Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer

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.