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A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration

BACKGROUND: Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear....

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Autores principales: Zhang, Moxuan, Cheng, Yanhao, Xue, Zhengchun, Sun, Qiang, Zhang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8653573/
https://www.ncbi.nlm.nih.gov/pubmed/34876094
http://dx.doi.org/10.1186/s12885-021-09046-2
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author Zhang, Moxuan
Cheng, Yanhao
Xue, Zhengchun
Sun, Qiang
Zhang, Jian
author_facet Zhang, Moxuan
Cheng, Yanhao
Xue, Zhengchun
Sun, Qiang
Zhang, Jian
author_sort Zhang, Moxuan
collection PubMed
description BACKGROUND: Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear. METHODS: In this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort. RESULTS: In this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated. CONCLUSION: We established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09046-2.
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spelling pubmed-86535732021-12-08 A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration Zhang, Moxuan Cheng, Yanhao Xue, Zhengchun Sun, Qiang Zhang, Jian BMC Cancer Research BACKGROUND: Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear. METHODS: In this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort. RESULTS: In this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated. CONCLUSION: We established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09046-2. BioMed Central 2021-12-07 /pmc/articles/PMC8653573/ /pubmed/34876094 http://dx.doi.org/10.1186/s12885-021-09046-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Moxuan
Cheng, Yanhao
Xue, Zhengchun
Sun, Qiang
Zhang, Jian
A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
title A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
title_full A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
title_fullStr A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
title_full_unstemmed A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
title_short A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
title_sort novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8653573/
https://www.ncbi.nlm.nih.gov/pubmed/34876094
http://dx.doi.org/10.1186/s12885-021-09046-2
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