Identification of key candidate genes and pathways in hepatitis B virus-associated acute liver failure by bioinformatical analysis

Abstract

Hepatitis B virus-associated acute liver failure (HBV-ALF) is a rare but life-threatening syndrome that carried a high morbidity and mortality. Our study aimed to explore the possible molecular mechanisms of HBV-ALF by means of bioinformatics analysis. In this study, genes expression microarray datasets of HBV-ALF from Gene Expression Omnibus were collected, and then we identified differentially expressed genes (DEGs) by the limma package in R. After functional enrichment analysis, we constructed the protein-protein interaction (PPI) network by the Search Tool for the Retrieval of Interacting Genes online database and weighted genes coexpression network by the WGCNA package in R. Subsequently, we picked out the hub genes among the DEGs. A total of 423 DEGs with 198 upregulated genes and 225 downregulated genes were identified between HBV-ALF and normal samples. The upregulated genes were mainly enriched in immune response, and the downregulated genes were mainly enriched in complement and coagulation cascades. Orosomucoid 1 (ORM1), orosomucoid 2 (ORM2), plasminogen (PLG), and aldehyde oxidase 1 (AOX1) were picked out as the hub genes that with a high degree in both PPI network and weighted genes coexpression network. The weighted genes coexpression network analysis found out 3 of the 5 modules that upregulated genes enriched in were closely related to immune system. The downregulated genes enriched in only one module, and the genes in this module majorly enriched in the complement and coagulation cascades pathway. In conclusion, 4 genes (ORM1, ORM2, PLG, and AOX1) with immune response and the complement and coagulation cascades pathway may take part in the pathogenesis of HBV-ALF, and these candidate genes and pathways could be therapeutic targets for HBV-ALF.

Figure 1

Volcano plots representation of differential expression analyses. The criteria for selection of DEGs was set as |fold change| ≥2 and adjusted P <.01.

Figure 2

The Venn plot of DEGs. There were a total of 423 overlapped genes from 3 different datasets.

Figure 3

The top 10 most enriched GO terms of (A) upregulated genes and (B) downregulated genes.

Figure 4

The top 5 most enriched KEGG pathways of (A) upregulated genes and (B) downregulated genes.

Figure 5

The PPI network. The left panel showed the interactions between the upregulated (red nodes) and downregulated (blue nodes) genes. The right panel showed the degree of the genes, only top 30 genes were listed out.

Figure 6

Meta-analysis of expression level of top ten hub genes in PPI network (KNG1, F5, IGF1, PLG, HLA-DRA, HLA-DRB1, SERPINF2, AOX1, C5, and CFD).

Figure 7

The weighted genes coexpression network of upregulated DEGs. There were 5 modules that the upregulated DEGs enriched in. Each module was named as the color assigned by the WGCNA analysis.

Figure 8

The weighted genes coexpression network of downregulated DEGs (the blue module of WGCNA analysis). The left panel showed the coexpression interactions between the genes. The right panel showed the degree of the genes, only top 30 genes were listed out.

Figure 9

The microRNAs-genes network. The green nodes represented the DEmiRNAs and the blue nodes were the target genes of the DEmiRNAs.

Figure 10

Connectivity Map analysis. The mean was the computed score. A positive score indicates that the drug exhibits an expression pattern that is synergistic with the disease. A negative score indicates that the drug exhibits an expression pattern that is opposite to the disease.