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Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma
Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognosti...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904333/ https://www.ncbi.nlm.nih.gov/pubmed/31867276 http://dx.doi.org/10.3389/fonc.2019.01361 |
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author | Zhou, Jian-Guo Liang, Bo Jin, Su-Han Liao, Hui-Ling Du, Guo-Bo Cheng, Long Ma, Hu Gaipl, Udo S. |
author_facet | Zhou, Jian-Guo Liang, Bo Jin, Su-Han Liao, Hui-Ling Du, Guo-Bo Cheng, Long Ma, Hu Gaipl, Udo S. |
author_sort | Zhou, Jian-Guo |
collection | PubMed |
description | Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death. |
format | Online Article Text |
id | pubmed-6904333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69043332019-12-20 Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma Zhou, Jian-Guo Liang, Bo Jin, Su-Han Liao, Hui-Ling Du, Guo-Bo Cheng, Long Ma, Hu Gaipl, Udo S. Front Oncol Oncology Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death. Frontiers Media S.A. 2019-12-04 /pmc/articles/PMC6904333/ /pubmed/31867276 http://dx.doi.org/10.3389/fonc.2019.01361 Text en Copyright © 2019 Zhou, Liang, Jin, Liao, Du, Cheng, Ma and Gaipl. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Zhou, Jian-Guo Liang, Bo Jin, Su-Han Liao, Hui-Ling Du, Guo-Bo Cheng, Long Ma, Hu Gaipl, Udo S. Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma |
title | Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma |
title_full | Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma |
title_fullStr | Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma |
title_full_unstemmed | Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma |
title_short | Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma |
title_sort | development and validation of an rna-seq-based prognostic signature in neuroblastoma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904333/ https://www.ncbi.nlm.nih.gov/pubmed/31867276 http://dx.doi.org/10.3389/fonc.2019.01361 |
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