Published online Feb 28, 2019. doi: 10.3748/wjg.v25.i8.941
Peer-review started: November 20, 2018
First decision: December 12, 2018
Revised: January 10, 2019
Accepted: January 28, 2019
Article in press: January 28, 2019
Published online: February 28, 2019
Processing time: 99 Days and 1.5 Hours
Liver fibrosis is a common chronic progressive liver disease, and alanine aminotransferase is a commonly used diagnostic indicator for liver disease. Magnetic resonance imaging- and ultrasound-based elastography has been used for further assessment of hepatic steatosis and fibrosis, but these techniques are not able to diagnose inflammation and cell damage very well. Therefore, new liver fibrosis and functional biomarkers are needed as supplements.
Metabolomics is an important component of systems biology which provides quantitative measurements of global changes in individual metabolic characteristics in biological fluids responding to a variety of physiological and pathological stimuli, and it can be used to discover new biomarkers for differential diagnosis of disease.
The main objectives were to investigate the dynamic changes in metabolic profiles during the liver fibrosis progression, and seek for potential novel biomarkers for early diagnosis of liver fibrosis.
A liver fibrosis model was induced by subcutaneous injection with CCl4. The dynamic changes in metabolic profiles during the progression of liver fibrosis were analyzed by ultra-performance liquid chromatography-mass spectrometry, and independent sample test and receiver operating characteristic analysis were used to identify potential biomarkers.
A liver fibrosis model was successfully established, which was evaluated by liver chemical tests, liver histopathology, Masson’s trichrome staining, and the expression levels of α-smooth muscle actin and transforming growth factor β1. Principal component analysis and orthogonal partial least squares discriminant analysis were used to characterize the metabolic profiles, which can clearly distinguish early liver fibrosis and advanced groups. We identified 21 metabolites associated with liver fibrosis, and two of them, β-muricholic acid (β-MCA) and cervonoyl ethanolamide (CEA), had excellent diagnostic value.
The dynamic metabolomics profile is useful for screening early metabolic characteristics associated with progression of fibrosis. Two new metabolic biomarkers identified in this study, β-MCA and CEA, can correctly classify the disease stage in our fibrosis animal model.
According to the results of rat experiments, further mechanistic studies are needed to investigate the involvement of these metabolites in fibrotic progression. We also need to collect clinical samples for further verification, and the markers identified may be used for clinical diagnosis in the future.