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Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma

Tumor mutation burden (TMB) is a useful biomarker to predict prognosis and the efficacy of immune checkpoint inhibitors (ICIs). In this study, we aimed to explore the prognostic value of TMB and the potential association between TMB and immune infiltration in lower-grade gliomas (LGGs). Somatic muta...

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Autores principales: Yin, Wen, Jiang, Xingjun, Tan, Jun, Xin, Zhaoqi, Zhou, Quanwei, Zhan, Chaohong, Fu, Xianyong, Wu, Zhaoping, Guo, Youwei, Jiang, Zhipeng, Ren, Caiping, Tang, Guihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468526/
https://www.ncbi.nlm.nih.gov/pubmed/32974146
http://dx.doi.org/10.3389/fonc.2020.01409
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author Yin, Wen
Jiang, Xingjun
Tan, Jun
Xin, Zhaoqi
Zhou, Quanwei
Zhan, Chaohong
Fu, Xianyong
Wu, Zhaoping
Guo, Youwei
Jiang, Zhipeng
Ren, Caiping
Tang, Guihua
author_facet Yin, Wen
Jiang, Xingjun
Tan, Jun
Xin, Zhaoqi
Zhou, Quanwei
Zhan, Chaohong
Fu, Xianyong
Wu, Zhaoping
Guo, Youwei
Jiang, Zhipeng
Ren, Caiping
Tang, Guihua
author_sort Yin, Wen
collection PubMed
description Tumor mutation burden (TMB) is a useful biomarker to predict prognosis and the efficacy of immune checkpoint inhibitors (ICIs). In this study, we aimed to explore the prognostic value of TMB and the potential association between TMB and immune infiltration in lower-grade gliomas (LGGs). Somatic mutation and RNA-sequencing (RNA-seq) data were downloaded from the Cancer Genome Atlas (TCGA) database. TMB was calculated and patients were divided into high- and low-TMB groups. After performing differential analysis between high- and low-risk groups, we identified six hub TMB and immune-related genes that were correlated with overall survival in LGGs. Then, Gene Set Enrichment Analysis was performed to screen significantly enriched GO terms between the two groups. Moreover, an immune-related risk score system was developed by LASSO Cox analysis based on the six hub genes and was validated with the Chinese Glioma Genome Atlas dataset. Using the TIMER database, we further systematically analyzed the relationships between mutants of the six hub genes and immune infiltration levels, as well as the relationships between the immune-related risk score system and the immune microenvironment in LGGs. The results showed that TMB was negatively correlated with OS and high TMB might inhibit immune infiltration in LGGs. Furthermore, the risk score system could effectively stratify patients into low- and high-risk groups in both the training and validation datasets. Multivariate Cox analysis demonstrated that TMB was not an independent prognostic factor, but the risk score was. Higher infiltration of immune cells (B cells, CD4(+) T cells, CD8(+) T cells, neutrophils, macrophages, and dendritic cells) and higher levels of immune checkpoints (PD-1, CTLA-4, LAG-3, and TIM-3) were found in patients in the high-risk group. Finally, a novel nomogram model was constructed and evaluated to estimate the overall survival of LGG patients. In summary, our study provided new insights into immune infiltration in the tumor microenvironment and immunotherapies for LGGs.
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spelling pubmed-74685262020-09-23 Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma Yin, Wen Jiang, Xingjun Tan, Jun Xin, Zhaoqi Zhou, Quanwei Zhan, Chaohong Fu, Xianyong Wu, Zhaoping Guo, Youwei Jiang, Zhipeng Ren, Caiping Tang, Guihua Front Oncol Oncology Tumor mutation burden (TMB) is a useful biomarker to predict prognosis and the efficacy of immune checkpoint inhibitors (ICIs). In this study, we aimed to explore the prognostic value of TMB and the potential association between TMB and immune infiltration in lower-grade gliomas (LGGs). Somatic mutation and RNA-sequencing (RNA-seq) data were downloaded from the Cancer Genome Atlas (TCGA) database. TMB was calculated and patients were divided into high- and low-TMB groups. After performing differential analysis between high- and low-risk groups, we identified six hub TMB and immune-related genes that were correlated with overall survival in LGGs. Then, Gene Set Enrichment Analysis was performed to screen significantly enriched GO terms between the two groups. Moreover, an immune-related risk score system was developed by LASSO Cox analysis based on the six hub genes and was validated with the Chinese Glioma Genome Atlas dataset. Using the TIMER database, we further systematically analyzed the relationships between mutants of the six hub genes and immune infiltration levels, as well as the relationships between the immune-related risk score system and the immune microenvironment in LGGs. The results showed that TMB was negatively correlated with OS and high TMB might inhibit immune infiltration in LGGs. Furthermore, the risk score system could effectively stratify patients into low- and high-risk groups in both the training and validation datasets. Multivariate Cox analysis demonstrated that TMB was not an independent prognostic factor, but the risk score was. Higher infiltration of immune cells (B cells, CD4(+) T cells, CD8(+) T cells, neutrophils, macrophages, and dendritic cells) and higher levels of immune checkpoints (PD-1, CTLA-4, LAG-3, and TIM-3) were found in patients in the high-risk group. Finally, a novel nomogram model was constructed and evaluated to estimate the overall survival of LGG patients. In summary, our study provided new insights into immune infiltration in the tumor microenvironment and immunotherapies for LGGs. Frontiers Media S.A. 2020-08-20 /pmc/articles/PMC7468526/ /pubmed/32974146 http://dx.doi.org/10.3389/fonc.2020.01409 Text en Copyright © 2020 Yin, Jiang, Tan, Xin, Zhou, Zhan, Fu, Wu, Guo, Jiang, Ren and Tang. http://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
Yin, Wen
Jiang, Xingjun
Tan, Jun
Xin, Zhaoqi
Zhou, Quanwei
Zhan, Chaohong
Fu, Xianyong
Wu, Zhaoping
Guo, Youwei
Jiang, Zhipeng
Ren, Caiping
Tang, Guihua
Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma
title Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma
title_full Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma
title_fullStr Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma
title_full_unstemmed Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma
title_short Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma
title_sort development and validation of a tumor mutation burden–related immune prognostic model for lower-grade glioma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468526/
https://www.ncbi.nlm.nih.gov/pubmed/32974146
http://dx.doi.org/10.3389/fonc.2020.01409
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