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.
背景/目的：卵巢癌作为致死率最高的妇科恶性肿瘤之一，其早期检测仍是临床重大挑战。虽然新一代测序技术已被探索用于筛查，但其高昂成本限制了大规模应用。为开发更易普及的诊断方案，本研究设计了一种基于qPCR的检测算法，重点针对最具侵袭性的高级别浆液性卵巢癌亚型进行早期检测优化。 方法：通过RNA测序技术分析19例卵巢癌患者、37例良性肿瘤患者及34例无症状对照者的外周血样本，筛选出在良性样本中表达量极低而在卵巢癌中显著升高的剪接连接位点生物标志物。 结果：最终确定的10个标志物经qPCR验证与测序数据高度吻合（R²=0.44–0.98）。该分类算法达到94.1%的敏感度与94.4%的特异度（AUC=0.933）。 结论：基于血小板RNA谱分析的方法具有高特异性、易操作性和早期检测潜力。后续研究将聚焦于扩大组织学亚型覆盖范围并优化标志物组合，以进一步提升诊断效能。
肿瘤(癌症)患者之家