Ovarian cancer is the leading cause of gynecologic
cancer mortality, due to the difficulty of early detection. Current screening methods lack sufficient accuracy, and it is still challenging to propose a new early detection method that improves patient outcomes with less-invasiveness. Although many studies have suggested the utility of circulating
microRNAs in
cancer detection, their potential for early detection remains elusive. Here, we develop novel predictive models using a combination of 8 circulating serum
miRNAs. This method was able to successfully distinguish
ovarian cancer patients from healthy controls (area under the curve, 0.97; sensitivity, 0.92; and specificity, 0.91) and early-stage
ovarian cancer from patients with benign
tumors (0.91, 0.86 and 0.83, respectively). This method also enables subtype classification in 4 types of
epithelial ovarian cancer. Furthermore, it is found that most of the 8
miRNAs were packaged in extracellular vesicles, including exosomes, derived from
ovarian cancer cells, and they were circulating in murine blood stream. The circulating
miRNAs described in this study may serve as
biomarkers for
ovarian cancer patients. Early detection and subtype determination prior to surgery are crucial for clinicians to design an effective treatment strategy for each patient, as is the goal of
precision medicine.