Dataset Information

Candidate genes investigation for severe nonalcoholic fatty liver disease based on bioinformatics analysis.

ABSTRACT:

Background

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver condition worldwide. However, its etiology and fundamental pathophysiology for the disease process are poorly understood. In this study, we thus used bioinformatics to identify candidate genes potentially causative of severe NAFLD.

Methods

Gene expression profile data GSE49541 were downloaded from the Gene Expression Omnibus database. Tissues samples from 32 severe and 40 mild NAFLD patients were evaluated to identify differentially expressed genes (DEGs) between the 2 groups, followed by analyses of Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes pathways. Then, a weighted protein-protein interaction (PPI) network was constructed, and subnetworks and candidate genes were screened. Moreover, the GSE48452 data (14 normal liver tissue samples and 18 nonalcoholic steatohepatitis samples) were used to verify the results obtained from the above analyses.

Results

A total of 100 upregulated genes and 24 downregulated ones were identified in severe NAFLD. Functional enrichment and pathway analyses showed that these DEGs were mainly associated with cell adhesion, inflammatory response, and chemokine activity. The top 5 subnetworks were selected based on the PPI network. A total of 5 hub genes, including ubiquilin 4 (UBQLN4), amyloid-beta precursor protein (APP), sex hormone-binding globulin (SHBG), cadherin-associated protein beta 1 (CTNNB1) and collagen type I alpha 1 (COL1A1), were considered to be candidate genes for NAFLD. In addition, the verification data confirmed the status of COL1A1, SHBG, and APP as candidate genes.

Conclusion

UBQLN4, APP, CTNNB1, SHBG, and COL1A1 might be involved in the development of NAFLD, and are proposed as the potential markers for predicting the development of this condition.

SUBMITTER: Qi S

PROVIDER: S-EPMC5556226 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Candidate genes investigation for severe nonalcoholic fatty liver disease based on bioinformatics analysis.

Qi Shan S Wang Changhong C Li Chunfu C Wang Pu P Liu Minghui M

Medicine 20170801 32

<h4>Background</h4>Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver condition worldwide. However, its etiology and fundamental pathophysiology for the disease process are poorly understood. In this study, we thus used bioinformatics to identify candidate genes potentially causative of severe NAFLD.<h4>Methods</h4>Gene expression profile data GSE49541 were downloaded from the Gene Expression Omnibus database. Tissues samples from 32 severe and 40 mild NAFLD patients were ...[more]

PMID: 28796060

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Project description:BackgroundNonalcoholic fatty liver disease (NAFLD) is a prevalent form of liver damage, affecting ~25% of the global population. NAFLD comprises a spectrum of liver pathologies, from hepatic steatosis to nonalcoholic steatohepatitis (NASH), and may progress to liver fibrosis and cirrhosis. The presence of NAFLD correlates with metabolic disorders such as hyperlipidemia, obesity, blood hypertension, cardiovascular, and insulin resistance. Fenofibrate is an agonist drug for peroxisome proliferator-activated receptor alpha (PPARα), used principally for treatment of hyperlipidemia. However, fenofibrate has recently been investigated in clinical trials for treatment of other metabolic disorders such as diabetes, cardiovascular disease, and NAFLD. The evidence to date indicates that fenofibrate could improve NAFLD. While PPARα is considered to be the main target of fenofibrate, fenofibrate may exert its effect through impact on other genes and pathways thereby alleviating, and possibly reversing, NAFLD. In this study, using bioinformatics tools and gene-drug, gene-diseases databases, we sought to explore possible targets, interactions, and pathways involved in fenofibrate and NAFLD.MethodsWe first determined significant protein interactions with fenofibrate in the STITCH database with high confidence (0.7). Next, we investigated the identified proteins on curated targets in two databases, including the DisGeNET and DISEASES databases, to determine their association with NAFLD. We finally constructed a Venn diagram for these two collections (curated genes-NAFLD and fenofibrate-STITCH) to uncover possible primary targets of fenofibrate. Then, Gene Ontology (GO) and KEGG were analyzed to detect the significantly involved targets in molecular function, biological process, cellular component, and biological pathways. A P value < 0.01 was considered the cut-off criterion. We also estimated the specificity of targets with NAFLD by investigating them in disease-gene associations (STRING) and EnrichR (DisGeNET). Finally, we verified our findings in the scientific literature.ResultsWe constructed two collections, one with 80 protein-drug interactions and the other with 95 genes associated with NAFLD. Using the Venn diagram, we identified 11 significant targets including LEP, SIRT1, ADIPOQ, PPARA, SREBF1, LDLR, GSTP1, VLDLR, SCARB1, MMP1, and APOC3 and then evaluated their biological pathways. Based on Gene Ontology, most of the targets are involved in lipid metabolism, and KEGG enrichment pathways showed the PPAR signaling pathway, AMPK signaling pathway, and NAFLD as the most significant pathways. The interrogation of those targets on authentic disease databases showed they were more specific to both steatosis and steatohepatitis liver injury than to any other diseases in these databases. Finally, we identified three significant genes, APOC3, PPARA, and SREBF1, that showed robust drug interaction with fenofibrate.ConclusionFenofibrate may exert its effect directly or indirectly, via modulation of several key targets and pathways, in the treatment of NAFLD.

Project description:Coronary artery disease (CAD) is the principal cause of death in patients with nonalcoholic fatty liver disease (NAFLD). The aim of the present study was to investigate whether NAFLD is causally involved in the pathogenesis of CAD. For this, previously reported NAFLD susceptibility genes were clustered and tested for an association with CAD in the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis plus the Coronary Artery Disease Genetics (CARDIoGRAMplusC4D) Consortium data set. The role of plasma lipids as a potential mediator was explored by using data from the Global Lipids Genetics Consortium. Statistical analyses revealed that the combination of 12 NAFLD genes was not associated with CAD in 60,801 CAD cases and 123,504 controls (odds ratio [OR] per NAFLD risk allele, 1.0; 95% confidence interval [CI], 0.99-1.00). In a subsequent sensitivity analysis, a positive relationship was observed after exclusion of gene variants that are implicated in NAFLD through impaired very low-density lipoprotein secretion (i.e., microsomal triglyceride transfer protein [MTTP], patatin-like phospholipase domain containing 3 [PNPLA3], phosphatidylethanolamine N-methyltransferase [PEMT], and transmembrane 6 superfamily member 2 [TM6SF2]) (OR, 1.01; 95% CI, 1.00-1.02). Clustering of the excluded genes showed a significant negative relationship with CAD (OR, 0.97; 95% CI, 0.96-0.99). A substantial proportion of the observed heterogeneity between the individual NAFLD genes in relation to CAD could be explained by plasma lipids, as reflected by a strong relationship between plasma lipids and CAD risk conferred by the NAFLD susceptibility genes (r = 0.76; P = 0.004 for low-density lipoprotein cholesterol). Conclusion: NAFLD susceptibility genes do not cause CAD per se. The relationship between these genes and CAD appears to depend to a large extent on plasma lipids. These observations strongly suggest taking plasma lipids into account when designing a new drug to target NAFLD.

Dataset Information

Candidate genes investigation for severe nonalcoholic fatty liver disease based on bioinformatics analysis.

Background

Methods

Results

Conclusion

Publications

Candidate genes investigation for severe nonalcoholic fatty liver disease based on bioinformatics analysis.

Similar Datasets

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