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Network analysis of ChIP-Seq data reveals key genes in prostate cancer

European Journal of Medical Research volume 19, Article number: 47 (2014) Cite this article

Abstract

Background

Prostate cancer (PC) is the second most common cancer among men in the United States, and it imposes a considerable threat to human health. A deep understanding of its underlying molecular mechanisms is the premise for developing effective targeted therapies. Recently, deep transcriptional sequencing has been used as an effective genomic assay to obtain insights into diseases and may be helpful in the study of PC.

Methods

In present study, ChIP-Seq data for PC and normal samples were compared, and differential peaks identified, based upon fold changes (with P-values calculated with t-tests). Annotations of these peaks were performed. Protein–protein interaction (PPI) network analysis was performed with BioGRID and constructed with Cytoscape, following which the highly connected genes were screened.

Results

We obtained a total of 5,570 differential peaks, including 3,726 differentially enriched peaks in tumor samples and 1,844 differentially enriched peaks in normal samples. There were eight significant regions of the peaks. The intergenic region possessed the highest score (51%), followed by intronic (31%) and exonic (11%) regions. The analysis revealed the top 35 highly connected genes, which comprised 33 differential genes (such as YWHAQ, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein and θ polypeptide) from ChIP-Seq data and 2 differential genes retrieved from the PPI network: UBA52 (ubiquitin A-52 residue ribosomal protein fusion product 1) and SUMO2 (SMT3 suppressor of mif two 3 homolog 2) .

Conclusions

Our findings regarding potential PC-related genes increase the understanding of PC and provides direction for future research.

Background

Prostate cancer (PC) is the second most common cancer among men worldwide [1]. The most common symptoms are difficulty in urinating, erectile dysfunction and problems during sexual intercourse [2],[3]. Genetic background contributes to PC risk, as suggested by associations with race, family and specific gene variants [4],[5]. Many genes have been found to be involved in PC. For example, mutations in BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset) are important risk factors for PC [6]. The authors of a previous article reported that PTEN (phosphatase and tensin homolog) deletions are related to tumor aggression in PC [7].

Determining how proteins interact with DNA is important to fully view many biological processes and disease states. The information thus derived can lead to a deeper understanding of tumor development. Chromatin immunoprecipitation sequencing (ChIP-Seq) is used to investigate interactions between chromatin-associated protein and DNA [8]. It provides the ability to identify the binding sites of any DNA-associated proteins [9]. CCCTC binding factor (CTCF) binds to three regularly spaced repeats of the core sequence CCCTC, and it is widely applied in ChIP-Seq [9],[10].

ChIP-Seq analysis has recently been used to study PC. The results of these studies provide some meaningful ChIP-Seq data in comprehending the molecular mechanisms of androgen receptors (AR) in PC cells, which may be used to develop novel drugs [11],[12]. Yu et al. [13] systematically mapped the genomic landscape of transcription factors and histone marks across multiple PC cell lines and tissues. ChIP-Seq has also been used to reveal direct binding of AR and ERG (v-ets avian erythroblastosis virus E26 oncogene homolog) to the promoter of glycine N-methyltransferase in VCaP (vertebral cancer of the prostate) cells [14]. There has been little study of the identification of PC-related genes by ChIP-Seq analysis.

Protein–protein interaction (PPI) networks provide valuable information in the understanding of cellular function and biological processes. With the tremendous increase in human protein interaction data, the PPI network approach is used to understand molecular mechanisms of disease, particularly to analyze cancer-related phenomena [15]. PPI networks also provide insights into distinct topological features of cancer genes [16]. Therefore, in the present study, we analyzed ChIP-Seq data derived from PC samples and normal controls, and differential peaks were screened out. Annotations were given for those peaks, and PPI network analysis was performed to identify critical genes related to PC.

Methods

Data source

Two Sequence Read Archive files, SRR513122 for normal and SRR513123 for tumor, were downloaded from National Center for Biotechnology Information Gene Expression Omnibus database with accession number [GEO:GSE38684] [17]. The original ChIP-Seq data were acquired from two normal primary prostate epithelial cells (PrEC cell lines) and three PC cell lines (LNCaP cell lines) by Bert et al. [17].

