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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...

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Autores principales: Li, Xiaomin, Chen, Rui, Ren, Ci, Huang, Anni, Ding, Wencheng, Wang, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
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.
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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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