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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2016
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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. |
format | Online Article Text |
id | pubmed-5029949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
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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