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Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma
Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151008/ https://www.ncbi.nlm.nih.gov/pubmed/32214002 http://dx.doi.org/10.3390/diagnostics10030177 |
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author | Liang, Ping Chai, Yi Zhao, He Wang, Guihuai |
author_facet | Liang, Ping Chai, Yi Zhao, He Wang, Guihuai |
author_sort | Liang, Ping |
collection | PubMed |
description | Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify predictive biomarkers for GBM. Genomic data and clinical characteristic information of patients with GBM were evaluated by Kaplan–Meier analysis and proportional hazard modeling. Deseq2 software was used for differential expression analysis. Immune-related genes from ImmPort Shared Data and the Cistrome Project were evaluated. The model performance was determined based on the area under the receiver operating characteristic (ROC) curve. CIBERSORT was used to assess the infiltration of immune cells. The results of differential expression analyses showed a significant difference in the expression levels of 2942 genes, comprising 1338 upregulated genes and 1604 downregulated genes (p < 0.05). A population of 24 immune-related genes that predicted GBM patient survival was identified. A risk score model established on the basis of the expressions of the 24 immune-related genes was used to evaluate a favorable outcome of GBM. Further validation using the ROC curve confirmed the model was an independent predictor of GBM (AUC = 0.869). In the GBM microenvironment, eosinophils, macrophages, activated NK cells, and follicular helper T cells were associated with prognostic risk. Our study confirmed the importance of immune-related genes and immune infiltrates in predicting GBM patient prognosis. |
format | Online Article Text |
id | pubmed-7151008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71510082020-04-20 Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma Liang, Ping Chai, Yi Zhao, He Wang, Guihuai Diagnostics (Basel) Article Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify predictive biomarkers for GBM. Genomic data and clinical characteristic information of patients with GBM were evaluated by Kaplan–Meier analysis and proportional hazard modeling. Deseq2 software was used for differential expression analysis. Immune-related genes from ImmPort Shared Data and the Cistrome Project were evaluated. The model performance was determined based on the area under the receiver operating characteristic (ROC) curve. CIBERSORT was used to assess the infiltration of immune cells. The results of differential expression analyses showed a significant difference in the expression levels of 2942 genes, comprising 1338 upregulated genes and 1604 downregulated genes (p < 0.05). A population of 24 immune-related genes that predicted GBM patient survival was identified. A risk score model established on the basis of the expressions of the 24 immune-related genes was used to evaluate a favorable outcome of GBM. Further validation using the ROC curve confirmed the model was an independent predictor of GBM (AUC = 0.869). In the GBM microenvironment, eosinophils, macrophages, activated NK cells, and follicular helper T cells were associated with prognostic risk. Our study confirmed the importance of immune-related genes and immune infiltrates in predicting GBM patient prognosis. MDPI 2020-03-24 /pmc/articles/PMC7151008/ /pubmed/32214002 http://dx.doi.org/10.3390/diagnostics10030177 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liang, Ping Chai, Yi Zhao, He Wang, Guihuai Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma |
title | Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma |
title_full | Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma |
title_fullStr | Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma |
title_full_unstemmed | Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma |
title_short | Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma |
title_sort | predictive analyses of prognostic-related immune genes and immune infiltrates for glioblastoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151008/ https://www.ncbi.nlm.nih.gov/pubmed/32214002 http://dx.doi.org/10.3390/diagnostics10030177 |
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