Identification of differentially expressed genes and biological pathways in para-carcinoma tissues of HCC with different metastatic potentials

Abstract

Hepatocellular carcinoma (HCC) is a malignant tumor with extensive metastasis. Changes in the tumor microenvironment provide favorable conditions for tumor metastasis. However, the role of changes to the tumor microenvironment in HCC metastasis is yet to be elucidated. The Gene Expression Omnibus expression profile GSE5093 consists of 20 noncancerous tissues surrounding HCC tissues, including 9 metastasis-inclined microenvironment samples with detectable metastases and 11 metastasis-averse microenvironment samples without detectable metastases. The present study assessed 35 HCC samples to verify the results of chip analysis. In total, 712 upregulated and 459 downregulated genes were identified, with 1,033 nodes, 7,589 edges and 10 hub genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the differentially expressed genes were significantly enriched in ‘cell-cell adhesion’, ‘cell proliferation’ and ‘protein binding’. The top 10 hub genes were identified via a protein-protein interaction analysis. The 3 most significant modules were identified from the protein-protein network. Moreover, an association between hub genes and patient prognosis was identified. In conclusion, these candidate genes and pathways may help elucidate the mechanisms underlying HCC metastasis and identify more options for targeted therapy.

Introduction

Hepatocellular carcinoma (HCC) is the major histological subtype of primary liver cancer accounting for 85% of all liver cancer cases worldwide, and it was reported to be the most common liver malignancy in 2016 (1). HCC is one of the most aggressive types of cancer with a high mortality rate, whereby 326,000 people died of HCC in 2015 (2). There are several definite risk factors for HCC, such as chronic hepatitis B/C virus infection, nonalcoholic fatty liver diseases, aflatoxin consumption and smoking (2). In addition, there have been a number of identified prognostic markers of HCC, such as alpha fetoprotein, vascular endothelial growth factor and transforming growth factor β, in both large-scale clinical trials and research projects. However, the early diagnosis and effective treatment of HCC remains problematic (3).

To date, surgical resection remains the gold standard treatment for HCC; however, postoperative recurrence and metastasis is common (4). In addition, a large number of patients with HCC are diagnosed at the advanced stages, in which the tumor has already metastasized to other organs prior to surgery (4). Therefore, metastasis is a key challenge in the treatment of HCC. Tumor metastasis is a malignant biological process involving multiple factors and complex signaling pathways that depend not only on the genetic changes of malignant tumor cells, but also on the changes in the tumor microenvironment, such as the stroma, blood vessels and infiltrating inflammatory cells (5). Dysfunction of gene expression in the microenvironment surrounding a tumor serves an important role in the metastatic behavior of tumor cells, such as adhesion of tumor cells, degradation of the extracellular matrix, invasion of basal tissues, homing ability to enter specific tissues, movement and migration in the circulatory system and the promotion of the angiogenesis (6). Therefore, the identification of key genes which function in the tumor microenvironment of HCC may be helpful to identify new targeted therapeutic strategies for HCC metastasis.

In the present study, an original dataset (GSE5093) was obtained from the NCBI-Gene Expression Omnibus database (ncbi.nlm.nih.gov/geo/) containing 20 samples of noncancerous tissues surrounding HCC tissues from two distinct groups of patients with HCC, including 9 metastasis-inclined microenvironment (MIM) samples, with detectable metastases and 11 metastasis-averse microenvironment (MAM) samples, without detectable metastases (7). Differentially expressed genes (DEGs) were filtered in using the Morpheus Website with a data processing standard. Gene-Spring software (version 13.1.1; Agilent Technologies Inc.) was employed to screen the DEGs, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG; kegg.jp) pathway enrichment analysis. Furthermore, a protein-protein interaction (PPI) network was established and three significant modules were analyzed. A total of 35 HCC tissue samples were assessed to verify the results of chip analysis. The present study aimed to investigate the genetic molecular mechanisms underlying the metastatic phenotype of HCC and improve the diagnosis and treatment of metastases of primary hepatic carcinoma patients.

Materials and methods

Data collection

The gene expression profile GSE5093 was obtained from Gene Expression Omnibus (GEO) (7). The GSE5093 dataset was based on the GPL1262 dataset [National Cancer Institute (NCI)/Advanced Technology Corner (ATC) Hs-UniGEM2, Advanced Technology Center Microarray Facility (National Institutes of Health (NIH)/NCI/Cancer Research Center (CCR)/ATC)] and contained 20 samples, including 9 MIM samples and 11 MAM samples.

Patients and specimens

The present study was approved by The Institutional Review Board of the Second Affiliated Hospital of Chongqing Medical University (Chongqing, China) and written informed consent was provided by all patients according to The Declaration of Helsinki. A total of 35 HCC specimens (from 21 men and 14 women; age range, 22–73 years; mean age, 53 years) were collected during resection of HCC tumor at The Second Affiliated Hospital of Chongqing Medical University (Chongqing, China) between May 2015 and November 2016. Tissue samples included 16 noncancerous surrounding hepatic tissue samples with MIM and 19 noncancerous surrounding hepatic tissue samples with MAM (<5 cm away from HCC tissue). Specimens were stored in liquid nitrogen for subsequent experimentation. The clinicopathological features of the HCC specimens are listed in Table SI.

Identification and analysis of DEGs

MIM and MAM samples were divided into two groups and GEO2R (ncbi.nlm.nih.gov/geo/geo2r/) was used to analyze the DEGs between the two groups. The raw expression data files, which included TXT files (Agilent platform), were used for analysis by processing using the Morpheus Website (software.broadinstitute.org). A unpaired t-test was used to identify the DEGs and |log fold change (FC)|≥1 and P<0.05 were used as the cut-off criteria for statistical significance.

