ArticleClinical and translational medicine2025
Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer.
Article in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Fragmentomic features of cell-free DNA in human spent blastocyst medium and their potential for non-invasive embryo ploidy assessment.Journal of assisted reproduction and genetics · 2026Article
- Immune response to DNA and RNA: structural insights, molecular mechanisms, and therapeutic targeting.Molecular biomedicine · 2026Review
- Gene-based lung cancer detection system through omix data and optimized convolutional neural network.Journal of computer-aided molecular design · 2026Article
- Emerging Role of ctDNA Fragmentomics and Epigenetic Signatures in the Early Detection, Minimal Residual Disease Assessment, and Precision Monitoring of Renal Cell Carcinoma.Journal of cellular and molecular medicine · 2026Review
- Lung cancer in never-smoking females: epidemiology, risk factors and screening.Frontiers in public health · 2026Review
- Epigenetic profiling of circulating cell-free DNA for early detection and minimal residual disease assessment in lung cancer: a focus on DNA methylation.Frontiers in oncology · 2026Review
- Non-targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer.Oncology letters · 2026Article
- Genomics and Epigenomics Approaches for the Quantification of Circulating Tumor DNA in Liquid Biopsy: Relevance of a Multimodal Strategy.International journal of molecular sciences · 2025Review
- Advances and challenges in circulating tumor DNA-based early detection of lung cancer.Translational cancer research · 2025Review
- Liquid biopsy-based multi-cancer early detection: an exploration road from evidence to implementation.Science bulletin · 2025Review
- Regulation of cisplatin resistance in lung cancer by epigenetic mechanisms.Clinical epigenetics · 2025Review
- Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer.Clinical and translational medicine · 2025Article
- Application of Liquid Biopsy Technology in Lung Cancer: A Bibliometric Study and Visualization Analysis.Cancer management and research · 2025Review
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Authors and funding
33 authors.
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Abstract
backgroundLung 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.
methodsTo 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.
resultsCancer-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.
conclusionsWith 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.
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