ArticleNature communications2023
DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Machine learning-driven proteomics classifier deciphers tumor origins of primary and metastatic squamous cell carcinomas.Biomarker research · 2026Article
- Case Report: Site-specific therapy guided by the 90-gene expression assay in cancer of unknown primary: lenvatinib plus camrelizumab for occult renal cell carcinoma.Frontiers in oncology · 2026Article
- Tumor Genomic and Transcriptomic Analysis Integrated With Liquid Biopsy ctDNA Monitoring: Analytical Validation and Clinical Insights.Cancer medicine · 2025Article
- Conserved methylation signature accurately predicts heavily irradiated CNS tumour with perplexing histopathology: A case report.Biomedical reports · 2025Article
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression.BMC medicine · 2025Article
- Mutational landscape and DNA methylation-based classification of squamous cell carcinoma and urothelial carcinoma.Clinical epigenetics · 2025Article
- Advancements in Diagnostics and Therapeutics for Cancer of Unknown Primary in the Era of Precision Medicine.MedComm · 2025Review
- Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours.Epigenomics · 2025Article
- DNA methylation in esophageal cancer: technological advances and early detection clinical applications.Frontiers in oncology · 2025Review
- Early Detection, Precision Treatment, Recurrence Monitoring: Liquid Biopsy Transforms Colorectal Cancer Therapy.Current cancer drug targets · 2025Review
- MethylGPT: a foundation model for the DNA methylome.bioRxiv : the preprint server for biology · 2024Article
- Comprehensive in silico CpG methylation analysis in hepatocellular carcinoma identifies tissue- and tumor-type specific marks disconnected from gene expression.Journal of physiology and biochemistry · 2024Article
- PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.Briefings in bioinformatics · 2024Article
- Evaluation of targeted sequencing for pathogen identification in bone and joint infections: a cohort study from China.Annals of clinical microbiology and antimicrobials · 2024Article
- Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies.Briefings in bioinformatics · 2024Article
- Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies.Biosensors · 2024Review
- Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns.Biology methods & protocols · 2024Article
- methylClass: an R package to construct DNA methylation-based classification models.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).
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