Circulating proteomic panels for diagnosis and risk stratification of acute-on-chronic liver failure in patients with viral hepatitis B.

Abstract

Chronic HBV infection (CHB) can lead to acute-on-chronic liver failure (HBV-ACLF) characterized by high mortality. This study aimed to reveal ACLF-related proteomic alterations, from which protein based diagnostic and prognostic scores for HBV-ACLF were developed.Methods: Ten healthy controls, 16 CHB, and 19 HBV-ACLF according to COSSH (Chinese group on the study of severe hepatitis B) criteria were enrolled to obtain the comprehensive proteomic portrait related to HBV-ACLF initiation and progression. Potential markers of HBV-ACLF were further selected based on organ specificity and functionality. An additional cohort included 77 healthy controls, 92 CHB and 71 HBV-ACLF was used to validate the proteomic signatures via targeted proteomic assays.Results: Significant losses of plasma proteins related to multiple functional clusters, including fatty acid metabolism/transport, immuno-response, complement and coagulation systems, were observed in ACLF patients. In the validation study, 28 proteins were confirmed able to separate ACLF, CHB patients. A diagnostic classifier P4 (APOC3, HRG, TF, KLKB1) was built to differentiate ACLF from CHB with high accuracy (auROC = 0.956). A prognostic model P8 (GC, HRG, HPR, SERPINA6, age, NEU, INR and total protein) was built to distinguish survivors from non-survivors in 28 and 90-days follow-up (auROC = 0.882, 0.871), and to stratify ACLF patients into risk subgroups showing significant difference in 28 and 90-days mortality (HR=7.77, 7.45, both P<0.0001). In addition, P8 score correlated with ACLF grades and numbers of extra-hepatic organ failures in ACLF patients, and was able to predict ACLF-associated coagulation and brain failure within 90 days (auROC = 0.815, 0.842).Conclusions: Proteomic signatures developed in this study reflected the deficiency of key hematological functions in HBV-ACLF patients, and show potential for HBV-ACLF diagnosis and risk prediction in complementary to current clinical based parameters.