Early and accurate detection of ovarian cancer by profiling cell-free mitochondrial DNA fragmentomics in multi-ethnic population.
10 affiliations
- 1Department of Obstetrics and Gynecology, Xijing Hospital, Air Force Medical University, Xi'an, China.
- 2Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai, China.
- 3College of Life Sciences, Northwest University, Xi'an, China.
- 4State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Physiology and Pathophysiology, Air Force Medical University, Xi'an, China.
- 5State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Clinical Diagnosis, Tangdu Hospital, Air Force Medical University, Xi'an, China.
- 6Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, China.
- 7State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Physiology and Pathophysiology, Air Force Medical University, Xi'an, China. [email protected].
- 8Department of Obstetrics and Gynecology, Xijing Hospital, Air Force Medical University, Xi'an, China. [email protected].
- 9Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai, China. [email protected].
- 10State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Clinical Diagnosis, Tangdu Hospital, Air Force Medical University, Xi'an, China. [email protected].
Abstract
Background
Ovarian cancer (OC) remains highly lethal due to frequent late-stage diagnosis and the challenge of distinguishing malignant from benign adnexal masses (BAM) preoperatively. Reliable non-invasive diagnostic biomarkers remain an unmet clinical need. This multi-center study aimed to evaluate the diagnostic potential of plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics for improving OC management.Methods
We developed two machine learning models based on ccf-mtDNA features: an OC Detection (OD) model to distinguishing OC from healthy controls (HC), and a Benign vs. Malignant Differential diagnosis (BMD) model to discriminate OC from BAM.Results
The OD model achieved an area under the curve (AUC) of 0.987 in the training cohort and maintained high performance (AUC above 0.979) in internal and external validation cohorts, with 91.67% sensitivity for Stage I OC at 95% specificity. The BMD model demonstrated strong discriminatory power (AUC = 0.982) and generalizability (AUC ranging from 0.961 to 0.980 in four validation cohorts), significantly outperforming serum biomarkers. Remarkably, both models performed robustly in the Chinese minority cohort.Conclusions
These findings establish ccf-mtDNA fragmentomics as a powerful liquid biopsy approach for the early and accurate detection of OC, with significant potential for clinical translation in high-risk populations and differential diagnosis of benign vs. malignant adnexal masses.
Related
- Cell-free DNA fragmentomics and epigenomic profiling in hematologic malignancies: current evidence, translational limitations, and future directions
- Noninvasive Detection and Subtyping of Esophageal Cancer by Circulating Cell-Free mtDNA Fragmentomics: A Multi-Center Validation Study.
- FragMIL: A Fragment-Token Multiple-Instance Learning Framework for Cancer Detection from Cell-Free DNA Fragmentomics
- Multi-omics profiling uncovers diagnostic biomarker panel, progression-associated subtypes, and prognostic signature in pediatric mitochondrial disease
- Language model-assisted label refinement for accurate sepsis detection from electronic health records
BibTeX
@article{guo2026early,
title = {{Early and accurate detection of ovarian cancer by profiling cell-free mitochondrial DNA fragmentomics in multi-ethnic population.}},
author = {Xinyan Guo and Zhiyun Gong and Ziyi Li and Tianlei Sun and Yue Fu and Huanmin Jiao and Huanqin Zhang and Siyuan Wang and Yue Liu and Wenying Yang and Fan Peng and Zhiyang Xu and Mengxin Wang and Tianyi Cheng and Ke Dong and Zhaoquan Su and Xiumin Ma and Jinliang Xing and Shujuan Liu and Renquan Lu and Yang Liu},
journal = {Genome medicine},
year = {2026},
month = jul,
doi = {10.1186/s13073-026-01735-6},
url = {https://europepmc.org/article/MED/42908017},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Early and accurate detection of ovarian cancer by profiling cell-free mitochondrial DNA fragmentomics in multi-ethnic population.
AU - Xinyan Guo
AU - Zhiyun Gong
AU - Ziyi Li
AU - Tianlei Sun
AU - Yue Fu
AU - Huanmin Jiao
AU - Huanqin Zhang
AU - Siyuan Wang
AU - Yue Liu
AU - Wenying Yang
AU - Fan Peng
AU - Zhiyang Xu
AU - Mengxin Wang
AU - Tianyi Cheng
AU - Ke Dong
AU - Zhaoquan Su
AU - Xiumin Ma
AU - Jinliang Xing
AU - Shujuan Liu
AU - Renquan Lu
AU - Yang Liu
PY - 2026
DA - 2026/07/01
JO - Genome medicine
DO - 10.1186/s13073-026-01735-6
UR - https://europepmc.org/article/MED/42908017
AB - Background Ovarian cancer (OC) remains highly lethal due to frequent late-stage diagnosis and the challenge of distinguishing malignant from benign adnexal masses (BAM) preoperatively. Reliable non-invasive diagnostic biomarkers remain an unmet clinical need. This multi-center study aimed to evaluate the diagnostic potential of plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics for improving OC management.Methods We developed two machine learning models based on ccf-mtDNA features: an OC Detection (OD) model to distinguishing OC from healthy controls (HC), and a Benign vs. Malignant Differential diagnosis (BMD) model to discriminate OC from BAM.Results The OD model achieved an area under the curve (AUC) of 0.987 in the training cohort and maintained high performance (AUC above 0.979) in internal and external validation cohorts, with 91.67% sensitivity for Stage I OC at 95% specificity. The BMD model demonstrated strong discriminatory power (AUC = 0.982) and generalizability (AUC ranging from 0.961 to 0.980 in four validation cohorts), significantly outperforming serum biomarkers. Remarkably, both models performed robustly in the Chinese minority cohort.Conclusions These findings establish ccf-mtDNA fragmentomics as a powerful liquid biopsy approach for the early and accurate detection of OC, with significant potential for clinical translation in high-risk populations and differential diagnosis of benign vs. malignant adnexal masses.
SN - 1756-994X
ER -