Identification of hub genes and analysis of prognostic values in hepatocellular carcinoma by bioinformatics analysis

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Identification of hub genes and analysis of prognostic values in hepatocellular carcinoma by bioinformatics analysis Liangfei Xu PhD , Tong Tong MD , Ziran Wang MD , Yawen Qiang MD , Fan Ma MD , Xiaoling Ma PhD PII: DOI: Reference:

S0002-9629(20)30021-5 https://doi.org/10.1016/j.amjms.2020.01.009 AMJMS 982

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The American Journal of the Medical Sciences

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22 July 2019 14 January 2020

Please cite this article as: Liangfei Xu PhD , Tong Tong MD , Ziran Wang MD , Yawen Qiang MD , Fan Ma MD , Xiaoling Ma PhD , Identification of hub genes and analysis of prognostic values in hepatocellular carcinoma by bioinformatics analysis, The American Journal of the Medical Sciences (2020), doi: https://doi.org/10.1016/j.amjms.2020.01.009

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Identification of hub genes and analysis of prognostic values in hepatocellular carcinoma by bioinformatics analysis Short Title: Bioinformatics analysis of hub genes in Hepatoma Liangfei Xu, PhD 1,3, Tong Tong, MD2, Ziran Wang, MD1,3, Yawen Qiang, MD1,3, Fan Ma, MD1,3, Xiaoling Ma, PhD3 1. Anhui Medical University, Hefei, Anhui, China 2. The First Affiliated Hospital of Anhui Medical University, Department of Clinical Laboratory, Hefei, Anhui, China 3.The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, P.R. China

Authors’ Email: Liangfei Xu: [email protected] Tong Tong: [email protected] Ziran Wang: [email protected] Yawen Qiang: [email protected] Fan Ma: [email protected]

Corresponding author:

Xiaoling Ma 1

Address: Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Lujiang Rd 17, Hefei, Anhui, 230001, China. Tel: (0086-0551) 62283454 ;

Email address:

[email protected]

Competing interests The authors declare that they have no competing interests. Funding Statement This research was supported by “the Fundamental Research Funds for Central Universities (WK9110000007)”. Key Terms: oncology; Basic Research/Genetics/Molecular Medicine Abstract Background Hepatocellular carcinoma (HCC) is one of the most frequent cancers in the world. In this study, differentially expressed genes between tumor tissues and normal tissues were identified using the comprehensive analysis method in bioinformatics. Methods We downloaded three mRNA expression profiles from the Gene Expression Omnibus database (GEO) to identify differentially expressed genes (DEGs) between tumor tissues and adjacent normal tissues. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction (PPI) network was performed to

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understand the function of DEGs. OncoLnc, which was linked to TCGA survival data, was used to investigate the prognostic values of hub genes. The expression of selected hub genes was validated by the quantitative real-time polymerase chain reaction (qRT-PCR). Results A total of 235 DEGs, consisting of 36 up-regulated and 199 down-regulated genes, were identified between tumor tissue and normal tissue. The GO and KEGG analysis results showed the up-regulated DEGs to be significantly enriched in cell division, mid-body, ATP binding, and oocyte meiosis pathways. The down-regulated DEGs were mainly involved in epoxygenase P450 pathway, extracellular region, oxidoreductase activity, and metabolic pathways. Ten hub genes, including AURKA, CDC20, FTCD, UBE2C, CCNB2, PTTG1, CDKN3, CKS1B, TOP2A, and KIF20A, were identified as the key genes in HCC. Survival analysis found the expression of hub genes to be significantly correlated with the survival of patients with HCC. Conclusions The present study identified hub genes and pathways in HCC that may be potential targets for diagnosis, treatment, and prognostic prediction. Key words Hepatocellular carcinoma, HCC, Bioinformatics, Gene.

Introduction Hepatocellular carcinoma (HCC) is known to have a high morbidity and mortality rate, 3

especially in developing countries.1, 2 Studies have shown liver cirrhosis, diabetes, HBV/HCV infection, and alcohol intake to complicate the pathogenesis of HCC.3, 4 Despite new developments in the treatment of liver cancer in the recent years, the 5-year survival rate has remained dissatisfactory.5, 6 Therefore, exploring new therapeutic targets and treatments would be required for the successful clinical treatment of hepatocellular carcinoma.7

Recently, accumulating evidence has shown many genes and molecular pathways to be complicating the HCC process.8 Therefore, identification of the hub genes and crucial signaling pathways is considered very important for new research, and diagnostic and therapeutic strategies.

