Dataset Information

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Non-alcoholic Fatty Liver Disease.

ABSTRACT: Background: Non-alcoholic fatty liver disease (NAFLD) is a well-known cause of liver dysfunction and has become a common chronic liver disease in many countries. However, the intrinsic molecular mechanisms underlying the pathogenesis of NAFLD have not yet been fully elucidated. Methods: We obtained the gene expression datasets of NAFLD through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between NAFLD patients and controls. Gene functional annotation and PPI network analysis were performed to explore the potential function of the DEGs. Finally, we used a sequencing dataset GSE126848 to validate our results. Results: In this study, GSE48452, GSE66676, GSE72756, GSE63067, GSE89632, and GSE107231 were included, including 125 NAFLD patients and 116 control patients. The RRA integrated analysis determined 96 significant DEGs (50 up-regulated and 46 down-regulated) and the most significant gene aberrantly expressed in NAFLD was ENO3 (P-value = 7.17E-05), followed by CYP7A1 (P-value = 9.04E-05), and P4HA1 (P-value = 1.67E-04). Carboxylic acid metabolic process (GO:0019752; P-value = 1.39E-03) was the most significantly enriched for biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that steroid hormone biosynthesis (hsa00140; P-value = 6.68E-03) and PPAR signaling pathway (hsa03320; P-value = 9.95E-03) were significantly enriched. Based on the results of the PPI and the results of the RRA, we finally defined the four most critical genes as the hub genes, including ENO3, CYP7A1, P4HA1, and CYP1A1. Conclusions: Our integrated analysis identified novel gene signatures and will contribute to the understanding of comprehensive molecular changes in NAFLD.

SUBMITTER: Jia X

PROVIDER: S-EPMC6736562 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Non-alcoholic Fatty Liver Disease.

Jia Xi X Zhai Tianyu T

Frontiers in endocrinology 20190830

<b>Background:</b> Non-alcoholic fatty liver disease (NAFLD) is a well-known cause of liver dysfunction and has become a common chronic liver disease in many countries. However, the intrinsic molecular mechanisms underlying the pathogenesis of NAFLD have not yet been fully elucidated. <b>Methods:</b> We obtained the gene expression datasets of NAFLD through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed ...[more]

PMID: 31551930

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Project description:Background: Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated. Methods: All UC datasets published in the GEO database were analyzed and summarized. Subsequently, the robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between UC patients and controls. Gene functional annotation and PPI network analysis were performed to illustrate the potential functions of the DEGs. Some important functional modules from the protein-protein interaction (PPI) network were identified by molecular complex detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analyses were performed. The results of CytoHubba, a plug for integrated algorithm for biomolecular interaction networks combined with RRA analysis, were used to identify the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium salt (DSS) solution to verify the expression of hub genes. Results: A total of 6 datasets met the inclusion criteria (GSE38713, GSE59071, GSE73661, GSE75214, GSE87466, GSE92415). The RRA integrated analysis revealed 208 significant DEGs (132 upregulated genes and 76 downregulated genes). After constructing the PPI network by MCODE plug, modules with the top three scores were listed. The CytoHubba app and RRA identified six hub genes: LCN2, CXCL1, MMP3, IDO1, MMP1, and S100A8. We found through enrichment analysis that these functional modules and hub genes were mainly related to cytokine secretion, immune response, and cancer progression. With the mouse model, we found that the expression of all six hub genes in the UC group was higher than that in the control group (P < 0.05). Conclusion: The hub genes analyzed by the RRA method are highly reliable. These findings improve the understanding of the molecular mechanisms in UC pathogenesis.

Project description:Background & aimsNon-alcoholic fatty liver disease (NAFLD) is a progressive liver disease with potentially severe complications including cirrhosis and hepatocellular carcinoma. Previously, we have identified circulating lipid signatures associating with liver fat content and non-alcoholic steatohepatitis (NASH). Here, we develop a metabolomic map across the NAFLD spectrum, defining interconnected metabolic signatures of steatosis (non-alcoholic fatty liver, NASH, and fibrosis).MethodsWe performed mass spectrometry analysis of molecular lipids and polar metabolites in serum samples from the European NAFLD Registry patients (n = 627), representing the full spectrum of NAFLD. Using various univariate, multivariate, and machine learning statistical approaches, we interrogated metabolites across 3 clinical perspectives: steatosis, NASH, and fibrosis.ResultsFollowing generation of the NAFLD metabolic network, we identify 15 metabolites unique to steatosis, 18 to NASH, and 15 to fibrosis, with 27 common to all. We identified that progression from F2 to F3 fibrosis coincides with a key pathophysiological transition point in disease natural history, with n = 73 metabolites altered.ConclusionsAnalysis of circulating metabolites provides important insights into the metabolic changes during NAFLD progression, revealing metabolic signatures across the NAFLD spectrum and features that are specific to NAFL, NASH, and fibrosis. The F2-F3 transition marks a critical metabolic transition point in NAFLD pathogenesis, with the data pointing to the pathophysiological importance of metabolic stress and specifically oxidative stress.Clinical trials registrationThe study is registered at Clinicaltrials.gov (NCT04442334).Lay summaryNon-alcoholic fatty liver disease is characterised by the build-up of fat in the liver, which progresses to liver dysfunction, scarring, and irreversible liver failure, and is markedly increasing in its prevalence worldwide. Here, we measured lipids and other small molecules (metabolites) in the blood with the aim of providing a comprehensive molecular overview of fat build-up, liver fibrosis, and diagnosed severity. We identify a key metabolic 'watershed' in the progression of liver damage, separating severe disease from mild, and show that specific lipid and metabolite profiles can help distinguish and/or define these cases.

Dataset Information

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Non-alcoholic Fatty Liver Disease.

Publications

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Non-alcoholic Fatty Liver Disease.

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