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A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them
Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug pert...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804649/ https://www.ncbi.nlm.nih.gov/pubmed/35116059 http://dx.doi.org/10.3389/fgene.2021.832627 |
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author | Ma, Shuzhi Guo, Zhen Wang, Bo Yang, Min Yuan, Xuelian Ji, Binbin Wu, Yan Chen, Size |
author_facet | Ma, Shuzhi Guo, Zhen Wang, Bo Yang, Min Yuan, Xuelian Ji, Binbin Wu, Yan Chen, Size |
author_sort | Ma, Shuzhi |
collection | PubMed |
description | Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas. |
format | Online Article Text |
id | pubmed-8804649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88046492022-02-02 A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them Ma, Shuzhi Guo, Zhen Wang, Bo Yang, Min Yuan, Xuelian Ji, Binbin Wu, Yan Chen, Size Front Genet Genetics Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas. Frontiers Media S.A. 2022-01-17 /pmc/articles/PMC8804649/ /pubmed/35116059 http://dx.doi.org/10.3389/fgene.2021.832627 Text en Copyright © 2022 Ma, Guo, Wang, Yang, Yuan, Ji, Wu 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 Ma, Shuzhi Guo, Zhen Wang, Bo Yang, Min Yuan, Xuelian Ji, Binbin Wu, Yan Chen, Size A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them |
title | A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them |
title_full | A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them |
title_fullStr | A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them |
title_full_unstemmed | A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them |
title_short | A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them |
title_sort | computational framework to identify biomarkers for glioma recurrence and potential drugs targeting them |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804649/ https://www.ncbi.nlm.nih.gov/pubmed/35116059 http://dx.doi.org/10.3389/fgene.2021.832627 |
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