Advanced
ovarian cancer is one of the most lethal gynecological
tumor, mainly due to late diagnoses and acquired drug resistance.
MicroRNAs (
miRNAs) are
small-non coding RNA acting as
tumor suppressor/oncogenes differentially expressed in normal and
epithelial ovarian cancer and has been recognized as a new class of
tumor early detection
biomarkers as they are released in blood fluids since
tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating
miRNAs in serum samples from healthy (N = 105) and untreated
ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian
tumors from health controls women. The selection of 45 candidate
miRNAs to be evaluated in the discovery set was based on
miRNAs represented in
ovarian cancer explorative commercial panels. We found six
miRNAs showing increased levels in the blood of early or late-stage
ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of
malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising
miRNAs (miR-320b and miR-141-3p) and
miRNAs combined with well-established
ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The
miRNA-based classifier was able to accurately discriminate early-stage
ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the
serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the
miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the
miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an
ovarian cancer early detection tool.