Non-alcoholic Fatty Liver Disease (NAFLD): A Systematic Review and Meta-analysis from an Omics Perspective

Abstract

Background and objectives: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in developed countries, contributing to ∼24% of cases worldwide and includes non-alcoholic steatohepatitis. NAFLD is characterized by lipid accumulation rather than alcohol consumption. There are several diagnostic/prognostic biomarkers for NAFLD including CK-18, ALT, AST, GGT, and haptoglobin, but with limited sensitivity and specificity. Therefore, high-throughput OMICS approaches have been used to characterize NAFLD conditions for the identification of potential molecular signatures or differentially regulated molecules (DEMs) and early detection of NAFLD. Methods:We analyzed the publically available data set (accession number: GSE63067) from the Gene Expression Omnibus (GEO) using the GEO2R program. The differentially expressed genes (DEGs) were filtered using the criteria where genes with p-value ≤0.05 and fold-change ≥2.0-fold (upregulated), and fold-change ≤0.5-fold (downregulated).Results: We identified 264 differentially expressed genes (DEGs) between NAFLD and normal liver tissue samples, where 211 were upregulated and 53 were downregulated in NAFLD. Additionally, we identified novel genes SGMS2, and WNK3 that were not well understood in the molecular pathophysiology of NAFLD. Further gene ontology-based analysis revealed that among biological processes, cellular components, and molecular functions were also dysregulated in NAFLD. Collectively, this integration of a systemic-cum-meta-analysis approach suggests that an OMICS-based analysis may provide better solutions if microarray and other high-throughput study-based DEMs are to be cataloged systematically for sharing with the scientific community. This will allow potential candidates (DEMs) to be interrogated either alone or in combination with existing biomarkers for effective early detection and or diagnosis of NAFLD. Conclusions:Our study shows that meta-analysis of publicly available data could be a good source for identification of DEGs in NAFLD, and it can be easily extrapolated in other disease conditions.