| Sumario: | Simple Summary: Ovarian cancer is one of the deadliest gynecologic cancers, largely due to the difficulty of detecting it early. Current screening methods, such as CA125 blood tests and ultrasound, lack accuracy, while advanced genetic tests are costly and impractical for widespread use. This study aims to develop a cost-effective and accessible diagnostic method using qPCR-based platelet RNA profiling to detect ovarian cancer earlier, particularly the aggressive high-grade serous ovarian cancer (HGSOC). By analyzing blood samples, we identified RNA-based biomarkers that can differentiate ovarian cancer from benign conditions with over 94% accuracy. These findings suggest that platelet RNA biomarkers could improve early detection, potentially leading to better survival rates. This research contributes to the advancement of liquid biopsy diagnostics, offering a promising alternative to current screening approaches. Background/Objectives: Ovarian cancer (OC) remains one of the most lethal gynecologic malignancies, largely due to the challenges of early detection. While next-generation sequencing (NGS) has been explored for screening, its high cost limits large-scale implementation. To develop a more accessible diagnostic solution, we designed a qPCR-based algorithm optimized for early OC detection, with a focus on high-grade serous ovarian cancer (HGSOC), the most aggressive subtype. Methods: Peripheral blood samples from 19 ovarian cancer patients, 37 benign tumor patients, and 34 asymptomatic controls were analyzed using RNA sequencing to identify splice junction-based biomarkers with minimal expression in benign samples but elevated in OC. Results: A final panel of 10 markers was validated via qPCR, demonstrating strong agreement with sequencing data (R2 = 0.44–0.98). The classification algorithm achieved 94.1% sensitivity and 94.4% specificity (AUC = 0.933). Conclusions: By leveraging platelet RNA profiling, this approach offers high specificity, accessibility, and potential for early OC detection. Future studies will focus on expanding histologic diversity and refining biomarker panels to further enhance diagnostic performance.
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