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Multiomics biomarkers for the prediction of nonalcoholic fatty liver disease severity.

ABSTRACT: This review intends to uncover how information from large-scale genetic profiling (whole genome sequencing, and whole exome sequencing) of nonalcoholic fatty liver disease (NAFLD), as well as information from circulating transcriptomics (cell-free miRNAs) and metabolomics, contributes to the understanding of NAFLD pathogenesis. A further aim is to address the question of whether OMICs information is ready to be implemented in the clinics. The available evidence suggests that any new knowledge pertaining to molecular signatures associated with NAFLD and nonalcoholic steatohepatitis should be promptly translated into the clinical setting. Nevertheless, rigorous steps that must include validation and replication are mandatory before utilizing OMICs biomarkers in diagnostics to identify patients at risk of advanced disease, including liver cancer.

SUBMITTER: Pirola CJ

PROVIDER: S-EPMC5910543 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Multiomics biomarkers for the prediction of nonalcoholic fatty liver disease severity.

Pirola Carlos J CJ Sookoian Silvia S

World journal of gastroenterology 20180401 15

This review intends to uncover how information from large-scale genetic profiling (whole genome sequencing, and whole exome sequencing) of nonalcoholic fatty liver disease (NAFLD), as well as information from circulating transcriptomics (cell-free miRNAs) and metabolomics, contributes to the understanding of NAFLD pathogenesis. A further aim is to address the question of whether OMICs information is ready to be implemented in the clinics. The available evidence suggests that any new knowledge pe ...[more]

PMID: 29686467

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Project description:BackgroundNonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients.MethodsGSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein-protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the "Least absolute shrinkage and selection operator regression" and "Random Forest" algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level.ResultsFirst, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group.ConclusionMetabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD.

Project description:Background & aimsAlterations in the gut microbiome have been associated with the severity of nonalcoholic fatty liver disease (NAFLD). Previous studies focused exclusively on the bacteria in the microbiome; we investigated changes in the viral microbiome (virome) in patients with NAFLD.MethodsIn a prospective, cross-sectional, observational study, we extracted RNA and DNA virus-like particles from fecal samples from 73 patients with NAFLD: 29 patients had an NAFLD Activity Score (NAS) of 0-4, 44 patients had an NAS of 5-8 or liver cirrhosis (LCI), 37 patients had F0-F1 fibrosis, and 36 patients had F2-F4 fibrosis. As controls, 9 individuals without liver disease and 13 patients with mild primary biliary cholangitis were included in the analysis. We performed shotgun metagenomic sequencing of virus-like particles.ResultsPatients with NAFLD and NAS 5-8/LCI had a significant decrease in intestinal viral diversity compared with patients with NAFLD and NAS 0-4 or control individuals. The presence of more advanced NAFLD was associated with a significant reduction in the proportion of bacteriophages compared with other intestinal viruses. Using multivariate logistic regression analysis with leave-1-out cross validation, we developed a model, including a viral diversity index and simple clinical variables, that identified patients with NAS 5-8/LCI with an area under the curve of 0.95 (95% confidence interval, 0.91-0.99) and F2-F4 fibrosis with an area under the curve of 0.88 (95% confidence interval, 0.80-0.95). Addition of data on viral diversity significantly improved multivariate models, including those based on only clinical parameters or bacterial diversity.ConclusionsIn a study of fecal viromes from patients with NAFLD and control individuals, we associated histologic markers of NAFLD severity with significant decreases in viral diversity and proportion of bacteriophages. We developed a model based on fecal viral diversity and clinical data that identifies patients with severe NAFLD and fibrosis more accurately than models based only on clinical or bacterial data.

Dataset Information

Multiomics biomarkers for the prediction of nonalcoholic fatty liver disease severity.

Publications

Multiomics biomarkers for the prediction of nonalcoholic fatty liver disease severity.

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