Introduction: Ovarian cancer (OvCa) has poor prognosis as it predominantly presents as advanced disease. Therefore, it is essential that effective early detection strategies are identified. Exosomes are a specific subtype of vesicles and their content are cell type specific, making them fingerprints of the releasing cells and their metabolic status. Thus, we suggest that analysis of exosomal content may provide an approach to enrich tumor biomarker detection.
Methods: Exosomes were isolated from plasma obtained from patients with epithelial OvCa and characterised. The exosomal proteomic profile was identified by Liquid chromatography mass spectrometry (LC-MS/MS) and SWATH analysis. An Illumina TrueSeq Small RNA kit was used to construct a small RNA library from exosomal RNA.
Results: 300 significant proteins and miRNAs were identified to change in expression (demonstrating either an increase or decrease) across OvCa progression. Functional analysis of the exosomal content revealed that the miRNAs and proteins identified as changing with OvCa progression belonged to cell-to-cell communication and migration. We built an algorithm using five exosomal miRNAs and proteins that are significantly different between benign and Stage I/Stage II. The model delivered discrimination between women with ovarian cancer compared to benign. The classification efficiency was assessed by ROC curve analysis (area under the curves (AUC) were 0.785 ± 0.091 (p = 0.0106)) with positive and negative predictive values of 75% and 76%, respectively.
Conclusion: We propose that the combined measurement of exosomal biomarkers might allow the early identification of women with OvCa, however, a larger trial is required for further validation.