In recent years, microarray technology has been widely used for the detection of genetic changes during tumorigenesis and cancer progression.9-11 In this study, we used GEO database and bioinformatics analysis to identity the overlapped differentially expression genes (DEGs) between tumor and normal tissues; DAVID website was used to analyze the functional and pathway enrichment, CytoScape software was used to identify the possible hub genes, and OncoLnc website was used to predict the prognostic values of the hub genes.

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Methods Microarray data In order to identify the differences in gene expression between hepatocellular tumor tissues and adjacent normal tissues, three gene expression datasets (GSE121248, GSE84402, and GSE76427) were downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo).12, 13 The array data of GSE121248 included 70 tumor tissues from patients with chronic hepatitis B-induced HCC and 37 adjacent normal tissues. The GSE84402 dataset consisted of 14 pairs of tumor tissues and normal tissues. The above two datasets were based on both the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). The GSE76427 dataset contained 115 HCC tissues (46% with HBV infection and 54% with cirrhosis) and 52 normal tissues and was based on the GPL10558 platform (Illumina HumanHT-12 v4.0 Expression BeadChip).

Identification of DEGs GEO2R was used to identify the DEGs between hepatocellular tumor tissues and adjacent normal tissues,14 and the cutoff value was adjusted to P < 0.05 and |log 2 FC| > 1.0 (tumor tissue to normal tissue). The Venn diagram of overlapped genes was generated using an online tool (http://bioinformatics.psb.ugent.be/webtools/Venn /). 5

Gene ontology and pathway enrichment analysis of DEGs To explore the biological function and molecular pathways of DEGs, we used DAVID (http://david.abcc.ncifcrf.gov/) for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the cutoff value was P < 0.05.15

Construction of protein-protein interaction (PPI) network and module analysis To clarify the interaction across differentially expressed proteins, we used STRING database (Version 11.0 https://string-db.org/) to construct a PPI network,16 and the cut-off criterion was combined score > 0.4. Furthermore, we used CytoScape (Version 3.7.1)17 to identify the hub genes and performed module analysis. The criterion of hub genes was node degree ≥ 20, and top 10 genes were selected therefrom. Molecular Complex Detection (MCODE) plugin was used to screen modules from the PPI network with degree cut off = 2, node score cutoff = 0.2, k-score = 2, maximum depth = 100. The functional and pathway enrichment analyses in the modules were performed via DAVID.

Survival analysis of hub genes 6

We downloaded the survival data from OncoLnc (http://www.oncol nc.org/), 18 and divided the patients with HCC into two groups on the basis of expression of hub genes: low (≤ 25%) and high (≥ 75%), and then used SPSS Statistics Version 24.0.0.0 to perform the survival analysis and calculate the HR and 95% CI.

Patients and tissue specimens We analyzed samples from 10 pairs of patients with HCC, and our study was approved by the Ethics Committee and Institutional Review Board of University of Science and Technology of China, Anhui, China (Approval number: 2019-N(H)-101). Tumor tissues and corresponding normal tissues were immediately frozen in liquid nitrogen, after harvest, and stored at -80 ℃ until further analysis. Every specimen was anonymously handled based on ethical standards. All participants provided written informed consent and our study had full approval from the hospital’s Ethics Review Committee.

RNA extraction and quantitative real-time PCR TRIzol reagent (Ambion, USA) was used to extract total RNA from the tissue samples according to the manufacturer’s protocol. Advantage® RT-PCR Kit and random primers were used to synthesize cDNA (Clontech, Takara, Japan). Quantitative real-time PCR (qRT-PCR) was 7

conducted on the LightCycler 480 Detection System with SYBR Green dye (Clontech, Takara, Japan). The reaction parameters included a denaturation program (30s at 95℃, 1 cycle), followed by an amplification and quantification program over 40 cycles (5 s at 95℃ and 20 s at 60℃). Each sample was tested in triplicate, and each underwent a melting curve analysis to check the specificity of amplification. Table 1 illustrates the primer sequences of hub genes.