Functional and pathway enrichment of DEGs

After identifying the DEGs, GO enrichment and KEGG pathway analyses were performed for gene annotation and functional enrichment analysis using the online tool Database for Annotation, Visualization and Integrated Discovery (DAVID; david.abcc.ncifcrf.gov/). The resulting GO terms and KEGG pathways with P<0.05 were considered significantly enriched for the obtained DEGs.

Survival analysis of the hub genes in HCC

Kaplan-Meier survival analysis and the log-rank test were employed to determine the association between hub genes and HCC, using the Gene Expression Profiling Interactive Analysis (GEPIA) database (8) and prognostic data from The Cancer Genome Atlas (cancer.gov/tcga) database. The percentage of low and high expression groups was set at 50%.

Constructing the PPI network of DEGs

The Search Tool for the Retrieval of Interacting Genes (STRING) database offers both experimental and predicted interactive information (9). In the present study, the STRING database was used to explore the enrichment analysis results according to the biological process, molecular function and cell components determined for the DEGs. Finally, the interactions were selected, of which combined scores of >0.7 were used to construct the PPI network using the Molecular Complex Detection (MCODE) plugin within Cytoscape software [version 3.7.2; (10,11)].

RNA preparation and reverse transcription-quantitative (RT-q)PCR

Total RNA from tissue samples was extracted using TRIzol^® reagent (Takara Bio, Inc.), according to the manufacturer's protocol. RNA was reverse transcribed into cDNA using the PrimeScript RT reagent (Takara Bio, Inc.). The temperature protocol for RT was as follows: 37°C for 5 min, followed by 45°C for 42 min and 75°C for 5 min. qPCR was subsequently performed using SYBR-Green Real-time PCR Master mix (Beijing Transgen Biotech Co., Ltd.). The primer sequences used for qPCR are listed in Table SII. The following thermocycling conditions were used for qPCR: Initial denaturation at 92°C for 15 min; 37 cycles of 95°C for 15 sec, 55°C for 30 sec; and a final extension at 72°C for 30 sec. Relative mRNA levels were measured using the 2^−ΔΔCq method (12) and normalized to the internal reference gene GAPDH.

Statistical analysis

All data are presented as the mean ± standard deviation and analyzed using SPSS 20.0 software (IBM Corp.) and GraphPad Prism 7 (GraphPad Software, Inc.). Differences between groups were analyzed using paired Student's t-test and one-way analysis of variance, followed by Newman-Keuls post hoc test. P<0.05 was considered to indicate a statistically significant difference.

Results

Identification of DEGs between MIM and MAM samples

Through the analysis and processing of data in GSE5093, a total of 1,171 DEGs were identified, which included 712 upregulated and 459 downregulated genes. The heat map of DEG expression levels (top 50 upregulated and 50 downregulated genes) is exhibited in (Fig. 1).

Figure 1.

Heat map of the top 100 differentially expressed genes in metastasis-inclined microenvironment and metastasis-averse microenvironment samples (50 upregulated and 50 downregulated genes). Red, upregulation; blue, downregulation.

GO and pathway enrichment analyses of DEGs

The 1,171 DEGs were selected for functional analysis, performed using the DAVID database. GO analysis of DEGs was carried out from three aspects, which covered molecular function (MF), cellular component (CC) and biological process (BP). BP analysis revealed that the upregulated DEGs were enriched in ‘cell-cell adhesion’, ‘mRNA splicing via spliceosome’ and ‘platelet degranulation’ (Table I), whereas the downregulated DEGs were enriched in ‘cell-cell adhesion’, ‘cell proliferation’ and ‘positive regulation to tyrosine phosphorylation of STAT5 protein’ (Table II). For CC, the upregulated DEGs were enriched in the ‘extracellular exosome and cytosol’, as well as the ‘membrane’ (Table I) and the downregulated DEGs were enriched in ‘receptor complex’, ‘integral component of plasma membrane’ and ‘postsynaptic density’ (Table II). In addition, the MF of upregulated DEGs were enriched in ‘protein binding’, ‘poly(A) RNA binding’ and ‘cadherin binding involved in cell-cell adhesion’ (Table I), whereas the downregulated DEGs were enriched in ‘protein binding’, ‘epidermal growth factor receptor binding’ and ‘amino acid transmembrane transfer activity’ (Table II). These results suggest that the DEGs were associated with the biological processes of cell migration and metastasis.

Table I.

Gene ontology analysis of upregulated differentially expressed genes associated with metastasis-inclined microenvironment and metastasis-averse microenvironment.

