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Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network

Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but wide...

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Autores principales: Zheng, Jianglin, Qiu, Yue, Wu, Zhipeng, Wang, Xuan, Jiang, Xiaobing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464945/
https://www.ncbi.nlm.nih.gov/pubmed/36105808
http://dx.doi.org/10.3389/fimmu.2022.944030
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author Zheng, Jianglin
Qiu, Yue
Wu, Zhipeng
Wang, Xuan
Jiang, Xiaobing
author_facet Zheng, Jianglin
Qiu, Yue
Wu, Zhipeng
Wang, Xuan
Jiang, Xiaobing
author_sort Zheng, Jianglin
collection PubMed
description Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM.
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spelling pubmed-94649452022-09-13 Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network Zheng, Jianglin Qiu, Yue Wu, Zhipeng Wang, Xuan Jiang, Xiaobing Front Immunol Immunology Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM. Frontiers Media S.A. 2022-08-29 /pmc/articles/PMC9464945/ /pubmed/36105808 http://dx.doi.org/10.3389/fimmu.2022.944030 Text en Copyright © 2022 Zheng, Qiu, Wu, Wang and Jiang 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 Immunology
Zheng, Jianglin
Qiu, Yue
Wu, Zhipeng
Wang, Xuan
Jiang, Xiaobing
Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
title Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
title_full Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
title_fullStr Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
title_full_unstemmed Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
title_short Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
title_sort exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464945/
https://www.ncbi.nlm.nih.gov/pubmed/36105808
http://dx.doi.org/10.3389/fimmu.2022.944030
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