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Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeq...

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Autores principales: Li, Yanan, Min, Weijie, Li, Mengmeng, Han, Guosheng, Dai, Dongwei, Zhang, Lei, Chen, Xin, Wang, Xinglai, Zhang, Yuhui, Yue, Zhijian, Liu, Jianmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029949/
https://www.ncbi.nlm.nih.gov/pubmed/27572852
http://dx.doi.org/10.3892/ijmm.2016.2717
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author Li, Yanan
Min, Weijie
Li, Mengmeng
Han, Guosheng
Dai, Dongwei
Zhang, Lei
Chen, Xin
Wang, Xinglai
Zhang, Yuhui
Yue, Zhijian
Liu, Jianmin
author_facet Li, Yanan
Min, Weijie
Li, Mengmeng
Han, Guosheng
Dai, Dongwei
Zhang, Lei
Chen, Xin
Wang, Xinglai
Zhang, Yuhui
Yue, Zhijian
Liu, Jianmin
author_sort Li, Yanan
collection PubMed
description Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeqV2 was downloaded by TCGA-Assembler, containing 169 GBM and 5 normal samples. Gene expression was calculated by the reads per kilobase per million reads measurement, and nor malized with tag count comparison. Following subgroup classification by the non-negative matrix factorization, the differentially expressed genes (DEGs) were screened in 4 GBM subgroups using the method of significance analysis of microarrays. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction (PPI) network was constructed based on the HPRD database. The subgroup-related microRNAs (miRNAs or miRs), transcription factors (TFs) and small molecule drugs were predicted with predefined criteria. A cohort of 19,515 DEGs between the GBM and control samples was screened, which were predominantly enriched in cell cycle- and immunoreaction-related pathways. In the PPI network, lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1–4, respectively. Paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific TFs in subgroups 1–4, respectively. miR-147b, miR-770-5p, miR-220a and miR-1247 were the particular miRNAs in subgroups 1–4, respectively. Natalizumab was the predicted small molecule drug in subgroup 2. In conclusion, the molecular regulatory mechanisms of GBM pathogenesis were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different GBM subgroups were identified, which may be used as potential markers. However, further experimental validations may be required.
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spelling pubmed-50299492016-09-22 Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis Li, Yanan Min, Weijie Li, Mengmeng Han, Guosheng Dai, Dongwei Zhang, Lei Chen, Xin Wang, Xinglai Zhang, Yuhui Yue, Zhijian Liu, Jianmin Int J Mol Med Articles Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeqV2 was downloaded by TCGA-Assembler, containing 169 GBM and 5 normal samples. Gene expression was calculated by the reads per kilobase per million reads measurement, and nor malized with tag count comparison. Following subgroup classification by the non-negative matrix factorization, the differentially expressed genes (DEGs) were screened in 4 GBM subgroups using the method of significance analysis of microarrays. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction (PPI) network was constructed based on the HPRD database. The subgroup-related microRNAs (miRNAs or miRs), transcription factors (TFs) and small molecule drugs were predicted with predefined criteria. A cohort of 19,515 DEGs between the GBM and control samples was screened, which were predominantly enriched in cell cycle- and immunoreaction-related pathways. In the PPI network, lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1–4, respectively. Paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific TFs in subgroups 1–4, respectively. miR-147b, miR-770-5p, miR-220a and miR-1247 were the particular miRNAs in subgroups 1–4, respectively. Natalizumab was the predicted small molecule drug in subgroup 2. In conclusion, the molecular regulatory mechanisms of GBM pathogenesis were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different GBM subgroups were identified, which may be used as potential markers. However, further experimental validations may be required. D.A. Spandidos 2016-10 2016-08-26 /pmc/articles/PMC5029949/ /pubmed/27572852 http://dx.doi.org/10.3892/ijmm.2016.2717 Text en Copyright: © Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Li, Yanan
Min, Weijie
Li, Mengmeng
Han, Guosheng
Dai, Dongwei
Zhang, Lei
Chen, Xin
Wang, Xinglai
Zhang, Yuhui
Yue, Zhijian
Liu, Jianmin
Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis
title Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis
title_full Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis
title_fullStr Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis
title_full_unstemmed Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis
title_short Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis
title_sort identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by rna-seq data analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029949/
https://www.ncbi.nlm.nih.gov/pubmed/27572852
http://dx.doi.org/10.3892/ijmm.2016.2717
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