Category	Term	Gene function	Gene count	P-value
BP	GO:0098609	Cell-cell adhesion	37	2.9×10−10
BP	GO:0000398	mRNA splicing, via spliceosome	28	3.1×10−7
BP	GO:0002576	Platelet degranulation	18	7.1×10−7
BP	GO:0006457	Protein folding	22	1.2×10−5
BP	GO:0048013	Ephrin receptor signaling pathway	14	3.8×10−5
BP	GO:0043066	Movement of cell or subcellular component	14	3.8×10−5
BP	GO:0043066	Negative regulation of apoptotic process	38	4.2×10−5
BP	GO:0007165	Signal transduction	75	5.4×10−5
BP	GO:0019886	Antigen processing and presentation of exogenous peptide antigen via MHC class II	14	7.8×10−5
BP	GO:0050900	Leukocyte migration	16	1.1×10−4
CC	GO:0070062	Extracellular exosome	220	1.1×10−28
CC	GO:0016020	Membrane	170	7.6×10−21
CC	GO:0005829	Cytosol	218	1.4×10−18
CC	GO:0005654	Nucleoplasm	177	4.2×10−13
CC	GO:0005913	Cell-cell adherens junction	44	5.2×10−13
CC	GO:0031012	Extracellular matrix	37	5.5×10−10
CC	GO:0042470	Melanosome	21	9.2×10−10
CC	GO:0043209	Myelin sheath	25	2.5×10−9
CC	GO:0005615	Extracellular space	90	1.1×10−7
CC	GO:0030529	Intracellular ribonucleoprotein complex	21	1.8×10−7
MF	GO:0005515	Protein binding	448	2.8×10−14
MF	GO:0044822	Poly(A) RNA binding	100	7.6×10−14
MF	GO:0098641	Cadherin binding involved in cell-cell adhesion	42	1.8×10−12
MF	GO:0032403	Protein complex binding	23	2.8×10−5
MF	GO:0003723	RNA binding	43	4.3×10−5
MF	GO:0051287	NAD binding	9	1.1×10−4
MF	GO:0051082	Unfolded protein binding	15	1.3×10−4
MF	GO:0008134	Transcription factor binding	25	4.8×10−4
MF	GO:0005524	ATP binding	86	5.9×10−4
MF	GO:0019899	Enzyme binding	27	9.6×10−4

P<0.001. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

Table II.

Gene ontology analysis of downregulated differentially expressed genes associated with metastasis-inclined microenvironment and metastasis-averse microenvironment.

Category	Term	Gene function	Gene count	P-value
BP	GO:0007155	Cell adhesion	28	c2.8×10−5
BP	GO:0008283	Cell proliferation	22	c3.0×10−4
BP	GO:0042523	Positive regulation of tyrosine phosphorylation of Stat5 protein	5	c6.7×10−4
BP	GO:0007165	Signal transduction	47	c9.4×10−4
BP	GO:0031532	Actin cytoskeleton reorganization	6	b5.8×10−3
BP	GO:0009267	Cellular response to starvation	6	b5.8×10−3
BP	GO:0060749	Mammary gland alveolus development	4	b7.8×10−3
BP	GO:0007169	Transmembrane receptor protein tyrosine kinase signaling pathway	8	b9.5×10−3
BP	GO:0006865	Amino acid transport	5	b1.0×10−2
BP	GO:0045944	Positive regulation of transcription from RNA polymerase II promoter	37	a1.1×10−2
CC	GO:0043235	Receptor complex	90	c1.9×10−4
CC	GO:0005887	Integral component of plasma membrane	47	c4.1×10−4
CC	GO:0014069	Postsynaptic density	78	b1.3×10−3
CC	GO:0045211	Postsynaptic membrane	64	b1.4×10−3
CC	GO:0005829	Cytosol	52	b2.1×10−3
CC	GO:0005886	Plasma membrane	52	b3.6×10−3
CC	GO:0009986	Cell surface	52	b4.5×10−3
CC	GO:0030054	Cell junction	15	b5.8×10−3
CC	GO:0005794	GOlgi apparatus	8	b6.7×10−3
CC	GO:0005737	Cytoplasm	8	b7.5×10−3
MF	GO:0005515	Protein binding	258	c1.5×10−5
MF	GO:0005154	Epidermal growth factor receptor binding	6	c8.6×10−4
MF	GO:0015171	Amino acid transmembrane transporter activity	7	c8.6×10−4
MF	GO:0005524	ATP binding	56	c9.5×10−4
MF	GO:0042803	Protein homodimerization activity	32	b1.5×10−3
MF	GO:0004714	Transmembrane receptor protein tyrosine kinase activity	6	b2.1×10−3
MF	GO:0008179	Adenylate cyclase binding	3	a1.9×10−2
MF	GO:0001078	Transcriptional repressor activity, RNA polymerase II core promoter proximal region sequence-specific binding	8	a1.9×10−2
MF	GO:0005096	Gtpase activator activity	14	a2.0×10−2
MF	GO:0043236	Laminin binding promoter	4	a2.2×10−2

^aP<0.05

^bP<0.01

^cP<0.001. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

KEGG pathway analysis of MIM and MAM DEGs

To further analysis the functions of DEGs, KEGG pathway analysis was conducted to determine the most significantly enriched pathways of the upregulated DEGs and downregulated DEGs. The upregulated DEGs were enriched in ‘protein processing in the endoplasmic reticulum’, ‘antigen processing and presentation’ and ‘phagosome’ pathways (Table III). The downregulated DEGs were enriched in the ‘arrhythmogenic right ventricular cardiomyopathy’, ‘phosphatidylinositol signaling system’ and ‘inositol phosphate metabolism’ pathways (Table III).

Table III.

Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs associated with metastasis-inclined microenvironment and metastasis-averse microenvironment.

