Identification of candidate genes that may contribute to the metastasis of prostate cancer by bioinformatics analysis

  • Authors:
    • Lingyun Liu
    • Kaimin Guo
    • Zuowen Liang
    • Fubiao Li
    • Hongliang Wang
  • View Affiliations

  • Published online on: November 14, 2017     https://doi.org/10.3892/ol.2017.7404
  • Pages: 1220-1228
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations

Abstract

To screen for marker genes associated with to the metastasis of prostate cancer (PCa), in silico analysis of the Gene Expression Omnibus dataset GSE27616, which included 4 metastatic and 5 localized PCa tissue samples, was performed. Differentially expressed genes (DEGs) were identified. Their potential functions were identified by Gene Ontology and Kyoto Encyclopedia of Gene Genomes pathway enrichment analyses. Furthermore, protein‑protein interaction (PPI) networks for DEGs were constructed using Cytoscape. Module analysis of the PPI networks was performed with Cluster ONE. A total of 561 DEGs were screened, including 208 upregulated and 353 downregulated genes. Proliferating cell nuclear antigen (PCNA) and cluster of differentiation 4 (CD4) exhibited the highest degrees of connectivity in the PPI networks for up‑ and down‑regulated DEGs, respectively. The DEGs in module A, including CD58, 2, 4 and major histocompatibility complex, class II DP‑β1 were enriched in ‘cell adhesion molecules’. Anaphase promoting complex subunit 4, cell division cycle 20 and cell division cycle 16 in module B were primarily enriched in ‘cell cycle’. The DEGs, including CD4, PCNA and baculoviral IAP repeat containing 5, may have critical roles in PCa metastasis and could thus be used as novel biomarker candidates for metastatic PCa. However, further studies are required to verify these results.

Introduction

Prostate cancer (PCa) is the most common malignancy among men worldwide (1) as well as the second leading cause of cancer-associated mortality (2,3). According to a previous study, PCa resulted in ~256,000 incidences of mortality in 2010 globally (4). This type of cancer is caused by a combination of and environmental factors, including age, ethnicity and diet, and genetic causes (5). Although PCa is curable in the early stages by radiation therapy or surgical resection, the majority of patients with locally advanced or metastatic PCa lack a curative treatment option (6).

Metastasis, the most common cause of cancer-associated mortality, represents the final and the most devastating stage of tumor progression (7). The majority of cancer types originate from epithelial tissues and, upon metastasis, leave the primary tumor and invade the adjacent tissue (8). PCa cells may thus spread from the prostate to other regions of the body. Pelvic lymph node involvement is the first indication of metastasis in the majority of cases of PCa, followed by transfers to other organs, including bones, lungs and liver (9). The metastasis of PCa to regional lymph nodes is a frequent early event that is associated with poor clinical treatment (10). Aurora kinase A is overexpressed in metastatic PCa and is associated with tumor development and progression (11). In addition, the expression of extracellular signal-regulated kinase, p38 mitogen-activated protein kinase and c-Jun-N-terminal kinase were demonstrated to be significantly reduced in metastatic PCa tissue compared with primary PCa (12). At present, prostate specific antigen (PSA), encoded by the prostate-specific gene kallikrein-3 (KLK3), is a serum biomarker used for the detection of PCa (13). However, the prognostic value of PSA is limited due to the variation in its specificity and sensitivity (14). Therefore, it is urgent to screen for valid biomarkers for metastatic PCa.

The present study investigated the global gene expression profile of localized and metastatic PCa, and identified the differentially expressed genes (DEGs) between localized and metastatic PCa tissue samples. Protein-protein interaction (PPI) networks were constructed for the DEGs, and pathway analyses of the PPI networks were performed. Finally, a set of genes associated with PCa metastasis was identified. The present study aimed to identify key genes that were involved in the metastasis of PCa and provide clues for the treatment of PCa, by bioinformatics methods.

Materials and methods

Affymetrix microarray data

Gene expression profiles (identification no., GSE27616) from the study of Kim et al (15), which were deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) were extracted, including 4 metastatic and 5 localized PCa tissue sample expression profiles. Raw data had been collected using the Agilent-014850 Whole Human Genome Microarray 4×44K G4112F array platform (Agilent Technologies, Inc., Santa Clara, CA, USA).

