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Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma
BACKGROUND: Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlight...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539462/ https://www.ncbi.nlm.nih.gov/pubmed/33028341 http://dx.doi.org/10.1186/s12967-020-02552-0 |
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author | Liang, Qingyu Guan, Gefei Li, Xue Wei, Chunmi Wu, Jianqi Cheng, Peng Wu, Anhua Cheng, Wen |
author_facet | Liang, Qingyu Guan, Gefei Li, Xue Wei, Chunmi Wu, Jianqi Cheng, Peng Wu, Anhua Cheng, Wen |
author_sort | Liang, Qingyu |
collection | PubMed |
description | BACKGROUND: Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated. METHODS: Gene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed. RESULTS: According to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma. CONCLUSIONS: We profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression. |
format | Online Article Text |
id | pubmed-7539462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75394622020-10-08 Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma Liang, Qingyu Guan, Gefei Li, Xue Wei, Chunmi Wu, Jianqi Cheng, Peng Wu, Anhua Cheng, Wen J Transl Med Research BACKGROUND: Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated. METHODS: Gene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed. RESULTS: According to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma. CONCLUSIONS: We profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression. BioMed Central 2020-10-07 /pmc/articles/PMC7539462/ /pubmed/33028341 http://dx.doi.org/10.1186/s12967-020-02552-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liang, Qingyu Guan, Gefei Li, Xue Wei, Chunmi Wu, Jianqi Cheng, Peng Wu, Anhua Cheng, Wen Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma |
title | Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma |
title_full | Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma |
title_fullStr | Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma |
title_full_unstemmed | Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma |
title_short | Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma |
title_sort | profiling pro-neural to mesenchymal transition identifies a lncrna signature in glioma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539462/ https://www.ncbi.nlm.nih.gov/pubmed/33028341 http://dx.doi.org/10.1186/s12967-020-02552-0 |
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