Results Identification of DEGs Overall, we identified 373, 92, and 740 up-regulated DEGs, and 653, 400, and 526 down-regulated DEGs in the GSE121248, GSE76427, and GSE84402 datasets, respectively. Among them, 235 were overlapped DEGs, 36 were up-regulated DEGs, and 199 were down-regulated DEGs (Figure 1).

Functional enrichment analysis of overlapped DEGs GO-based biological process (BP) analysis indicated the up-regulated DEGs to be significantly enriched in cell division, mitotic nuclear division, anaphase-promoting 8

complex-dependent catabolic process, protein ubiquitination, and cell cycle while the down-regulated genes were significantly involved in epoxygenase P450 pathway, oxidation-reduction process, exogenous drug catabolic process, complement activation, and drug metabolic process (Figure 2A). For GO cell component (CC), the up-regulated DEGs were significantly enriched in mid-body, spindle, nucleoplasm, nucleus, and microtubule, and the down-regulated genes were mainly involved in extracellular region, organelle membrane, extracellular exosome, extracellular space, and blood microparticle (Figure 2B). Regarding molecular function (MF), the up-regulated DEGs were significantly enriched in ATP binding, protein binding, microtubule binding, and protein kinase binding and activity, whereas the down-regulated genes were mainly involved in heme binding, oxidoreductase activity, oxygen binding, iron ion binding, and monooxygenase activity (Figure 2C). Furthermore, KEGG analysis indicated the up-regulated DEGs to be significantly enriched in oocyte meiosis (hsa04114) and cell cycle (hsa04110) while the down-regulated genes were mainly involved in metabolic pathways (hsa01100), retinol metabolism (hsa00830), complement and coagulation cascades (hsa04610), tryptophan metabolism (hsa00380) and Chemical carcinogenesis (hsa05204) (Figure 2D) (Figure 2, top five terms were ranked by -Log(P-value)).

Protein-protein interaction (PPI) network analysis

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Based on the STRING database, there were 205 nodes and 915 edges mapped in the PPI network for the overlapped DEGs, with a local clustering coefficient of 0.40 (Figure 3). We chose the top 10 genes, ranked by degree, as the hub genes, including AURKA, CDC20, FTCD, UBE2C, CCNB2, PTTG1, CDKN3, CKS1B, TOP2A, and KIF20A. AURKA had the highest degree of nodes (27 nodes) while KIF20A had the lowest (23 nodes) (Table 2). Among the 10 hub genes, only FTCD belonged to down-regulated genes. Furthermore, we used MCODE to construct the entire PPI network, following which, three modules were chosen for bioinformatics analysis (Figure 4). As shown in Table 3, module 1 genes were mainly enriched in oocyte meiosis and cell cycle pathways, module 2 genes were significantly enriched in complement and coagulation cascades, prion diseases, and systemic lupus erythematosus, and module 3 genes were mainly involved in retinol metabolism, drug metabolism, cytochrome P450, chemical carcinogenesis, metabolic pathways, and metabolism of xenobiotics by cytochrome P450 (Table 3).

Survival analysis of hub genes Survival analysis was conducted by SPSS software, and the results indicated high expression of CDC20 (HR: 2.41, Log-rank P-value: 0.02), UBE2C (HR: 2.016, Log-rank P-value: 0.039), CCNB2 (HR: 3.18, Log-rank P-value: 0.007), PTTG1 (HR: 2.488, Log-rank P-value: 0.015), CDKN3 (HR: 2.168, Log-rank P-value: 0.023), TOP2A (HR: 2.563, Log-rank P-value: 0.02), and

