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Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer
BACKGROUND: Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. T...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650235/ https://www.ncbi.nlm.nih.gov/pubmed/34872564 http://dx.doi.org/10.1186/s12920-021-01137-0 |
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author | Li, Albert Yu, Wen-Hsuan Hsu, Chia-Lang Huang, Hsuan-Cheng Juan, Hsueh-Fen |
author_facet | Li, Albert Yu, Wen-Hsuan Hsu, Chia-Lang Huang, Hsuan-Cheng Juan, Hsueh-Fen |
author_sort | Li, Albert |
collection | PubMed |
description | BACKGROUND: Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD). RESULTS: We used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient’s survival, including module N1 (HR = 0.62, 95% CI = 0.46–0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50–0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52–0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50–0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37–0.69, p value = 2.00e−05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT). CONCLUSION: Taken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-021-01137-0. |
format | Online Article Text |
id | pubmed-8650235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86502352021-12-07 Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer Li, Albert Yu, Wen-Hsuan Hsu, Chia-Lang Huang, Hsuan-Cheng Juan, Hsueh-Fen BMC Med Genomics Research BACKGROUND: Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD). RESULTS: We used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient’s survival, including module N1 (HR = 0.62, 95% CI = 0.46–0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50–0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52–0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50–0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37–0.69, p value = 2.00e−05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT). CONCLUSION: Taken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-021-01137-0. BioMed Central 2021-12-06 /pmc/articles/PMC8650235/ /pubmed/34872564 http://dx.doi.org/10.1186/s12920-021-01137-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Li, Albert Yu, Wen-Hsuan Hsu, Chia-Lang Huang, Hsuan-Cheng Juan, Hsueh-Fen Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer |
title | Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer |
title_full | Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer |
title_fullStr | Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer |
title_full_unstemmed | Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer |
title_short | Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer |
title_sort | modular signature of long non-coding rna association networks as a prognostic biomarker in lung cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650235/ https://www.ncbi.nlm.nih.gov/pubmed/34872564 http://dx.doi.org/10.1186/s12920-021-01137-0 |
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