Processing and alignment

Reads with low quality were first discarded. To prevent high-quality reads from being rejected during quality-filtering or assembly processes, we trimmed bases from poor-quality ends of reads [18]. ChIP-Seq reads were aligned to the human genome (hg19) using the Bowtie tool [19], which allows up to two mismatches in the alignment. Locations with one or more exact matches were kept for further analysis.

Screening and annotation of differential peaks

Model-based analysis of ChIP-Seq (MACS) [20] was applied to identify peaks (read-enriched regions) from Sequence Alignment/Map (SAM) format files as well as differential peaks between cancer and normal samples. MACS is a command line tool designed to analyze ChIP-Seq data in eukaryotes, especially in mammals [21]. The parameters for calling peaks are as follows: (1) effective genome size = 2.70e + 09, (2) bandwidth = 300 bp and (3) P-value cutoff = 1.00e-10. To provide further functional context for the biological interpretation, peaks with fold enrichment scores above 2.95 were picked out and annotated with respect to known genomic sequence features (such as genes and transcripts) according to the hg19 refGenes data. Some peaks of various widths were assigned to a gene based on the closest transcription start sites (TSSs), then the locations of those peaks were extracted by MACS. DNA motifs were identified using MDSeqPos (P < 0.0001).

Protein–protein interaction network analysis

Proteins (genes) work together to exert certain biological functions, with the proteins exhibiting more interactions with others likely to play more important roles in the whole process. Therefore, PPI network analysis of the genes obtained above was performed with BioGRID (Biological General Repository for Interaction Datasets) [22], which is a public database of physical and genetic interactions for all major model organism species (combined score above 0.4). This network was then constructed using Cytoscape [23], which is a software package available on the internet for biological network visualization, data integration and interaction network generation.

Results

Differential peaks between prostate cancer tissue samples and normal samples

A total of 5,570 differential peaks were found between the PC sample and normal sample (Additional file 1: Table S1 and Additional file 2: Table S2). Among these peaks, 3,726 peaks were differentially enriched in the PC sample and 1,844 peaks were differentially enriched in the normal sample. Two typical differential binding regions which were in the vicinity of POTEH (POTE ankyrin domain family, member H) are shown in Figure 1. We found that, in the tumor sample, both of these two peaks were significantly higher than those in the same positions of the normal sample, which may mean they are at the CTCF binding site in tumors. The widths of the two peaks in the tumor sample were approximately 200 to 300 bp.

Figure 1
figure 1

Differential CCCTC binding factor binding peaks between the prostate cancer sample and the normal sample. The chromosomal location is shown in the upper panel. Blue peaks represent CCCTC binding factor binding peaks in these regions.

Full size image

Annotation of differential peaks

Differential peak regions were annotated and their functional consequence on genes were examined (Additional file 3: Table S3). The most significantly regions of the peaks were shown in Figure 2A. The intergenic region was the highest with a score of 51%, followed by the intronic region (31%) and the exonic region (11%). Other regions of the peaks were upstream (2%), splicing (1%), the 5’ untranslated region (5’ UTR) (1%), 3’ UTR (1%) and downstream (1%). One of the top enriched DNA binding motifs is shown in Figure 2B. The - log10 (motif enrichment P-value) was 69.078.

Figure 2
figure 2

The distribution regions and motifs with annotation of differential peaks. (A) A pie chart of the peaks mapping to the splicing, upstream, exonic, downstream, 5’ untranslated region (UTR5), UTR3, intronic and intergenic genomic regions. (B) One of the top enriched DNA binding motifs.