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KIF20A (HR: 2.194, Log-rank P-value: 0.038) to predict a poor survival and that of FTCD (HR: 0.415, Log-rank P-value < 0.01), which belongs to down-regulated genes, to predict prolonged survival (Figure 5). However, the relationship between AURKA and CKS1B expression and survival may be debatable, since the HR of AURKA was 1.728 (Log-rank P-value: 0.081) and HR of CKS1B was 1.105 (Log-rank P-value: 0.72). Verification of hub genes using quantitative real-time PCR To further verify the results of bioinformatics analysis, mRNA levels of the 10 hub genes were determined in 10 paired tumor and adjacent normal tissues using qRT-PCR. As illustrated in Figure 6, seven identified hub genes (AURKA, CDC20, UBE2C, CCNB2, CDKN3, TOP2A, PTTG1) was significantly up-regulated in tumor tissue (P < 0.05), and FTCD was significantly down-regulated in tumor tissue(P < 0.001), as predicted by bioinformatics analysis. Discussion In recent years, diagnosis and treatment of HCC have made great progress; however, early diagnosis and targeted treatment of HCC remain difficult.19 Hence, we investigated the detailed molecular mechanisms of HCC initiation and considered the process to be significant for further prevention and treatment of HCC. Recently, with the development of microarray and high-throughput sequencing technology, the promising targets in preventing and treating HCC have become easier to discover. 11

In the present study, we downloaded 3 datasets, for comparing the differences in mRNA levels between tumor tissues and adjacent normal tissues, from GEO database. In total, 235 DEGs were identified, including 36 up-regulated genes and 199 down-regulated genes. GO-based analysis indicated the up-regulated DEGs to be significantly enriched in cell division, mitotic division, mid-body, spindle, ATP binding, and protein binding. Meanwhile, the down-regulated DEGs were mainly involved in epoxygenase P450 pathway, oxidation-reduction process, extracellular region, organelle membrane, heme binding, and oxidoreductase activity. Furthermore, KEGG pathway analysis showed the up-regulated DEGs to be significantly enriched in oocyte meiosis and cell cycle pathways while the down-regulated DEGs were mainly enriched in metabolic pathways, retinol metabolism, and complement and coagulation cascades. Moreover, top 10 genes, ranked by degree, including AURKA, CDC20, FTCD, UBE2C, CCNB2, PTTG1, CDKN3, CKS1B, TOP2A, and KIF20A were identified as the key genes in HCC.

AURKA (Aurora kinase A) is a kinase of the aurora kinase family. Studies have reported the abnormal expression of AURKA to be related to tumor generation, such as in gastric cancer, HCC, and pancreatic cancer.20-22 In HCC, the expression of AURKA was positively correlated with tumor-stage and tumor-grade, and negatively correlated with outcome. In a previous study,

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Singh et al had reported that AURKA was comprise of high degree nodes in case of HCC with HCV-related cirrhosis.23 In this study, the expression of AURKA in tumor tissue was seen to be higher than in normal tissue; however, the relationship between AURKA and survival of patients had no significant correlation (HR: 1.728, Log-rank P-value: 0.081). Further studies may be needed to confirm this result.

Cell division cycle 20 (CDC20), acts as an important molecule in cell cycle and human tumorigenesis and development.24, 25 Gayyed et al 26 reported the expression of CDC20 to have a close relationship with tumor grade and stage. In HCC, Li et al27 reported the overexpression of CDC20 to be closely associated with neoplasia and progression of HCC. In this study, we found CDC20 to be overexpressed in tumor tissue, predicting a poor outcome, consistent with a previous study.23 However, the underlying mechanisms of CDC20 involved in tumorigenesis and progression still requires further study.

UBE2C, a member of the E2 ubiquitin-conjugating enzyme family, not only plays an important role in normal cell renewal, but also plays a key role in cell cycle regulation and proliferation of cancer cells. Liu et al had reported the overexpression of UBE2C in melanoma to have a negative relationship with overall survival; knock down of UBE2C could inhibit cell 13

growth via deactivating ERK/Akt signaling pathways.28 Zhang et al 29 had reported that UBE2C could promote cell proliferation via ERK pathway in NSCLC. In the present study, we found the patient with high expression of UBE2C to have a poor survival, which is consistent with previous study.30 Thus, UBE2C may be a potential biomarker for cancer diagnosis and may be used to predict cancer prognosis and sensitivity to cancer treatment.

