Cargando…

Development of a prognostic model of glioma based on immune-related genes

Glioma is the most common type of primary brain cancer, and the prognosis of most patients with glioma, and particularly that of patients with glioblastoma, is poor. Tumor immunity serves an important role in the development of glioma. However, immunotherapy for glioma has not been completely succes...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Jing-Jing, Wang, Han, Zhu, Bao-Long, Wang, Xiang, Qian, Yong-Hong, Xie, Lei, Wang, Wen-Jie, Zhu, Jie, Chen, Xing-Yu, Wang, Jing-Mei, Ding, Zhi-Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751470/
https://www.ncbi.nlm.nih.gov/pubmed/33376548
http://dx.doi.org/10.3892/ol.2020.12377
_version_ 1783625673984704512
author Wang, Jing-Jing
Wang, Han
Zhu, Bao-Long
Wang, Xiang
Qian, Yong-Hong
Xie, Lei
Wang, Wen-Jie
Zhu, Jie
Chen, Xing-Yu
Wang, Jing-Mei
Ding, Zhi-Liang
author_facet Wang, Jing-Jing
Wang, Han
Zhu, Bao-Long
Wang, Xiang
Qian, Yong-Hong
Xie, Lei
Wang, Wen-Jie
Zhu, Jie
Chen, Xing-Yu
Wang, Jing-Mei
Ding, Zhi-Liang
author_sort Wang, Jing-Jing
collection PubMed
description Glioma is the most common type of primary brain cancer, and the prognosis of most patients with glioma, and particularly that of patients with glioblastoma, is poor. Tumor immunity serves an important role in the development of glioma. However, immunotherapy for glioma has not been completely successful, and thus, comprehensive examination of the immune-related genes (IRGs) of glioma is required. In the present study, differentially expressed genes (DEGs) and differentially expressed IRGs (DEIRGs) were identified using the edgeR package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used for functional enrichment analysis of DEIRGs. Survival-associated IRGs were selected via univariate Cox regression analysis. A The Cancer Genome Atlas prognostic model and GSE43378 validation model were established using lasso-penalized Cox regression analysis. Based on the median risk score value, patients were divided into high-risk and low-risk groups for clinical analysis. Receiver operating characteristic curve and nomogram analyses were used to assess the accuracy of the models. Reverse transcription-quantitative PCR was performed to measure the expression levels of relevant genes, such as cyclin-dependent kinase 4 (CDK4), interleukin 24 (IL24), NADPH oxidase 4 (NOX4), bone morphogenetic protein 2 (BMP2) and baculoviral IAP repeat containing 5 (BIRC5). A total of 3,238 DEGs, including 1,950 upregulated and 1,288 downregulated DEGs, and 97 DEIRGs, including 60 upregulated and 37 downregulated DEIRGs, were identified. ‘Neuroactive ligand-receptor interaction’ and ‘Cytokine-cytokine receptor interaction’ were the most significantly enriched pathways according to KEGG pathway analysis. A prognostic model and a validation prognostic model were created for glioma, including 15 survival-associated IRGs (FCER1G, NOX4, TRIM5, SOCS1, APOBEC3C, BIRC5, VIM, TNC, BMP2, CMTM3, IL24, JAG1, CALCRL, HNF4G and CDK4). Furthermore, multivariate Cox regression analysis results suggested that age, high WHO Grade by histopathology, wild type isocitrate dehydrogenase 1 and high risk score were independently associated with poor overall survival. The infiltration of B cells, CD8(+) T cells, dendritic cells, macrophages and neutrophils was positively associated with the prognostic risk score. In the present study, several clinically significant survival-associated IRGs were identified, and a prognosis evaluation model of glioma was established.
