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

Sep 22, 2026

Upload by Fans

Early detection of ovarian cancer, especially high-grade serous ovarian cancer (HGSOC), remains difficult due to insufficient reliable biomarkers. This study evaluates tRNA-derived small RNAs (tsRNAs) as non-invasive diagnostic biomarkers using liquid biopsy. Relative tsRNA abundance measured by qRT-PCR (Ct values) in plasma from healthy controls and early/late-stage HGSOC patients was used to build and validate a logistic regression model, showing high sensitivity and specificity. The selected tsRNA is dysregulated in tumor versus normal tissue, supporting potential clinical use for early HGSOC after further mechanistic study.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Keywords

Introduction

Challenge of early ovarian cancer detection

Rationale for tsRNAs and liquid biopsy

Study approach and sample cohorts

A novel diagnostic model for early-stage high-grade serous ovarian cancer based on serum tRNA-derived fragments Cancer Biomarkers Volume 43: 1–11 © The Author(s) 2026 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions) [DOI: 10.1177/18758592261463300](DOI: 10.1177/18758592261463300)[ ](DOI: 10.1177/18758592261463300)[journals.sagepub.com/home/cbm](journals.sagepub.com/home/cbm) Xueyuan Zhao1,2,* , Weijia Wen1,2,*, Yan Jia1,2,*, Li Yuan1,2, Linna Chen1,2, Songlin Liu1,2, Haolin Fan1,2, Hongye Jiang1,2, Chaoyun Pan3, Chunyu Zhang1,2, and Shuzhong Yao1,2 Abstract 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. Keywords ovarian cancer, molecular biomarkers, diagnostic Received: 2 March 2025; Revised: 23 February 2026; Accepted: 7 June 2026 Introduction Ovarian cancer (OC), with high-grade serous ovarian cancer (HGSOC) accounting for the majority of pathological subtypes, is one of the most lethal malignancies in gynecology due to its insidious symptoms and the difficulty of early diagnosis. 1,2 Despite the availability of various screening methods for early detection of OC, including imaging techniques (ultrasound, CT, MRI), blood biomarker assessments (e.g., CA-125, HE4), and multifarious diagnostic models (e.g., the ROMA model), yet none of these methods has demonstrateed satisfactory efficacy and convenience, resulting in only a limited number of cases being detected at early stages.3,4 This delayed detection greatly complicates treatment, leading to poor cure rates and 1Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China 2Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, Guangzhou, Guangdong, P. R. China 3Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China *These authors contributed equally: Xueyuan Zhao, Weijia Wen, Yan Jia. Corresponding authors: Shuzhong Yao, Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P.R. China; Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, Guangzhou, Guangdong, 510000 P.R. China. Email: [yaoshuzh@mail.sysu.edu.cn](yaoshuzh@mail.sysu.edu.cn) Chunyu Zhang, Department ofObstetricsand Gynecology, the First Affiliated Hospital, SunYat-sen University, Guangzhou, Guangdong, P.R. China; Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases, Guangzhou, Guangdong, 510000 P.R. China. Email: [zhangchy266@mail.sysu.edu.cn](zhangchy266@mail.sysu.edu.cn) Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work withou

FAQs

What problem does the study address in ovarian cancer detection?

It targets the difficulty of early detection of ovarian cancer, particularly high-grade serous ovarian cancer (HGSOC), due to a lack of reliable biomarkers.

How was the diagnostic model developed in this study?

Selected tsRNAs were quantified from plasma using qRT-PCR, and the relative Ct values were used to construct a logistic regression model for distinguishing HGSOC patients from healthy controls.

What evidence supports the usefulness of the identified tsRNA biomarker?

The model was validated in a multi-center cohort, showed high sensitivity and specificity for early-stage cases, and the tsRNA was dysregulated in tumor tissue compared with normal tissue.

Sep 22, 2026

Upload by Fans