CCNB2 (Cyclin B2), a member of B-type cyclin family, functions as an important cell cycle regulating factor. Wu et al31 had suggested that the repression of CCNB2 by menin could inhibit G2/M transition and cell proliferation. Furthermore, Li et al. had reported the overexpression of CCNB2 in HCC to be associated with a poor prognosis.32 In this study, the expression of CCNB2 in HCC was higher than in normal tissues and indicated a poor prognosis, thereby suggesting CCNB2 as a potential biomarker in HCC diagnosis.

PTTG1 (pituitary tumor-transforming gene 1) acts as a cell cycle regulating factor.33 Numerous evidence have shown PTTG1 to be an oncogene, overexpressed in different kinds of cancer, promoting tumor cell growth, and being associated with poor prognosis.34-36 Our research also found PTTG1 to be highly expressed in HCC and indicated a poor overall survival. Thus, PTTG1 may be a potential biomarker for HCC diagnosis. 14

CDKN3, encoded by CDKN3 gene, has been reported to play an important role in carcinogenesis.37 Liu et al 38 had reported the overexpression of CDKN3 in esophageal squamous cell carcinoma (ESCC) and that it promoted G1/S transition through the regulation of pAKT-p53-p21 axis. Wang et al39 found CDKN3 to enhance esophageal cancer growth and cisplatin resistance via increased RAD51 expression, and to be associated with a poor overall survival. In this study, we found CDKN3 to be overexpressed in HCC and to correlate with a low survival in patients with HCC.

High expression of TOP2A (Topoisomerase-II alpha) has been reported to be significantly associated with poor prognosis in various types of cancer and is considered as a potential target for some chemotherapeutic drugs in the treatment of human tumors.40-42 Wong et al. 43 had reported the expression of TOP2A at mRNA and protein levels to be higher in tumor tissue than in non-tumor samples. According to previous studies, our research showed TOP2A to be highly expressed in HCC and associated with a poor prognosis.

Accumulating evidence indicate KIF20A to be overexpressed in different types of human cancers. Taniuchi et al 44 had reported the expression of KIF20A to be increased in pancreatic 15

cancer, and down-regulation of KIF2A to inhibit the growth of pancreatic cancer cell. Shen et al 45

had found patients with bladder cancer and high expression of KIF2A to have a poor

prognosis, and that KIF2A could enhance the growth and invasion of bladder cancer cells.

FTCD (formiminotransferase cyclodeaminase) has been reported to be mainly expressed in the liver.46, 47 Accumulating evidence indicate FTCD to have high expression in many human cancers, thereby identifying it as a tumor marker and a therapeutic target of HCC. 48-52 However, Chen et al 53 reported that FTCD acted as a gene suppressor by promoting DNA damage and inducing cell apoptosis due to up-regulated PTEN expression in HCC cells. In this study, FTCD was the only down-regulated gene amongst the 10 selected hub genes. Besides, we also found the high expression of FTCD to indicate a good outcome in patients with HCC, based on survival analysis. Therefore, the role of FTCD in HCC and the underlying mechanisms need to be studied further.

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Conclusions This study identified 235 DEGs, including AURKA, CDC20, FTCD, UBE2C, CCNB2, PTTG1, CDKN3, CKS1B, TOP2A, and KIF20A. High expression of CDC20, UBE2C, CCNB2, PTTG1, CDKN3, TOP2A, and KIF20A may be predictor of poor survival while high expression of FTCD may be a predictor of good survival. The hub genes may open up brand-new possibilities for early detection and treatment of HCC; however, further research is warranted for understanding the mechanism of progression of HCC. Author Contributions XLF and TT designed the study, performed the bioinformatics analysis. XLF and WZR wrote the paper. MF and QYW performed the Statistical analysis. MXL reviewed the manuscript. All authors read and approved the final manuscript.

