Volume 15, Issue 2 e70225
RESEARCH ARTICLE
Open Access

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer

Yadong Wang

Yadong Wang

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Qiang Guo

Qiang Guo

Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China

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Zhicheng Huang

Zhicheng Huang

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Liyang Song

Liyang Song

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Fei Zhao

Fei Zhao

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Tiantian Gu

Tiantian Gu

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Zhe Feng

Zhe Feng

Department of Cardiothoracic Surgery, the Sixth Hospital of Beijing, Beijing, China

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Haibo Wang

Haibo Wang

Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China

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Bowen Li

Bowen Li

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Daoyun Wang

Daoyun Wang

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Bin Zhou

Bin Zhou

Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China

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Chao Guo

Chao Guo

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Yuan Xu

Yuan Xu

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Yang Song

Yang Song

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Zhibo Zheng

Zhibo Zheng

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Zhongxing Bing

Zhongxing Bing

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Haochen Li

Haochen Li

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Xiaoqing Yu

Xiaoqing Yu

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Ka Luk Fung

Ka Luk Fung

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Heqing Xu

Heqing Xu

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Jianhong Shi

Jianhong Shi

Department of Scientific Research, Affiliated Hospital of Hebei University, Baoding, China

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Meng Chen

Meng Chen

Department of Scientific Research, Affiliated Hospital of Hebei University, Baoding, China

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Shuai Hong

Shuai Hong

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Haoxuan Jin

Haoxuan Jin

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Shiyuan Tong

Shiyuan Tong

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Sibo Zhu

Sibo Zhu

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Chen Zhu

Chen Zhu

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Jinlei Song

Jinlei Song

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Jing Liu

Jing Liu

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

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Shanqing Li

Shanqing Li

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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Hefei Li

Corresponding Author

Hefei Li

Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China

Correspondence

Hefei Li, Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China.

Email: [email protected]

Xueguang Sun, Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China.

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Naixin Liang, Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Email: [email protected]

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Xueguang Sun

Corresponding Author

Xueguang Sun

Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China

Correspondence

Hefei Li, Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China.

Email: [email protected]

Xueguang Sun, Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China.

Email: [email protected]

Naixin Liang, Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Email: [email protected]

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Naixin Liang

Corresponding Author

Naixin Liang

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

Correspondence

Hefei Li, Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, China.

Email: [email protected]

Xueguang Sun, Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China.

Email: [email protected]

Naixin Liang, Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Email: [email protected]

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First published: 05 February 2025
Citations: 1

Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song and Fei Zhao contributed equally to this work.

Abstract

Background

Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

Methods

To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

Results

Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

Conclusions

With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

Keypoints

  • Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features.
  • Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model.
  • The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

CONFLICT OF INTEREST STATEMENT

L. S., F. Z., T. G., S. H., H. J., S. T., S. Z., C. Z., J. S., J. L. and X. S. are employees of Shanghai Weihe Medical Laboratory Co., Ltd, Shanghai, China. All other authors have declared no conflicts of interest.

DATA AVAILABILITY STATEMENT

The data supporting the findings of this study have been deposited in Genome Sequence Archive (Genome Sequence Archive for Human in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences) under accession number PRJCA033843. This study does not employ any new algorithms, and the code used for statistical analysis is available on request from the corresponding author Naixin Liang.

ETHICS STATEMENT AND CONSENT TO PARTICIPATE

The study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of Affiliated Hospital of Hebei University (Approval No. HDFYLL-IIT-023-005) and Peking Union Medical College Hospital (Approval No. I-23PJ1205). Written informed consent was obtained from all enrolled participants prior participation.

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