DEGs analysis

The original expression data from all conditions was scaled using the robust multi-array average (RMA) method (16), with the default settings in Bioconductor (http://bioconductor.org/help/search/index.html?q=AgiMicroRna) (17) and a linear model was constructed. Linear Models for Microarray Data package (18) was applied to identify DEGs. To reduce the likelihood of false positive results, the Benjamini-Hochberg method (19) was used to adjust the raw P-value. Finally, DEGs with the cut-off criteria [log2 Fold change (FC)]>1 and P<0.05 were selected.

Gene ontology (GO) and pathway enrichment analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.7, http://david.abcc.ncifcrf.gov/) is an annotation tool that allows for the extraction of biological meaning from a large list of genes (20). In the present study, DAVID was used to identify over-represented GO terms in the biological process category based on the hypergeometric distribution with a false discovery rate (FDR) <0.05 and gene count >2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was applied to identify the main functional and metabolic pathways enriched in DEGs from PCa metastasis. P<0.05 was selected as the cut-off criterion.

PPI network construction

To demonstrate potential PPIs, PPI data from the Human Protein Reference Database (HPRD) (21) and the Biological General Repository for Interaction Database (BioGRID) (22) were used to identify interactions among the DEGs. On the basis of this dataset, PPI networks of the DEGs were constructed in the Search Tool for the Retrieval of Interacting Gene database (23) using Cytoscape (version 3.2.0; http://www.cytoscape.org/release_notes_3_2_0.html) software (24).

Screening and analyses of relevant regulatory network modules

The functional modules of the network for downregulated DEGs were investigated with Cluster ONE in Cytoscape (25). The sub-modules were screened with a criterion of P<0.01. Finally, the two most significant sub-modules from the modularity analysis were selected for GO functional enrichment analysis and KEGG pathway enrichment analysis.

Results

DEGs between metastatic and localized PCa cells

To identify DEGs between metastatic and localized PCa samples, the publicly available microarray dataset GSE27616 was obtained from the GEO database. DEGs with a (log2 FC)>1 and P<0.05 were identified. A total of 561 DEGs were obtained, including 208 upregulated genes and 353 downregulated genes.

KEGG pathway enrichment and GO analysis of DEGs

KEGG pathway enrichment analyses were performed for upregulated and downregulated DEGs. The results revealed that the upregulated DEGs were enriched in ‘renal cell carcinoma’ (P=0.00848), ‘cell cycle’ (P=0.01438) and ‘homologous recombination’ (P=0.04125; Table I). The downregulated DEGs were enriched in ‘vascular smooth muscle contraction’ (P=0.00889), ‘calcium signaling pathway’ (P=0.03116) and ‘dilated cardiomyopathy’ (P=0.0436; Table II).

Table I.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for the upregulated differentially expressed genes.

Table I.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for the upregulated differentially expressed genes.

TermCountP-valueGenes
hsa05211: Renal cell carcinoma50.00848BRAF, CREBBP, EGLN3, EGLN1, FLCN
hsa04110: Cell cycle60.01438CDKN2D, CREBBP, PKMYT1, CHEK1, CDC20, PTTG1
hsa03440: Homologous recombination30.04125POLD1, RAD52, RAD54L

Table II.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for the downregulated differentially expressed genes.

Table II.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for the downregulated differentially expressed genes.

TermCountP-valueGenes
hsa04270: Vascular smooth muscle contraction80.00889KCNMA1, ACTG2, PLA2G4A, PLCB4, ACTA2, PPP1R12B, MYLK, ITPR2
hsa04020: Calcium signaling pathway90.03116CD38, SLC8A1, PLCB4, PHKB, PLN, GRPR, PPP3CB, MYLK, ITPR2
hsa05414: Dilated cardiomyopathy60.04364SLC8A1, DES, PLN, DMD, TPM2, CACNA2D2

To investigate whether DEGs share specific functional features, the online biological classification software DAVID was used to identify overrepresented GO categories in biological process. Gene count >2 and P<0.05 were selected as threshold values. There were 48 categories enriched in upregulated DEGs and 94 categories enriched in downregulated DEGs that met these criteria. The most frequent GO functions of upregulated DEGs included ‘response to hypoxia’ (P=2.72×10−8), ‘response to oxygen levels’ (P=4.81×10−8) and ‘cell cycle phase’ (P=6.60×10−4; Table III). The GO functions of downregulated DEGs included ‘response to organic substance’ (P=3.50×10−5), ‘response to cytokine stimulus’ (P=2.70×10−4) and ‘response to hypoxia’ (P=3.07×10−4; Table IV).

