Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis

Abstract

Background Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses. Methods In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein–protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC. Results First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival. Conclusions The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

“…FOXM1 contributes to multiple cancers by promoting cellular proliferation and tumor initiation via β-catenin and cyclin D1 (Kim et al, 2019;Shukla et al, 2019). Bioinformatics analysis showed that FOXM1 was also involved in the development of hepatitis B virus (HBV)-related HCC (Xie et al, 2019). Aberrant CENPA expression participates in multiple stages of cancer progression by regulating the cell cycle (Sun et al, 2016).…”

<p>ANLN Directly Interacts with RhoA to Promote Doxorubicin Resistance in Breast Cancer Cells</p>

“…Bioinformatics analysis shows that ANLN participates in HBV-related hepatocellular carcinoma. 25 Surveys such as that conducted by Dai et al show that ANLN down-regulation takes part in the prognostic of breast cancer survival by interacting with KDR. 26,27 ANLN overexpression leads to poor prognosis and poor clinical outcome to anthracycline-based chemotherapy in breast cancer patients.…”

<p>MCM10 Acts as a Potential Prognostic Biomarker and Promotes Cell Proliferation in Hepatocellular Carcinoma: Integrated Bioinformatics Analysis and Experimental Validation</p>

“…6,7 Among these studies, integrative bioinformatics strategies have been employed to analyze the DEGs in a large number of tumor samples. For example, Xie et al, 8 analyzed four Gene Expression Omnibus (GEO) datasets including GSE19665, GSE55092, GSE94660, and GSE121248 and identified key genes and pathways involved in HBVrelated HCC. 8 Ma et al, 9 obtained the mRNA expression profiles from the GEO database and the Cancer Genome Atlas-Liver Hepatocellular Carcinoma database, and the results showed that eleven hub genes with the most interactions were suggested as potential biomarkers in HCC.…”

“…For example, Xie et al, 8 analyzed four Gene Expression Omnibus (GEO) datasets including GSE19665, GSE55092, GSE94660, and GSE121248 and identified key genes and pathways involved in HBVrelated HCC. 8 Ma et al, 9 obtained the mRNA expression profiles from the GEO database and the Cancer Genome Atlas-Liver Hepatocellular Carcinoma database, and the results showed that eleven hub genes with the most interactions were suggested as potential biomarkers in HCC. 9 Yan et al, 10 conducted a comprehensive study on bioinformatics, clinical analysis and in vitro validation and found that Misato family member 2, pseudogene had significant diagnostic and prognostic value in HCC.…”