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Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
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Frequency-Domain Transformation of cfDNA End-Motif Profiles Enhances Robust Cancer Detection.

Xinwei Sheng1, Xinming Du1, Qianqian Shi1

  • 1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Genes
|June 26, 2026
PubMed
Summary

A new frequency-domain analysis of cell-free DNA (cfDNA) end-motifs (EDMs) enhances cancer detection. This method improves diagnostic accuracy across datasets by analyzing cfDNA fragment patterns more effectively than previous approaches.

Keywords:
cancer detectioncell-free DNAdiscrete Fourier transformend-motifliquid biopsy

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Area of Science:

  • Biochemistry
  • Genomics
  • Computational Biology

Background:

  • Cell-free DNA (cfDNA) end-motifs (EDMs) show promise for noninvasive cancer detection.
  • Current EDM methods face limitations in robustness across datasets due to background cfDNA signals.
  • Existing approaches like Motif Diversity Score (MDS) and raw motif frequency classifiers lack consistent performance.

Purpose of the Study:

  • To develop a robust analytical framework for cfDNA end-motif analysis in cancer detection.
  • To improve the diagnostic utility of fragmentomic features by addressing cross-dataset variability.
  • To enhance the accuracy and reliability of noninvasive cancer detection using cfDNA.

Main Methods:

  • Developed a frequency-domain analytical framework using Discrete Fourier Transform (DFT) on k-mer EDM frequency profiles.
  • Converted EDM profiles into amplitude spectral features for improved data representation.
  • Constructed an Ensemble Spectral Model (ESM) integrating multi-scale spectral features from 4-6-mer EDMs.

Main Results:

  • DFT transformation improved the separability of cfDNA spectral features between cancer and non-cancer samples.
  • The ESM achieved a mean AUC of 0.843 across six datasets from four independent studies (1782 samples).
  • ESM demonstrated significant improvements in sensitivity (0.585 at 95% specificity) over raw EDM and MDS methods.

Conclusions:

  • Frequency-domain transformation offers a more robust representation of cfDNA EDM profiles.
  • The developed ESM provides a powerful and cross-dataset applicable analytical framework for cancer detection.
  • This approach enhances the utility of cfDNA fragmentomics for noninvasive cancer diagnostics.