A, Upregulated DEGs
Pathway	Name	Gene count	P-value	Genes
hsa04141	Protein processing in endoplasmic reticulum	26	c7.50×10−6	HSP90AB1, SEC31A, GANAB, PDIA3, PDIA4, PRKCSH, CALR, SEC62, DERL3, CANX, SSR1, OS9, HSPH1,xBP1, RPN1, DNAJA1, HSPA6, HSPA5, HSPA8, SEC23A, P4HB, BCAP31, CAPN1, SEC23B, SEL1L, UBE2E1
hsa04612	Antigen processing and presentation	15	c7.23×10−5	HSP90AB1, CIITA, PDIA3, CD8B, LGMN, CTSS, CALR, CANX, TAPBP, HSPA6, HLA-DRB5, CTSB, HLA-DPB1, HSPA8, HLA-DRA
hsa04145	Phagosome	22	c9.24×10−5	ACTB, C3, ATP6AP1, CTSS, CALR, ATP6V1B1, ITGB1, CANX, ITGAM, ACTG1, ATP6V1A, LAMP2, HLA-DRB5, MPO, VAMP3, HLA-DPB1, THBS1, FCGR3A, DYNC1H1, THBS2, CD14, HLA-DRA
hsa03040	Spliceosome	20	c1.51×10−4	SRSF1, TRA2A, PRPF3, DDX5, SF3A1, SF3B4, RBMX, HNRNPA1, HNRNPU, SRSF4, HNRNPK, PLRG1, DHX38, PCBP1, USP39, HSPA6, DHX15, ACIN1, HSPA8, THOC1
hsa04918	Thyroid hormone synthesis	13	c4.73×10−4	TG, GPX2, ADCY7, GNAQ, ATP1B2, PAX8, TPO, CREB3L1, PRKCG, PRKACB, HSPA5, PDIA4, CANX
hsa04610	Complement and coagulation cascades	12	b1.50×10−3	KNG1, C8B, C7, A2M, FGA, C3, CFB, CD46, C6, SERPING1, C1S, C2
hsa04520	Adherens junction	12	b1.91×10−3	ACTB, ACTG1, PTPN6, TGFBR2, CTNND1, CDH1, SMAD2, PTPN1, WASL, TCF7L2, IQGAP1, VCL
hsa05142	Chagas disease (American trypanosomiasis)	15	b1.98×10−3	CFLAR, GNAI3, GNAI2, C3, RELA, TGFBR2, MAP2K4, NFKB1, SMAD2, CALR, GNAQ, PPP2CB, IL12A, PPP2R2B, PPP2R2A
hsa05110	Vibrio cholerae infection	10	b2.23×10−3	ACTB, ACTG1, ATP6V1A, KDELR2, ATP6AP1, PRKCG, PRKACB, PDIA4, ATP6V1B1, TJP2
hsa05146	Amoebiasis	15	b2.37×10−3	IL1R1, RELA, PRKCG, NFKB1, ITGAM, VCL, ARG1, C8B, SERPINB9, GNAQ, IL12A, PRKACB, COL1A1, CD14, FN1
B, Downregulated DEGs
hsa05412	Arrhythmogenic right ventricular cardiomyopathy	8	b2.35×10−3	ITGA8, SGCD, DSC2, GJA1, ITGA2, CACNB4, ITGA4, CACNA1C (ARVC)
hsa04070	Phosphatidylinositol signaling system	9	b5.51×10−3	PIK3CG, MTM1, DGKB, PIK3C2G, IMPA1, PIP5K1B, ITPKB, PTEN, ITPR2
hsa00562	Inositol phosphate metabolism	7	a1.33×10−2	PIK3CG, MTM1, PIK3C2G, IMPA1, PIP5K1B, ITPKB, PTEN
hsa04020	Calcium signaling pathway	11	a2.61×10−2	P2RX7, SLC8A1, CCKBR, ERBB4, PHKB, GRPR, ITPKB, PPP3CA, PTGFR, CACNA1C, ITPR2
hsa05202	Transcriptional misregulation in cancer	10	a4.13×10−2	MAF, CCNT2, CSF2, HHEX, LMO2, FLT3, GZMB, SMAD1, JMJD1C, HMGA2
hsa04151	PI3K-Akt signaling pathway	16	a5.39×10−2	PHLPP1, PIK3CG, COL4A3, PPP2R5A, TCL1A, ITGA2, FGF13, ITGA4, KIT, PTEN, COL5A2, PRLR, ITGA8, PDGFD, GHR, IL2
hsa05410	Hypertrophic cardiomyopathy (HCM)	6	a6.39×10−2	ITGA8, SGCD, ITGA2, CACNB4, ITGA4, CACNA1C
hsa04810	Regulation of actin cytoskeleton	11	a6.54×10−2	PIK3CG, DOCK1, ARHGEF7, DIAPH2, ITGA8, PIP5K1B, ITGA2, IQGAP2, FGF13, PDGFD, ITGA4
hsa04080	Neuroactive ligand-receptor interaction	13	a8.12×10−2	CCKBR, NPY2R, GABRA5, PTGFR, P2RX7, AGTR2, P2RY10, PRLR, GRPR, CHRNA5, CHRNB3, TSHR, GHR
hsa05414	Dilated cardiomyopathy	6	a8.21×10−2	ITGA8, SGCD, ITGA2, CACNB4, ITGA4, CACNA1C

^aP<0.05

^bP<0.01

^cP<0.001. DEGs, differentially expressed genes.

