Machine learning–based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of hepatocellular carcinoma

Sasimol Udomruk; Songphon Sutthitthasakul; Nuttida Bunsermvicha; Kanokwan Pinyopornpanish; Dumnoensun Pruksakorn; Phasit Charoenkwan; Petlada Yongpitakwattana; Kanlaya Khounkaew; Thanapak Jaimalai; Treephum Duangsan; Santhasiri Orrapin; Sutpirat Moonmuang; Pitiporn Noisagul; Arnat Pasena; Pathacha Suksakit; Ratikorn Gamngoen; Pimpisa Teeyakasem; Chaiyut Charoentum; Sarawut Kongkarnka; Worakitti Lapisatepun; Trairak Pisitkun

doi:10.1038/s43856-026-01437-5

Key Points

The aim is to establish a method for early detection of hepatocellular carcinoma using cfDNA fragmentation profiling.
Utilized a machine learning approach for cfDNA fragmentation analysis.
Employed automated capillary electrophoresis for high-dimensional fragmentomics.
Developed a strategy to bridge research-grade technologies with clinical application.
Demonstrated practical accuracy in detecting early-stage hepatocellular carcinoma.
Showed feasibility for applying the method in clinical settings for HCC surveillance.

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Implication

Automated capillary electrophoresis enables accurate early detection of hepatocellular carcinoma, suggesting a practical clinical tool.

Abstract

CEliver provides a practical and accurate strategy for early HCC detection. By enabling high-dimensional cfDNA fragmentomics analysis on a widely accessible electrophoresis platform, it bridges the gap between research-grade cfDNA technologies and real-world clinical implementation. This method represents a simple and scalable approach that could potentially be applied in HCC surveillance.

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Udomruk et al. (2026) studied this question.

synapsesocial.com/papers/6997f984ad1d9b11b34523f9 https://doi.org/10.1038/s43856-026-01437-5

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Machine learning–based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of hepatocellular carcinoma

Key Points

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Cite This Study

Machine learning–based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of hepatocellular carcinoma

Key Points

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Implication

Abstract

Discussion

Cite This Study

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