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Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer

In endometrial carcinoma, the clinical outcome directly correlates with the TNM stage, but the lack of sufficient information prevents accurate prediction. The molecular mechanism underlying the competing endogenous RNA (ceRNA) hypothesis has not been investigated in endometrial cancer. Multi-bioinf...

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Autores principales: Xia, Leilei, Wang, Ye, Meng, Qi, Su, Xiaoling, Shen, Jizi, Wang, Jing, He, Haiwei, Wen, Biwei, Zhang, Caihong, Xu, Mingjuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549402/
https://www.ncbi.nlm.nih.gov/pubmed/31192139
http://dx.doi.org/10.3389/fonc.2019.00448
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author Xia, Leilei
Wang, Ye
Meng, Qi
Su, Xiaoling
Shen, Jizi
Wang, Jing
He, Haiwei
Wen, Biwei
Zhang, Caihong
Xu, Mingjuan
author_facet Xia, Leilei
Wang, Ye
Meng, Qi
Su, Xiaoling
Shen, Jizi
Wang, Jing
He, Haiwei
Wen, Biwei
Zhang, Caihong
Xu, Mingjuan
author_sort Xia, Leilei
collection PubMed
description In endometrial carcinoma, the clinical outcome directly correlates with the TNM stage, but the lack of sufficient information prevents accurate prediction. The molecular mechanism underlying the competing endogenous RNA (ceRNA) hypothesis has not been investigated in endometrial cancer. Multi-bioinformatic analyses, including differentially expressed gene analysis, ceRNA network construction, Cox regression analysis, function enrichment analysis, and protein-protein network analysis, were performed on the sequence data acquired from The Cancer Genome Atlas (TCGA) data bank. A ceRNA network comprising 366 mRNAs, 27 microRNAs (miRNAs), and 66 long non-coding RNAs (lncRNAs) was established. Survival analysis performed with the univariate Cox regression analysis revealed nine lncRNAs with prognostic power in endometrial carcinoma. In multivariate Cox regression analysis, a signature comprising LINC00491, LINC00483, ADARB2-AS1, and C8orf49 showed remarkable prognostic power. Risk score and neoplasm status, but not TNM stage, were independent prognostic factors of endometrial carcinoma. A ceRNA network comprising differentially expressed mRNAs, miRNAs, and lncRNAs may reveal the molecular events involved in the progression of endometrial carcinoma. In addition, the signature with prognostic value may discriminate patients with increased risk for poor outcome, which may allow physicians to take accurate decisions.
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spelling pubmed-65494022019-06-12 Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer Xia, Leilei Wang, Ye Meng, Qi Su, Xiaoling Shen, Jizi Wang, Jing He, Haiwei Wen, Biwei Zhang, Caihong Xu, Mingjuan Front Oncol Oncology In endometrial carcinoma, the clinical outcome directly correlates with the TNM stage, but the lack of sufficient information prevents accurate prediction. The molecular mechanism underlying the competing endogenous RNA (ceRNA) hypothesis has not been investigated in endometrial cancer. Multi-bioinformatic analyses, including differentially expressed gene analysis, ceRNA network construction, Cox regression analysis, function enrichment analysis, and protein-protein network analysis, were performed on the sequence data acquired from The Cancer Genome Atlas (TCGA) data bank. A ceRNA network comprising 366 mRNAs, 27 microRNAs (miRNAs), and 66 long non-coding RNAs (lncRNAs) was established. Survival analysis performed with the univariate Cox regression analysis revealed nine lncRNAs with prognostic power in endometrial carcinoma. In multivariate Cox regression analysis, a signature comprising LINC00491, LINC00483, ADARB2-AS1, and C8orf49 showed remarkable prognostic power. Risk score and neoplasm status, but not TNM stage, were independent prognostic factors of endometrial carcinoma. A ceRNA network comprising differentially expressed mRNAs, miRNAs, and lncRNAs may reveal the molecular events involved in the progression of endometrial carcinoma. In addition, the signature with prognostic value may discriminate patients with increased risk for poor outcome, which may allow physicians to take accurate decisions. Frontiers Media S.A. 2019-05-29 /pmc/articles/PMC6549402/ /pubmed/31192139 http://dx.doi.org/10.3389/fonc.2019.00448 Text en Copyright © 2019 Xia, Wang, Meng, Su, Shen, Wang, He, Wen, Zhang and Xu. 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
Xia, Leilei
Wang, Ye
Meng, Qi
Su, Xiaoling
Shen, Jizi
Wang, Jing
He, Haiwei
Wen, Biwei
Zhang, Caihong
Xu, Mingjuan
Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer
title Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer
title_full Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer
title_fullStr Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer
title_full_unstemmed Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer
title_short Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer
title_sort integrated bioinformatic analysis of a competing endogenous rna network reveals a prognostic signature in endometrial cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549402/
https://www.ncbi.nlm.nih.gov/pubmed/31192139
http://dx.doi.org/10.3389/fonc.2019.00448
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