Analysis of the PPI network with DEGs in MIM and MAM

According to the information provided by the STRING, the interaction of the DEGs and acquired hub genes of potential diagnosis and treatment associated DEGs were analyzed. As depicted in Figs. 2A and S1, there were 955 nodes and 7,589 edges, of which 638 nodes represented upregulated DEGs and nodes represented downregulated DEGs (Fig. 2B). The top 10 hub genes with the highest degrees included: Heat shock protein family A member 8 (HSPA8); PH domain and leucine rich repeat protein phosphatase 1 (PHLPP1); phosphoribosylglycinamide formyltransferase (GART); carbamoyl-phosphate synthetase 2 (CAD); actin beta (ACTB); cadherin 1 (CDH1); phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG); nuclear factor κB subunit 1 (NFκB1); signal transducer and activator of transcription 3 (STAT3); and heat shock protein family A (Hsp70) member 5 (HSPA5). Then, the expression of the top 10 hub genes were verified using RT-qPCR. There were 4 hub upregulated genes and 6 downregulated genes in MAM, compared with MIM samples (Fig. 3). Moreover, the top 3 significant modules were selected from the DEG PPI network using MCODE (Fig. 4A-C). Module 1 included 31 nodes and 458 edges, module 2 included 37 nodes and 267 edges and module 3 included 45 nodes. Furthermore, GO and KEGG pathway analysis results were used to analyze the functional and signal pathway enrichment of the three modules. The results showed that module 1 was primarily associated with ‘RNA splicing’, ‘catalytic step 2 spliceosome’, ‘nucleotide binding’ and ‘spliceosome’ pathways (Table IV). Module 2 was primarily enriched in ‘protein modification by small protein conjugation’, ‘catalytic complex’, ‘ubiquitin-protein transferase activity’ and ‘chemokine signaling’ pathways (Table V). Module 3 was most enriched in ‘protein folding’, ‘extracellular exosomes’, ‘protein disulfide isomerase activity’ and ‘chemokine signaling’ pathways (Table VI). These results indicated that the 10 hub genes may function in the biological behavior processes of cell migration and metastasis.

Figure 2.

DEG protein-protein interaction network in metastasis-inclined microenvironment and metastasis-averse microenvironment. (A) The most significant interaction network of the upregulated DEGs interaction network. (B) Interaction network of the 317 downregulated DEGs interaction network. DEG, differentially expressed gene.

Figure 3.

Top 10 hub genes were detected by reverse transcription-quantitative PCR. Expression of (A) CAD, (B) GART, (C) HSPA5, (D) NFKB1, (E) ACTB, (F) CDH1, (G) HSPA8, (H) PHLPP1, (I) PIK3GC and (J) STAT3 The data are presented as the mean ± standard deviation of 3 independent experiments. **P<0.01 MAM vs. MIM. CAD, carbamoyl-phosphate synthetase 2; GART, phosphoribosylglycinamide formyltransferase; HSPA5, heat shock protein family A member 5; NFκB1, nuclear factor κB subunit 1; ACTB, actin beta; CDH1, Cadherin 1; HSPA8, heat shock protein family A member 8; PHLPP1, PH domain and leucine rich repeat protein phosphatase 1; PIK3CG, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ; STAT3, signal transducer and activator of transcription 3.

Figure 4.

Top 3 modules from the protein-protein interaction network determined by the molecular complex detection score. (A) Module 1 and (B) Module 2. Top 3 modules from the protein-protein interaction network determined by the molecular complex detection score. (C) Module 3.

Table IV.

Functional and pathway enrichment analysis of the genes in module 1.

Category	Term	Gene function	Gene count	P-value
BP	GO:0008380	RNA splicing	6	c2.27×10−9
BP	GO: 0000398	mRNA splicing, via spliceosome	6	c8.01×10−7
BP	GO:0006397	mRNA processing	5	c2.06×10−6
BP	GO:0048025	Negative regulation of mRNA splicing, via spliceosome	4	c3.81×10−6
BP	GO:0000381	Regulation of alternative mRNA splicing, via spliceosome	4	c4.06×10−5
CC	GO:0071013	Catalytic step 2 spliceosome	3	c6.60×10−19
CC	GO:0005681	Spliceosomal complex	2	c1.58×10−10
CC	GO:0030529	Intracellular ribonucleoprotein complex	2	c8.66×10−9
CC	GO:0019013	Viral nucleocapsid	2	c7.85×10−8
CC	GO:0005654	Nucleoplasm	12	c8.50×10−6
MF	GO:0000166	Nucleotide binding	7	c3.01×10−10
MF	GO:0044822	Poly(A) RNA binding	7	c1.66×10−9
MF	GO:0003723	RNA binding	6	c1.95×10−5
MF	GO:0003729	mRNA binding	12	c4.65×10−5
MF	GO:0003676	Nucleic acid binding	7	c8.67×10−4
KEGG_PATHWAY	has:03040	Spliceosome	20	c1.54×10−28
KEGG_PATHWAY	has: 03015	mRNA surveillance pathway	4	b4.32×10−3
KEGG_PATHWAY	has: 05168	Herpes simplex infection	4	a3.18×10−2

^aP<0.05

^bP<0.01

^cP<0.001. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Table V.

Functional and pathway enrichment analysis of the genes in module 2.

Category	Term	Gene function	Gene count	P-value
BP	GO:0032446	Protein modification by small protein conjugation	12	b6.57×10−9
BP	GO:0070647	Protein modification by small protein conjugation or removal	12	b5.38×10−8
BP	GO:0007186	G-protein coupled receptor signaling pathway	11	b2.93×10−7
BP	GO:0016567	Protein ubiquitination	10	b5.90×10−7
BP	GO:0007193	Adenylate cyclase-inhibiting G-protein coupled receptor signaling pathway	5	b6.02×10−6
CC	GO:1902494	Catalytic complex	11	b9.45×10−6
CC	GO:0000151	Ubiquitin ligase complex	7	b1.07×10−5
CC	GO:0098552	Side of membrane	5	b2.94×10−4
CC	GO:1990234	Transferase complex	6	b3.96×10−4
CC	GO:0000152	Nuclear ubiquitin ligase complex	3	a1.53×10−3
MF	GO:0004842	Ubiquitin-protein transferase activity	8	b3.79×10−6
MF	GO:0001664	G-protein coupled receptor binding	6	b4.47×10−6
MF	GO:0019787	Ubiquitin-like protein transferase activity	8	b5.20×10−6
MF	GO:0008528	G-protein coupled peptide receptor activity	5	b1.42×10−4
MF	GO:0001653	Peptide receptor activity	5	b1.52×10−4
KEGG_PATHWAY	has: 04062	Chemokine signaling pathway	11	b4.43×10−10
KEGG_PATHWAY	has: 04120	Ubiquitin mediated proteolysis	6	b1.26×10−4
KEGG_PATHWAY	has: 04914	Progesterone-mediated oocyte maturation	5	b2.19×10−4

^aP<0.01

^bP<0.001. GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Table VI.

