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Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma
Inflammatory response plays a crucial role in the development and progression of gliomas. Whereas the prognostic esteem of inflammatory response-related genes has never been comprehensively explored in glioma, the RNA-seq information and clinical data of patients with glioma were extracted from TCGA...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516783/ https://www.ncbi.nlm.nih.gov/pubmed/37488455 http://dx.doi.org/10.1007/s12031-023-02142-x |
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author | Zhao, Zhen Zheng, Baoping Zheng, Jianglin Zhang, Yi Jiang, Cheng Nie, Chuansheng Jiang, Xiaobing Yao, Dongxiao Zhao, Hongyang |
author_facet | Zhao, Zhen Zheng, Baoping Zheng, Jianglin Zhang, Yi Jiang, Cheng Nie, Chuansheng Jiang, Xiaobing Yao, Dongxiao Zhao, Hongyang |
author_sort | Zhao, Zhen |
collection | PubMed |
description | Inflammatory response plays a crucial role in the development and progression of gliomas. Whereas the prognostic esteem of inflammatory response-related genes has never been comprehensively explored in glioma, the RNA-seq information and clinical data of patients with glioma were extracted from TCGA, CGGA, and Rembrandt databases. The differentially expressed genes (DEGs) were picked out between glioma tissue and non-tumor brain tissue (NBT). Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct the prognostic signature in the TCGA cohort and verified in other cohorts. Kaplan–Meier survival analyses were conducted to compare the overall survival (OS) between the high and low-risk groups. Univariate and multivariate Cox analyses were subsequently used to confirm the independent prognostic factors of OS, and then, the nomogram was established based them. Furthermore, immune infiltration, immune checkpoints, and immunotherapy were also probed and compared between high and low-risk groups. The four genes were also analyzed by qRT-PCR, immunohistochemistry, and western blot trials between glioma tissue and NBT. The 39 DEGs were identified between glioma tissue and NBT, of which 31 genes are associated to the prognosis of glioma. The 8 optimal inflammatory response-related genes were selected to construct the prognostic inflammatory response-related signature (IRRS) through the LASSO regression. The effectiveness of the IRRS was verified in the TCGA, CGGA, and Rembrandt cohorts. Meanwhile, a nomogram with better accuracy was established to predict OS based on the independent prognostic factors. The IRRS was highly correlated with clinicopathological features, immune infiltration, and genomic alterations in glioma patients. In addition, four selective genes also verified the difference between glioma tissue and NBT. A novel prognostic signature was associated with the prognosis, immune infiltration, and immunotherapy effect in patients with gliomas. Thus, this study could provide a perspective for glioma prognosis and treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12031-023-02142-x. |
format | Online Article Text |
id | pubmed-10516783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-105167832023-09-24 Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma Zhao, Zhen Zheng, Baoping Zheng, Jianglin Zhang, Yi Jiang, Cheng Nie, Chuansheng Jiang, Xiaobing Yao, Dongxiao Zhao, Hongyang J Mol Neurosci Research Inflammatory response plays a crucial role in the development and progression of gliomas. Whereas the prognostic esteem of inflammatory response-related genes has never been comprehensively explored in glioma, the RNA-seq information and clinical data of patients with glioma were extracted from TCGA, CGGA, and Rembrandt databases. The differentially expressed genes (DEGs) were picked out between glioma tissue and non-tumor brain tissue (NBT). Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct the prognostic signature in the TCGA cohort and verified in other cohorts. Kaplan–Meier survival analyses were conducted to compare the overall survival (OS) between the high and low-risk groups. Univariate and multivariate Cox analyses were subsequently used to confirm the independent prognostic factors of OS, and then, the nomogram was established based them. Furthermore, immune infiltration, immune checkpoints, and immunotherapy were also probed and compared between high and low-risk groups. The four genes were also analyzed by qRT-PCR, immunohistochemistry, and western blot trials between glioma tissue and NBT. The 39 DEGs were identified between glioma tissue and NBT, of which 31 genes are associated to the prognosis of glioma. The 8 optimal inflammatory response-related genes were selected to construct the prognostic inflammatory response-related signature (IRRS) through the LASSO regression. The effectiveness of the IRRS was verified in the TCGA, CGGA, and Rembrandt cohorts. Meanwhile, a nomogram with better accuracy was established to predict OS based on the independent prognostic factors. The IRRS was highly correlated with clinicopathological features, immune infiltration, and genomic alterations in glioma patients. In addition, four selective genes also verified the difference between glioma tissue and NBT. A novel prognostic signature was associated with the prognosis, immune infiltration, and immunotherapy effect in patients with gliomas. Thus, this study could provide a perspective for glioma prognosis and treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12031-023-02142-x. Springer US 2023-07-25 2023 /pmc/articles/PMC10516783/ /pubmed/37488455 http://dx.doi.org/10.1007/s12031-023-02142-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Research Zhao, Zhen Zheng, Baoping Zheng, Jianglin Zhang, Yi Jiang, Cheng Nie, Chuansheng Jiang, Xiaobing Yao, Dongxiao Zhao, Hongyang Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma |
title | Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma |
title_full | Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma |
title_fullStr | Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma |
title_full_unstemmed | Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma |
title_short | Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma |
title_sort | integrative analysis of inflammatory response-related gene for predicting prognosis and immunotherapy in glioma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516783/ https://www.ncbi.nlm.nih.gov/pubmed/37488455 http://dx.doi.org/10.1007/s12031-023-02142-x |
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