Abstract
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.
Highlights
Ovarian cancer (OC) accounts to 2.5% of all women malignances [1] this tumor is the leading cause of gynecologic cancer mortality [2]
We have developed a high specific and sensitivity diagnostic classifier model with miR-320b, miR-141-3p, cancer antigen-125 (CA-125) and human epididymis protein 4 (HE4) markers allowing the discrimination between ovarian cancer and health controls
To select promising miRNA as OC circulating biomarkers, we considered candidates overlapped in an ovarian cancer focused commercial panel (miScript miRNA PCR Array Human Ovarian Cancer (Cat. no. 331221 MIHS-110ZA, QIAGEN) and a plasma/serum miRNA panel
Summary
Ovarian cancer (OC) accounts to 2.5% of all women malignances [1] this tumor is the leading cause of gynecologic cancer mortality [2]. The detection and characterization of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), and extracellular vesicles profiles in the human body fluids represents a promising clinical utility of liquid biopsy for cancer patients management [9,10,11]. One miRNA can potentially bind to hundreds of target genes and be involved in the regulation of various cellular processes, such as development, differentiation and cell proliferation [12] They display distinct expression profiles in tumors and are able to differentiate between cancer and normal tissue, as they are released by solid tumors in human body fluids [13,14,15]. We have developed a high specific and sensitivity diagnostic classifier model with miR-320b, miR-141-3p, CA-125 and HE4 markers allowing the discrimination between ovarian cancer and health controls
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