Hepatocellular carcinoma is one of the most common malignant tumors and the sixth most common cancer in the world, with the death rate ranked fourth among cancers (Bray et al., 2018). HCC ranks 5th among the top 10 high-incidence cancers in China. And it ranks 3rd in lethality in China, after lung cancer and gastric cancer (Chen et al., 2016). At present, although there are various methods for the treatment of HCC, such as hepatectomy, liver transplantation, and molecular targeted drugs (sorafenib), due to the high recurrence and high metastasis of liver cancer, the therapeutic effect is still not ideal (Bruix et al., 2014; Reig et al., 2016; Rossetto et al. 2019). Therefore, the search for genes related to the diagnosis and development of HCC is of great significance for the in-depth study of the pathogenesis of HCC and the development of diagnostic and therapeutic techniques.

The Gene Expression Omnibus Database (GEO) contains more than 32 000 public data sets from 13 000 laboratories, including data from multiple platforms such as gene chips, next-generation sequencing, and high-throughput sequencing. And it provides important support data for multi-sample research of tumors (Barrett et al., 2013). More and more researches are based on data mining conducted on the GEO, and many experiments have confirmed that the key genes discovered have an important role in the occurrence and development of cancers (Shen et al., 2019; Ye et al. 2019). This study uses the GEO data sets and a variety of bioinformatics analysis methods to find molecular markers that are closely related to the occurrence and development of HCC, providing a new basis for the diagnosis and treatment of HCC.

1 Results and Analysis

1.1 Screening of differentially expressed genes

GEO2R is an online analysis tool. After analyze two or more groups of data in the GEO database, it can obtain differentially expressed genes (DEGs). Use the GEO2R to analyze the GSE60502, GSE101685, GSE84402, GSE29721, and GSE33006. After that, selected the genes within the range of | log2FC | and p<0.05. The analysis results show that 5 827 differentially expressed genes were obtained in the 5 datasets. And the top 50 genes with the most obvious differences in each data set can distinguish between cancer and non-cancer samples (Figure 1A). In addition, there are 94 DEGs shared in the 5 datasets, including 28 up-regulated genes and 66 down-regulated genes (Figure 1B).

1.2 Enrichment analysis for DEGs

Metascape is an online website that integrates a variety of biological analyses such as gene annotation, functional enrichment analysis, and signal pathway analysis (Zhou et al., 2019). Using the Metascape performed functional enrichment and signaling pathway analysis on 28 up-regulated genes and 66 down-regulated genes related to HCC. It was found that the functions of up-regulated genes are mainly enriched in biological processes such as cell division, regulation of cell cycle process and chromosome condensation. And the signaling pathway is mainly related to the cell cycle and p53 signaling pathway (Figure 2A). The functions of down-regulated genes are mainly enriched in the biological processes of acute inflammation response, steroid metabolism process and cellular response to copper ion. And the signaling pathways are mainly related to retinol metabolism, mineral absorption, complement and coagulation cascades (Figure 2B).

1.3 Screening of genes related to liver cancer

National Center for Biotechnology Information is currently the largest medical and scientific literature retrieval database in the world. GEPIA is an online interactive tool that analyzes the expression of a gene in cancer, its correlation with patient survival, and other biological information (Tang et al., 2017).

In this study, NCBI was used to conduct a literature search on 94 differentially expressed genes. And it was found that 70 genes have reported related molecular mechanisms in the development of HCC, and 24 genes have not been searched for in-depth reports on the molecular mechanisms related to the development of HCC. Twenty-four genes that were not thoroughly studied were initially identified as candidate genes for subsequent analysis, including 2 up-regulated genes and 22 down-regulated genes (Table 1).

After analyzing the data in TCGA by GEPIA, the expressions of the 24 DEGs that were not thoroughly discussed are consistent with the analysis results of this study (Figure 3A). And GEPIA was used to analyze the association of these genes with the prognosis of HCC patients. It was found that only GINS1, CFHR4, and DNASE1L3 were significantly correlated with the overall survival and disease-free survival of liver cancer patients (Figure 3B).

1.4 Candidate gene verified by real-time fluorescent quantitative PCR

For the GINS1, CFHR4 and DNASE1L3 screened above, real-time fluorescence quantitative PCR was used to verify the expression level of 32 liver cancer and para-cancer samples. GINS1 was highly expressed in 81.3% of HCC tissue samples, CFHR4 and DNASE1L3 were lowly expressed in 71.9% and 93.8% of HCC tissue samples, respectively. The verification results of GINS1, CFHR4 and DNASE1L3 in HCC tissues are consistent with the analysis results (Figure 4).

