Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer
Joint Authors
Li, Li
Gu, Haiyan
Chen, Lingying
Zhu, Ping
Zhao, Li
Wang, Yuzhuo
Zhao, Xiang
Zhang, Xingguo
Zhang, Yonghu
Shu, Peng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-04
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Background.
Epithelial ovarian cancer (EOC) is a heterogeneous disease, which has been recently classified into four molecular subtypes, of which the mesenchymal subtype exhibited the worst prognosis.
We aimed to identify a microRNA- (miRNA-) based signature by incorporating the molecular modalities involved in the mesenchymal subtype for risk stratification, which would allow the identification of patients who might benefit from more rigorous treatments.
Method.
We characterized the regulatory mechanisms underlying the mesenchymal subtype using network analyses integrating gene and miRNA expression profiles from The Cancer Genome Atlas (TCGA) cohort to identify a miRNA signature for prognosis prediction.
Results.
We identified four miRNAs as the master regulators of the mesenchymal subtype and developed a risk score model.
The 4-miRNA signature significantly predicted overall survival (OS) and progression-free survival (PFS) in discovery (p=0.004 and p=0.04) and two independent public datasets (GSE73582: OS, HR: 2.26 (1.26-4.05), p=0.005, PFS, HR: 2.03 (1.34-3.09), p<0.001; GSE25204: OS, HR: 3.07 (1.73-5.46), p<0.001, PFS, HR: 2.59 (1.72-3.88), p<0.001).
Moreover, in multivariate analyses, the miRNA signature maintained as an independent prognostic predictor and achieved superior efficiency compared to the currently used clinical factors.
Conclusions.
In conclusion, our network analysis identified a 4-miRNA signature which has prognostic value superior to currently reported clinical covariates.
This signature warrants further testing and validation for use in clinical practice.
American Psychological Association (APA)
Li, Li& Gu, Haiyan& Chen, Lingying& Zhu, Ping& Zhao, Li& Wang, Yuzhuo…[et al.]. 2019. Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer. BioMed Research International،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1123155
Modern Language Association (MLA)
Li, Li…[et al.]. Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer. BioMed Research International No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1123155
American Medical Association (AMA)
Li, Li& Gu, Haiyan& Chen, Lingying& Zhu, Ping& Zhao, Li& Wang, Yuzhuo…[et al.]. Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1123155
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1123155