Predictive factors of cirrhosis regression in patients with advanced chronic liver disease

Principal Investigator:
Prof. Dr. med. Annalisa Berzigotti, Associate Professor of Hepatology, Senior Attending Physician, University Clinic for Visceral Surgery and Medicine, Department of Biomedical Research, Inselspital, University of Bern. http://www.swissliver.ch/en/

Co-Investigators:
Prof. Dr. Jaime Bosch, Guest Professor of Hepatology, University Clinic for Visceral Surgery and Medicine, Department of Biomedical Research, Inselspital, University of Bern.
Dr. Cédric Bovet, Senior Scientist, University Institute of Clinical Chemistry, Inselspital, Bern.
Dr. med. Yuly Mendoza, PhD candidate in Hepatology, University of Bern.

Background & aim:
Advanced chronic liver disease (ACLD)/cirrhosis was considered an irreversible condition, but evidence showed it can be reversed if the underlying cause is addressed. Since regression of cirrhosis implies improved prognosis non-invasive methods to identify it accurately are needed.
This pilot study aims at identifying candidate genetic, transcriptomic and metabolomic factors associated with cirrhosis regression in ACLD-patients. The discovered biomarkers will be validated in a later phase to develop a diagnostic and predictive algorithm of cirrhosis regression.
Approach:
In collaboration with the Clinical Metabolomics Facility, Inselspital Bern, we will perform a metabolomics analysis of blood samples (stored in the Biobank of Inselspital) of patients with and without cirrhosis regression. Additionally, we will perform a transcriptome analysis on liver tissue (FFPE) obtained before and on regression. This will be done using NGS with RNA-sequencing at the Clinical Genomic Laboratory, Inselspital; mRNA will be enriched using hybridization to overcome the fragmentation of RNA extraction from FFPE tissue.
Significance:
This proposal has the potential of identifying novel metabolomics biomarkers to be later validated, and used in an individualized medicine approach to non-invasively diagnose and predict cirrhosis regression. Furthermore, the transcriptomic approach proposed could lead to outline pathways involved in cirrhosis regression.