A cfDNA fragmentomics-based liquid biopsy assay for early detection of oral cancer.
5 affiliations
- 1Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China.
- 2Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, 210032, China.
- 3Department of Cancer Center, Chongqing University Three Gorges Hospital, Chongqing, No. 165 Xincheng Road, Wanzhou District, Chongqing, 404000, China. [email protected].
- 4Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China. [email protected].
- 5Clinical Laboratory of Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, No. 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, China. [email protected].
Abstract
Background
Early detection of oral cancer improves outcomes, but remains limited by the invasiveness and low sensitivity of current screening methods.Methods
This study included 342 participants (200 in the training and 142 in the validation cohorts). Plasma cfDNA underwent low-depth whole-genome sequencing, with data normalized to a standardized 5× coverage via down-sampling to ensure consistent analysis. Three cfDNA-derived features, including fragment size ratio (FSR), copy number variation (CNV), and repetitive element profiles (REP), were extracted and integrated using a stacked machine learning ensemble to generate prediction scores. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity with 95% confidence intervals.Results
In the training cohort, oral cancer patients had a median age of 53 years (91% male), while healthy individuals had a median age of 57 years (37% male). In the validation cohort, the median ages were 53 and 57 years, with male proportions of 83.1% and 38.0% in the cancer and healthy groups, respectively. Cancer samples exhibited shorter cfDNA fragments, recurrent CNV gains at 3q and 8q, and losses at 3p and 5q. The individual AUCs for FSR, CNV, and REP were 0.973, 0.983, and 0.979 in the training cohort, and 0.960, 0.982, and 0.972 in the validation cohort, respectively. The integrated stacked model achieved AUCs of 0.996 (95% CI: 0.991-1.000) and 0.988 (95% CI: 0.975-0.988) in the training and validation cohorts, respectively. To prioritize high specificity for large-scale screening, a decision threshold was established in the training cohort to target 98.0% specificity, which yielded a corresponding sensitivity of 98.0%. When this pre-fixed threshold was blindly applied to the independent validation cohort, a sensitivity of 94.4% and a specificity of 95.8% were achieved. Prediction scores were not significantly associated with age, sex, or tumor site.Conclusions
The cfDNA fragmentomics-based stacked model distinguished oral cancer from healthy individuals, including early stage disease, supporting its potential for noninvasive approach for oral cancer detection.
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BibTeX
@article{wang2026cfdna,
title = {{A cfDNA fragmentomics-based liquid biopsy assay for early detection of oral cancer.}},
author = {Weiwei Wang and Tao Ding and Cuicui Liu and Xinyue Hong and Peng He and Wei Yang and Maoxi Zhong and Yuan Jiang},
journal = {BMC cancer},
year = {2026},
month = aug,
doi = {10.1186/s12885-026-16606-x},
url = {https://europepmc.org/article/MED/43009804},
note = {CC BY},
}
RIS
TY - JOUR
TI - A cfDNA fragmentomics-based liquid biopsy assay for early detection of oral cancer.
AU - Weiwei Wang
AU - Tao Ding
AU - Cuicui Liu
AU - Xinyue Hong
AU - Peng He
AU - Wei Yang
AU - Maoxi Zhong
AU - Yuan Jiang
PY - 2026
DA - 2026/08/15
JO - BMC cancer
DO - 10.1186/s12885-026-16606-x
UR - https://europepmc.org/article/MED/43009804
AB - Background Early detection of oral cancer improves outcomes, but remains limited by the invasiveness and low sensitivity of current screening methods.Methods This study included 342 participants (200 in the training and 142 in the validation cohorts). Plasma cfDNA underwent low-depth whole-genome sequencing, with data normalized to a standardized 5× coverage via down-sampling to ensure consistent analysis. Three cfDNA-derived features, including fragment size ratio (FSR), copy number variation (CNV), and repetitive element profiles (REP), were extracted and integrated using a stacked machine learning ensemble to generate prediction scores. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity with 95% confidence intervals.Results In the training cohort, oral cancer patients had a median age of 53 years (91% male), while healthy individuals had a median age of 57 years (37% male). In the validation cohort, the median ages were 53 and 57 years, with male proportions of 83.1% and 38.0% in the cancer and healthy groups, respectively. Cancer samples exhibited shorter cfDNA fragments, recurrent CNV gains at 3q and 8q, and losses at 3p and 5q. The individual AUCs for FSR, CNV, and REP were 0.973, 0.983, and 0.979 in the training cohort, and 0.960, 0.982, and 0.972 in the validation cohort, respectively. The integrated stacked model achieved AUCs of 0.996 (95% CI: 0.991-1.000) and 0.988 (95% CI: 0.975-0.988) in the training and validation cohorts, respectively. To prioritize high specificity for large-scale screening, a decision threshold was established in the training cohort to target 98.0% specificity, which yielded a corresponding sensitivity of 98.0%. When this pre-fixed threshold was blindly applied to the independent validation cohort, a sensitivity of 94.4% and a specificity of 95.8% were achieved. Prediction scores were not significantly associated with age, sex, or tumor site.Conclusions The cfDNA fragmentomics-based stacked model distinguished oral cancer from healthy individuals, including early stage disease, supporting its potential for noninvasive approach for oral cancer detection.
SN - 1471-2407
ER -