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. 2023 Dec;21(4):e45.
doi: 10.5808/gi.23051. Epub 2023 Dec 29.

Identification of key genes and functional enrichment analysis of liver fibrosis in nonalcoholic fatty liver disease through weighted gene co-expression network analysis

Yue Hu  1 , Jun Zhou  1
Affiliations

Identification of key genes and functional enrichment analysis of liver fibrosis in nonalcoholic fatty liver disease through weighted gene co-expression network analysis

Yue Hu et al. Genomics Inform. 2023 Dec.

Abstract

Nonalcoholic fatty liver disease (NAFLD) is a common type of chronic liver disease, with severity levels ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH). The extent of liver fibrosis indicates the severity of NASH and the risk of liver cancer. However, the mechanism underlying NASH development, which is important for early screening and intervention, remains unclear. Weighted gene co-expression network analysis (WGCNA) is a useful method for identifying hub genes and screening specific targets for diseases. In this study, we utilized an mRNA dataset of the liver tissues of patients with NASH and conducted WGCNA for various stages of liver fibrosis. Subsequently, we employed two additional mRNA datasets for validation purposes. Gene set enrichment analysis (GSEA) was conducted to analyze gene function enrichment. Through WGCNA and subsequent analyses, complemented by validation using two additional datasets, we identified five genes (BICC1, C7, EFEMP1, LUM, and STMN2) as hub genes. GSEA analysis indicated that gene sets associated with liver metabolism and cholesterol homeostasis were uniformly downregulated. BICC1, C7, EFEMP1, LUM, and STMN2 were identified as hub genes of NASH, and were all related to liver metabolism, NAFLD, NASH, and related diseases. These hub genes might serve as potential targets for the early screening and treatment of NASH.

Keywords: functional enrichment analysis; gene set enrichment analysis; hub genes; liver fibrosis; nonalcoholic fatty liver disease; weighted gene co-expression network analysis.

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Conflict of interest statement

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.
Differentially expressed gene (DEG) analysis and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. (A) Volcano plot of DEGs in GSE49541. (B) Heatmap of the expression levels of the top 20 upregulated and top 20 downregulated DEGs. (C) KEGG analysis of downregulated and upregulated DEGs. (D–F) GO analysis of downregulated and upregulated DEGs.
Fig. 2.
Fig. 2.
Co-expression module analysis. (A) The relationship between the scale-free fit index and various soft-thresholding powers and between the mean connectivity and various soft-thresholding powers. (B) Clustering dendrogram of genes; various colors represent different modules.
Fig. 3.
Fig. 3.
Important module analysis. (A) The relationships between the liver fibrosis trait and seven modules. (B) Eigengene adjacency heatmap of differentially expressed gene expression levels in six modules. (C) Heatmap of the relationships among genes with high correlation values (Pearson correlation value > 0.7, p < 0.05) for liver fibrosis.
Fig. 4.
Fig. 4.
Gene expression levels of the five key genes in three mRNA datasets.
Fig. 5.
Fig. 5.
Gene set enrichment analysis results for five key genes. (A) Bicc1. (B) C7. (C) Efemp1. (D) Lum. (E) Stmn2.
Fig. 6.
Fig. 6.
Pathway analysis of gene sets related to immunity in five key genes. (A) Bicc1. (B) C7. (C) Efemp1. (D) Lum (E) Stmn2.
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