Functional and pathway enrichment analysis of the genes in module 3.

Category	Term	Gene function	Gene count	P-value
BP	GO:0006457	Protein folding	8	b1.45×10−7
BP	GO:0045454	Cell redox homeostasis	5	b3.19×10−5
BP	GO:0044710	Single-organism metabolic process	19	b4.36×10−5
BP	GO:0006091	Generation of precursor metabolites and energy	6	b3.46×10−4
BP	GO:0090066	Regulation of anatomical structure size	7	b3.49×10−4
CC	GO:0070062	Extracellular exosome	23	b1.72×10−8
CC	GO:0072562	Blood microparticle	5	b7.56×10−5
CC	GO:0005577	Fibrinogen complex	3	b1.64×10−4
CC	GO:0043209	Myelin sheath	5	b5.65×10−4
CC	GO:0005829	Cytosol	9	a2.90×10−3
MF	GO:0003756	Protein disulfide isomerase activity	3	a1.07×10−3
MF	GO:0005524	ATP binding	11	a1.31×10−3
MF	GO:0004070	Aspartate carbamoyltransferase activity	2	a5.77×10−3
MF	GO:0004088	Carbamoyl-phosphatesynthase (glutamine-hydrolyzing) activity	2	a5.77×10−3
MF	GO:0004087	Carbon metabolism	2	a5.77×10−3
KEGG_PATHWAY	has: 01200	Chemokine signaling pathway	5	a1.23×10−3
KEGG_PATHWAY	has: 04144	Endocytosis	6	a2.89×10−3
KEGG_PATHWAY	has: 05100	Bacterial invasion of epithelial cells	4	a4.33×10−3

^aP<0.01

^bP<0.001. GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

The Kaplan-Meier plot of hub genes in MIM and MAM

Through GEPIA prediction of the association between the 10 hub genes and HCC patient prognosis, it was observed that expression of CAD [Hazard Ratio (HR), 1.9; P<0.001], GART (HR, 1.8; P=0.018), HSPA5 (HR, 1.5; P=0.016), ACTB (HR, 1.6; P=0.0074), CDH1 (HR, 0.66; P=0.018) and HSPA8 (HR, 1.6; P=0.011) were associated with poor overall survival in patients with HCC (Fig. 5).

Figure 5.

Prognostic value of hub genes in patients with HCC. Kaplan-Meier analysis of OS time un patients with HCC with high and low expression levels of: (A) CAD, (B) GART, (C) HSPA5, (D) NFκB1, (E) ACTB, (F) CDH1, (G) HSPA8, (H) PHLPP1, (I) PIK3CG and (J) STAT3. The red dotted line above the red curve and the red dotted line below represent the high (50%) and low cutoff (50%) on the survival curve. The blue dotted line above the blue curve and the blue dotted line below represent high (50%) and low cutoff (50%) on the survival curve. HCC, hepatocellular carcinoma; OS, overall survival; CAD, carbamoyl-phosphate synthetase 2; GART, phosphoribosylglycinamide formyltransferase; HSPA5, heat shock protein family A member 5; NFκB1, nuclear factor κB subunit 1; ACTB, actin beta; CDH1, cadherin 1; HSPA8, heat shock protein family A member 8; PHLPP1, PH domain and leucine rich repeat protein phosphatase 1; PIK3CG, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ; STAT3, signal transducer and activator of transcription 3; HR, hazard ratio; TPM, transcripts per million.

Discussion

HCC is the most common malignant tumor with high morbidity and mortality, and was reported to be one of the major causes of cancer-associated mortalities worldwide, in 2002 (13). Patients with HCC are often diagnosed with extrahepatic metastasis, which poses a great challenge to the diagnosis and treatment of HCC (14). Currently, high-throughput gene chip technology can be used to explore the occurrence and development of diseases from the whole genome or transcriptome level, which has been widely used in gene expression analysis and biomarker discovery for a variety of illnesses (14). In order to further understand the molecular mechanisms underlying the recurrence and metastasis of HCC, the GSE5093 Biochip dataset on the metastatic microenvironment of HCC was obtained from the GEO database. This was used to perform a systematic and bioinformatics analysis, including enrichment analysis of differences in gene expression and protein-protein interaction.

