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Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma

BACKGROUND: Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper‐induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but coppe...

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Autores principales: Li, Ling, Leng, Wenyuan, Chen, Junying, Li, Shaoying, Lei, Bingxi, Zhang, Huasong, Zhao, Huiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166918/
https://www.ncbi.nlm.nih.gov/pubmed/36856182
http://dx.doi.org/10.1002/cam4.5688
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author Li, Ling
Leng, Wenyuan
Chen, Junying
Li, Shaoying
Lei, Bingxi
Zhang, Huasong
Zhao, Huiying
author_facet Li, Ling
Leng, Wenyuan
Chen, Junying
Li, Shaoying
Lei, Bingxi
Zhang, Huasong
Zhao, Huiying
author_sort Li, Ling
collection PubMed
description BACKGROUND: Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper‐induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism‐related genes associated with cancer development remain unclear. METHODS: We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism‐related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis. RESULTS: The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high‐risk group or low‐risk group, and the low‐risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high‐risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower‐grade glioma, and non‐tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients. CONCLUSIONS: Our study could contribute to the prognosis prediction and decision‐making in patients with gliomas.
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spelling pubmed-101669182023-05-10 Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma Li, Ling Leng, Wenyuan Chen, Junying Li, Shaoying Lei, Bingxi Zhang, Huasong Zhao, Huiying Cancer Med Research Articles BACKGROUND: Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper‐induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism‐related genes associated with cancer development remain unclear. METHODS: We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism‐related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis. RESULTS: The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high‐risk group or low‐risk group, and the low‐risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high‐risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower‐grade glioma, and non‐tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients. CONCLUSIONS: Our study could contribute to the prognosis prediction and decision‐making in patients with gliomas. John Wiley and Sons Inc. 2023-03-01 /pmc/articles/PMC10166918/ /pubmed/36856182 http://dx.doi.org/10.1002/cam4.5688 Text en © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Li, Ling
Leng, Wenyuan
Chen, Junying
Li, Shaoying
Lei, Bingxi
Zhang, Huasong
Zhao, Huiying
Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
title Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
title_full Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
title_fullStr Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
title_full_unstemmed Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
title_short Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
title_sort identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166918/
https://www.ncbi.nlm.nih.gov/pubmed/36856182
http://dx.doi.org/10.1002/cam4.5688
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