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A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the...

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Autores principales: Zhao, Yue, Yang, Shao-Min, Jin, Yu-Lan, Xiong, Guang-Wu, Wang, Pin, Snijders, Antoine M., Mao, Jian-Hua, Zhang, Xiao-Wei, Hang, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925684/
https://www.ncbi.nlm.nih.gov/pubmed/31885574
http://dx.doi.org/10.1155/2019/3614207
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author Zhao, Yue
Yang, Shao-Min
Jin, Yu-Lan
Xiong, Guang-Wu
Wang, Pin
Snijders, Antoine M.
Mao, Jian-Hua
Zhang, Xiao-Wei
Hang, Bo
author_facet Zhao, Yue
Yang, Shao-Min
Jin, Yu-Lan
Xiong, Guang-Wu
Wang, Pin
Snijders, Antoine M.
Mao, Jian-Hua
Zhang, Xiao-Wei
Hang, Bo
author_sort Zhao, Yue
collection PubMed
description The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.
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spelling pubmed-69256842019-12-29 A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer Zhao, Yue Yang, Shao-Min Jin, Yu-Lan Xiong, Guang-Wu Wang, Pin Snijders, Antoine M. Mao, Jian-Hua Zhang, Xiao-Wei Hang, Bo J Oncol Research Article The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment. Hindawi 2019-11-07 /pmc/articles/PMC6925684/ /pubmed/31885574 http://dx.doi.org/10.1155/2019/3614207 Text en Copyright © 2019 Yue Zhao et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhao, Yue
Yang, Shao-Min
Jin, Yu-Lan
Xiong, Guang-Wu
Wang, Pin
Snijders, Antoine M.
Mao, Jian-Hua
Zhang, Xiao-Wei
Hang, Bo
A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer
title A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer
title_full A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer
title_fullStr A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer
title_full_unstemmed A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer
title_short A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer
title_sort robust gene expression prognostic signature for overall survival in high-grade serous ovarian cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925684/
https://www.ncbi.nlm.nih.gov/pubmed/31885574
http://dx.doi.org/10.1155/2019/3614207
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