2 Discussion

At present, many studies have shown that DEGs in patients with HCC have a great effect on the occurrence and development of HCC (Huang et al., 2007; Xia et al., 2016; Yuan et al., 2019). However, the biomarkers that can be used for clinical diagnosis are mainly AFP. But used AFP alone for diagnosis has a high false negative rate (Yu et al., 2019). Therefore, biomarkers for diagnosis and treatment of HCC are still in the exploration stage. So, we need more researches to explore related genes that have an important role in the development of HCC. In this study, bioinformatics was used to analyze the data of 5 datasets (GSE60502, GSE101685, GSE84402, GSE29721, GSE33006) on the GEO database. And the bioinformatics analysis was carried out to find the unknown functional genes closely related to the development of HCC. These genes may lay the foundation for in-depth study of the pathogenesis of HCC and the development of new diagnostic and therapeutic targets.

By analyzing 5 datasets, a total of 94 common DEGs were obtained (28 up-regulated genes and 66 down-regulated genes). Subsequent functional enrichment analysis found that the functions of up-regulated genes are mainly enriched in biological processes closely related to cancer, such as cell division and cell cycle; down-regulated genes are mainly enriched in many biological metabolic processes, such as acute inflammation response, steroid metabolism process and cellular response to copper ion. At present, many studies have confirmed that mitotic abnormalities play an important role in the development of cancer. And inhibitors of many genes related to mitosis have entered the stage of clinical testing, hoping to play a role in cancer treatment (Dominguez-Brauer et al., 2015). Studies have shown that the occurrence of cancer is accompanied by changes in many metabolic pathways in the body, and these metabolic pathways may play an important role in the occurrence and development of cancer (DeBerardinis et al., 2008). This suggests that the key genes involved in the metabolic pathway and its pathway may be closely related to the occurrence and development of HCC. It may provide new ideas for the diagnosis of HCC. Analysis of the signal pathways of DEGs showed that the DEGs related to HCC in this study were mainly involved in the cell cycle, p53, retinol metabolism, mineral absorption, and complement and coagulation cascade pathways. The p53 signaling pathway is one of the most important apoptosis signaling pathways. Many genes in this pathway, such as TP53, play an important role in the development of HCC (Lai et al., 2007). The retinol metabolism pathway mainly is one of the pathways involved in liver fibrosis. Some studies have shown that the enzymes involved in retinol metabolism are related to HCC. Many genes in the pathway play an important role in occurrence and development, such as CYP1A2, CYP2C9 (Yu et al., 2015; Ren et al., 2016). In addition, some genes in metabolic pathways such as mineral absorption and complement and coagulation cascades have also been shown to be involved in the development of HCC (Seol et al., 2016; Liu et al., 2018). Some scientists have suggested that patients always with the change of multiple metabolic pathways during the development of HCC. Inhibiting key genes of tumor metabolism may become a new method of tumor treatment (Monte et al., 2005; Wu et al., 2010; Luengo et al., 2017). After analyzed by GO and KEGG, we found that the 94 candidate genes were play an important role in the occurrence and development of HCC. The key genes which involved in HCC metabolic pathways in the 94 candidate genes are likely to become targets for the treatment and diagnosis of HCC especially.

This research is to further study the molecular mechanism of the occurrence and development of HCC and find important genes with unknown functions in the pathogenesis of HCC. We conducted a literature search on 94 differential genes and found that 70 genes (such as TOP2A, CDK1, BIRC5, etc.) have been reported in the literature to be closely related to the development of HCC (Wong et al., 2009; Cao et al., 2013; Wu et al., 2018). But there are still 24 genes that have not been found to be related to the molecular mechanism of HCC in-depth research reports. Therefore, we listed 24 genes that were not reported in-depth as candidate genes, and conducted follow-up analysis. Using the GEPIA to verify the expression of 24 genes, it was found that 24 genes all showed differential expression in HCC. And the expression pattern was consistent with the analysis results. This further suggests the reliability of our analysis results. Further analysis found that only 3 of the 24 genes (GINS1, CFHR4, and DNASE1L3) were closely related to the disease-free survival and overall survival of HCC patients (p<0.05). In order to better verify our results, we used real-time fluorescence quantitative PCR technology to detect the expression differences of these three genes in 32 pairs of HCC and HCC adjacent tissue samples. The results showed that GINS1 had high expression in 81.3% of HCC tissues, CFHR4 and DNASE1L3 were down-regulated in 71.9% and 93.8% of HCC tissues, respectively.

The down-regulated of CFHR4 and DNASE1L3 and the up-regulated of GINS1 in HCC tissues found in this study has not been reported. Some scientists have found that CFHR4 may be associated with C-reactive protein (CRP). CRP can be used as an inflammatory marker that reflects the growth and invasion of HCC patients. This suggests that in-depth study of the relationship between CFHR4 and CRP may find new targets for liver cancer treatment (Mihlan et al., 2009; Suner et al., 2018).

