Ji Y et al. (2020), Integrated Bioinformatic Analysis Identifies Ne...

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Int J Genomics 2020 Jan 01;2020:2061024. doi: 10.1155/2020/2061024.

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Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma.

Ji Y , Yin Y , Zhang W .

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Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

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Species referenced: Xenopus
Genes referenced: aspm elavl2 kif2c tpx2

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	Figure 1. The workflow diagram of data acquisition, preprocessing, analysis, and validation.
	Figure 2. Volcano plots and Venn diagrams of differentially expressed genes (DEGs) selected from three Gene Expression Omnibus (GEO) datasets. (a) Volcano plots of DEGs in normal and tumoral liver samples of HCC patients with HBV infection in GSE47197, GSE55092, and GSE121248. DEGs were filtered by adjusted P value < 0.05 and ∣log2 (fold change) \| ≥1. The red and green dots display the distribution of all the significant upregulated (red dots) and downregulated (green dots) DEGs in the three datasets, respectively. (b, c) The Venn diagrams of overlapping DEGs from an intersection of upregulated and downregulated genes in the three datasets.
	Figure 3. Gene Ontology (GO) enrichment analysis of DEGs following the evaluation by (a) the DAVID functional annotation and (b, c) ClueGO plugin. (a) The histogram with orange, green, and yellow shows GO terms Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), respectively. (b) Functional connection of the enriched categories for DEGs (kappa score ≥ 0.4). The GO terms are depicted as nodes, whose size represents the degree of significance. (c) The pie chart shows the clusters of GO terms that were labeled with diverse colors.
	Figure 4. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs based on the DAVID functional annotation and ClueGO plugin of Cytoscape. (a) KEGG enrichment analyzed by DAVID is displayed by a scatter plot. (b) The network with terms connected to its kappa score point (≥0.4) as evaluated by the ClueGO plugin. (c) Overview of the chart containing the most significant KEGG terms with the corresponding colors.
	Figure 5. Protein-protein interaction (PPI) network, hub gene screening, and module analysis. (a) The PPI network constructed using STRING 11.0 was visualized by Cytoscape. The upregulated genes are shown in green, while the downregulated genes are shown in bluish violet. (b) The network directly associated with the top ten hub genes identified by CytoHubba. (c) The network of the top ten hub genes filtered according to the degree method provided by CytoHubba. (d, e) The key modules identified using the MCODE plugin of Cytoscape, which contains 27 nodes/344 edges and 16 nodes/34 edges, respectively.
	Figure 6. Validation of the expression level of the ten hub genes between the clinical liver samples from HBV-related HCC patients and those from healthy individuals. All the relative expression levels of hub genes were based on the dataset from Roessler et al., except for that of ASPM (Guichard et al.).
	Figure 7. Kaplan-Meier plotter reveals the overall survival (OS) and relapse-free survival (RFS) curves with a significant difference concerning the hub genes in a liver cancer RNA-seq cohort. As risk factors, alcohol consumption was excluded and hepatitis virus was included in the analysis. The analysis was run on 111 and 103 patients for OS and RFS, respectively. P < 0.05 was considered a statistically significant difference.

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