Table III.

Top ten enriched GO functions of upregulated differentially expressed genes.

Table III.

Top ten enriched GO functions of upregulated differentially expressed genes.

GO termCountP-valueGenes
0001666: Response to hypoxia13 2.72×10−8ALDOC, CREBBP, EGLN3, EGLN1, UBE2B, ADA, DDIT4, ADM, PLOD2, SERPINA1, SCNN1B, ANGPTL4, MT3
0070482: Response to oxygen levels13 4.81×10−8ALDOC, CREBBP, EGLN3, EGLN1, UBE2B, ADA, DDIT4, ADM, PLOD2, SERPINA1, SCNN1B, ANGPTL4, MT3
0022403: Cell cycle phase14 6.60×10−4PKMYT1, CDC20, CHEK1, PTTG1, RCC1, RAD52, RAD54L, KIF2C, CDCA8, CDKN2D, POLD1, STMN1, CIT, FANCA
0010033: Response to organic substance19 9.65×10−4F12, EIF2C2, AQP9, ALDOC, TAF9B, DUOX1, HSPA1A, UBE2B, SDC1, HSF1, ADM, SLC25A36, ENO2, HSPA6, HSPB1, SERPINA1, CA2, ABCC5, SPP1
0000279: M phase120.00104KIF2C, CDCA8, PKMYT1, CHEK1, CDC20, PTTG1, STMN1, CIT, RAD52, RCC1, RAD54L, FANCA
0006260: DNA replication  80.00508RFC4, POLD1, CDKN2D, CHTF18, CHEK1, C16ORF75, TK1, CDT1
0007049: Cell cycle180.00517CKS1B, PSRC1, PKMYT1, CHEK1, CDC20, PTTG1, RAD52, RCC1, RAD54L, CDT1, KIF2C, CDCA8, POLD1, CDKN2D, CHTF18, STMN1, CIT, FANCA
0042060: Wound healing  80.00522F12, FGG, SDC1, MST1, ENO3, SERPINA1, SCNN1B, TM4SF4
0010035: Response to inorganic substance  80.00761FGG, SDC1, AQP9, DUOX1, SERPINA1, CA2, ADA, MT3
0000278: Mitotic cell cycle110.00785KIF2C, CDCA8, POLD1, CDKN2D, PKMYT1, CHEK1, CDC20, PTTG1, STMN1, CIT, RCC1

[i] GO, Gene Ontology.

Table IV.

Top ten enriched GO functions of downregulated differentially expressed genes.

Table IV.

Top ten enriched GO functions of downregulated differentially expressed genes.

GO termCountP-valueGenes
0010033: Response to organic substance28 3.50×10−5CCL2, SNCA, ADH5, PDE3B, PTEN, ASAH1, STAT6, SORBS1, BCL2, PPP3CB, SRD5A2, GNG4, EIF2B4, GHR, KCNMA1, BSG, SLC8A1, SP100, SOCS2, FBP1, LIFR, COLEC12, SELS, CYP7B1, CD38, PLA2G4A, SMPD1, WFDC1
0034097: Response to cytokine stimulus8 2.70×10−4STAT6, CD38, SP100, BCL2, SNCA, LIFR, PPP3CB, GHR
0001666: Response to hypoxia10 3.07×10−4KCNMA1, CD38, SLC8A1, SMAD9, CCL2, BCL2, PSEN2, CABC1, DPP4, ITPR2
0009266: Response to temperature stimulus8 3.67×10−4PLA2G4A, CCL2, TRPM8, DIO2, BCL2, CIRBP, GMPR, EIF2B4
0070482 Response to oxygen levels10 4.47×10−4KCNMA1, CD38, SLC8A1, SMAD9, CCL2, BCL2, PSEN2, CABC1, DPP4, ITPR2
0007610 Behavior19 5.65×10−4KCNMA1, CCL2, SNCA, CXCL9, ATP1A2, FOSB, CXCL11, PTEN, CXCL12, SLIT2, PTGDS, CCR5, BCL2, PSEN2, GRPR, PTN, PBX3, FGF2, IL1RAPL1
0006955 Immune response24 7.18×10−4GBP5, SP100, CCL2, GZMA, IGJ, SNCA, TUBB2C, GPR65, CXCL9, COLEC12, CXCL11, CXCL12, CLEC10A, IGSF6, CCR5, BCL2, PSEN2, MS4A2, HLA-DRB5, HLA-DPB1, SPON2, IL1RAPL1, ERCC1, RAB27A
0009725 Response to hormone stimulus16 8.63×10−4KCNMA1, BSG, CCL2, SOCS2, FBP1, PDE3B, PTEN, CD38, PLA2G4A, SORBS1, BCL2, WFDC1, SRD5A2, GNG4, EIF2B4, GHR
0042110 T-cell activation9 9.58×10−4BCL2, LCK, PSEN2, PPP3CB, CD2, CXCL12, DPP4, RAB27A, RHOH
0048511 Rhythmic process90.00106KCNMA1, CYP7B1, HLF, PLA2G4A, EGR3, PTGDS, BCL2, LFNG, EIF2B4

