression subtypes, and chemo/ratio therapies. Immune-related biological processes were significantly different between MRGP risk groups. Compared with the low-risk group, the high-risk group was significantly enriched in humoral immune responses and phagocytosis processes, and had more monocyte infiltration and less activated DC, NK, and γδ T cell infiltration. scRNA-seq dataset analysis identified that the expression levels of 5 MRGs (ABCA1, HMOX1, MTHFD2, PIM1, and PTPRE) in TAMs increased with metabolic risk. With tumor progression, the expression level of ABCA1 in TAMs was positively correlated with the population of TAMs in tumor tissue. Downregulation of ABCA1 levels can promote TAM polarization towards an inflammatory phenotype and control tumor growth. CONCLUSIONS: The metabolic signature is expected to be used in the individualized management of primary IDH(WT) GBM patients. Frontiers Media S.A. 2022-09-30 /pmc/articles/PMC9561761/ /pubmed/36248907 http://dx.doi.org/10.3389/fimmu.2022.869061 Text en Copyright © 2022 Wang, Li, Zuo, Kong, Wei and Dong https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Wang, Shiqun
Li, Lu
Zuo, Shuguang
Kong, Lingkai
Wei, Jiwu
Dong, Jie
Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma
title Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma
title_full Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma
title_fullStr Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma
title_full_unstemmed Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma
title_short Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDH(WT) glioblastoma
title_sort metabolic-related gene pairs signature analysis identifies abca1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary idh(wt) glioblastoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561761/
https://www.ncbi.nlm.nih.gov/pubmed/36248907
http://dx.doi.org/10.3389/fimmu.2022.869061
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