Identification of prostate cancer hub genes and therapeutic agents using bioinformatics approach

Abstract

BACKGROUND:

Prostate cancer (PCa) is the most common and the second leading cause of cancer-related death among men in America. As the molecular mechanism of PCa has not yet been completely discovered, identification of hub genes and potential drug of this disease is an important area of research that could provide new insights into exploring the mechanisms underlying PCa.

OBJECTIVE:

The aim of this study was to identify potential biomarkers and novel drug for prostate cancer treatment.

METHODS:

The differentially expressed genes (DEGs) between prostate cancer and normal cells were screened using microarray data obtained from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were performed in order to investigate the functions of DEGs, and the protein-protein interaction (PPI) network of the DEGs was constructed using the Cytoscape software. DEGs were then mapped to the connectivity map database to identify molecular agents associated with the underlying mechanisms of PCa.

RESULTS:

Totally, 359 genes (155 upregulated and 204 downregulated genes) were found to be differentially expressed between prostate cancer and normal cells. The GO terms significantly enriched by DEGs included cell adhesion, protein binding involved in cell-cell adhesion, response to BMP, extracellular region and extracellular region part. KEGG pathway analysis showed that the most significant pathways included cell adhesion molecules (CAMs) and TGF-beta signaling pathway. The PPI network of up-regulated DEGs and down-regulated DEGs were established, respectively. While CDH1, BMP2, NKX3-1, PPARG and PRKAR2B were identified as the hub genes in the PPI network.

CONCLUSIONS:

The BMP2, PPARG and PRKAR2B genes may therefore be potential biomarkers in the treatment of PCa. Additionally, the small molecular agent phenoxybenzamine may be a potential drug for PCa.

Keywords

Prostate cancer bioinformatics analysis differently expressed genes hub genes therapeutic agent

1. Introduction

According to WHO, prostate cancer ranks second in the most incident cancer, and ranks fifth in the key cause of death by cancer among men in the world [1]. As a multi-factorial disease, many factors including age, ethnicity, diet, and geographic factors are supposed to play a key role in PCa development [2]. In North America, PCa remains the most common noncutaneous solid tumor. It ranks third and second in death caused by cancer among men in Canada and USA [3, 4]. In 2016, a total of 180,890 new cases and 26,120 deaths from the cancer are expected to occur in the United States [5]. Overall, the five-year survival rate is excellent (98.9%) but drops down to 30% in the case of aggressive form with distant metastasis and relapse after treatment [1].

As for the early detection of PCa, prostate-specific antigen (PSA) is the most widely used biomarker, but it has poor specificity (33%) resulting in a high number of positive reports. So, PSA does not represent an ideal biomarker. To date, and despite the high number of studies, only very few biomarkers for the prediction of PCa progression have been verified in different cohorts or using different techniques, yet none was translated into clinical practice [6]. Moreover, the molecular mechanism of PCa occurrence and progression remains unclarified. Therefore, it would be worthwhile to clarify the potential molecular mechanisms of PCa for the purpose of identifying new biomarkers and discovering candidate small molecular drugs.

In our study, the differentially expressed genes between prostate tumor and matched normal tissues were identified by the bioinformatics approach. Additionally, several methods including hierarchical clustering analysis, GO/KEGG pathway analysis, construction of protein-protein interaction network and identification of small molecules were used to analyze this data. The aim of this study was to produce a systematic view on understanding the underlying mechanisms and to investigate potential therapeutic targets for prostate cancer.

Figure 1.

Cartridge of expression values data before and after normalization. The x coordinate: the samples; the y coordinate: the gene expression values. Left: unnormalized data; right: normalized data.

Figure 2.

Heat map and clustering analysis of DEGs. The left of the heat map shows clustering of the DEGs, the above of the heat map shows clustering of the samples. Red: high expression level; green: low expression level.

2. Materials and methods

2.1 Data collection

The gene expression profile of six primary prostate tumors samples and six normal prostate tissues samples (GSE26910) based on the platform of GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) was collected from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) database, which was deposited by Planche et al. [7].

