Literature DB >> 31188000

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma.

Alessia Ferrarini, Cristina Di Poto, Shisi He, Chao Tu, Rency S Varghese, Abdalla Kara Balla1, Meth Jayatilake, Zhenzhi Li, Kian Ghaffari, Ziling Fan, Zaki A Sherif2, Deepak Kumar3, Alexander Kroemer1, Mahlet G Tadesse, Habtom W Ressom.   

Abstract

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

Entities:  

Keywords:  GC−MS; HCC; UPLC−MS; alpha-fetoprotein; cirrhosis; liver tissues; metabolomics

Mesh:

Substances:

Year:  2019        PMID: 31188000      PMCID: PMC6677583          DOI: 10.1021/acs.jproteome.9b00185

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


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