In the present study, a total of 1,171 DEGs were identified, of which 712 were upregulated and 459 were downregulated. GO term functional analysis demonstrated that the upregulated genes were primarily involved in ‘cell-cell adhesion’, ‘extracellular exosome’ and ‘cadherin binding’, while downregulated DEGs were involved in ‘cell-cell adhesion’, ‘integral component of plasma membrane’ and ‘epidermal growth factor receptor binding’. The tumor cells leave the primary site, invade the extracellular matrix, adhere to macromolecular protein components in the basement membrane, degrade the basement membrane and the extracellular matrix, and move through the extracellular matrix to invade the surrounding tissues (15). Once in the circulation system, tumor cells evade the surveillance of the immune system. When passing through the vessel wall to the secondary site, tumor cells adhere to the secondary site, proliferate and eventually metastasize (16,17). Cell adhesion, receptor complex, integral component of plasma membrane, postsynaptic density and epidermal growth factor receptor binding are key auxiliary components (18–20). Cell adhesion molecules suppress the adhesive function of tumor cells and glycoproteins located on the cell surface (21). Moreover, KEGG pathway analysis indicated that the upregulated DEGs influenced ‘protein processing in endoplasmic reticulum’, ‘antigen processing and presentation’ and ‘phagosome’; the downregulated DEGs were associated with ‘arrhythmogenic right ventricular cardiomyopathy’, ‘phosphatidylinositol signaling system’ and ‘inositol phosphate metabolism’. Tumor metastasis involves detachment from the primary site of the tumor, entrance into the surrounding stroma and the circulation or lymphatic system, adhesion to endothelial cell walls, extravasation and invasion through vascular proliferation for the formation a novel metastatic lesion (22,23). GO and KEGG analyses suggested that the aforementioned DEGs may influence signaling pathways associated with tumor cell metastasis. The identified DEGs may provide novel research directions and targets for the treatment of HCC metastasis.

Based on the established PPI network with DEGs, the following top 10 hub genes were identified: HSPA8, PHLPP1, GART, CAD, ACTB, CDH1, PIK3CG, NFκB1, STAT3 and HSPA5. HSPA8 is a member of the heat shock proteins family, which influences molecular signal transduction, apoptosis and protein regulation (24). HSPA8 is located in the cytoplasm and lysosomes and is involved in mediating cell autophagy by binding to the substrate protein and transporting it into the lysosomal cavity (25,26). Novel research has shown that the Hsp70 protein may inhibit apoptosis by regulating the caspase-dependent pathway (27). Moreover, tumor vaccines against HSP70S have been successfully completed in animal models and clinical trials are underway (27).

PHLPP1 is an important regulator of Akt serine-threonine kinases and conventional/novel protein kinase C isoforms (28). PHLPP1 can inhibit growth factor-induced signal transduction pathways in cancer cells, so it may be used as a growth inhibitor in several types of cancer (28). In the human breast cancer cell line 21T, AK294 phosphorylation was inhibited by LY294002, and the expression of PHLPP1 in two metastatic cell lines (MT1 and MT2) was much lower compared with early breast cancer cells (29). PHLPP1 can negatively regulate the activity of AKT and its downstream kinase by phosphorylating the hydrophobic group of AKT at the Serine473 site. This resulted in inhibition of the PI3K/AKT signaling pathway, and the activation of various cancer promoting signaling pathways, including amplification or gain-of-function mutations in upstream receptor protein tyrosine kinases (RPTKs), activating mutations in PI3K and Akt and loss-of-function mutations in the regulatory phosphatase PTEN, which affect differentiation and proliferation of cancer cells and apoptosis in breast cancer cells (29). Therefore, it is hypothesized that PHLPP1 is associated with tumor metastasis.

Trifunctional purine biosynthetic protein adenosine-3 is an enzyme that is encoded by the GART gene (30). GART participates in several aspects of energy metabolism, including as a ATP binding source, metal ion binding source and phosphoribosylamine-glycine ligase activity source (30,31). CAD is a fusion gene that encodes three enzymes involved in pyrimidine biosynthesis; carbamoyl-phosphate synthetase 2 and aspartate transcarbamylase fused with dihydroorotase (31). CAD is a key enzyme the regulates multiple biological processes in the first three steps of pyrimidine biosynthesis (32). These three genes serve a role in the synthesis and metabolism of nucleic acids and proteins in cells, and their synthesized products provide essential substances and energy for cell proliferation (32).

Actin is a major constituent of the muscular contractile apparatus and ACTB is one of six different actin isoforms that have been identified in humans (33). ACTB binds RNA-binding protein Sam68 and participates in the regulation of the synaptic formation of dendritic spines (34). ACTB supports the muscular cytoskeleton during the formation of novel tumor cells following tumor cell proliferation and metastasis. Thus, ACTB is considered to be associated with cell proliferation and metastasis (34).

CDH1, also known as epithelial cadherin (E-cadherin), is a protein encoded by the CDH1 gene (35). Mutations in CDH1 are closely associated with liver, colorectal and gastric cancer. Moreover, a functional deficit of CDH1 promotes the proliferation and invasiveness of tumor cells, and promotes the malignant development of tumors (35). E-cadherin is a member of the cadherin family and downregulation of E-cadherin reduces cell-cell adhesion, which results in cells that are more susceptible to migration (36). This change can easily result in cancer cells passing through the cell basement membrane and invading surrounding tissues (36). A previous study demonstrated that E-cadherin is an important protein that regulates cell-cell adhesion. On the cell membrane, E-cadherin binds to β-catenin via its cytoplasmic tail, allowing epithelial cells to bind tightly together. Knockdown of E-cadherin expression results in β-catenin release into the cytoplasm and translocation into the nucleus, and this process can result in the expression of epithelial mesenchymal transition (EMT)-inducible transcription factors. Therefore, E-cadherin is an important gene that regulates EMT (37).

The PIK3CG subunit λ isoform is an enzyme encoded by the PIK3CG gene (38). Several studies have demonstrated that the function of PIK3CG is to regulate the transmission of extracellular signals, including E-cadherin-mediated cell-cell adhesion, which serves an important role in maintaining the structural and functional integrity of the epithelium (39,40).