Table 1
The top 5 GO terms and KEGG pathway analysis for DEGs

Category	Term	Count	P-value
Upregulated DEGs
GOTERM_BP_FAT	GO:0072359 $\sim$ circulatory system development	26	6.56307E-08
GOTERM_BP_FAT	GO:0072358 $\sim$ cardiovascular system development	26	6.56307E-08
GOTERM_BP_FAT	GO:0061138 $\sim$ morphogenesis of a branching epithelium	12	1.39561E-07
GOTERM_BP_FAT	GO:0001763 $\sim$ morphogenesis of a branching structure	12	3.70616E-07
GOTERM_BP_FAT	GO:0007155 $\sim$ cell adhesion	34	5.70541E-07
GOTERM_CC_FAT	GO:0044421 $\sim$ extracellular region part	52	9.54967E-05
GOTERM_CC_FAT	GO:0043296 $\sim$ apical junction complex	8	0.000100073
GOTERM_CC_FAT	GO:0005911 $\sim$ cell-cell junction	16	0.000244776
GOTERM_CC_FAT	GO:0098590 $\sim$ plasma membrane region	19	0.000592773
GOTERM_CC_FAT	GO:0070062 $\sim$ extracellular exosome	39	0.00072087
GOTERM_MF_FAT	GO:0030284 $\sim$ estrogen receptor activity	3	0.000548711
GOTERM_MF_FAT	GO:0050839 $\sim$ cell adhesion molecule binding	12	0.00068612
GOTERM_MF_FAT	GO:0086080 $\sim$ protein binding involved in heterotypic cell-cell adhesion	3	0.002930346
GOTERM_MF_FAT	GO:0008201 $\sim$ heparin binding	6	0.007087593
GOTERM_MF_FAT	GO:0098632 $\sim$ protein binding involved in cell-cell adhesion	8	0.007541901
KEGG_PATHWAY	hsa04514: Cell adhesion molecules (CAMs)	5	0.018959723
Downregulated DEGs
GOTERM_BP_FAT	GO:0048468 $\sim$ cell development	41	3.8411E-06
GOTERM_BP_FAT	GO:0071773 $\sim$ cellular response to BMP stimulus	10	1.36915E-05
GOTERM_BP_FAT	GO:0071772 $\sim$ response to BMP	10	1.36915E-05
GOTERM_BP_FAT	GO:0003007 $\sim$ heart morphogenesis	12	1.58153E-05
GOTERM_BP_FAT	GO:0060429 $\sim$ epithelium development	26	3.3375E-05
GOTERM_CC_FAT	GO:0005576 $\sim$ extracellular region	66	0.000420581
GOTERM_CC_FAT	GO:0097458 $\sim$ neuron part	27	0.000537878
GOTERM_CC_FAT	GO:0044297 $\sim$ cell body	14	0.00091487
GOTERM_CC_FAT	GO:0044421 $\sim$ extracellular region part	56	0.00134134
GOTERM_CC_FAT	GO:0005615 $\sim$ extracellular space	27	0.001737652
GOTERM_MF_FAT	GO:0005102 $\sim$ receptor binding	28	0.001307403
GOTERM_MF_FAT	GO:1901681 $\sim$ sulfur compound binding	9	0.002418246
GOTERM_MF_FAT	GO:0005501 $\sim$ retinoid binding	4	0.00596409
GOTERM_MF_FAT	GO:0019840 $\sim$ isoprenoid binding	4	0.006913558
GOTERM_MF_FAT	GO:0070696 $\sim$ transmembrane receptor protein serine/threonine kinase binding	3	0.007229021
KEGG_PATHWAY	hsa04350: TGF-beta signaling pathway	5	0.010649457
KEGG_PATHWAY	hsa00982: Drug metabolism - cytochrome P450	4	0.032391713
KEGG_PATHWAY	hsa04360: Axon guidance	5	0.041140553
KEGG_PATHWAY	hsa05410: Hypertrophic cardiomyopathy (HCM)	4	0.045756951

2.2 Data processing and identification of DEGs

The Affy package [8] was applied to preprocess the raw expression data with the rma [9] algorithm in the R statistical software. Then, the limma package [10] was used to identify DEGs between PCa and matched normal samples. We selected differentially expressed genes with $|$ log ${}_{2}$ FC $|>$ 1 and $P<$ 0.01 in microarray data for further analysis. The heat map was constructed for samples and identified DEGs with pheatmap package [11] in R software.