format Online
Article
Text
id pubmed-7751470
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher D.A. Spandidos
record_format MEDLINE/PubMed
spelling pubmed-77514702020-12-28 Development of a prognostic model of glioma based on immune-related genes Wang, Jing-Jing Wang, Han Zhu, Bao-Long Wang, Xiang Qian, Yong-Hong Xie, Lei Wang, Wen-Jie Zhu, Jie Chen, Xing-Yu Wang, Jing-Mei Ding, Zhi-Liang Oncol Lett Articles Glioma is the most common type of primary brain cancer, and the prognosis of most patients with glioma, and particularly that of patients with glioblastoma, is poor. Tumor immunity serves an important role in the development of glioma. However, immunotherapy for glioma has not been completely successful, and thus, comprehensive examination of the immune-related genes (IRGs) of glioma is required. In the present study, differentially expressed genes (DEGs) and differentially expressed IRGs (DEIRGs) were identified using the edgeR package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used for functional enrichment analysis of DEIRGs. Survival-associated IRGs were selected via univariate Cox regression analysis. A The Cancer Genome Atlas prognostic model and GSE43378 validation model were established using lasso-penalized Cox regression analysis. Based on the median risk score value, patients were divided into high-risk and low-risk groups for clinical analysis. Receiver operating characteristic curve and nomogram analyses were used to assess the accuracy of the models. Reverse transcription-quantitative PCR was performed to measure the expression levels of relevant genes, such as cyclin-dependent kinase 4 (CDK4), interleukin 24 (IL24), NADPH oxidase 4 (NOX4), bone morphogenetic protein 2 (BMP2) and baculoviral IAP repeat containing 5 (BIRC5). A total of 3,238 DEGs, including 1,950 upregulated and 1,288 downregulated DEGs, and 97 DEIRGs, including 60 upregulated and 37 downregulated DEIRGs, were identified. ‘Neuroactive ligand-receptor interaction’ and ‘Cytokine-cytokine receptor interaction’ were the most significantly enriched pathways according to KEGG pathway analysis. A prognostic model and a validation prognostic model were created for glioma, including 15 survival-associated IRGs (FCER1G, NOX4, TRIM5, SOCS1, APOBEC3C, BIRC5, VIM, TNC, BMP2, CMTM3, IL24, JAG1, CALCRL, HNF4G and CDK4). Furthermore, multivariate Cox regression analysis results suggested that age, high WHO Grade by histopathology, wild type isocitrate dehydrogenase 1 and high risk score were independently associated with poor overall survival. The infiltration of B cells, CD8(+) T cells, dendritic cells, macrophages and neutrophils was positively associated with the prognostic risk score. In the present study, several clinically significant survival-associated IRGs were identified, and a prognosis evaluation model of glioma was established. D.A. Spandidos 2021-02 2020-12-15 /pmc/articles/PMC7751470/ /pubmed/33376548 http://dx.doi.org/10.3892/ol.2020.12377 Text en Copyright: © Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Wang, Jing-Jing
Wang, Han
Zhu, Bao-Long
Wang, Xiang
Qian, Yong-Hong
Xie, Lei
Wang, Wen-Jie
Zhu, Jie
Chen, Xing-Yu
Wang, Jing-Mei
Ding, Zhi-Liang
Development of a prognostic model of glioma based on immune-related genes
title Development of a prognostic model of glioma based on immune-related genes
title_full Development of a prognostic model of glioma based on immune-related genes
title_fullStr Development of a prognostic model of glioma based on immune-related genes
title_full_unstemmed Development of a prognostic model of glioma based on immune-related genes
title_short Development of a prognostic model of glioma based on immune-related genes
title_sort development of a prognostic model of glioma based on immune-related genes
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751470/
https://www.ncbi.nlm.nih.gov/pubmed/33376548
http://dx.doi.org/10.3892/ol.2020.12377
work_keys_str_mv AT wangjingjing developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT wanghan developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT zhubaolong developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT wangxiang developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT qianyonghong developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT xielei developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT wangwenjie developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT zhujie developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT chenxingyu developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT wangjingmei developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes
AT dingzhiliang developmentofaprognosticmodelofgliomabasedonimmunerelatedgenes