Plasma PDGFRβ and PSD4 methylation levels for non-invasive staging of liver fibrosis.

BACKGROUND AND AIM: While liver biopsy remains the reference standard for assessing hepatic fibrosis, the major prognostic factor in metabolic dysfunction-associated steatotic liver disease (MASLD), its inherent limitations have driven the search for innovative non-invasive diagnostic tests. In this study, we sought to evaluate the diagnostic accuracy of plasma PDGFRβ and PSD4 methylation levels, integrated within machine learning algorithms, for non-invasive staging of hepatic fibrosis in patients with MASLD, and to compare its performance against established non-invasive biomarkers.

MATERIALS AND

METHODS

Patients with biopsy-proven MASLD and healthy controls were recruited from the three institutions. Quantitative methylation of circulating cell-free DNA was assessed using bisulfite modification and pyrosequencing. The resulting data were used to develop linear discriminant analysis, random forest, and support vector machine algorithms to identify patients at different stages of fibrosis.

RESULTS

The study included 234 patients with histologically confirmed MASLD and 43 healthy controls. Each dataset was validated using an independent cohort. Advanced fibrosis was associated with elevated plasma PDGFRβ and PSD4 methylation levels in both the discovery and validation cohorts. The integration of plasma DNA methylation markers with clinical parameters demonstrated performance comparable to that of existing noninvasive biomarkers for detecting both significant and advanced fibrosis, achieving area under the curve scores exceeding 0.75 across all models.

CONCLUSION

Supervised machine learning algorithms incorporating plasma PDGFRβ and PSD4 DNA methylation levels show promise as a non-invasive approach for assessing hepatic fibrosis in MASLD.