The NFκB1 gene is a cellular transcription factor and encodes the NF-κB p105 subunit protein. The expression of NF-κB is activated via various stimuli, such as cytokines, oxidative free radicals and bacterial or viral products (41). Activated NF-κB is transported to the nucleus to participate in the transcription and synthesis of various proteins and signaling molecules, regulating various cell behaviors (41). It has been revealed that osteopontin can regulate the expression of MTI-MMP via the NF-κB signaling pathway and promote the migration of cancer cells and invasion into the extracellular matrix (42). A previous study confirmed that several adhesion molecules, such as ICAM-1, VCAM-1 and ELAM-1, are regulated by NF-κB, which serves an important role in tumor metastasis (43).

STAT3, which contains SH2 and SH3 domains, binds to specific phosphotyrosine-containing peptides. When STAT3 is activated by phosphorylation, it serves as a transcriptional activator, in either its homodimeric or heterodimeric form (44). Phosphorylated STAT3 binds a specific site in the target gene promoter sequence and promotes transcription of the gene (44). Dysregulation of this pathway in tumors leads to increased angiogenesis surrounding the tumor, promotes blood supply to the tumor, increases tumor survival and promotes malignant development. It has been demonstrated that tissue-specific inactivation of STAT3 in the lungs of mice is associated with the occurrence and progression of lung adenocarcinoma, which reduced the survival rate. Notably, decreased expression of STAT3 promotes the expression of NF-κB in the cytoplasm, thus regulating NF-κB-induced IL-8 expression (45). This process promotes IL-8-mediated tumor invasion, angiogenesis and progression (45).

Binding immunoglobulin protein, also known as HSPA5, is a protein that is encoded by the HSPA5 gene (46). A large number of studies have revealed the high expression of HSPA5 in all malignant tumors (47,48). Therefore, inhibiting HSPA5 gene expression may be used as an adjuvant therapy for cancer. In addition, reducing the large amount of HSPA5 produced during the stress response can increase the apoptosis of tumor cells and inhibit tumor growth (49).

In addition, tumor metastasis is a complex multi-step process, whereby tumor cells not only interact with each other and host cells, but also with extracellular matrix components (50). Certain characteristics, such as adhesion, metastasis, migration, skeleton assembly, signal transduction and proliferation are associated with tumor metastasis (50,51). Migration is considered a key factor in the process of tumor cell metastasis (51). Previous studies have demonstrated that RNA splicing, protein modification by small protein conjugation, catalytic complex, ubiquitin-protein transferase activity, chemokine signaling pathways, protein folding, extracellular exosomes, protein disulfide isomerase activity and chemokine signaling pathways are also involved in the process of tumor cell migration and metastasis, such as the expansion of the cell front, the formation of new adhesion sites and the release of the original adhesion sites in the tail of the cell (52–56).

A study reported that miR-26b-5p exerts metastatic properties and maintains epithelial cell adhesion molecule + cancer stem cells via HSPA8 in HCC (57). Moreover, a study investigating the role of PHLPP1 in HCC reported that miR-190 influences EMT by regulating the expression of PHLPP1, thus affecting the malignant biological behavior of HCC cells (58). Isobaric tags for relative and absolute quantification analysis of clinical samples of HCC indicated that ACTB serves an important role in the initiation stage of HCC (59). Long non-coding RNA ATRERNA1 promotes the metastasis and invasiveness of HCC cells by recruiting EHMT2 and/or ehmt2/snai1 complexes to inhibit CDH1 (60). Loss of NFκB1 promotes the occurrence of age-related chronic liver disease (CLD) which is characterized by steatosis, neutrophil proliferation, fibrosis, telomere damage of hepatocytes and HCC (61). The results of the present study demonstrated that STAT3 is one of the hub genes. Phosphorylated RPN2 activates the signal transducer and activator of STAT3 and is also responsible for the RPN2-stimulated elevated expression of MMP-9 and for invading HCC cells (62).

In the current study, biological function and signal pathway enrichment analysis revealed that the genes in module 1, 2 and 3 were primarily enriched in ‘RNA splicing’, ‘G-protein coupled receptor signaling pathway’ and ‘protein ubiquitination’. Studies have demonstrated that these signaling pathways are required for cell survival and metabolism and serve an important role in tumor recurrence and metastasis (63–65). Therefore, it is hypothesized that these molecular pathways may provide novel insight and potential targets for the diagnosis and treatment of HCC.

In conclusion, 1,171 DEGs were identified (including 712 upregulated and 459 downregulated genes, 1,033 nodes, 7,589 edges and 10 hub genes) via gene profile dataset and integrated bioinformatics analysis in noncancerous surrounding hepatic tissues. The 10 hub genes were significantly enriched in several signaling pathways which serve an important role in tumor metastasis. According to the present research and analysis, CAD, GART, HSPA5, ACTB, CDH1 and HSPA8 may have potential as targets for the diagnosis and treatment of metastatic HCC. The present study may provide a scientific basis for the investigation of these genes in HCC in the future.

Funding

The present study was supported by The Foundation of Scientific Research of Sichuan Medical Association (grant. no. S16007), The Sichuan Health Planning Commission Key Project Foundation (grant. no. 17ZD008), The Sichuan Science and Technology Project (grant. no. 2018JY0276) and The Chengdu Science and Technology Bureau Technology Innovation Project (grant. no. 2018-YF05-01228-SN).

Availability of data and materials

The datasets generated and/or analyzed during the present study are available in the [NCBI] repository, [https://www.ncbi.nlm.nih.gov/geo].

Authors' contributions

YL and HW performed the experiments, analyzed the data and drafted the manuscript. YW, HW, MD and CL conceptualized the study design and critically revised the manuscript. YL, MD, CL and HW wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by The Institutional Review Board of the Second Affiliated Hospital of Chongqing Medical University (Chongqing, China) and written informed consent was provided by all patients according to the Declaration of Helsinki.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.