2.3 GO and KEGG pathway analysis

GO is used to perform functional enrichment analysis on gene sets [12]. Additionally, the Kyoto Encyclopedia of Genes and Genomes database is used to understand the high-level functions and utilities of the biological system [13]. The Database for Annotation, Visualization and Integration Discovery (DAVID) online tool [14, 15] was used for DEGs analysis of GO annotation and KEGG pathway enrichment. $P<$ 0.05 was set as the threshold value.

2.4 Construction of PPI network

Search Tool for the Retrieval of Interacting Genes (STRING), a biological database and web resource, offers comprehensive information of predicted protein-protein interactions [16]. To evaluate the interactive relationship of DEGs, we mapped the DEGs to STRING. The combined score of $>$ 0.5 was considered as the threshold for significant protein pairs. Then, PPI network was visualized with Cytoscape software [17] and the hub genes were identified according to the degree score (number of neighbors).

2.5 Identification of small molecular drugs

The Connectivity Map (cMap) [18] database is a collection of more than 7,000 expression profiles from cultured human cells treated with small molecules. The DEGs in PPI network were mapped onto the cMap database. The $|$ connectivity score $|>$ 0.8 was used as cutoff value to identify candidate small molecular drugs associated with prostate cancer.

3. Results

3.1 Data processing and DEGs analysis

The results of data processed before and after normalization were shown in Fig. 1, and the lines in the boxes were nearly on the same straight line after standardization, showing the standardization level was satisfactory. Then, 359 genes were identified to be differentially expressed genes, including 155 upregulated genes and 204 downregulated genes in tumor tissue.

As shown in Fig. 2, DEGs were divided into 2 clusters according to the clustering analysis result. Meanwhile, tumor and normal tissues were also classified into 2 different groups.

3.2 GO and KEGG pathway analysis

The top five GO terms of up-regulated and down-regulated DEGs were shown in Table 1. The upregulated DEGs were mainly enriched in cell adhesion, extracellular region part, cell-cell junction, cell adhesion molecule binding and protein binding involved in cell-cell adhesion, and the downregulated DEGs mainly participated in cell development, response to BMP, extracellular region, extracellular region part, extracellular space, receptor binding and sulfur compound binding.

In Table 1, the pathways results showed that the up-regulated DEGs were significantly enriched in cell adhesion molecules (CAMs) pathway, while the downregulated DEGs were significantly enriched in TGF-beta signaling pathway, Drug metabolism-cytochrome P450, Axon guidance and Hypertrophic cardiomyopathy (HCM) (Table 1).

Figure 3.

The Protein network was constructed by online software string.

Figure 4.

Constructed PPI network of DEGs. A: The network of up-regulated DEGs. Pink nodes represent upregulated genes; B: The network of down-regulated DEGs. Green nodes represent downregulated genes.

Table 2

Enriched significant of small molecules

cMap name	Enrichment	P-value
phenoxybenzamine	$-$ 0.929	0.00002
emetine	$-$ 0.879	0.00052
prednicarbate	$-$ 0.846	0.00727
MG-262	$-$ 0.831	0.00957
calmidazolium	0.814	0.07018
laudanosine	0.819	0.00193
fendiline	0.846	0.00697

3.3 PPI network construction

Based on the STRING database, protein network was constructed (Fig. 3). Totally, 198 protein pairs were identified in the network (combined scores of $>$ 0.5). As represented in Fig. 4, the PPI network of up-regulated DEGs was constructed with of 141 nodes and 87 edges, and the PPI network of down-regulated DEGs was established with of 184 nodes and 111 edges. Then, CDH1 (degree score, 18), BMP2 (degree score, 9), NKX3-1 (degree score, 6), PPARG (degree score, 6) and PRKAR2B (degree score, 6) were identified as hub genes (degree $\geqslant$ 6).

