Non-invasive esophageal cancer screening in high-risk populations: a multi-omics integration model

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality in China, with over 95% of patients diagnosed at advanced stages, resulting in low survival rates. Early detection is critical for improving outcomes, yet current screening methods are invasive or lack sensitivity. We developed a non-invasive early-detection model by integrating circulating cell-free DNA (cfDNA) methylation and fragmentomics features. Using a three-layer stacked ensemble machine learning framework, we analyzed multicenter blood samples from 309 participants, including ESCA patients and high-risk individuals. The integrated model called ESim-seq (Esophageal Cancer Screening with Integrated Model) achieved an AUC of 0.994 (95% CI: 0.979–1.000) in validation. At 99% specificity, sensitivity reached 87.3%, with simulated screening showing a 24.8% increase in stage I detection. Stratified analyses confirmed robustness across demographics, risk factors, and tumor stages. Clinical benefit modeling projected a 56.3% stage I detection rate under ideal conditions, significantly improving survival outcomes compared to standard care. This study developed a cfDNA-based analytical framework and demonstrated the advantage of multi-feature integration for early detection of ESCA.

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Publication Details

Journal
npj Precision Oncology
Published
2026-07-23
DOI
https://doi.org/10.1038/s41698-026-01620-w
Primary Topic
Esophageal Cancer Research and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Non-invasive esophageal cancer screening in high-risk populations: a multi-omics integration model

Xiangli Liu, Mingrui Shao, Xiaotong Xi, Wanfu Men et al.
npj Precision Oncology
Esophageal Cancer Research and Treatment
article

Non-invasive esophageal cancer screening in high-risk populations: a multi-omics integration model

Xiangli Liu, Mingrui Shao, Xiaotong Xi, Wanfu Men, Xiaoqiang Wang, Zhenghua Liu, Peipei Zhang, Xing Zhang, Wenya Li, Yue Wang, Ying Liu, Dongsheng Chen, Ling Lv, Yu Ding, Shun Xu, Huidong Yu, Wanglong Deng, Jingcai Wu, Xiaohuan Hou, Lei Ye, Yong Li, Youbin Cui
article en

Abstract

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality in China, with over 95% of patients diagnosed at advanced stages, resulting in low survival rates. Early detection is critical for improving outcomes, yet current screening methods are invasive or lack sensitivity. We developed a non-invasive early-detection model by integrating circulating cell-free DNA (cfDNA) methylation and fragmentomics features. Using a three-layer stacked ensemble machine learning framework, we analyzed multicenter blood samples from 309 participants, including ESCA patients and high-risk individuals. The integrated model called ESim-seq (Esophageal Cancer Screening with Integrated Model) achieved an AUC of 0.994 (95% CI: 0.979–1.000) in validation. At 99% specificity, sensitivity reached 87.3%, with simulated screening showing a 24.8% increase in stage I detection. Stratified analyses confirmed robustness across demographics, risk factors, and tumor stages. Clinical benefit modeling projected a 56.3% stage I detection rate under ideal conditions, significantly improving survival outcomes compared to standard care. This study developed a cfDNA-based analytical framework and demonstrated the advantage of multi-feature integration for early detection of ESCA.

npj Precision Oncology
Chifeng University (CN), Jilin University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Wuhan Wudong Hospital (CN), National Cancer Center (US), Simcere Pharmaceutical (China) (CN), Chifeng Municipal Hospital (CN), First Hospital of Jilin University (CN), First Hospital of China Medical University (CN), Affiliated Hospital of Hebei University (CN), China Medical University (CN)
National Natural Science Foundation of China, China Medical University, Department of Science and Technology of Liaoning Province, Shengjing Hospital
Good health and well-being
Openalex Percentile: Top 7%
Esophageal Cancer Research and Treatment
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