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A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer

Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with dif...

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Autores principales: Song, Jia-Yi, Li, Xiao-Ping, Qin, Xiu-Jiao, Zhang, Jing-Dong, Zhao, Jian-Yu, Wang, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739968/
https://www.ncbi.nlm.nih.gov/pubmed/32831192
http://dx.doi.org/10.3233/CBM-190505
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author Song, Jia-Yi
Li, Xiao-Ping
Qin, Xiu-Jiao
Zhang, Jing-Dong
Zhao, Jian-Yu
Wang, Rui
author_facet Song, Jia-Yi
Li, Xiao-Ping
Qin, Xiu-Jiao
Zhang, Jing-Dong
Zhao, Jian-Yu
Wang, Rui
author_sort Song, Jia-Yi
collection PubMed
description Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox’s regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future.
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spelling pubmed-77399682020-12-18 A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer Song, Jia-Yi Li, Xiao-Ping Qin, Xiu-Jiao Zhang, Jing-Dong Zhao, Jian-Yu Wang, Rui Cancer Biomark Research Article Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox’s regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future. IOS Press 2020-11-20 /pmc/articles/PMC7739968/ /pubmed/32831192 http://dx.doi.org/10.3233/CBM-190505 Text en © 2020 – IOS Press and the authors. All rights reserved https://creativecommons.org/licenses/by-nc/4.0/ This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
spellingShingle Research Article
Song, Jia-Yi
Li, Xiao-Ping
Qin, Xiu-Jiao
Zhang, Jing-Dong
Zhao, Jian-Yu
Wang, Rui
A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer
title A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer
title_full A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer
title_fullStr A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer
title_full_unstemmed A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer
title_short A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer
title_sort fourteen-lncrna risk score system for prognostic prediction of patients with non-small cell lung cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739968/
https://www.ncbi.nlm.nih.gov/pubmed/32831192
http://dx.doi.org/10.3233/CBM-190505
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