3.4 Identification of small molecular agents

Based on the results of cMap database mapping, a total of 7 small molecular agents were obtained, such as phenoxybenzamine, emetine and fendiline. In Table 2, the small molecular agent phenoxybenzamine had the highest negative score.

4. Discussion

Prostate cancer is one of the most common urinary tumors and ranks second in death caused by cancer in men in the USA [19, 20]. In current study, we used the gene expression profile obtained from the GEO database, identified 359 DEGs, including 155 upregulated and 204 downregulated genes in PCa. Down-regulated genes were mainly enriched in several functional terms such as cellular response to BMP stimulus, response to BMP, extracellular region and extracellular region part, and pathways associated with TGF-beta signaling pathway. Up-regulated genes were significantly associated with other functional terms, such as cell adhesion, cell-cell junction, cell adhesion molecule binding and protein binding involved in cell-cell adhesion, and were mainly enriched in significant pathways such as cell adhesion molecules (CAMs). In order to clarify the interactions of DEGs, we constructed the PPI network. Five genes were selected as hub genes, including CDH1, NKX3-1, BMP2, PPARG and PRKAR2B. Among the five genes, CDH1 and NKX3-1 were up-regulated, while BMP2, PPARG and PRKAR2B were down-regulated. In addition, small molecular agent phenoxybenzamine was identified as an important potential drug for prostate cancer.

Cell adhesion molecules (CAMs), a group of cell surface receptors, are capable of modulating signal transduction, which is essential for regulation of processes including cellular adhesion, proliferation, differentiation, migration and apoptosis [21, 22]. Study reported that dysregulated CAMs might cause deregulation of these biological processes in tumors, indicating an important role of CAMs in cancer development [23, 24]. Cell adhesion is a common biological process, such as the interaction of cell-cell and cell-matrix [25], involves in cellular growth, differentiation and migration [26].

CDH1 gene is mainly involved in regulating cell adhesion, migration and proliferation of epithelial cells. Mutation of this gene promotes cancer progression by increasing proliferation, invasion, and/or metastasis [27]. The downregulation of CDH1 gene in epithelial cells is usually correlated with tumor formation and differentiation [28]. However, in our study, overexpression CDH1 gene was accordant with GO/KEGG pathway analysis. CDH1 gene expression is higher in prostate cancer than normal samples, which may suggest that overexpression CDH1 plays a minor effect in tumor development. In addition, previous study reported that tumor and normal cells enhanced cell adhesion in the late stage of tumor development [24]. Hence, the expression level of CDH1 in different stages in prostate cancer needs to further study.

TGF- $\beta$ signaling pathway is a key factor in the biology of metazoan, and its misregulation can lead to tumorigenesis. In healthy cells and early-stage cancer cells, this pathway functions as tumor-suppressor, including cell-cycle arrest and apoptosis. However, its activation in late-stage cancer can promote tumorigenesis, including metastasis and chemoresistance [29]. In our study, the downregulated DEGs were significantly enriched in TGF- $\beta$ signaling pathway, which weakened this pathway about tumor-suppressor function, was conducive to tumor progression.

Bone morphogenetic protein-2 (BMP-2) is a multifunctional growth factor, which strongly induces bone formation and belongs to the TGF- $\beta$ superfamily [30, 31]. BMP regulates adult tissue homeostasis through control of cell growth, differentiation and apoptosis. Several studies have examined the association of abnormal BMP signaling with cancer initiation and/or progression. A former study showed that BMP2 was downregulated in colorectal cancer and its overexpression reduced HCT116 cell growth, migration, sphere formation and colony formation [32]. In our study, BMP2 was a downregulated gene in prostate cancer tissue, which was consistent with previous study, and enriched in the TGF- $\beta$ signaling pathway. Therefore, the BMP2 gene was found to be a key regulator in the prostate cancer development.

