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Background <p>In multiple sclerosis (MS), there is a critical need for cost-effective blood biomarkers that can concurrently classify disease subtypes, monitor disability severity, and predict long-term disability progression.</p> Methods <p>In this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples collected from a prospective clinic cohort with longitudinal disability outcomes. We assessed the utility of cfDNA methylation profiles for differentiating MS patients from controls, classifying MS subtypes, estimating disability severity, and predicting disease trajectories.</p> Results <p>We identify hundreds of differentially methylated regions (DMRs) that significantly distinguish MS from controls, separate MS subtypes, and stratify disability severity levels. These DMRs are highly enriched in immunologically and neurologically relevant cis-regulatory elements and in motifs associated with neuronal function and T-cell differentiation. To distinguish MS subtypes and disability severity, models using DMRs achieve area-under-the-curve (AUC) values ranging from 0.75 to 0.95, and models using inferred tissue-of-origin patterns from cfDNA methylation achieve AUC values of 0.69 to 0.72, performing better than historical benchmark neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) measures in the same cohort. Finally, a linear mixed-effects model identifies “prognostic regions” where baseline cfDNA methylation levels are associated with subsequent disability progression and predict future disability severity (AUC = 0.74) within a 3-year evaluation window. Using these prognostic regions, we generate a cfDNA methylation-based progression risk score and stratify patient groups by differential progression risks.</p> Conclusions <p>These findings highlight circulating cfDNA methylation profiles as promising clinical MS biomarkers, warranting future validation using higher-depth WGBS data.</p>

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Circulating cell-free DNA methylation profiles as candidate noninvasive multiple sclerosis biomarkers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Hailu Fu,
  • Jocelyn Liang,
  • Kevin Huang,
  • Wen Zhu,
  • Lili Zhang,
  • Ravi Bandaru,
  • Shruthi Venkatesh,
  • Elizabeth Walker,
  • Li Wang,
  • Yaping Liu,
  • Zongqi Xia

摘要

Background

In multiple sclerosis (MS), there is a critical need for cost-effective blood biomarkers that can concurrently classify disease subtypes, monitor disability severity, and predict long-term disability progression.

Methods

In this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples collected from a prospective clinic cohort with longitudinal disability outcomes. We assessed the utility of cfDNA methylation profiles for differentiating MS patients from controls, classifying MS subtypes, estimating disability severity, and predicting disease trajectories.

Results

We identify hundreds of differentially methylated regions (DMRs) that significantly distinguish MS from controls, separate MS subtypes, and stratify disability severity levels. These DMRs are highly enriched in immunologically and neurologically relevant cis-regulatory elements and in motifs associated with neuronal function and T-cell differentiation. To distinguish MS subtypes and disability severity, models using DMRs achieve area-under-the-curve (AUC) values ranging from 0.75 to 0.95, and models using inferred tissue-of-origin patterns from cfDNA methylation achieve AUC values of 0.69 to 0.72, performing better than historical benchmark neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) measures in the same cohort. Finally, a linear mixed-effects model identifies “prognostic regions” where baseline cfDNA methylation levels are associated with subsequent disability progression and predict future disability severity (AUC = 0.74) within a 3-year evaluation window. Using these prognostic regions, we generate a cfDNA methylation-based progression risk score and stratify patient groups by differential progression risks.

Conclusions

These findings highlight circulating cfDNA methylation profiles as promising clinical MS biomarkers, warranting future validation using higher-depth WGBS data.