Identification and validation of plasma AGRN as a novel diagnostic biomarker of hepatitis B Virus-related chronic hepatitis and liver fibrosis/cirrhosis

Histol Histopathol. 2023 Dec 21:18695. doi: 10.14670/HH-18-695. Online ahead of print.

Abstract

Objective: The aim of this study was to find novel biomarkers and develop a non-invasive, effective diagnostic model for hepatitis B Virus-related chronic hepatitis and liver fibrosis/cirrhosis.

Method: Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to assess the expression of differentially expressed genes (AGRN, JAG1, CCL5, ID3, CCND1, and CAPN2) in peripheral blood mononuclear cells (PBMCs) from healthy subjects, chronic hepatitis B (CHB), and liver fibrosis/cirrhosis (LF/LC) patients. The molecular mechanisms underlying AGRN-regulated CHB were further explored and verified in LX2 cells, in which small interfering RNA (siRNA) was used to block AGRN gene expression. Finally, enzyme-linked Immunosorbent Assay (ELISA) was used to measure AGRN protein expression in 100 healthy volunteers, 100 CHB patients, and 100 LF/LC patients, and the efficacy of the diagnostic model was assessed by the Area Under the Curve (AUC).

Results: AGRN mRNA displayed a steady rise in the PBMCs of normal, CHB, and LF/LC patients. Besides, AGRN expression was markedly elevated in activated LX2 cells, whereas the expression of COL1 and α-SMA decreased when AGRN was inhibited using siRNA. In addition, downregulation of AGRN can reduce the gene expression of β-catenin and c-MYC while upregulating the expression of GSK-3β. Furthermore, PLT and AGRN were used to develop a non-invasive diagnostic model (PA). To identify CHB patients from healthy subjects, the AUC of the PA model was 0.951, with a sensitivity of 87.0% and a specificity of 91.0%. The AUC of the PA model was 0.922 with a sensitivity of 82.0% and a specificity of 90.0% when differentiating between LF/LC and CHB patients.

Conclusion: The current study indicated that AGRN could be a potential plasma biomarker and the established PA model could improve the diagnostic accuracy for HBV-related liver diseases.