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Introduction

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is a leading cause of cancer-related deaths worldwide (Balogh et al., 2016). HCC occurs frequently in persons with chronic liver diseases such as cirrhosis caused by independent risk factors, including chronic hepatitis B virus or hepatitis C virus infection (Balogh et al., 2016).

An early diagnosis is essential for obtaining the best treatment results for HCC patients.

Patients suffering from late-stage HCC have a poor prognosis with limited therapeutic options. α-Fetoprotein (AFP), which is often expressed at high levels in HCC patients, has historically been used as a clinical biomarker (Chen et al., 1984). Currently, there is a substantial body of research concerning HCC-specific targeted therapies. These studies have mainly focused on the serine–threonine kinases, Raf-1 and B-Raf, and the receptor tyrosine kinase activity of vascular endothelial growth factor receptors (VEGFR-1, VEGFR-2, and VEGFR-3) and platelet-derived growth factor receptor β (PDGFR-β) (Wilhelm et al., 2004; Chang et al., 2007). Although the prognosis for metastatic or unresectable HCC has improved in recent years due to the approval of the target drug, Sorafenib (Llovet et al., 2008), the overall survival rate is still poor. This suggests that the mechanisms underlying HCC progression require further exploration, which would hopefully lead to the discovery of novel therapeutic targets.

Gene chip microarrays can provide genome-wide expression profiles (Lee et al., 2013) that can be analyzed using unbiased bioinformatic tools to discover new therapeutic targets. Exporting data from these analyses into modern pathway profiling software could uncover meaningful clues toward novel understanding (Guo et al., 2017). Vast datasets have been developed from HCC patient specimens in recent years (Wang et al., 2015; Mou et al., 2017), and a lot of differentially expressed genes (DEGs) have been identified and characterized using gene ontology (GO) and signaling pathway analyses (Zhao et al., 2019).

In this study, we downloaded the GSE62043 mRNA expression profiles from the website of Gene Expression Omnibus (GEO). GEO is a public online database for uploading, archiving, retrieving, and querying microarray datasets. DEG analyses were carried out between primary tumor tissues (marked tumor) of HCC patients and paired, adjacent nonmalignant tissues (marked control) with R software (Dessau and Pipper, 2008) using the built-in limma et al. packages (Smyth, 2005). Then, functional and signaling pathway enrichment analyses were performed with the online bioinformatics resource DAVID. We also constructed protein–protein interaction (PPI) networks of the DEGs and identified those genes that appeared in two or more analyses (module genes). Finally, we validated these candidate module genes using separate datasets available through The Cancer Genome Atlas and Genotype-Tissue Expression (TCGA/GTEx) databases. Using this approach, we identified several potentially important HCC-related genes and pathways for further study to help define their roles in diagnosis, prognosis, and therapeutic treatment.

Materials and Methods

Microarray data and data preprocessing

Data from the Agilent microarray file GSE62043 (Thurnherr et al., 2016) were downloaded from the public NCBI-GEO database (Edgar et al., 2008; Barrett et al., 2009) and executed on the GPL6480 platform. GSE62043 contains 100 paired primary and adjacent nonmalignant tissue samples from HCC patients. After the normalized log2 ratio (Cy5/Cy3) representing tumor/normal of the GSE62043 dataset, normalized by the loess and quantile method (Smyth and Speed, 2003), was downloaded, probe identification numbers were converted to gene symbols using as a reference the Whole Human Genome Microarray 4?×?44K G4112F (Probe Name Version). When multiple probes corresponded to the same gene, the probe with the greatest p value from the downstream differential analysis was chosen to determine the differential gene expression value.

Identification of DEGs

DEG analysis refers to identification of genes that are identified as being expressed at significantly different levels by multiple modes of analysis (Korpelainen and Tuimala, 2014). We used limma (Smyth, 2005) and R (Dessau and Pipper, 2008) packages to identify the DEGs in primary tumor tissues (marked tumor) of HCC patients and compared them with adjacent nonmalignant tissues (marked normal) of HCC patients. Genes with |log2-fold change (FC)| ≥ 1.2 and adjusted p values <0.01 (moderated t-statistics, corrected by Benjamini and Hochberg's method) were used in the next analysis stage (Benjamini and Hochberg, 1995).

GO enrichment and pathway analysis

The GO (Ashburner et al., 2000) is a structured vocabulary of terms describing gene products according to the biological process (BP), molecular function (MF), and cellular component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) (Ogata et al., 1999) provides data resources of known biological metabolic pathways. We used DAVID (Dennis et al., 2003), a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries, to view the GO and KEGG enrichment of DEGs; a p value <0.05 was considered statistically significant.

PPI networks and module analysis

The Search Tool for the Retrieval of Interacting Genes (STRING, Version 11.0) (Franceschin et al., 2013) database was used to retrieve DEGs' encoded protein and PPI network information. This database contains >24.6 million proteins and 2 billion interactions involved in 5,090 organisms. We uploaded the DEGs into the STRING database and set the interaction score ≥0.900 (highest confidence) as the significant threshold. Then, the PPI networks were constructed using Cytoscape (Kohl et al., 2011; Smoot et al., 2011) software. The Molecular Complex Detection (MCODE) built in Cytoscape is an automated method that was used to analyze highly interconnected modules as molecular complexes or clusters. The analysis parameters were set by default. The functional enrichment analysis was executed for DEGs, from which two significant modules of genes were identified with p?<?0.05 set as the threshold.

