Dataset Information

WGCNA-Based Identification of Hub Genes and Key Pathways Involved in Nonalcoholic Fatty Liver Disease.

ABSTRACT:

Background

The morbidity of nonalcoholic fatty liver disease (NAFLD) has been rising, but the pathogenesis of NAFLD is still elusive. This study is aimed at determining NAFLD-related hub genes based on weighted gene coexpression network analysis (WGCNA).

Methods

GSE126848 dataset based construction of coexpression networks was performed based on WGCNA. Database for Annotation, Visualization, and Integrated Discovery (DAVID) was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Hub genes were identified and validated in independent datasets and mouse model.

Results

We found that the steelblue module was most significantly correlated with NAFLD. Total 15 hub genes (NDUFA9, UQCRQ, NDUFB8, COPS5, RPS17, UBL5, PSMA3, PSMA1, SF3B5, MRPL27, RPL26, PDCD5, PFDN6, SNRPD2, PSMB3) were derived from both the coexpression and PPI networks and considered "true" hub genes. Functional enrichment analysis showed that the hub genes were related to NAFLD pathway and oxidative phosphorylation. Independent dataset-based analysis and the establishment of NAFLD mouse model confirmed the involvement of two hub genes NDUFA9 and UQCRQ in the pathogenesis of NAFLD.

Conclusions

Oxidative phosphorylation and NAFLD pathway may be crucially involved in the pathogenesis of NAFLD, and two hub genes NDUFA9 and UQCRQ might be diagnostic biomarkers and therapeutic targets for NAFLD.

SUBMITTER: Zeng F

PROVIDER: S-EPMC8687832 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Hepatocellular carcinoma (HCC) is one of the leading causes of death from cancer worldwide. The histopathological features, risk factors, and prognosis of HCC caused by nonalcoholic fatty liver disease (NAFLD) appear to be significantly different from those of HCC caused by other etiologies of liver disease. Objective This article explores the shared gene and molecular mechanism between NAFLD and HCC through bioinformatics technologies such as weighted gene co-expression network analysis (WGCNA), so as to provide a reference for comprehensive understanding and treatment of HCC caused by NAFLD. Methods NAFLD complementary deoxyribonucleic acid microarrays (GSE185051) from the Gene Expression Omnibus database and HCC ribonucleic acid (RNA)-sequencing data (RNA-seq data) from The Cancer Genome Atlas database were used to analyze the differentially expressed genes (DEGs) between NAFLD and HCC. Then, the clinical traits and DEGs in the two disease data sets were analyzed by WGCNA to obtain W-DEGs, and cross-W-DEGs were obtained by their intersection. We performed subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses of the cross-W-DEGs and established protein-protein interaction networks. Then, we identified the hub genes in them by Cytoscape and screened out the final candidate genes. Finally, we validated candidate genes by gene expression, survival, and immunohistochemical analyses. Results The GO analysis of 79 cross-W-DEGs showed they were related mainly to RNA polymerase II (RNAP II) and its upstream transcription factors. KEGG analysis revealed that they were enriched predominantly in inflammation-related pathways (tumor necrosis factor and interleukin-17). Four candidate genes (JUNB, DUSP1, NR4A1, and FOSB) were finally screened out from the cross-W-DEGs. Conclusion JUNB, DUSP1, NR4A1, and FOSB inhibit NAFLD and HCC development and progression. Thus, they can serve as potential useful biomarkers for predicting and treating NAFLD progression to HCC.