[i] GO, Gene Ontology.

Construction of an integrated PPI network

PPI interaction data were obtained from HPRD and BioGRID, and PPI networks were constructed for upregulated and downregulated DEGs (Figs. 1 and 2). In the PPI network for upregulated DEGs, the top ten nodes with the highest connectivity were proliferating cell nuclear antigen (PCNA), cell division cycle associated 8 (CDCA8), cell division cycle 20 (CDC20), baculoviral IAP repeat containing 5 (BIRC5), DNA polymerase δ-1, chromatin licensing and DNA replication factor 1, kinesin family member 2C, checkpoint kinase 1, aurora kinase B (AURKB) and thymidine kinase 1. In the PPI network for downregulated DEGs, the top ten nodes with the highest connectivity were CD4, CCR5, LCK, C-X-C motif chemokine ligand 12 (CXCL12), fibromodulin, fibroblast growth factor 2, CXCL9, CD2, CD69 and CCL5. The connectivity degree of these proteins was >10 (Table V).

Table V.

Differentially expressed genes with the top-20 highest connectivity degree in the protein-protein interaction network.

Table V.

Differentially expressed genes with the top-20 highest connectivity degree in the protein-protein interaction network.

StatusGene symbolDegree
UpregulatedPCNA34
CDCA830
CDC2030
BIRC530
POLD129
CDT129
KIF2C28
CHEK128
AURKB28
TK127
RFC427
BUB1B27
RAD54L25
UBE2T22
CDC25C21
GMNN20
C16orf7519
PTTG119
EZH218
CDC25A18
DownregulatedCD425
CCR515
LCK15
CXCL1214
FMOD13
FGF213
CXCL912
CD212
CD6912
CCL512
PTEN11
ACTA211
GZMA11
BIRC510
CCL29
DES9
CDC209
AURKB9
SFRP49
MYLK9
Analysis of modules

Module A (P=3.029×10−5) and module B (P=2.901×10−4; Fig. 3) were isolated from the PPI network of the downregulated DEGs using Cluster ONE software. GO and KEGG pathway analysis were then performed to analyze the modules.

In module A, genes including CCL2, CCR5, CXCL9, CXCL11 and CCL5 were enriched in ‘chemokine signaling pathway’ (P=4.77×10−4) and ‘cytokine-cytokine receptor interaction’ (P=0.00171); genes including CD58, CD2, CD4 and major histocompatibility complex, class II, DP-β1 (HLA-DPB1) were enriched in ‘adhesion molecules’ (P=0.00242); genes including CD38, 2 and 4 were enriched in ‘hematopoietic cell lineage’ (P=0.01408); genes including CXCL9, CXCL11 and CCL5 were enriched in ‘toll-like receptor signaling pathway’ (P=0.01912).

In module B, genes including anaphase promoting complex subunit 4 (ANAPC4), protein phosphatase 3 catalytic subunit-β, CDC20 and CDC16 were enriched in ‘oocyte meiosis’ (P=3.88×10−5); genes including ANAPC4, CDC20 and CDC16 were enriched in ‘cell cycle’ (P=0.003482) and ‘ubiquitin mediated proteolysis’ (P=0.004173; Table VI).

