ArticleBioMed research international2019
Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Search for New Participants in the Pathogenesis of High-Grade Serous Ovarian Cancer with the Potential to Be Used as Diagnostic Molecules.Life (Basel, Switzerland) · 2022Article
- KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data.BMC cancer · 2022Article
- Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Comprehensive analysis of prognosis-related alternative splicing events in ovarian cancer.RNA biology · 2022Article
- Development of a novel transcription factors-related prognostic signature for serous ovarian cancer.Scientific reports · 2021Article
- Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer.PloS one · 2021Article
- Systematic Profiling of Alternative Splicing Events in Ovarian Cancer.Frontiers in oncology · 2021Article
- Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer.PeerJ · 2020Article
- Identification of Stably Expressed Reference microRNAs in Epithelial Ovarian Cancer.In vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEpithelial 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.
methodWe 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.
resultsWe 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 (
conclusionsIn 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.
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