Cargando…
Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas
PURPOSE: Low-grade glioma is the most common type of primary intracranial tumour, and the overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past few decades. Therefore, it is crucial to understand the precise molecular mechanisms involved in the c...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254561/ https://www.ncbi.nlm.nih.gov/pubmed/34234557 http://dx.doi.org/10.2147/CMAR.S314011 |
_version_ | 1783717753066094592 |
---|---|
author | Wang, Ming Cui, Yan Cai, Yang Jiang, Yugang Peng, Yong |
author_facet | Wang, Ming Cui, Yan Cai, Yang Jiang, Yugang Peng, Yong |
author_sort | Wang, Ming |
collection | PubMed |
description | PURPOSE: Low-grade glioma is the most common type of primary intracranial tumour, and the overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past few decades. Therefore, it is crucial to understand the precise molecular mechanisms involved in the carcinogenesis of LGG. METHODS: To investigate the regulatory mechanisms of mRNA–miRNA networks related to LGG, in the present study, a comprehensive analysis of the genomic landscape between low-grade gliomas and normal brain tissues from the GEO and TCGA datasets was first conducted to identify differentially expressed genes (DEGs) and differentially expressed miRNAs in LGG. Following a series of analyses, including WGCNA, GO and KEGG analyses, PPI and key model analyses, and survival analysis of the DEGs with clinical phenotypes, the potential key genes were screened and identified, and the related miRNA–mRNA networks were subsequently constructed through miRWalk 3.0. Finally, the potential miRNA–mRNA networks were further validated in CGGA (Chinese Glioma Genome Atlas) datasets and clinical specimens by qRT-PCR. RESULTS: In our results, six hub genes, MELK, NCAPG, KIF4A, NUSAP1, CEP55, and TOP2A, were ultimately identified. Two regulatory pathways, miR-495-3p-TOP2A and miR-1224-3p-MELK, that regulate the pathogenesis of LGG were ultimately identified. Furthermore, the expression of miR-495-3p-TOP2A and miR-1224-3p-MELK in solid tissues was validated by qRT-PCR. CONCLUSION: Our study identified hub genes and related miRNA–mRNA regulatory pathways that contribute to the carcinogenesis of LGG, which may help us reveal the mechanisms underlying the development of LGG. |
format | Online Article Text |
id | pubmed-8254561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-82545612021-07-06 Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas Wang, Ming Cui, Yan Cai, Yang Jiang, Yugang Peng, Yong Cancer Manag Res Original Research PURPOSE: Low-grade glioma is the most common type of primary intracranial tumour, and the overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past few decades. Therefore, it is crucial to understand the precise molecular mechanisms involved in the carcinogenesis of LGG. METHODS: To investigate the regulatory mechanisms of mRNA–miRNA networks related to LGG, in the present study, a comprehensive analysis of the genomic landscape between low-grade gliomas and normal brain tissues from the GEO and TCGA datasets was first conducted to identify differentially expressed genes (DEGs) and differentially expressed miRNAs in LGG. Following a series of analyses, including WGCNA, GO and KEGG analyses, PPI and key model analyses, and survival analysis of the DEGs with clinical phenotypes, the potential key genes were screened and identified, and the related miRNA–mRNA networks were subsequently constructed through miRWalk 3.0. Finally, the potential miRNA–mRNA networks were further validated in CGGA (Chinese Glioma Genome Atlas) datasets and clinical specimens by qRT-PCR. RESULTS: In our results, six hub genes, MELK, NCAPG, KIF4A, NUSAP1, CEP55, and TOP2A, were ultimately identified. Two regulatory pathways, miR-495-3p-TOP2A and miR-1224-3p-MELK, that regulate the pathogenesis of LGG were ultimately identified. Furthermore, the expression of miR-495-3p-TOP2A and miR-1224-3p-MELK in solid tissues was validated by qRT-PCR. CONCLUSION: Our study identified hub genes and related miRNA–mRNA regulatory pathways that contribute to the carcinogenesis of LGG, which may help us reveal the mechanisms underlying the development of LGG. Dove 2021-06-29 /pmc/articles/PMC8254561/ /pubmed/34234557 http://dx.doi.org/10.2147/CMAR.S314011 Text en © 2021 Wang et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Wang, Ming Cui, Yan Cai, Yang Jiang, Yugang Peng, Yong Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas |
title | Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas |
title_full | Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas |
title_fullStr | Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas |
title_full_unstemmed | Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas |
title_short | Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA–mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas |
title_sort | comprehensive bioinformatics analysis of mrna expression profiles and identification of a mirna–mrna network associated with the pathogenesis of low-grade gliomas |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254561/ https://www.ncbi.nlm.nih.gov/pubmed/34234557 http://dx.doi.org/10.2147/CMAR.S314011 |
work_keys_str_mv | AT wangming comprehensivebioinformaticsanalysisofmrnaexpressionprofilesandidentificationofamirnamrnanetworkassociatedwiththepathogenesisoflowgradegliomas AT cuiyan comprehensivebioinformaticsanalysisofmrnaexpressionprofilesandidentificationofamirnamrnanetworkassociatedwiththepathogenesisoflowgradegliomas AT caiyang comprehensivebioinformaticsanalysisofmrnaexpressionprofilesandidentificationofamirnamrnanetworkassociatedwiththepathogenesisoflowgradegliomas AT jiangyugang comprehensivebioinformaticsanalysisofmrnaexpressionprofilesandidentificationofamirnamrnanetworkassociatedwiththepathogenesisoflowgradegliomas AT pengyong comprehensivebioinformaticsanalysisofmrnaexpressionprofilesandidentificationofamirnamrnanetworkassociatedwiththepathogenesisoflowgradegliomas |