Table VI.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for genes in module A and module B.

Table VI.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for genes in module A and module B.

ModuleTermCountP-valueGenes
Ahsa04062: Chemokine signaling pathway5 4.77×10−4CCL2, CCR5, CXCL9, CXCL11, CCL5
hsa04060: Cytokine-cytokine receptor interaction50.00171CCL2, CCR5, CXCL9, CXCL11, CCL5
hsa04514: Cell adhesion molecules40.00242CD58, CD2, CD4, HLA-DPB1
hsa04640: Hematopoietic cell lineage30.01408CD38, CD2, CD4
hsa04620: Toll-like receptor signaling pathway30.01912CXCL9, CXCL11, CCL5
Bhsa04114: Oocyte meiosis43.88 ×10−5ANAPC4, PPP3CB, CDC20, CDC16
hsa04110: Cell cycle30.003482ANAPC4, CDC20, CDC16
hsa04120: Ubiquitin mediated proteolysis30.004173ANAPC4, CDC20, CDC16

The enriched GO analysis results revealed that the enriched GO functions for genes in module A were predominantly enriched in ‘immune response’ (P=6.77×10−10), ‘taxis’ (P=3.07×10−5) and ‘chemotaxis’ (P=3.07×10−5). The enriched GO functions for genes in module B were mainly enriched in ‘mitotic cell cycle’ (P=1.08×10−11), ‘cell cycle phase’ (P=2.39×10−11) and ‘mitosis’ (P=1.19×10−10; Table VII).

Table VII.

Top ten enriched GO functions for genes in module A and module B.

Table VII.

Top ten enriched GO functions for genes in module A and module B.

ModuleGO termCountP-valueGenes
A0006955 Immune response11 6.77×10−10IGSF6, GBP5, CCL2, CCR5, GZMA, GPR65, CXCL9, CD4, HLA-DPB1, CXCL11, CCL5
0042330 Taxis5 3.07×10−5CCL2, CCR5, CXCL9, CXCL11, CCL5
0006935 Chemotaxis5 3.07×10−5CCL2, CCR5, CXCL9, CXCL11, CCL5
0006874 Cellular calcium ion homeostasis5 5.19×10−5CD38, CCL2, CCR5, LCK, CCL5
0055074 Calcium ion homeostasis5 5.77×10−5CD38, CCL2, CCR5, LCK, CCL5
0006875 Cellular metal ion homeostasis5 6.79×10−5CD38, CCL2, CCR5, LCK, CCL5
0007166 Cell surface receptor linked signal transductio10 7.77×10−5IGSF6, CCL2, CCR5, LCK, GPR65, CD2, CXCL9, CD4, CXCL11, CCL5
0055065 Metal ion homeostasis5 8.08×10−5CD38, CCL2, CCR5, LCK, CCL5
0030005 Cellular di-, tri-valent inorganic Cation homeostasis5 1.20×10−4CD38, CCL2, CCR5, LCK, CCL5
0055066 Di-, tri-valent inorganic cation homeostasis5 1.46×10−4CD38, CCL2, CCR5, LCK, CCL5
B0000278 Mitotic cell cycle8 1.08×10−11CDCA8, INCENP, ANAPC4, PPP3CB, BIRC5, CDC20, CDC16, AURKB
0022403 Cell cycle phase8 2.39×10−11CDCA8, INCENP, ANAPC4, PPP3CB, BIRC5, CDC20, CDC16, AURKB
0007067 Mitosis7 1.19×10−10CDCA8, INCENP, ANAPC4, BIRC5, CDC20, CDC16, AURKB
0000280 Nuclear division7 1.19×10−10CDCA8, INCENP, ANAPC4, BIRC5, CDC20, CDC16, AURKB
0000087 M phase of mitotic cell cycle7 1.33×10−10CDCA8, INCENP, ANAPC4, BIRC5, CDC20, CDC16, AURKB
0048285 Organelle fission7 1.52×10−10CDCA8, INCENP, ANAPC4, BIRC5, CDC20, CDC16, AURKB
0022402 Cell cycle process8 2.14×10−10CDCA8, INCENP, ANAPC4, PPP3CB, BIRC5, CDC20, CDC16, AURKB
0051301 Cell division7 7.03×10−10CDCA8, INCENP, ANAPC4, BIRC5, CDC20, CDC16, AURKB
0000279 M-phase7 1.36×10−9CDCA8, INCENP, ANAPC4, BIRC5, CDC20, CDC16, AURKB
0007049 Cell cycle8 1.99×10−9CDCA8, INCENP, ANAPC4, PPP3CB, BIRC5, CDC20, CDC16, AURKB

[i] GO, Gene Ontology.

