Cargando…

Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The...

Descripción completa

Detalles Bibliográficos
Autores principales: Gong, Xiaoyong, Ning, Bobin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785998/
https://www.ncbi.nlm.nih.gov/pubmed/33084528
http://dx.doi.org/10.1177/1533033820963578
_version_ 1783632540979953664
author Gong, Xiaoyong
Ning, Bobin
author_facet Gong, Xiaoyong
Ning, Bobin
author_sort Gong, Xiaoyong
collection PubMed
description Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.
format Online
Article
Text
id pubmed-7785998
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-77859982021-01-14 Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis Gong, Xiaoyong Ning, Bobin Technol Cancer Res Treat Original Article Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa. SAGE Publications 2020-10-21 /pmc/articles/PMC7785998/ /pubmed/33084528 http://dx.doi.org/10.1177/1533033820963578 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Gong, Xiaoyong
Ning, Bobin
Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis
title Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis
title_full Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis
title_fullStr Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis
title_full_unstemmed Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis
title_short Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis
title_sort five lncrnas associated with prostate cancer prognosis identified by coexpression network analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785998/
https://www.ncbi.nlm.nih.gov/pubmed/33084528
http://dx.doi.org/10.1177/1533033820963578
work_keys_str_mv AT gongxiaoyong fivelncrnasassociatedwithprostatecancerprognosisidentifiedbycoexpressionnetworkanalysis
AT ningbobin fivelncrnasassociatedwithprostatecancerprognosisidentifiedbycoexpressionnetworkanalysis