Non-invasive candidate protein signature predicts hepatic venous pressure gradient reduction in cirrhotic patients after sustained virologic response

Liver international : official journal of the International Association for the Study of the Liver(2023)

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摘要
Background and AimsA reduction in hepatic venous pressure gradient (HVPG) is the most accurate marker for assessing the severity of portal hypertension and the effectiveness of intervention treatments. This study aimed to evaluate the prognostic potential of blood-based proteomic biomarkers in predicting HVPG response amongst cirrhotic patients with portal hypertension due to Hepatitis C virus (HCV) and had achieved sustained virologic response (SVR). MethodsThe study comprised 59 patients from two cohorts. Patients underwent paired HVPG (pretreatment and after SVR), liver stiffness (LSM), and enhanced liver fibrosis scores (ELF) measurements, as well as proteomics-based profiling on serum samples using SomaScan & REG; at baseline (BL) and after SVR (EOS). Machine learning with feature selection (Caret, Random Forest and RPART) methods were performed to determine the proteins capable of classifying HVPG responders. Model performance was evaluated using AUROC (pROC R package). ResultsPatients were stratified by a change in HVPG (EOS vs. BL) into responders (greater than 20% decline in HVPG from BL, or 10 mmHg at BL) and non-responders. LSM and ELF decreased markedly after SVR but did not correlate with HVPG response. SomaScan (SomaLogic, Inc., Boulder, CO) analysis revealed a substantial shift in the peripheral proteome composition, reflected by 82 significantly differentially abundant proteins. Twelve proteins accurately distinguished responders from non-responders, with an AUROC of .86, sensitivity of 83%, specificity of 83%, accuracy of 83%, PPV of 83%, and NPV of 83%. ConclusionsA combined non-invasive soluble protein signature was identified, capable of accurately predicting HVPG response in HCV liver cirrhosis patients after achieving SVR.
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关键词
hepatic venous pressure gradient,machine learning,predictive analysis,proteomics,sustained virologic response
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