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A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma

BACKGROUND: As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all abo...

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Detalles Bibliográficos
Autores principales: Wen, Jun, Zhao, Wenting, Shu, Xiaolei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909527/
https://www.ncbi.nlm.nih.gov/pubmed/36776374
http://dx.doi.org/10.3389/fonc.2022.1087762
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author Wen, Jun
Zhao, Wenting
Shu, Xiaolei
author_facet Wen, Jun
Zhao, Wenting
Shu, Xiaolei
author_sort Wen, Jun
collection PubMed
description BACKGROUND: As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG. METHODS: RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA. RESULTS: We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well. CONCLUSION: Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients’ survival of LGG and provide ideas for the exploration of new targets for LGG in the future.
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spelling pubmed-99095272023-02-10 A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma Wen, Jun Zhao, Wenting Shu, Xiaolei Front Oncol Oncology BACKGROUND: As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG. METHODS: RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA. RESULTS: We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well. CONCLUSION: Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients’ survival of LGG and provide ideas for the exploration of new targets for LGG in the future. Frontiers Media S.A. 2023-01-26 /pmc/articles/PMC9909527/ /pubmed/36776374 http://dx.doi.org/10.3389/fonc.2022.1087762 Text en Copyright © 2023 Wen, Zhao and Shu 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 Oncology
Wen, Jun
Zhao, Wenting
Shu, Xiaolei
A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma
title A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma
title_full A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma
title_fullStr A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma
title_full_unstemmed A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma
title_short A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma
title_sort novel cuproptosis-related lncrna signature: prognostic and therapeutic value for low grade glioma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909527/
https://www.ncbi.nlm.nih.gov/pubmed/36776374
http://dx.doi.org/10.3389/fonc.2022.1087762
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