In addition, NKX3.1 is a homologous domain gene regulated by androgen, which functions mainly as a prostatic tumor suppressor [33]. However, previous studies showed that NKX3.1 protein is positive in primary prostatic adenocarcinomas, downregulated in advanced prostate cancers, and completely lost in metastatic prostate cancers [34, 35]. Furthermore, Xu et al. [36] found NKX3.1 overexpression in 31% tumor specimens, no change in 48% tumor specimens and decreased expression in 21% tumor specimens. In our result, NKX3.1 was an up-regulated gene in primary prostate cancer, suggesting that overexpression of NKX3.1 might lead to tumor development.

PPARG is thought to be a tumor suppressor in various types of tumors including prostatic cancer [37], liposarcoma [38], breast cancer [39], and cervical cancer [40, 41], which induces tumor apoptosis and growth arrest. The lower expression levels of PPARG was reported in cervical adenocarcinoma than matched normal cervical tissue [42]. In our result, PPARG was downregulated in tumor tissue, which is accordance with former studies. Therefore, we further confirm that PPARG might be capable of inhibiting tumor development in PCa. It may be a potential therapeutic biomarker in PCa.

PRKAR2B is a Protein Coding gene, which encodes cAMP-dependent protein kinase type II-beta regulatory subunit in humans [43]. Previous research has demonstrated that PRKAR2B was downregulated in human CCA tissues and CCA cell lines [44]. In addition, our results also revealed PRKAR2B gene was downregulated in prostate cancer. Therefore, PRKAR2B may play an inhibitory role in the development of primary prostate cancer.

At last, the study found candidate small molecules that may be involved in promoting or suppressing the development of PCa. Phenoxybenzamine was considered to be small molecule drug with the highest negative score, which means it maybe suppress tumor progression. It has been reported that Phenoxybenzamine significantly reduced PC3 proliferation, because of the capable to decrease cell proliferation, migration and adhesion [45]. Another molecule, Phenoxybenzamine hydrochloride (PHEN) could inhibit cell growth and invasion in glioma [46], induce cell cycle arrest and apoptosis in prostate cancer cell lines [47]. Additionally, a high-throughput screening study has demonstrated that PHEN was thought to be a novel small molecule inhibitor because of potential anti-cancer effect [48]. Therefore, Phenoxybenzamine or Phenoxybenzamine hydrochloride may be a potential drug to treat PCa.

In conclusion, our study found several significant genes in PCa progression, such as BMP2, PPARG and PRKAR2B, which may have the inhibitory effect in the progression of primary prostate cancer. Furthermore, phenoxybenzamine or Phenoxybenzamine hydrochloride may act as a potential molecular drug to treat PCa. At present, our study may be helpful to elucidate the underlying molecular mechanisms of PCa, and identifies potential candidate targets to treat PCa. However, the current study is performed based on bioinformatics methods and no experiments have been performed to confirm the conclusions. Therefore, further experimental study is required to confirm the findings of the present result.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Acknowledgments

This study was supported by the National Key Research and Development Program of China (2016YFC0902301) and the grants from the Science, Technology and Innovation Committee of Shenzhen Municipality (JSGG20140702161347218 and KJYY20140530141443717).

References

Brawley

O.W.

, Trends in prostate cancer in the United States, J Natl Cancer Inst Monogr 2012 (2012), 152–156.

Bostwick

D.G.

Burke

H.B.

Djakiew

Euling

S.M.

Landolph

Morrison

Sonawane

Shifflett

Waters

D.J.

and Timms

, Human prostate cancer risk factors, Cancer 101 (2004), 2371–2490.

The Canadian Cancer Society, http://www.cancer.ca/en/cancer-information/cancer-type/prostate/statistics/?region=qc (accessed June 2015).

Society

AC.

,What are the Key Statistics about Prostate Cancer, http://www.cancer.org/cancer/prostatecancer/detailedguide/ prostate-cancer-key-statistics(accessed June 2015).