Validation of gene expression and overall survival of module genes

Gene Expression Profiling Interactive Analysis (Tang et al., 2017) is a web sever, which contains the TCGA and GTEx datasets. First, we uploaded the genes identified by the PPI networks to validate the consistency of gene expression between the GSE62043 and TCGA/GTEx liver hepatocellular carcinoma (LIHC) datasets; we set the parameters, |log2FC| cutoff: 1.2 and p value cutoff: 0.01, as the threshold criteria. Next, we performed the overall survival analysis. The patients in the TCGA/GTEx dataset were divided into high and low expression groups using the median transcripts per kilobase million (TPM) as a breakpoint, and significance was determined using a log-rank test with p?<?0.05. The functional enrichment analysis was executed to annotate the final potential key genes with the filter parameter, p value <1?×?10^?10, as the threshold in the GenCLiP 2.0 website (Wang et al., 2014).

Results

Identification of DEGs

In this study, we identified DEGs from 100 paired primary tumor tissues (tumor) using limma packages, compared with adjacent nonmalignant tissues (control). Using both |log2-fold change (FC)| ≥ 1.2 and adjusted p value <0.01 criteria, a total of 425 genes were identified, including 48 upregulated genes and 377 downregulated genes (Table 1).

GO enrichment and pathway analysis

To further explore potential targets of these DEGs in HCC, we performed GO and pathway analyses on HCC with the criterion of p value <0.05 (Fig. 1). As shown in Figure 1, it shows the top six significant terms for each of the following: the BP, CC, MF, and KEGG pathways of DEGs, respectively.

We also show annotation of the upregulated genes as well as downregulated genes. As shown in Table 2, in the BP group, the upregulated DEGs were mainly enriched for genes involved in the mitotic/cell cycle processes, cell division, and chromosome segregation. The downregulated DEGs were enriched genes in the carboxylic acid metabolic processes, oxoacid metabolic process, and other organic acid metabolic processes. In the CC group, the upregulated DEGs were mainly enriched for genes associated with the chromosome, centromeric regions, and condensed chromosome. The downregulated DEGs were enriched for genes associated with the extracellular space, extracellular region, and extracellular region part. In the MF group, the upregulated DEGs were mainly enriched from genes associated with chromatin binding, enzyme binding, and macromolecular complex binding; and the downregulated DEGs were enriched for genes encoding proteins for cofactor binding, receptor binding, and anion binding. In the KEGG pathway group, the upregulated DEGs were enriched for genes in the cell cycle, signaling pathway, and microRNAs in cancer; and the downregulated DEGs were enriched for those associated with drug metabolism, including cytochrome P450s, chemical carcinogenesis, and the complement and coagulation cascades.

PPI networks and module analysis

All of the DEGs were uploaded onto the STRING website and were analyzed with Cytoscape software. A total of 211 nodes with 475 edges with scores >0.900 (highest confidence) were selected to construct the PPI networks (Fig. 2). Two significant modules were selected using the MCODE plug-in. Genes in module 1 are downregulated, while the genes in module 2 are upregulated (Fig. 2B, C). Module 1 consisted of 17 nodes/genes and 99 edges (Fig. 2B), which are mainly associated with platelet degranulation (BP), secretory granule lumen (CC), receptor binding (MF), and complement and coagulation cascades (KEGG) (Table 3). Module 2 consisted of 11 nodes/genes and 49 edges (Fig. 2C), which are mainly associated with sister chromatid segregation (BP), chromosomal region (CC), histone kinase activity (MF), and the p53 signaling pathway (KEGG) (Table 4).

Validation of gene expression and overall survival of module genes

To assess whether gene expression changes are consistent between specimens from the test HCC (GSE62043) and validation LIHC (TCGA/GTEx) datasets, we validated these two module genes (total 28 genes) in the GEPIA website. We found that CYR61, IGF1, IGF2, and IGFBP3 in module 1 were downregulated in tumor versus normal in the LIHC datasets (built in TGCA/GTEx), which is in accordance with GSE62043 HCC samples (Table 5). The genes of module 2, including NDC80, CDK1, CENPF, CDCA8, CCNB1, BIRC5, NCAPG, SPC25, CDCA5, and CENPU, were upregulated in tumor versus normal in the LIHC datasets, which is in accordance with GSE62043 HCC patients (Table 5).

