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Pan-Cancer Detection Through DNA Methylation Profiling Using Enzymatic Conversion Library Preparation with Targeted
Alvida Qvick1, Emma Adolfsson2, Lina Tornéus3
1Clinical Research Center, Faculty of Medicine and Health, Örebro University, SE-701 85 Örebro, Sweden.
International Journal of Molecular Sciences
|October 29, 2025
Summary
Circulating cell-free DNA (cfDNA) methylation patterns differ significantly between cancer patients and those with severe symptoms. This cfDNA methylation analysis shows promise as a biomarker for early cancer detection, even in complex cases.
Area of Science:
- Biomarkers
- Genomics
- Oncology
Background:
- Distinguishing cancer from severe nonspecific symptoms is clinically challenging.
- Circulating cell-free DNA (cfDNA) methylation is an emerging area for cancer detection.
Purpose of the Study:
- To investigate cfDNA methylation differences between cancer patients and individuals with severe, nonspecific symptoms.
- To develop and validate a cfDNA methylation-based classifier for cancer detection.
Main Methods:
- Plasma cfDNA methylation analysis in 229 patients (37 with cancer).
- Utilized NEBNext workflow, Twist pan-cancer methylation panel, nf-core/methylseq, and DMRichR for analysis.
- Developed a machine learning classifier with cross-validation and external validation.
Main Results:
- Cancer samples exhibited higher overall CpG methylation (1.82% vs. 1.34%, p < 0.001).
- Identified 162 differentially methylated regions (DMRs), with 95.7% hypermethylated in cancer.
- The machine learning classifier achieved an AUC of 0.88 (83.8% sensitivity, 83.8% specificity) in the final model.
Conclusions:
- Distinct cfDNA methylation patterns serve as a robust biomarker for cancer detection.
- The developed classifier shows potential for identifying cancer in patients with confounding conditions.
- cfDNA methylation analysis offers a promising non-invasive approach for early cancer diagnosis.