Siegel

R.L.

Miller

K.D.

and Jemal

, Cancer statistics, 2016, CA Cancer J Clin 66 (2016), 7–30.

Gaudreau

P.O.

Stagg

Soulieres

and Saad

, The present and future of biomarkers in prostate cancer: Proteomics, genomics, and immunology advancements, Biomark Cancer 8 (2016), 15–33.

Planche

Bacac

Provero

Fusco

Delorenzi

Stehle

J.C.

and Stamenkovic

, Identification of prognostic molecular features in the reactive stroma of human breast and prostate cancer, PLoS One 6 (2011), e18640.

Gautier

Cope

Bolstad

B.M.

and Irizarry

R.A.

, Affy – analysis of Affymetrix GeneChip data at the probe level, Bioinformatics 20 (2004), 307–315.

Irizarry

R.A.

Hobbs

Collin

Beazer-Barclay

Y.D.

Antonellis

K.J.

Scherf

and Speed

T.P.

, Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics 4 (2003), 249–264.

10.

Ritchie

M.E.

Phipson

Law

C.W.

Shi

and Smyth

G.K.

, Limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res 43 (2015), e47.

11.

Wang

Cao

Zeng

Wang

Cheng

Zhu

Nian

and Wang

, RNA-seq analyses of multiple meristems of soybean: Novel and alternative transcripts, evolutionary and functional implications, BMC Plant Biol 14 (2014), 169.

12.

Ashburner

Ball

C.A.

Blake

J.A.

Botstein

Butler

Cherry

J.M.

Davis

A.P.

Dolinski

Dwight

S.S.

Eppig

J.T.

Harris

M.A.

Hill

D.P.

Issel-Tarver

Kasarskis

Lewis

Matese

J.C.

Richardson

J.E.

Ringwald

Rubin

G.M.

and Sherlock

, Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium, Nat Genet 25 (2000), 25–29.

13.

Altermann

and Klaenhammer

T.R.

, PathwayVoyager: Pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, BMC Genomics 6 (2005), 60.

14.

Huang da

Sherman

B.T.

and Lempicki

R.A.

, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat Protoc 4 (2009), 44–57.

15.

Huang

D.W.

Sherman

B.T.

and Lempicki

R.A.

, Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists, Nucleic Acids Res 37 (2009), 1–13.

16.

Szklarczyk

Franceschini

Wyder

Forslund

Heller

Huerta-Cepas

Simonovic

Roth

Santos

Tsafou

K.P.

Kuhn

Bork

Jensen

L.J.

and von Mering

, STRING v10: Protein-protein interaction networks, integrated over the tree of life, Nucleic Acids Res 43 (2015), D447–452.

17.

Shannon

Markiel

Ozier

Baliga

N.S.

Wang

J.T.

Ramage

Amin

Schwikowski

and Ideker

, Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res 13 (2003), 2498–2504.

18.

Lamb

Crawford

E.D.

Peck

Modell

J.W.

Blat

I.C.

Wrobel

M.J.

Lerner

Brunet

J.P.

Subramanian

Ross

K.N.

Reich

Hieronymus

Wei

Armstrong

S.A.

Haggarty

S.J.

Clemons

P.A.

Wei

Carr

S.A.

Lander

E.S.

and Golub

T.R.

, The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease, Science 313 (2006), 1929–1935.

19.

Gutman

A.B.

and Gutman

E.B.

, An “acid” phosphatase occurring in the serum of patients with metastasizing carcinoma of the prostate gland, J Clin Invest 17 (1938), 473–478.

20.

Bhavsar

McCue

and Birbe

, Molecular diagnosis of prostate cancer: Are we up to age? Semin Oncol 40 (2013), 259–275.

21.

Edelman

G.M.

and Crossin

K.L.

, Cell adhesion molecules: Implications for a molecular histology, Annu Rev Biochem 60 (1991), 155–190.

22.

Hunter

Sawa

Edlund

and Obrink

, Evidence for regulated dimerization of cell-cell adhesion molecule (C-CAM) in epithelial cells, Biochem J 320(Pt 3) (1996), 847–853.

