Abstract
Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.
Highlights
Hepatocellular carcinoma (HCC) is the sixth most commonly diagnosed cancer and the fourth leading cause of cancerrelated mortality globally [1, 2] and the second in China [3]
While hepatitis B virus (HBV) and hepatitis C virus (HCV) are both included in International Agency for Research on Cancer (IARC) group 1, hepatitis delta virus (HDV) was assigned several years ago to group 3 [12], due to inadequacy to support the contribution of HDV to HBV-induced HCC
We aimed to investigate potential HDV carcinogenesis mechanisms by protein-to-protein interaction (PPI) network, Gene Expression Profiling Interactive Analysis (GEPIA), and Weighted Gene Coexpression Network Analysis (WGCNA), to screen and identify key genes and pathways to determine their possible role in HCC pathogenesis, and to help determine their mechanism of inhibition of HBV replication and their effects in diagnosis, treatment and prognosis
Summary
Hepatocellular carcinoma (HCC) is the sixth most commonly diagnosed cancer and the fourth leading cause of cancerrelated mortality globally [1, 2] and the second in China [3]. The risk of HCC is thought to be higher when a HBV-infected patient is superinfected with HDV, the molecular mechanisms of carcinogenesis remain unclear [10]. It has been found that the intrahepatic HBV DNA levels in patients with HDV-associated HCC and non-HCC cirrhosis are significantly reduced [11]. This phenomenon of HDVmediated inhibition of HBV replication suggests that the effects of HDV are mediated through a unique molecular mechanism. Due to the dependency of HDV on HBV, there are still controversies regarding the increased risk of HCC development in chronically HDVinfected patients [4], and the available data on the particular mechanism by which HDV contributes to HCC are sparse. Researchers have taken advantage of gene ontology (GO) and signal pathway analysis tools to identify and characterize many differentially expressed genes (DEGs)
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