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Figure 1. Identification of overlapping DEGs. (A) Venn diagram of 36 overlapping up-regulated genes in GSE121248, GSE76427, and GSE84402; (B) Venn diagram of 199 overlapping down-regulated genes in GSE121248, GSE76427, and GSE84402 datasets. (Abbreviations: DEGs: Defferentially Expressed Genes) Figure 2. Functional and pathway enrichment analysis of DEGs. (A) Analysis of BP; (B) Analysis of CC; (C)Analysis of MF; (D)Analysis of KEGG Pathway. (Abbreviations: BP: Biological processes; CC: Cellular components; DEGs: Defferentially Expressed Genes; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: Molecular function) Figure 3. Protein-Protein interaction (PPI) network construction and top 10 hub genes. Red nodes represent up-regulated genes and green nodes represent down-regulated genes. Figure 4. Top 3 modules from the protein-protein interaction networks. Figure 5. Prognostic curve of 10 hub genes in patients with Hepatocellular carcinoma . Figure 6. Quantitative real-time PCR results for the 10 gene biomarkers. Expression of these DEGs was normalized against GAPDH expression. The statistical significance of differences was calculated by the Student’s t-test. (* P < 0.05, ***P < 0.001). (Abbreviations: DEGs: Defferentially Expressed Genes; Quantitative real-time PCR: Quantitative real-time polymerase chain reaction)

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Table 1. Primer sequences of PCR. cDNA

Forward primer (5′–3′)

Reverse primer (5′–3′)

AURKA

CATTCCTTTGCAAGCACAAAAG

ATTTCAAAGTCTTCCAAAGCCC

CDC20

AGCAGCAGATGAGACCCTGAGG CAGCGGATGCCTTGGTGGATG

FTCD

CAAGAACACACCTGAGGAAAAG GATGAGCACGTTGAAATATGCG

UBE2C

CAACCTTTTCAAATGGGTAGGG

CAGGATGTCCAGGCATATGTTA

PTTG1

GACTTTGAGAGTTTTGACCTGC

GAGACTGCAACAGATTGGATTC

CDKN3

GCCCAGTTCAATACAAACAAGT

CAACCTGGAAGAGCACATAAAC

CKS1B

CTGCCCAAGGACATAGCCAAGC

CCTGACTCTGCTGAACGCCAAG

TOP2A

AAGATTCATTGAAGACGCTTCG

GCTGTAAAATGCCATTTCTTGC

CCNB2

GAGATATAAATGGACGCATGCG

AAATCGATCCATAATGCCAACG

KIF20A

GAATGTGGAGACCCTTGTTCTA

CCATCTCCTTCACAGTTAGGTT

32

Table 2. Top 10 hub genes with higher degree of connectivity

Gene

Degree of connectivity

AURKA

27

CDC20

26

FTCD

26

UBE2C

25

CCNB2

25

PTTG1

24

CDKN3

24

CKS1B

24

TOP2A

24

KIF20A

23

33

Table 3. KEGG pathway of genes in three modules

KEGG ID

Term

P-Val ue

Genes

hsa04114

Oocyte meiosis

2.16E04

CCNB2, AURKA, CDC20, PTTG1

hsa04110

Cell cycle

3.00E04

CCNB2, CDC20, PTTG1, MCM6

hsa04610

Complement and coagulation cascades

2.25E11

F11, C8A, MBL2, C8B, KLKB1, C6, F9

hsa05020

Prion diseases

hsa05322

Systemic lupus erythematosus

Module 1

Module 2

6.52E04

C8A, C8B, C6

9.77E03

C8A, C8B, C6

Module 3

hsa00830

Retinol metabolism

8.33E10

CYP3A4, CYP2B6, CYP2C9, CYP2C8, CYP26A1, CYP2A6, CYP1A2

hsa00982

Drug metabolism cytochrome P450

9.84E08

CYP3A4, CYP2B6, CYP2C9, CYP2C8, CYP2A6, CYP1A2

hsa05204

Chemical carcinogenesis

2.24E-

CYP3A4, CYP2C9, CYP2C8, NAT2, CYP2A6, 34

07

hsa01100

Metabolic pathways

CYP1A2

CYP3A4, ACSM3, CYP2B6, 2.86E- CYP2C9, CYP2C8, NAT2, 07 HAO2, IDO2, CYP26A1, CYP2A6, KMO, CYP1A2

Metabolism of xenobiotics by 8.20Ehsa00980 cytochrome P450 06

CYP3A4, CYP2B6, CYP2C9, CYP2A6, CYP1A2

35