23.

Thomas

G.J.

and Speight

P.M.

, Cell adhesion molecules and oral cancer, Crit Rev Oral Biol Med 12 (2001), 479–498.

24.

Albelda

S.M.

, Role of integrins and other cell adhesion molecules in tumor progression and metastasis, Lab Invest 68 (1993), 4–17.

25.

Gumbiner

B.M.

, Cell adhesion: The molecular basis of tissue architecture and morphogenesis, Cell 84 (1996), 345–357.

26.

Albelda

S.M.

and Buck

C.A.

, Integrins and other cell adhesion molecules, FASEB J 4 (1990), 2868–2880.

27.

The CDH1 gene, http://www.genecards.org/cgi-bin/carddisp.pl?gene=CDH1, (accessed April 2017).

28.

Cavallaro

and Christofori

, Cell adhesion and signalling by cadherins and Ig-CAMs in cancer, Nat Rev Cancer 4 (2004), 118–132.

29.

Mishra

Derynck

and Mishra

, Transforming growth factor-beta signaling in stem cells and cancer, Science 310 (2005), 68–71.

30.

C.J.

, Smad signal pathway in BMP-2-induced osteogenesis-a mini review, Journal of Dental Sciences 3 (2008), 13–21.

31.

Reddi

A.H.

, Role of morphogenetic proteins in skeletal tissue engineering and regeneration, Nat Biotechnol 16 (1998), 247–252.

32.

Vishnubalaji

Yue

Alfayez

Kassem

Liu

F.F.

Aldahmash

and Alajez

N.M.

, Bone morphogenetic protein 2 (BMP2) induces growth suppression and enhances chemosensitivity of human colon cancer cells, Cancer Cell Int 16 (2016), 77.

33.

Abate-Shen

Shen

M.M.

and Gelmann

, Integrating differentiation and cancer: The Nkx3.1 homeobox gene in prostate organogenesis and carcinogenesis, Differentiation 76 (2008), 717–727.

34.

Gurel

Ali

T.Z.

Montgomery

E.A.

Begum

Hicks

Goggins

Eberhart

C.G.

Clark

D.P.

Bieberich

C.J.

Epstein

J.I.

and De Marzo

A.M.

, NKX3.1 as a marker of prostatic origin in metastatic tumors, Am J Surg Pathol 34 (2010), 1097–1105.

35.

Bowen

Bubendorf

Voeller

H.J.

Slack

Willi

Sauter

Gasser

T.C.

Koivisto

Lack

E.E.

Kononen

Kallioniemi

O.P.

and Gelmann

E.P.

, Loss of NKX3.1 expression in human prostate cancers correlates with tumor progression, Cancer Res 60 (2000), 6111–6115.

36.

L.L.

Srikantan

Sesterhenn

I.A.

Augustus

Dean

Moul

J.W.

Carter

K.C.

and Srivastava

, Expression profile of an androgen regulated prostate specific homeobox gene NKX3.1 in primary prostate cancer, J Urol 163 (2000), 972–979.

37.

Kubota

Koshizuka

Williamson

E.A.

Asou

Said

J.W.

Holden

Miyoshi

and Koeffler

H.P.

, Ligand for peroxisome proliferator-activated receptor gamma (troglitazone) has potent antitumor effect against human prostate cancer both in vitro and in vivo, Cancer Res 58 (1998), 3344–3352.

38.

Demetri

G.D.

Fletcher

C.D.

Mueller

Sarraf

Naujoks

Campbell

Spiegelman

B.M.

and Singer

, Induction of solid tumor differentiation by the peroxisome proliferator-activated receptor-gamma ligand troglitazone in patients with liposarcoma, Proc Natl Acad Sci USA 96 (1999), 3951–3956.

39.

Elstner

Muller

Koshizuka

Williamson

E.A.

Park

Asou

Shintaku

Said

J.W.

Heber

and Koeffler

H.P.

