Unveiling novel prognostic biomarkers and therapeutic targets for HBV-associated hepatocellular carcinoma through integrated bioinformatic analysis

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, with limited treatment options. The goal of this study was to use integrated bioinformatic analysis to find possible biomarkers for prognosis and therapeutic targets for hepatitis B (HBV)-associated HCC. Three microarray datasets (GSE84402, GSE121248, and E-GEOD-19665) from patients with HBV-associated HCC were combined and analyzed. We identified differentially expressed genes (DEGs) and performed pathway enrichment analysis. We constructed protein-protein interaction networks to identify hub genes. We identified a total of 374 DEGs, which included 90 up-regulated and 284 down-regulated genes. Pathway enrichment analysis revealed associations with cell cycle, oocyte meiosis, and the p53 signaling pathway for up-regulated DEGs. Twenty hub genes were identified, and 9 of them (ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1) were validated using the Cancer Genome Atlas data and Kaplan-Meier survival analysis. These genes were significantly associated with a poor prognosis in HCC patients. Our research shows that ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1 may be useful for predicting how HBV-associated HCC will progress and for finding new ways to treat it. In addition to these further studies are needed to elucidate the functions of the remaining 11 identified hub genes (RRM2, NUSAP1, PBK, CCNB1, CCNB2, BUB1B, NEK2, CENPF, ASPM, TOP2A, and BUB1) in HCC development and progression.

Figure 1.

Before the batch adjustment, 3 distinct clusters that corresponded to each dataset could be observed, which vanished after the batch adjustment. Instead, 2 clusters were emerged, which represent 2 outcome conditions (HCC vs non-tumor), indicating that the biological signal of interest has been preserved. HCC = hepatocellular carcinoma.

Figure 2.

The volcano plot of the DEGs between HCC samples and the non-tumor group. The magenta dots represent the up- and down-regulated genes with adjusted P-value < 0.01 and the absolute value of log fold change >1.5; the purple dots represent the up- and down-regulated genes with adjusted P-value < .01 and the absolute value of log fold change <1.5; and green spots show the genes with no significant differences between study groups. DEGs = differentially expressed genes, HCC = hepatocellular carcinoma.

Figure 3.

Bubble maps for gene ontology (GO) pathway analyses of up- and down-regulated DEGs. Pathways with a minimum of 3 genes and an adjusted P value <.01 in at least 15 occurrences out of 25 iterations were considered statistically significant. (A) up-regulated genes biological process (BP); (B) down-regulated genes BP; (C) up-regulated genes cellular components (CC); (D) down-regulated genes CC; (E) up-regulated genes molecular function (MF); and (F) down-regulated genes MF.

Figure 4.

Bubble maps for the Kyoto Encyclopedia of Genes and Genomes (KEGG) of up- and down-regulated DEGs. Pathways with a minimum of 3 genes and an adjusted P value <0.01 in at least 15 occurrences out of 25 iterations were considered statistically significant. (A) up-regulated genes; (B) down-regulated genes. DEGs = differentially expressed genes

Figure 5.

PPI network diagrams of (A) up-regulated DEGs; (B) subnetworks; and (C) hub genes using the Cytoscape software. DEGs = differentially expressed genes, PPI = protein–protein interaction.

Figure 6.

PPI network diagrams of (A) down-regulated DEGs; (B) subnetworks; and (C) hub genes using the Cytoscape software. DEGs = differentially expressed genes, PPI = protein–protein interaction.

Figure 7.

PPI network diagrams of (A) overall DEGs; (B) subnetworks; and (C) hub genes using the Cytoscape software. DEGs = differentially expressed genes, PPI = protein–protein interaction.

Figure 8.

Comparisons of expression levels of the selected hub genes between the HCC and non-tumor groups in the present study. HCC = hepatocellular carcinoma.

Figure 9.

Comparisons of expression levels of the selected hub genes between the HCC and normal groups in the TCGA dataset. Besides NUSAP1, HCC samples showed high expression of other chosen hub genes compared to normal tissues. HCC = hepatocellular carcinoma, TCGA = The Cancer Genome Atlas.

Figure 10.

Survival curves were generated by the Kaplan–Meier plotter online tool, utilizing the low and high expression levels of hub genes in hepatocellular carcinoma patients who were infected with the hepatitis virus. A log-rank P-value of <.05 was considered to be statistically significant.

Figure 11.

A forest plot illustrating the hub genes associated with prognosis in patients with hepatocellular carcinoma (HCC). The hazard ratio (HR) of the gene is shown by each point in the forest plot, while the 95% confidence interval (95% CI) is represented by the line on both sides of the point. Genes exhibiting statistical significance, are highlighted and denoted with asterisks.