Noninvasive Detection and Subtyping of Esophageal Cancer by Circulating Cell-Free mtDNA Fragmentomics: A Multi-Center Validation Study.
6 affiliations
- 1State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Department of Physiology and Pathophysiology, Fourth Military Medical University, Xi'an, China.
- 2College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China.
- 3Department of Clinical Laboratory, The First Affiliated Hospital of Air Force Medical University, Xi'an, China.
- 4Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, China.
- 5Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai, China.
- 6State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Department of Clinical Diagnosis, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Abstract
Esophageal cancer (EC) is highly lethal due to late diagnosis, and accurate non-invasive tools for early detection and subtyping remain lacking. Circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics offers a promising biomarker source. In this large multi-center study enrolling 1,260 participants spanning healthy control (HC), benign esophageal diseases (BED), precancerous lesions and EC patients, we developed two machine learning models based on plasma ccf-mtDNA fragmentomic profiles: an EC detection (ED) model and a pathological subtyping prediction (PSP) model. The ED model achieved an AUC of 0.9706 (EC vs. non-EC) in the training cohort, with high performance in internal (0.9762), external (0.9748), and Chinese minority (0.9668, Uyghur and Kazakh) validation cohorts. At the optimal cut-off, it yielded 92.16% sensitivity for stage I EC in the combined validation cohort and correctly identified 93.10% of early EC cases missed by preoperative biopsy. It also discriminated high-grade intraepithelial neoplasia from non-EC with an AUC of 0.9597. The PSP model distinguished ESCC from EAC with 89.71% accuracy, improving to 90.74% after integrating key clinical parameters. In summary, ccf-mtDNA fragmentomics provides a robust, non-invasive platform for early EC detection, precancerous risk stratification, and histological subtyping, demonstrating strong potential to transform clinical management.
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BibTeX
@article{liu2026noninvasive,
title = {{Noninvasive Detection and Subtyping of Esophageal Cancer by Circulating Cell-Free mtDNA Fragmentomics: A Multi-Center Validation Study.}},
author = {Yue Liu and Wenying Yang and Yu Li and Siyuan Wang and Xiang Li and Fan Peng and Miao Dang and Huanqin Zhang and Huanmin Jiao and Yuqing Yang and Zhaoquan Su and Xiumin Ma and Zhiyun Gong and Renquan Lu and Jinliang Xing and Yang Liu},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = oct,
doi = {10.1002/advs.78377},
url = {https://europepmc.org/article/MED/42949655},
note = {CC BY},
}
RIS
TY - JOUR
TI - Noninvasive Detection and Subtyping of Esophageal Cancer by Circulating Cell-Free mtDNA Fragmentomics: A Multi-Center Validation Study.
AU - Yue Liu
AU - Wenying Yang
AU - Yu Li
AU - Siyuan Wang
AU - Xiang Li
AU - Fan Peng
AU - Miao Dang
AU - Huanqin Zhang
AU - Huanmin Jiao
AU - Yuqing Yang
AU - Zhaoquan Su
AU - Xiumin Ma
AU - Zhiyun Gong
AU - Renquan Lu
AU - Jinliang Xing
AU - Yang Liu
PY - 2026
DA - 2026/10/28
JO - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
DO - 10.1002/advs.78377
UR - https://europepmc.org/article/MED/42949655
AB - Esophageal cancer (EC) is highly lethal due to late diagnosis, and accurate non-invasive tools for early detection and subtyping remain lacking. Circulating cell-free mitochondrial DNA (ccf-mtDNA) fragmentomics offers a promising biomarker source. In this large multi-center study enrolling 1,260 participants spanning healthy control (HC), benign esophageal diseases (BED), precancerous lesions and EC patients, we developed two machine learning models based on plasma ccf-mtDNA fragmentomic profiles: an EC detection (ED) model and a pathological subtyping prediction (PSP) model. The ED model achieved an AUC of 0.9706 (EC vs. non-EC) in the training cohort, with high performance in internal (0.9762), external (0.9748), and Chinese minority (0.9668, Uyghur and Kazakh) validation cohorts. At the optimal cut-off, it yielded 92.16% sensitivity for stage I EC in the combined validation cohort and correctly identified 93.10% of early EC cases missed by preoperative biopsy. It also discriminated high-grade intraepithelial neoplasia from non-EC with an AUC of 0.9597. The PSP model distinguished ESCC from EAC with 89.71% accuracy, improving to 90.74% after integrating key clinical parameters. In summary, ccf-mtDNA fragmentomics provides a robust, non-invasive platform for early EC detection, precancerous risk stratification, and histological subtyping, demonstrating strong potential to transform clinical management.
SN - 2198-3844
ER -