Discussion

PCa is a relatively common cancer in men, with a subset ultimately developing metastatic disease to other regions of the body (25). The severity of the disease and its clinical heterogeneity, combined with the lack of effective diagnostic markers and therapeutic strategies, make the treatment of PCa a major challenge (26,27). Therefore, it is urgent to screen key genes that are associated with the metastasis of PCa with the aim of improving its treatment. The present study used bioinformatics methods to identify the DEGs between metastatic and localized PCa expression profiles. The results revealed that the expression of 561 genes (including 208 upregulated and 353 downregulated genes) was altered in metastatic PCa, compared with localized PCa. PPI networks were then constructed to reveal the associations among these genes. Furthermore, two potentially important modules were selected from the downregulated DEG set and their functions were determined by GO and KEGG pathway analyses.

The upregulated DEGs were significantly enriched in ‘response to hypoxia’ and ‘response to oxygen levels’. Hypoxia increases the likelihood of tumor invasion and metastasis by activating relevant gene expression through the expression of hypoxia-inducible factor (HIF) (28). HIF is a transcription factor that responds to changes in the available oxygen in the cell (29). A previous study demonstrated that hypoxia may regulate each step in the process of tumor metastasis, from the initial epithelial-mesenchymal transition to the final organotropic colonization (28). Hypoxia also increases the mRNA expression of lysyl oxidase, which is associated with the early and later stages of metastasis (30). It also has been reported that hypoxia promotes cell invasion in PCa cells (31). Thus, these genes may be associated with the metastasis of PCa.

In addition, it was identified that the downregulated DEGs were enriched in ‘calcium signaling pathway’. Calcium, as a ubiquitous second messenger, is an important signaling molecule that is involved in numerous fundamental physiological functions, including the cell cycle, apoptosis and migration (32). Certain human diseases have been associated with the dysregulation of calcium homeostasis, including hypertension, diabetes, Alzheimer's disease, cardiovascular disease and cancer (33,34). Calcium signaling in cancer cells has been demonstrated to be associated with events during tumor progression, including migration, invasion and metastasis (35). Several membrane-bound Ca2+ channels have been reported to have critical roles in regulating cancer cell migration and malignant metastasis (32,36). Cationic channels of the transient receptor potential (TRP) family are considered to be key players in calcium homoeostasis (37). The expression of transient receptor potential melastain 8 (TRPM8), part of the TRP channel subfamily, has been demonstrated to reduce the migration of PCa PC-3 cells (37,38). TRP vanilloid 2, another TRP channel, enhances PCa cell migration and invasion (39). Therefore, the DEGs enriched in ‘response to hypoxia’ and ‘calcium signaling pathway’ may contribute to the metastasis of PCa.

Based on the result of a PPI network construction from the DEGs, it was identified that a number of DEGs may be linked with others. CD4, LCK and CCR5 had high degrees of connectivity in the PPI network and were associated with ‘immune system’. It has been reported that a range of T-cell subsets found in solid tumors are associated with tumor progression and metastasis (40). CD4 encodes a T-lymphocyte membrane glycoprotein and functions as an adhesion molecule that binds to non-polymorphic regions of major histocompatibility complex class II antigens (41,42). CD4+ cells are involved in the pulmonary metastasis of mammary carcinoma (43). It has been reported that the infiltrating CD4+ T cells may promote PCa metastasis (44). Furthermore, CD4 was also associated with ‘cell adhesion molecules (CAMs)’ in module A. CAMs, which belong to the family of membrane receptors that mediate cell-matrix and cell-cell interactions, are essential for transducing intracellular signals, which are responsible for facilitating cell adhesion, invasion, migration and metastasis (45). LCK is a member of the Src tyrosine kinase family expressed primarily in T lymphocytes and natural killer cells (46). LCK localizes to the surface of the plasma membrane and binds to transmembrane receptors, including CD4 (47). CCR5, a protein on the surface of white blood cells, has been demonstrated to alter the proliferation of PCa cells (47). In the present study, LCK and CCR5 were connected to CD4 in the PPI network. Therefore, we hypothesize that CD4, LCK and CCR5 could have important roles in the metastasis of PCa.