We picked out those 14 genes with the criterion: the trend of gene expression in GSE62043 is consistent with LIHC datasets built in TCGA/GTEx. For further verification, overall survival of each of the above 14 candidate genes was calculated. As shown in Figure 3A and B, the downregulated genes (including IGF1 and IGF2) have lower percent survival in the low expression group compared with the high expression group as well as the other upregulated genes (including NDC80, CDK1, CENPF, CDCA8, CCNB1, BIRC5, NCAPG, SPC25, CDCA5, and CENPU). The reason we discarded CYR61 and IGFBP3 is that the log-rank p value of CYR61 did not meet standard criteria and the percent survival of IGFBP3 is opposite to the other downregulated genes (IGF1 and IGF2) (Supplementary Fig. S1). All retained candidate genes were involved in cell cycle, mitotic cell cycle, and organelle organization pathways (Fig. 4).

Discussion

This study was designed to identify potential HCC-related genes by comparing tumor tissues with adjacent nonmalignant tissues of patients suffering from HCC. Forty-eight upregulated and 377 downregulated DEGs were identified. Then, we performed GO and KEGG annotation analyses for DEGs. Subsequently, a DEG PPI network was constructed and 211 nodes/genes were identified with 475 edges, and two most significant modules were chosen from the PPIs, of which 28 central node genes were selected for further validation of gene expression and overall survival aspects in the TCGA/GTEx database. Finally, 12 genes, IGF1, IGF2, NDC80, CDK1, CENPF, CDCA8, CCNB1, BIRC5, NCAPG, SPC25, CDCA5, and CENPU, which were significantly associated with cell cycle, mitotic cell cycle, and organelle organization, were identified.

Insulin-like growth factor 1 (IGF1) acts in response to growth hormone. It plays an important role in childhood growth and continues to have anabolic effects in adults (Keating, 2008). Wang et al. (2017) demonstrated that the serum IGF1 level is predictive of progression and survival in HCC patients. In addition to IGF1, the insulin-like growth factor 2 (IGF2) expression level is also related to the HCC stage (Adamek and Kasprzak, 2018). The nuclear division cycle 80 (NDC80) gene encodes one type of outer kinetochore protein. It forms a heterotetramer with the proteins NUF2, SPC25, and SPC24. This protein complex forms microtubule-binding domains (D'Archivio and Wickstead, 2017). Ju et al. (2017) showed that NDC80 contributes to progression of HCC by reducing apoptosis and overcoming cell cycle arrest. Cyclin-dependent kinase 1 (CDK1) is a small protein that is highly conserved, which when coupled with Sox2, is closely related to tumorigenesis in human melanoma (Morgan, 2006; Ravindran Menon et al., 2018). Centromere protein F (CENPF) is a protein encoded by the CENPF gene in humans (Zhu et al., 1995; Chan et al., 1998; Li et al., 1998). Studies have shown that coexpression of FOXM1 and CENPF is a robust prognostic indicator of poor survival and metastasis in human prostate cancer (Aytes et al., 2014). Hayama et al.'s research implied that phosphorylation and activation of the cell division cycle-associated 8 (CDCA8) protein by aurora kinase B play an important role in human lung tumorigenesis (Satoshi et al., 2007). G2/mitotic-specific cyclin-B1 (CCNB1), known as survivin, is a regulatory protein involved in mitosis (Sartor et al., 1992). Baculoviral inhibitor of apoptosis (IAP) repeat-containing 5 (BIRC5) is a member of the IAP family. BIRC5 is involved in negative regulation of apoptosis. Survivin is known to be highly expressed in most tumor cell types and absent in normal cells, making it a good target for cancer therapy (Pennati et al., 2007, 2008; Mita et al., 2008; Zaffaroni et al., 2010). Non-SMC condensin I complex subunit G (NCAPG) is a protein encoded by the NCAPG gene (J?ger et al., 2000; Kimura et al., 2001; Geiman et al., 2004). The Liu et al. (2018) group suggested that silencing NCAPG inhibits proliferation and induces apoptosis in HCC cells. Spindle pole body component 25 homolog (SPC25), a component of the NDC80 kinetochore complex, plays a crucial role in regulating mitotic chromosome segregation in prostate cancer (Liu et al., 2018). Cell division cycle-associated 5 (CDCA5) serves an important role in promoting colorectal cancer progression by activating the ERK signaling pathway (Shen et al., 2019). Additionally, CDCA5 could be associated with HCC (Tian et al., 2018), breast cancer (Phan et al., 2018), and lung carcinogenesis (Nguyen et al., 2010). Centromere protein U (CENPU) promotes nonsmall cell lung cancer proliferation involving the Wnt/β-catenin signaling pathway and predicts poor survival through the expression of FOXM1 (Wang et al., 2018; Zhang et al., 2018).

In conclusion, in this study, we have identified 12 potential candidate HCC-related genes, most of which have previously been implicated in multiple pathways associated with tumorigenesis. In addition, we identified two novel genes, CENPU and SPC25, as potential markers of HCC that have not been previously implicated in carcinogenesis. All of these DEG candidate HCC-related genes should be confirmed through molecular biological experiments.

Authors' Contributions

All authors participated in the data analysis. Bin Zhao and Zheng Wan performed the comparative analysis using bioinformatic tools. Bin Zhao interpreted the data and wrote the manuscript. All authors read and approved the final manuscript.

Author Disclosure Statement

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding Information

No funding was received for this article.

Supplementary Material

Supplementary Figure S1