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Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme
BACKGROUND: Glioblastoma (GBM) is an intracranial brain tumor characterized by a high final lethality rate and recurrence rate, and limited available therapies. With the development of high-throughput sequencing technology, the genomic and transcriptomic features of GBM have been fully characterized...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798109/ https://www.ncbi.nlm.nih.gov/pubmed/35116328 http://dx.doi.org/10.21037/tcr-21-1162 |
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author | Li, Xiaomin Chen, Rui Ren, Ci Huang, Anni Ding, Wencheng Wang, Hui |
author_facet | Li, Xiaomin Chen, Rui Ren, Ci Huang, Anni Ding, Wencheng Wang, Hui |
author_sort | Li, Xiaomin |
collection | PubMed |
description | BACKGROUND: Glioblastoma (GBM) is an intracranial brain tumor characterized by a high final lethality rate and recurrence rate, and limited available therapies. With the development of high-throughput sequencing technology, the genomic and transcriptomic features of GBM have been fully characterized. Therefore, our study aimed to identify its underlying genetic mechanisms, thus facilitating the development of novel therapies for GBM. METHODS: Based on the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, differential expression of RNAs in GBM and control group was analyzed. After constructing the long noncoding RNA (lncRNA)-miRNA-mRNA regulatory network of GBM, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were performed to analyze related key nodes and the lncRNAs interacting with them. Further univariate Cox regression was conducted to explore independent factors, and then multivariate Cox regression was performed to construct risk prediction models. RESULTS: We first constructed the lncRNA-miRNA-mRNA regulatory network of GBM and two effective prediction models that included 2 mRNAs [transcription factor 12 (TCF12) and discoidin, CUB and LCCL domain containing 2 (DCBLD2)] and 5 lncRNAs (C10orf25, LINC00343, HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1), FGF12 antisense RNA 2 (FGF12-AS2) and H19). Additionally, we identified several key molecules [TCF12, integrin β3 (ITGB3), high mobility group AT-hook 2 (HMGA2), C10orf25 and LINC00336] closely associated with GBM prognosis. C10orf25/miR-218/DCBLD2 may be an important regulatory pathway in GBM. CONCLUSIONS: Key molecules (TCF12, ITGB3, HMGA2, C10orf25, LINC00336 and H19) that are independent prognostic factors may be possible biomarkers to further optimize GBM prognosis. Two effective prognostic risk models that include 2 mRNAs (TCF12 and DCBLD2) and 5 lncRNAs (C10orf25, LINC00343, HOTAIRM1, FGF12-AS2 and H19) were constructed. C10orf25/miR-218/DCBLD2 may be an important regulatory pathway associated with the pathogenesis of GBM. Our findings contribute to further understanding the pathogenesis of GBM and finding possible candidate genes for prognostic and therapeutic usage with GBM. |
format | Online Article Text |
id | pubmed-8798109 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-87981092022-02-02 Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme Li, Xiaomin Chen, Rui Ren, Ci Huang, Anni Ding, Wencheng Wang, Hui Transl Cancer Res Original Article BACKGROUND: Glioblastoma (GBM) is an intracranial brain tumor characterized by a high final lethality rate and recurrence rate, and limited available therapies. With the development of high-throughput sequencing technology, the genomic and transcriptomic features of GBM have been fully characterized. Therefore, our study aimed to identify its underlying genetic mechanisms, thus facilitating the development of novel therapies for GBM. METHODS: Based on the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, differential expression of RNAs in GBM and control group was analyzed. After constructing the long noncoding RNA (lncRNA)-miRNA-mRNA regulatory network of GBM, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were performed to analyze related key nodes and the lncRNAs interacting with them. Further univariate Cox regression was conducted to explore independent factors, and then multivariate Cox regression was performed to construct risk prediction models. RESULTS: We first constructed the lncRNA-miRNA-mRNA regulatory network of GBM and two effective prediction models that included 2 mRNAs [transcription factor 12 (TCF12) and discoidin, CUB and LCCL domain containing 2 (DCBLD2)] and 5 lncRNAs (C10orf25, LINC00343, HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1), FGF12 antisense RNA 2 (FGF12-AS2) and H19). Additionally, we identified several key molecules [TCF12, integrin β3 (ITGB3), high mobility group AT-hook 2 (HMGA2), C10orf25 and LINC00336] closely associated with GBM prognosis. C10orf25/miR-218/DCBLD2 may be an important regulatory pathway in GBM. CONCLUSIONS: Key molecules (TCF12, ITGB3, HMGA2, C10orf25, LINC00336 and H19) that are independent prognostic factors may be possible biomarkers to further optimize GBM prognosis. Two effective prognostic risk models that include 2 mRNAs (TCF12 and DCBLD2) and 5 lncRNAs (C10orf25, LINC00343, HOTAIRM1, FGF12-AS2 and H19) were constructed. C10orf25/miR-218/DCBLD2 may be an important regulatory pathway associated with the pathogenesis of GBM. Our findings contribute to further understanding the pathogenesis of GBM and finding possible candidate genes for prognostic and therapeutic usage with GBM. AME Publishing Company 2021-11 /pmc/articles/PMC8798109/ /pubmed/35116328 http://dx.doi.org/10.21037/tcr-21-1162 Text en 2021 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/. |
spellingShingle | Original Article Li, Xiaomin Chen, Rui Ren, Ci Huang, Anni Ding, Wencheng Wang, Hui Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
title | Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
title_full | Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
title_fullStr | Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
title_full_unstemmed | Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
title_short | Systematic analysis of the long noncoding RNA (lncRNA)-miRNA-mRNA competing endogenous RNA network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
title_sort | systematic analysis of the long noncoding rna (lncrna)-mirna-mrna competing endogenous rna network to identify prognostic biomarkers and the potential regulatory axis in glioblastoma multiforme |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798109/ https://www.ncbi.nlm.nih.gov/pubmed/35116328 http://dx.doi.org/10.21037/tcr-21-1162 |
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