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A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma
Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome A...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3069027/ https://www.ncbi.nlm.nih.gov/pubmed/21483847 http://dx.doi.org/10.1371/journal.pone.0017438 |
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author | Srinivasan, Sujaya Patric, Irene Rosita Pia Somasundaram, Kumaravel |
author_facet | Srinivasan, Sujaya Patric, Irene Rosita Pia Somasundaram, Kumaravel |
author_sort | Srinivasan, Sujaya |
collection | PubMed |
description | Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR] = 2.4; 95% CI = 1.4–3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1–2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4–2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04–1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy. |
format | Text |
id | pubmed-3069027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-30690272011-04-11 A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma Srinivasan, Sujaya Patric, Irene Rosita Pia Somasundaram, Kumaravel PLoS One Research Article Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR] = 2.4; 95% CI = 1.4–3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1–2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4–2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04–1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy. Public Library of Science 2011-03-31 /pmc/articles/PMC3069027/ /pubmed/21483847 http://dx.doi.org/10.1371/journal.pone.0017438 Text en Srinivasan et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Srinivasan, Sujaya Patric, Irene Rosita Pia Somasundaram, Kumaravel A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma |
title | A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma |
title_full | A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma |
title_fullStr | A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma |
title_full_unstemmed | A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma |
title_short | A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma |
title_sort | ten-microrna expression signature predicts survival in glioblastoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3069027/ https://www.ncbi.nlm.nih.gov/pubmed/21483847 http://dx.doi.org/10.1371/journal.pone.0017438 |
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