ArticleScientific reports2024
Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research trends and hotspots of circulating tumor DNA in colorectal cancer: a bibliometric study.Frontiers in oncology · 2025Pooled it
- Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning.Journal of advanced research · 2026Article
- Establishment and Performance Evaluation of a Multiplexed TET2-APOBEC-Mediated cfDNA Methylation Detection Workflow Using qPCR and dPCR Readouts.Journal of personalized medicine · 2026Article
- A comprehensive toolkit for analyzing cell-free DNA genomic sequencing data in liquid biopsy.iScience · 2026Article
- Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.World journal of surgical oncology · 2026Review
- Methylated circulating tumor DNA: technical challenges and clinical applications in non-small cell lung cancer patients-a narrative review.Translational lung cancer research · 2026Review
- DNMT/TET Imbalance and Network-Level DNA Methylation Remodeling in Ovarian Aging: Mechanistic Perspectives.Biology · 2026Review
- Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.Frontiers in immunology · 2026Review
- Article
- 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
- DeepFRAG: a method for cancer detection based on DNA fragmentomics and deep learning.Bioinformatics advances · 2026Article
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Targeted long-read methylation analysis using hybridization capture suitable for clinical specimens.Cell reports methods · 2025Article
- Early Detection of Lung Cancer: A Review of Innovative Milestones and Techniques.Journal of clinical medicine · 2025Review
- Circulating tumor DNA in lung cancer: a bibliometric analysis of studies published from 2001 to 2024.Journal of thoracic disease · 2025Article
- Comparison of enzymatic and bisulfite conversion of circulating cell-free tumor DNA for DNA methylation analyses.Clinical epigenetics · 2025Article
- Innovative Approaches to Early Detection of Cancer-Transforming Screening for Breast, Lung, and Hard-to-Screen Cancers.Cancers · 2025Review
- Cost-effective shallow genome-wide sequencing for profiling plasma cfDNA signatures to enhance lung cancer detection.Future oncology (London, England) · 2025Article
- cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling.Briefings in bioinformatics · 2025Article
- Plasma Small Extracellular Vesicle microRNAs as Non-Invasive Biomarkers for Lung Cancer Detection.International journal of nanomedicine · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we sought to develop a deep learning model incorporating cfDNA methylation and fragment size profiles. To achieve this, we utilized methylation data collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Then we generated methylated DNA immunoprecipitation sequencing and genome-wide Enzymatic Methyl-seq (EM-seq) form lung cancer tissue and plasma. Using these data, we selected 366 methylation markers. A targeted EM-seq panel was designed using the selected markers, and 142 lung cancer and 56 healthy samples were produced with the panel. Additionally, cfDNA samples from healthy individuals and lung cancer patients were diluted to evaluate sensitivity. Its lung cancer detection performance reached an accuracy of 81.5% and an area under the receiver operating characteristic curve of 0.87. In the serial dilution experiment, we achieved tumor fraction detection of 1% at 98% specificity and 0.1% at 80% specificity. In conclusion, we successfully developed and validated a combination of methylation panel and a deep learning model that can distinguish between patients with lung cancer and healthy individuals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.