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Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis
Background: Glioma is the most common primary tumor of the central nervous system. The conventional glioma treatment strategies include surgical excision and chemo- and radiation-therapy. Interferon Gamma (IFN-γ) is a soluble dimer cytokine involved in immune escape of gliomas. In this study, we sou...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880184/ https://www.ncbi.nlm.nih.gov/pubmed/36712869 http://dx.doi.org/10.3389/fgene.2022.1053263 |
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author | Zhang, Zhe Shen, Xiaoli Tan, Zilong Mei, Yuran Lu, Tianzhu Ji, Yulong Cheng, Sida Xu, Yu Wang, Zekun Liu, Xinxian He, Wei Chen, Zhen Chen, Shuhui Lv, Qiaoli |
author_facet | Zhang, Zhe Shen, Xiaoli Tan, Zilong Mei, Yuran Lu, Tianzhu Ji, Yulong Cheng, Sida Xu, Yu Wang, Zekun Liu, Xinxian He, Wei Chen, Zhen Chen, Shuhui Lv, Qiaoli |
author_sort | Zhang, Zhe |
collection | PubMed |
description | Background: Glioma is the most common primary tumor of the central nervous system. The conventional glioma treatment strategies include surgical excision and chemo- and radiation-therapy. Interferon Gamma (IFN-γ) is a soluble dimer cytokine involved in immune escape of gliomas. In this study, we sought to identify IFN-γ-related genes to construct a glioma prognostic model to guide its clinical treatment. Methods: RNA sequences and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the China Glioma Genome Atlas (CGGA). Using univariate Cox analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, IFN-γ-related prognostic genes were selected to construct a risk scoring model, and analyze its correlation with the clinical features. A high-precision nomogram was drawn to predict prognosis, and its performance was evaluated using calibration curve. Finally, immune cell infiltration and immune checkpoint molecule expression were analyzed to explore the tumor microenvironment characteristics associated with the risk scoring model. Results: Four out of 198 IFN-γ-related genes were selected to construct a risk score model with good predictive performance. The expression of four IFN-γ-related genes in glioma tissues was significantly increased compared to normal brain tissue (p < 0.001). Based on ROC analysis, the risk score model accurately predicted the overall survival rate of glioma patients at 1 year (AUC: The Cancer Genome Atlas 0.89, CGGA 0.59), 3 years (AUC: TCGA 0.89, CGGA 0.68), and 5 years (AUC: TCGA 0.88, CGGA 0.70). Kaplan-Meier analysis showed that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group (p < 0.0001). Moreover, high-risk scores were associated with wild-type IDH1, wild-type ATRX, and 1P/19Q non-co-deletion. The nomogram predicted the survival rate of glioma patients based on the risk score and multiple clinicopathological factors such as age, sex, pathological grade, and IDH Status, among others. Risk score and infiltrating immune cells including CD8 T-cell, resting CD4 memory T-cell, regulatory T-cell (Tregs), M2 macrophages, resting NK cells, activated mast cells, and neutrophils were positively correlated (p < 0.05). In addition, risk scores closely associated with expression of immune checkpoint molecules such as PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, TIGIT, CD48, CD226, and CD96. Conclusion: Our risk score model reveals that IFN-γ -associated genes are an independent prognostic factor for predicting overall survival in glioma, which is closely associated with immune cell infiltration and immune checkpoint molecule expression. This model will be helpful in predicting the effectiveness of immunotherapy and survival rate in patients with glioma. |
format | Online Article Text |
id | pubmed-9880184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98801842023-01-28 Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis Zhang, Zhe Shen, Xiaoli Tan, Zilong Mei, Yuran Lu, Tianzhu Ji, Yulong Cheng, Sida Xu, Yu Wang, Zekun Liu, Xinxian He, Wei Chen, Zhen Chen, Shuhui Lv, Qiaoli Front Genet Genetics Background: Glioma is the most common primary tumor of the central nervous system. The conventional glioma treatment strategies include surgical excision and chemo- and radiation-therapy. Interferon Gamma (IFN-γ) is a soluble dimer cytokine involved in immune escape of gliomas. In this study, we sought to identify IFN-γ-related genes to construct a glioma prognostic model to guide its clinical treatment. Methods: RNA sequences and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the China Glioma Genome Atlas (CGGA). Using univariate Cox analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, IFN-γ-related prognostic genes were selected to construct a risk scoring model, and analyze its correlation with the clinical features. A high-precision nomogram was drawn to predict prognosis, and its performance was evaluated using calibration curve. Finally, immune cell infiltration and immune checkpoint molecule expression were analyzed to explore the tumor microenvironment characteristics associated with the risk scoring model. Results: Four out of 198 IFN-γ-related genes were selected to construct a risk score model with good predictive performance. The expression of four IFN-γ-related genes in glioma tissues was significantly increased compared to normal brain tissue (p < 0.001). Based on ROC analysis, the risk score model accurately predicted the overall survival rate of glioma patients at 1 year (AUC: The Cancer Genome Atlas 0.89, CGGA 0.59), 3 years (AUC: TCGA 0.89, CGGA 0.68), and 5 years (AUC: TCGA 0.88, CGGA 0.70). Kaplan-Meier analysis showed that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group (p < 0.0001). Moreover, high-risk scores were associated with wild-type IDH1, wild-type ATRX, and 1P/19Q non-co-deletion. The nomogram predicted the survival rate of glioma patients based on the risk score and multiple clinicopathological factors such as age, sex, pathological grade, and IDH Status, among others. Risk score and infiltrating immune cells including CD8 T-cell, resting CD4 memory T-cell, regulatory T-cell (Tregs), M2 macrophages, resting NK cells, activated mast cells, and neutrophils were positively correlated (p < 0.05). In addition, risk scores closely associated with expression of immune checkpoint molecules such as PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, TIGIT, CD48, CD226, and CD96. Conclusion: Our risk score model reveals that IFN-γ -associated genes are an independent prognostic factor for predicting overall survival in glioma, which is closely associated with immune cell infiltration and immune checkpoint molecule expression. This model will be helpful in predicting the effectiveness of immunotherapy and survival rate in patients with glioma. Frontiers Media S.A. 2023-01-13 /pmc/articles/PMC9880184/ /pubmed/36712869 http://dx.doi.org/10.3389/fgene.2022.1053263 Text en Copyright © 2023 Zhang, Shen, Tan, Mei, Lu, Ji, Cheng, Xu, Wang, Liu, He, Chen, Chen and Lv. 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 | Genetics Zhang, Zhe Shen, Xiaoli Tan, Zilong Mei, Yuran Lu, Tianzhu Ji, Yulong Cheng, Sida Xu, Yu Wang, Zekun Liu, Xinxian He, Wei Chen, Zhen Chen, Shuhui Lv, Qiaoli Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
title | Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
title_full | Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
title_fullStr | Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
title_full_unstemmed | Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
title_short | Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
title_sort | interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880184/ https://www.ncbi.nlm.nih.gov/pubmed/36712869 http://dx.doi.org/10.3389/fgene.2022.1053263 |
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