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An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration

As the traditional treatment for glioma, the most common central nervous system malignancy with poor prognosis, the efficacy of high-intensity surgery combined with radiotherapy and chemotherapy is not satisfactory. The development of individualized scientific treatment strategy urgently requires th...

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Autores principales: Fan, Xin, Zhang, Lingling, Huang, Junwen, Zhong, Yun, Fan, Yanting, Zhou, Tong, Lu, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114310/
https://www.ncbi.nlm.nih.gov/pubmed/35601497
http://dx.doi.org/10.3389/fgene.2022.889629
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author Fan, Xin
Zhang, Lingling
Huang, Junwen
Zhong, Yun
Fan, Yanting
Zhou, Tong
Lu, Min
author_facet Fan, Xin
Zhang, Lingling
Huang, Junwen
Zhong, Yun
Fan, Yanting
Zhou, Tong
Lu, Min
author_sort Fan, Xin
collection PubMed
description As the traditional treatment for glioma, the most common central nervous system malignancy with poor prognosis, the efficacy of high-intensity surgery combined with radiotherapy and chemotherapy is not satisfactory. The development of individualized scientific treatment strategy urgently requires the guidance of signature with clinical predictive value. In this study, five prognosis-related differentially expressed immune-related genes (PR-DE-IRGs) (CCNA2, HMGB2, CASP3, APOBEC3C, and BMP2) highly associated with glioma were identified for a prognostic model through weighted gene co-expression network analysis, univariate Cox and lasso regression. Kaplan-Meier survival curves, receiver operating characteristic curves and other methods have shown that the model has good performance in predicting the glioma patients’ prognosis. Further combined nomogram provided better predictive performance. The signature’s guiding value in clinical treatment has also been verified by multiple analysis results. We also constructed a comprehensive competing endogenous RNA (ceRNA) regulatory network based on the protective factor BMP2 to further explore its potential role in glioma progression. Numerous immune-related biological functions and pathways were enriched in a high-risk population. Further multi-omics integrative analysis revealed a strong correlation between tumor immunosuppressive environment/IDH1 mutation and signature, suggesting that their cooperation plays an important role in glioma progression.
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spelling pubmed-91143102022-05-19 An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration Fan, Xin Zhang, Lingling Huang, Junwen Zhong, Yun Fan, Yanting Zhou, Tong Lu, Min Front Genet Genetics As the traditional treatment for glioma, the most common central nervous system malignancy with poor prognosis, the efficacy of high-intensity surgery combined with radiotherapy and chemotherapy is not satisfactory. The development of individualized scientific treatment strategy urgently requires the guidance of signature with clinical predictive value. In this study, five prognosis-related differentially expressed immune-related genes (PR-DE-IRGs) (CCNA2, HMGB2, CASP3, APOBEC3C, and BMP2) highly associated with glioma were identified for a prognostic model through weighted gene co-expression network analysis, univariate Cox and lasso regression. Kaplan-Meier survival curves, receiver operating characteristic curves and other methods have shown that the model has good performance in predicting the glioma patients’ prognosis. Further combined nomogram provided better predictive performance. The signature’s guiding value in clinical treatment has also been verified by multiple analysis results. We also constructed a comprehensive competing endogenous RNA (ceRNA) regulatory network based on the protective factor BMP2 to further explore its potential role in glioma progression. Numerous immune-related biological functions and pathways were enriched in a high-risk population. Further multi-omics integrative analysis revealed a strong correlation between tumor immunosuppressive environment/IDH1 mutation and signature, suggesting that their cooperation plays an important role in glioma progression. Frontiers Media S.A. 2022-05-03 /pmc/articles/PMC9114310/ /pubmed/35601497 http://dx.doi.org/10.3389/fgene.2022.889629 Text en Copyright © 2022 Fan, Zhang, Huang, Zhong, Fan, Zhou and Lu. 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
Fan, Xin
Zhang, Lingling
Huang, Junwen
Zhong, Yun
Fan, Yanting
Zhou, Tong
Lu, Min
An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration
title An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration
title_full An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration
title_fullStr An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration
title_full_unstemmed An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration
title_short An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration
title_sort integrated immune-related bioinformatics analysis in glioma: prognostic signature’s identification and multi-omics mechanisms’ exploration
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114310/
https://www.ncbi.nlm.nih.gov/pubmed/35601497
http://dx.doi.org/10.3389/fgene.2022.889629
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