Cargando…

Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is one of the most important type of bladder cancer, with a high morbidity and mortality rate. Studies have found that long non-coding RNA (lncRNA) plays a key role in maintaining genomic instability. However, Identification of lncRNAs related to gen...

Descripción completa

Detalles Bibliográficos
Autores principales: Liang, Yingchun, Ye, Fangdie, Cheng, Zhang, Ou, Yuxi, Zou, Lujia, Hu, Yun, Hu, Jimeng, Jiang, Haowen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424867/
https://www.ncbi.nlm.nih.gov/pubmed/34496843
http://dx.doi.org/10.1186/s12935-021-02185-3
_version_ 1783749745329569792
author Liang, Yingchun
Ye, Fangdie
Cheng, Zhang
Ou, Yuxi
Zou, Lujia
Hu, Yun
Hu, Jimeng
Jiang, Haowen
author_facet Liang, Yingchun
Ye, Fangdie
Cheng, Zhang
Ou, Yuxi
Zou, Lujia
Hu, Yun
Hu, Jimeng
Jiang, Haowen
author_sort Liang, Yingchun
collection PubMed
description BACKGROUND: Muscle-invasive bladder cancer (MIBC) is one of the most important type of bladder cancer, with a high morbidity and mortality rate. Studies have found that long non-coding RNA (lncRNA) plays a key role in maintaining genomic instability. However, Identification of lncRNAs related to genomic instability (GIlncRNAs) and their clinical significance in cancers have not been extensively studied yet. METHODS: Here, we downloaded the lncRNA expression profiles, somatic mutation profiles and clinical related data in MIBC patients from The Cancer Genome Atlas (TCGA) database. A lncRNA computational framework was used to find differentially expressed GIlncRNAs. Multivariate Cox regression analysis was used to construct a genomic instability-related lncRNA signature (GIlncSig). Univariate and multivariate Cox analyses were used to assess the independent prognostic for the GIlncSig and other key clinical factors. RESULTS: We found 43 differentially expressed GIlncRNAs and constructed the GIlncSig with 6 GIlncRNAs in the training cohort. The patients were divided into two risk groups. The overall survival of patients in the high-risk group was lower than that in the low-risk group (P < 0.001), which were further verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression showed that the GIlncSig was an independent prognostic factor. In addition, the GIlncSig correlated with the genomic mutation rate of MIBC, indicating its potential as a measure of the degree of genomic instability. The GIlncSig was able to divide FGFR3 wild- and mutant-type patients into two risk groups, and effectively enhanced the prediction effect. CONCLUSION: Our study introduced an important reference for further research on the role of GIlncRNAs, and provided prognostic indicators and potential biological therapy targets for MIBC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02185-3.
format Online
Article
Text
id pubmed-8424867
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-84248672021-09-10 Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer Liang, Yingchun Ye, Fangdie Cheng, Zhang Ou, Yuxi Zou, Lujia Hu, Yun Hu, Jimeng Jiang, Haowen Cancer Cell Int Primary Research BACKGROUND: Muscle-invasive bladder cancer (MIBC) is one of the most important type of bladder cancer, with a high morbidity and mortality rate. Studies have found that long non-coding RNA (lncRNA) plays a key role in maintaining genomic instability. However, Identification of lncRNAs related to genomic instability (GIlncRNAs) and their clinical significance in cancers have not been extensively studied yet. METHODS: Here, we downloaded the lncRNA expression profiles, somatic mutation profiles and clinical related data in MIBC patients from The Cancer Genome Atlas (TCGA) database. A lncRNA computational framework was used to find differentially expressed GIlncRNAs. Multivariate Cox regression analysis was used to construct a genomic instability-related lncRNA signature (GIlncSig). Univariate and multivariate Cox analyses were used to assess the independent prognostic for the GIlncSig and other key clinical factors. RESULTS: We found 43 differentially expressed GIlncRNAs and constructed the GIlncSig with 6 GIlncRNAs in the training cohort. The patients were divided into two risk groups. The overall survival of patients in the high-risk group was lower than that in the low-risk group (P < 0.001), which were further verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression showed that the GIlncSig was an independent prognostic factor. In addition, the GIlncSig correlated with the genomic mutation rate of MIBC, indicating its potential as a measure of the degree of genomic instability. The GIlncSig was able to divide FGFR3 wild- and mutant-type patients into two risk groups, and effectively enhanced the prediction effect. CONCLUSION: Our study introduced an important reference for further research on the role of GIlncRNAs, and provided prognostic indicators and potential biological therapy targets for MIBC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02185-3. BioMed Central 2021-09-08 /pmc/articles/PMC8424867/ /pubmed/34496843 http://dx.doi.org/10.1186/s12935-021-02185-3 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 Primary Research
Liang, Yingchun
Ye, Fangdie
Cheng, Zhang
Ou, Yuxi
Zou, Lujia
Hu, Yun
Hu, Jimeng
Jiang, Haowen
Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
title Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
title_full Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
title_fullStr Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
title_full_unstemmed Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
title_short Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
title_sort calculated identification of mutator-derived lncrna signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424867/
https://www.ncbi.nlm.nih.gov/pubmed/34496843
http://dx.doi.org/10.1186/s12935-021-02185-3
work_keys_str_mv AT liangyingchun calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT yefangdie calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT chengzhang calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT ouyuxi calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT zoulujia calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT huyun calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT hujimeng calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer
AT jianghaowen calculatedidentificationofmutatorderivedlncrnasignaturesofgenomicinstabilitytopredicttheclinicaloutcomeofmuscleinvasivebladdercancer