In summary, this study found three HCC-related genes and verified their expression differences in HCC tissues. Besides, we initially discussed the relationship between the three HCC-related genes and the prognosis of HCC. Our research results suggest that in-depth study of the role of these three genes in the pathogenesis of HCC may find new molecular targets for diagnosis and treatment of HCC and provide a certain research basis for the development of diagnosis and treatment technology.

3 Materials and Methods

3.1 Experimental reagents

PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time) and TB Green® Premix Ex Taq™ (Tli RNaseH Plus) were purchased from TAKARA. The reagents were used for reverse transcription of sample RNA and determination of the relative expression of genes in the sample.

3.2 Microarray data

GSE60502, GSE101685, GSE84402, GSE29721, and GSE33006 were obtained from the GEO database. GSE60502 is a dataset based on the GPL96 platform submitted by Kao K.J. et al. It includes 36 samples from HCC and normal liver tissue of 18 patients. GSE101685, GSE84402, GSE29721 and GSE33006 are datasets based on the GPL570 platform. Among them, GSE101685 was submitted by Sen-Yung H. et al., including 8 normal samples and 24 HCC samples. GSE84402 was submitted by Qin W. et al., including 28 samples from 14 patients. GSE29721 was submitted by Bhattacharyya B., etc., including 10 normal samples and 10 HCC samples. GSE33006 was submitted by Huang Y. et al., including 3 HCC samples and 3 normal samples.

3.3 Screening of DEGs

The online analysis tool GEO2R was used to analyze DEGs (https://www.ncbi.nlm.nih.gov/geo/geo2r/). The DEGs between HCC tissues and normal HCC tissues were screened DEGs with the threshold of |log Fold Chang(FC)|≥2 and p<0.05. After that, we used Morpheus (https://software.broadinstitute.org/morpheus/) to raw a heat map of the TOP50 DEGs in 5 datasets. In addition, we used the R language to draw the Venn map of the DEGs obtained from the 5 datasets to obtain the DEGs shared by 5 datasets.

3.4 Functional enrichment analysis

The online analysis tool Matescape (http://metascape.org/) performs GO enrichment and KEGG analysis on the DEGs shared by the five datasets. Screening biological functions and signaling pathways with p<0.01 and at least 3 genes involved in.

3.5 Screening of DEGs related to HCC

The DEGs related to HCC were searched with NCBI (https://www.ncbi.nlm.nih.gov/). In order to find the genes that had no in-depth reports on molecular mechanisms related to HCC. Subsequently, the online analysis tool GEPIA (http://gepia.cancer-pku.cn/) was used to analyze the expression levels of DEGs related to HCC, as well as the correlation with the patient's overall survival and disease-free survival. Genes with a correlation between survival and disease-free survival are listed as candidate genes.

3.6 Real-time quantitative PCR verification

The candidate genes screened above were verified by real-time fluorescence quantitative PCR. Take 1 µg sample RNA for reverse transcription and follow the instructions of PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time) to obtain cDNA. The real-time fluorescence quantitative PCR instrument StepOnePlus is used for quantitative PCR analysis and the subsequent steps and program settings are operated according to the TB Green® Premix Ex Taq™ (Tli RNaseH Plus) instructions. In the experiment, β-ACTIN was used as the internal reference gene, and 2^-ΔΔCt was used to calculate the relative expression level.

The real-time fluorescence quantitative PCR primers are as follows:

β-ACTIN Forward primer: 5'-CACCAACTGGGACGACAT-3', Reverse primer: 5'-ACAGCCTGGATAGCAA CG-3'; GINS1 Forward primer: 5'-CCCAGATACCATTTTGGCGTG-3', Reverse primer: 5'-GTCTGAGTCCA TCCTCGTTG-3'; CFHR4 Forward primer: 5'-TCAAACCCCAAACAGTGCAAC-3', Reverse primer: 5'-ACA AGGTTTCACTTCTTGTCCA-3'; DNASE1L3 Forward primer: 5'-TCAACGTCAGGTCC TTTGGG-3', Reverse primer: 5'-GCTTTTCCCTGTTCAGCTTCTC-3'.

3.7 Statistical analysis

GraphPad Prism 5.0, Microsoft Excel and Microsoft PowerPoint were used for data and graph processing. The data were expressed in the form of mean ± standard deviation in statistical analysis. The difference between cancer and adjacent samples was calculated by unpaired t test, p<0.05 indicates statistical significance.

Authors’ contributions

Qiu Lin is responsible for the entire data analysis, experimental operation and thesis writing; Professor Huang Jian is the main person in charge of this research, responsible for the design of the experimental ideas, technical guidance and thesis modification. Both authors read and agreed to the final text.

Acknowledgement

This research was jointly funded by the Natural Science Foundation Commission Fund (No. 18Z103010173) and the key project of Shanghai Science and Technology Commission (No. 19Z111220022).

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