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Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma

Few breakthroughs have been achieved in the treatment of lower-grade glioma (LGG) in recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study id...

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Autores principales: Zhu, Yunyang, Feng, Songwei, Song, Zhaoming, Wang, Zhong, Chen, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136245/
https://www.ncbi.nlm.nih.gov/pubmed/35646050
http://dx.doi.org/10.3389/fgene.2022.894865
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author Zhu, Yunyang
Feng, Songwei
Song, Zhaoming
Wang, Zhong
Chen, Gang
author_facet Zhu, Yunyang
Feng, Songwei
Song, Zhaoming
Wang, Zhong
Chen, Gang
author_sort Zhu, Yunyang
collection PubMed
description Few breakthroughs have been achieved in the treatment of lower-grade glioma (LGG) in recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study identified four immunotypes of lower-grade glioma (clusters A, B, C, and D) by bioinformatics methods in TCGA-LGG and two CGGA datasets. Cluster A was an “immune-cold” phenotype with the lowest immune infiltration and longest survival expectation, whereas cluster D was an “immune-rich” subtype with the highest immune infiltration and poor survival expectation. The expression of immune checkpoints increased along with immune infiltration degrees among the clusters. It was notable that immune clusters correlated with a variety of clinical and immunogenomic factors such as age, WHO grades, IDH1/2 mutation, PTEN, EGFR, ATRX, and TP53 status. In addition, LGGs in cluster D were sensitive to cisplatin, gemcitabine, and immune checkpoint PD-1 inhibitors. RTK-RAS and TP53 pathways were affected in cluster D. Functional pathways such as cytokine–cytokine receptor interaction, antigen processing and presentation, cell adhesion molecules (CAMs), and ECM–receptor interaction were also enriched in cluster D. Hub genes were selected by the Matthews correlation coefficient (MCC) algorithm in the blue module of a gene co-expression network. Our studies might provide an immunogenomics subtyping reference for immunotherapy in LGG.
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spelling pubmed-91362452022-05-28 Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma Zhu, Yunyang Feng, Songwei Song, Zhaoming Wang, Zhong Chen, Gang Front Genet Genetics Few breakthroughs have been achieved in the treatment of lower-grade glioma (LGG) in recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study identified four immunotypes of lower-grade glioma (clusters A, B, C, and D) by bioinformatics methods in TCGA-LGG and two CGGA datasets. Cluster A was an “immune-cold” phenotype with the lowest immune infiltration and longest survival expectation, whereas cluster D was an “immune-rich” subtype with the highest immune infiltration and poor survival expectation. The expression of immune checkpoints increased along with immune infiltration degrees among the clusters. It was notable that immune clusters correlated with a variety of clinical and immunogenomic factors such as age, WHO grades, IDH1/2 mutation, PTEN, EGFR, ATRX, and TP53 status. In addition, LGGs in cluster D were sensitive to cisplatin, gemcitabine, and immune checkpoint PD-1 inhibitors. RTK-RAS and TP53 pathways were affected in cluster D. Functional pathways such as cytokine–cytokine receptor interaction, antigen processing and presentation, cell adhesion molecules (CAMs), and ECM–receptor interaction were also enriched in cluster D. Hub genes were selected by the Matthews correlation coefficient (MCC) algorithm in the blue module of a gene co-expression network. Our studies might provide an immunogenomics subtyping reference for immunotherapy in LGG. Frontiers Media S.A. 2022-05-13 /pmc/articles/PMC9136245/ /pubmed/35646050 http://dx.doi.org/10.3389/fgene.2022.894865 Text en Copyright © 2022 Zhu, Feng, Song, Wang and Chen. 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
Zhu, Yunyang
Feng, Songwei
Song, Zhaoming
Wang, Zhong
Chen, Gang
Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma
title Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma
title_full Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma
title_fullStr Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma
title_full_unstemmed Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma
title_short Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma
title_sort identification of immunological characteristics and immune subtypes based on single-sample gene set enrichment analysis algorithm in lower-grade glioma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136245/
https://www.ncbi.nlm.nih.gov/pubmed/35646050
http://dx.doi.org/10.3389/fgene.2022.894865
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