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Metabolomics Reveals Metabolic Characteristics of Functional Cure in Chronic Hepatitis B Treated With Entecavir Combined With Pegylated Interferon Alpha

Journal 2026 · IF 2.9 · 2026(1): 1031453 · 发表于 2026 年 9 月 3 日
作者: Junling Wang,Xianghong Kong,Huiping Xu,Wei Zhang,Peng Peng
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摘要

Background Entecavir (ETV) combined with pegylated interferon alpha (PEG-IFN α ) improves chronic hepatitis B (CHB) functional cure rates, but therapeutic heterogeneity and underlying metabolic mechanisms remain unclear. This study used untargeted metabolomics to identify metabolic signatures, mechanisms, and predictive biomarkers of functional cure with ETV-PEG-IFN α . Methods Thirty-eight CHB patients were grouped into ETV monotherapy (Group E, n = 12) and ETV-PEG-IFN α combination therapy (Group Z, n = 26); Group Z was subdivided into cured (Group A, n = 13) and noncured (Group B, n = 13). Serum metabolomic profiling, multivariate statistics, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified differential metabolites. A random forest model was built using key metabolites. Results Three hundred eighty-eight metabolites were identified. Four differential metabolites distinguished Group A and B (upregulated guanidinoacetic acid, uracil 5-carboxylate; downregulated L-methionine S-oxide, oleamide), enriching amino acid metabolism pathways. Nine differential metabolites between Group E and Z implicated amino acid, immune, and fatty acid pathways. The random forest model based on the four Group A/B metabolites showed 88.5% cross-validation accuracy (AUC = 0.920), with L-methionine S-oxide and oleamide as key predictors. Conclusions This study reveals metabolic rewiring in CHB functional cure via ETV-PEG-IFN α therapy, involving energy metabolism, oxidative stress, and immunomodulation, based on which we propose a tentative metabolism-immunity synergy model to guide future research. Key metabolites, especially L-methionine S-oxide and oleamide, show exploratory predictive potential for functional cure that warrants further validation in independent cohorts.