Cargando…

A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients

Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA ex...

Descripción completa

Detalles Bibliográficos
Autores principales: Xian, Junmin, Zhang, Quanzhong, Guo, Xiwen, Liang, Xiankun, Liu, Xinhua, Feng, Yugong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443874/
https://www.ncbi.nlm.nih.gov/pubmed/30984542
http://dx.doi.org/10.1002/2211-5463.12602
_version_ 1783407914026795008
author Xian, Junmin
Zhang, Quanzhong
Guo, Xiwen
Liang, Xiankun
Liu, Xinhua
Feng, Yugong
author_facet Xian, Junmin
Zhang, Quanzhong
Guo, Xiwen
Liang, Xiankun
Liu, Xinhua
Feng, Yugong
author_sort Xian, Junmin
collection PubMed
description Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA expression profiles. We downloaded gene expression profiles of glioma patients along with their clinical information from the Gene Expression Omnibus and extracted ncRNA expression profiles via a microarray annotation file. Correlations between ncRNAs and glioma patients’ OS were first evaluated through univariate Cox regression analysis and a permutation test, followed by random survival forest analysis for further screening of valuable ncRNA signatures. Prognostic signatures could be established as a risk score formula by including ncRNA signature expression values weighted by their estimated regression coefficients. Patients could be divided into high risk and low risk subgroups by using the median risk score as cutoff. As a result, glioma patients with a high risk score tended to have shorter OS than those with low risk scores, which was confirmed by analyzing another set of glioma patients in an independent dataset. Additionally, gene set enrichment analysis showed significant enrichment of cancer development‐related biological processes and pathways. Our study may provide further insights into the evaluation of glioma patients’ prognosis.
format Online
Article
Text
id pubmed-6443874
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-64438742019-04-12 A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients Xian, Junmin Zhang, Quanzhong Guo, Xiwen Liang, Xiankun Liu, Xinhua Feng, Yugong FEBS Open Bio Research Articles Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA expression profiles. We downloaded gene expression profiles of glioma patients along with their clinical information from the Gene Expression Omnibus and extracted ncRNA expression profiles via a microarray annotation file. Correlations between ncRNAs and glioma patients’ OS were first evaluated through univariate Cox regression analysis and a permutation test, followed by random survival forest analysis for further screening of valuable ncRNA signatures. Prognostic signatures could be established as a risk score formula by including ncRNA signature expression values weighted by their estimated regression coefficients. Patients could be divided into high risk and low risk subgroups by using the median risk score as cutoff. As a result, glioma patients with a high risk score tended to have shorter OS than those with low risk scores, which was confirmed by analyzing another set of glioma patients in an independent dataset. Additionally, gene set enrichment analysis showed significant enrichment of cancer development‐related biological processes and pathways. Our study may provide further insights into the evaluation of glioma patients’ prognosis. John Wiley and Sons Inc. 2019-03-07 /pmc/articles/PMC6443874/ /pubmed/30984542 http://dx.doi.org/10.1002/2211-5463.12602 Text en © 2019 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Xian, Junmin
Zhang, Quanzhong
Guo, Xiwen
Liang, Xiankun
Liu, Xinhua
Feng, Yugong
A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients
title A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients
title_full A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients
title_fullStr A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients
title_full_unstemmed A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients
title_short A prognostic signature based on three non‐coding RNAs for prediction of the overall survival of glioma patients
title_sort prognostic signature based on three non‐coding rnas for prediction of the overall survival of glioma patients
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443874/
https://www.ncbi.nlm.nih.gov/pubmed/30984542
http://dx.doi.org/10.1002/2211-5463.12602
work_keys_str_mv AT xianjunmin aprognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT zhangquanzhong aprognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT guoxiwen aprognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT liangxiankun aprognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT liuxinhua aprognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT fengyugong aprognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT xianjunmin prognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT zhangquanzhong prognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT guoxiwen prognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT liangxiankun prognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT liuxinhua prognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients
AT fengyugong prognosticsignaturebasedonthreenoncodingrnasforpredictionoftheoverallsurvivalofgliomapatients