PCNA exhibited a high degree of connectivity in the PPI network for upregulated DEGs. PCNA belongs to the family of DNA sliding clamps, is essential for DNA replication and is associated with DNA repair, chromatin remodeling and epigenetics (48). The phosphorylation of PCNA is a frequent event in the development of prostate cancer (49). It was demonstrated that PCNA was highly expressed in triple-negative breast cancer and associated with axillary lymph node metastasis (50). Although the regulation of PCNA function in prostate cancer cells has not been fully characterized, the data of the present study indicated that PCNA may have a critical role in PCa metastasis.

GO analysis of module B revealed that the DEGs were enriched in terms associated with the cell cycle. For example, BIRC5, CDCA8, inner centromere protein, ANAPC4, CDC20, CDC16 and AURKB were enriched in ‘mitosis’, ‘nuclear division’, ‘M phase of mitotic cell cycle’ and ‘organelle fission’. Cell cycle deregulation is a common feature of tumors (51). BIRC5 is a member of the inhibitor of apoptosis protein family, which has a critical role in the occurrence and progression of tumors (52). BIRC5 has been reported to be associated with colorectal cancer tumorigenesis and progress (53). BIRC5, as a component of the chromosomal passenger complex, is involved in the microtubule-kinetochore attachment that ensures cohesion between sister chromatids and centrosome aggregation (54). The regulation of centrosome coalescence may link mitosis to cell adhesion (55). Therefore, BIRC5 may be associated with PCa cell metastasis.

There are certain limitations to the present study; due to the in silico nature of the analysis of the PCa expression profiles, the obtained results were only forecasted with contrived criteria. Therefore, a number of important genes may have been ignored. The absence of experiments verifying the expression of DEGs was also a limitation in the present study. The limited sample size may also represent a limitation and restrict the ability to draw a valid conclusion. Despite these caveats, the results identified in the present study may provide a novel stimulus for the further experimental study of PCa metastasis.

The present study analyzed the gene expression profiles of localized and metastatic PCa tissue using bioinformatics analysis. It was identified that DEGs, including CD4, PCNA and BIR5C, may serve roles in driving the metastasis of PCa. These genes may be novel potential biomarkers and/or therapeutic targets for patients with PCa. However, further research is required to confirm these results.

Acknowledgements

The present study was supported by a grant from the Scientific Research program of Jilin provincial Health department (grant no. 3D5157343428).

Glossary

Abbreviations

Abbreviations:

CAMs

cell adhesion molecules

DAVID

database for annotation visualization and integrated discovery

DEGs

differentially expressed genes

FDR

false discovery rate

GEO

gene expression omnibus

GO

Gene Ontology

HIF

hypoxia inducible factor

HPRD

Human Protein Reference Database

KEGG

Kyoto Encyclopedia of Genes and Genomes

PPI

protein-protein interaction

PCa

prostate cancer

PSA

prostate specific antigen

TRP

transient receptor potential

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January 2018
Volume 15 Issue 1

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APA
Liu, L., Guo, K., Liang, Z., Li, F., & Wang, H. (2018). Identification of candidate genes that may contribute to the metastasis of prostate cancer by bioinformatics analysis. Oncology Letters, 15, 1220-1228. https://doi.org/10.3892/ol.2017.7404
MLA
Liu, L., Guo, K., Liang, Z., Li, F., Wang, H."Identification of candidate genes that may contribute to the metastasis of prostate cancer by bioinformatics analysis". Oncology Letters 15.1 (2018): 1220-1228.
Chicago
Liu, L., Guo, K., Liang, Z., Li, F., Wang, H."Identification of candidate genes that may contribute to the metastasis of prostate cancer by bioinformatics analysis". Oncology Letters 15, no. 1 (2018): 1220-1228. https://doi.org/10.3892/ol.2017.7404