A novel diagnostic model for early-stage high-grade serous ovarian cancer based on serum tRNA-derived fragments

Early detection of ovarian cancer, particularly high-grade serous ovarian cancer (HGSOC), remains challenging due to the lack of reliable biomarkers. This study investigated the potential of tRNA-derived small RNAs (tsRNAs) as non-invasive diagnostic biomarkers using liquid biopsy. Using qRT-PCR, we analyzed the relative Ct values of selected tsRNAs in plasma samples from healthy controls and patients with early- and late-stage HGSOC. A logistic regression model constructed using the relative Ct value of the selected tsRNA demonstrated high sensitivity and specificity for distinguishing ovarian cancer patients from healthy controls, including those with early-stage disease. The model was validated in a multi-center cohort, and analysis confirmed the dysregulation of this tsRNA in tumor tissue compared to normal tissue. These findings suggest that this single plasma tsRNA may serve as a promising biomarker for early HGSOC detection, supporting its potential clinical utility pending further mechanistic studies.