, Ligands for peroxisome proliferator-activated receptorgamma and retinoic acid receptor inhibit growth and induce apoptosis of human breast cancer cells in vitro and in BNX mice, Proc Natl Acad Sci USA 95 (1998), 8806–8811.

40.

Girnun

G.D.

Smith

W.M.

Drori

Sarraf

Mueller

Eng

Nambiar

Rosenberg

D.W.

Bronson

R.T.

Edelmann

Kucherlapati

Gonzalez

F.J.

and Spiegelman

B.M.

, APC-dependent suppression of colon carcinogenesis by PPARgamma, Proc Natl Acad Sci USA 99 (2002), 13771–13776.

41.

Sarraf

Mueller

Jones

King

F.J.

DeAngelo

D.J.

Partridge

J.B.

Holden

S.A.

Chen

L.B.

Singer

Fletcher

and Spiegelman

B.M.

, Differentiation and reversal of malignant changes in colon cancer through PPARgamma, Nat Med 4 (1998), 1046–1052.

42.

Jung

T.I.

Baek

W.K.

Suh

S.I.

Jang

B.C.

Song

D.K.

Bae

J.H.

Kwon

K.Y.

Bae

J.H.

Cha

S.D.

Bae

and Cho

C.H.

, Down-regulation of peroxisome proliferator-activated receptor gamma in human cervical carcinoma, Gynecol Oncol 97 (2005), 365–373.

43.

Solberg

Sistonen

Traskelin

A.L.

Berube

Simard

Krajci

Jahnsen

and de la Chapelle

, Mapping of the regulatory subunits RI beta and RII beta of cAMP-dependent protein kinase genes on human chromosome 7, Genomics 14 (1992), 63–69.

44.

Loilome

Juntana

Namwat

Bhudhisawasdi

Puapairoj

Sripa

Miwa

Saya

Riggins

G.J.

and Yongvanit

, PRKAR1A is overexpressed and represents a possible therapeutic target in human cholangiocarcinoma, Int J Cancer 129 (2011), 34–44.

45.

Colciago

Mornati

Ferri

Castelnovo

L.F.

Fumagalli

Bolchi

Pallavicini

Valoti

and Negri-Cesi

, A selective alpha1D-adrenoreceptor antagonist inhibits human prostate cancer cell proliferation and motility “in vitro”, Pharmacol Res 103 (2016), 215–226.

46.

Lin

X.B.

Jiang

Ding

M.H.

Chen

Z.H.

Bao

Chen

Sun

Zhang

C.R.

H.K.

Cai

C.Y.

Zhou

J.Y.

Qian

X.J.

Jin

W.L.

and Hu

G.H.

, Anti-tumor activity of phenoxybenzamine hydrochloride on malignant glioma cells, Tumour Biol 37 (2016), 2901–2908.

47.

Liou

S.F.

Lin

H.H.

Liang

J.C.

Chen

I.J.

and Yeh

J.L.

, Inhibition of human prostate cancer cells proliferation by a selective alpha1-adrenoceptor antagonist labedipinedilol-A involves cell cycle arrest and apoptosis, Toxicology 256 (2009), 13–24.

48.

Wang

Zhao

Cui

Yao

Ren

Hao

Smollen

Nie

Jin

Liu

and Wong

S.T.

, Identification of novel small-molecule inhibitors of glioblastoma cell growth and invasion by high-throughput screening, Biosci Trends 6 (2012), 192–200.

Identification of prostate cancer hub genes and therapeutic agents using bioinformatics approach

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2.1 Data collection

Table 1 The top 5 GO terms and KEGG pathway analysis for DEGs

2.3 GO and KEGG pathway analysis

2.4 Construction of PPI network

2.5 Identification of small molecular drugs

3. Results

3.1 Data processing and DEGs analysis

3.2 GO and KEGG pathway analysis

3.4 Identification of small molecular agents

4. Discussion

Conflict of interest

Footnotes

Acknowledgments

References

Table 1
The top 5 GO terms and KEGG pathway analysis for DEGs