Full size image

Protein–protein interaction network of peak-associated genes

The PPI network of peak-associated genes and some other interactional genes is shown in Figure 3 (P < 0.05). The network contains 4,920 protein nodes and 7,018 interaction edges. Genes closely related to others might have important roles in biological processes. Therefore, interactions between genes were calculated for each gene, and the top 35 genes were obtained (Figure 4). Among them, 33 differential genes were identified from the ChIP-Seq data, and 2 others were derived from the PPI network. Each of them had a neighbor number near or above 100. UBA52 (ubiquitin A-52 residue ribosomal protein fusion product 1), with the highest weight (above 500), was a PPI network–associated gene. Next, the subsequent genes were identified from ChIP-Seq data, such as YWHAQ (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, θ polypeptide), NEDD4 (neural precursor cell expressed, developmentally downregulated 4, E3 ubiquitin protein ligase) and EGFR (epidermal growth factor receptor). Some other genes, including AR (androgen receptor), HAPA4 (heat shock 70 kDa protein 4), CDK9 (cyclin-dependent kinase 9) and SUMO2 (SMT3 suppressor of mif two 3 homolog 2), may be PC-related.

Figure 3
figure 3

The protein–protein interaction network of differential peak–related genes. Nodes stand for genes or interacting genes. Edges indicate that some interaction exists in two nodes, such as the same pathways, biological processes or molecular functions. The interaction network was visualized with Cytoscape.

Full size image
Figure 4
figure 4

Top 35 highly connected genes in the network of differential peak–related genes. The ordinate represents the degree of protein. Genes marked with asterisks are differential peak–related genes that were identified from ChIP-Seq data.

Full size image

Discussion

Unlike sequence-specific transcription activators such as ER and p53, CTCF binding sites are ubiquitously and universally present throughout the genome, and their chromosomal distribution is strongly correlated with genes [24]. Bert et al. used these data to identify the relationship between long-range epigenetic activation and domain gene deregulation, which is quite different from our results [17]. We used MACS was in the present study and found 5,570 differential peaks comprising 3,726 differentially enriched peaks in the PC sample and 1,844 differentially enriched peaks in the normal sample. According to the annotation results (Figure 2), CTCF binding sites were scattered in chromosomes. About one-half of the CTCF binding sites flanked genes. In addition, some differentially expression genes were obtained with the PPI network analysis.

Figure 4 shows the top 35 highly connected genes in a network of differential genes. Some of them are biomarkers of PC. AR had the sixth most interactors in the network, and the peak of it was enriched in the intergenic region of chromosome X, which is in accordance with the results reported by Taslim et al. [25]. The AR signaling axis plays a critical role in PC development and progression [26]. In a previous study, researchers discovered that AR is highly expressed in PC and that it may inhibit PC progression by suppressing AR expression and activity [27]. An international patent has been filed for the use of AR variants as biomarkers and therapeutic targets in advanced PC [28].

CDK9 is a regulator of cell cycles. The peaks of CDK9 were aligned upstream of chromosome 9. Shore et al. showed that one isoform of CDK9 was transcribed from an alternative upstream promoter [29]. Gordon et al. found that CDK9 regulated AR promoter selectivity and cell growth through serine 81 phosphorylation [30]. The combined inhibition of Cdk9 and Akt can be utilized to induce apoptosis of metastatic PC cells [31].

EGFR is also an important player in the PPI network. The position of differential peaks annotation was exonic at chromosome 7. EGFR is the cell-surface receptor for members of the epidermal growth factor family of extracellular protein ligands [32]. EGFR overexpression may serve as a reasonable target for therapeutic intervention in this otherwise difficult-to-treat subset of PC patients [33]. The progression of PC is accompanied by the overexpression of EGFR in a very large majority of cases, suggesting that it may play a crucial role in PC [34].

NEDD4, whose peak annotation was located in the intergenic region of chromosome 15, was in the center of the PPI network. It is a proto-oncogenic ubiquitin ligase for PTEN, and its upregulation is found in many human cancers [35]. Farooqi et al. reported that SMURF and NEDD4 interference offers therapeutic potential in chaperoning genome integrity [36].

The peak of HSPA4 (heat shock protein 70 (HSP70)) was located in the intergenic region of chromosome 5. HSPA4 has been implicated in PC [37],[38], and it may also be differentially regulated according to our analysis. Kottke et al. found that induction of hsp70-mediated Th17 autoimmunity can be exploited in immunotherapy for metastatic PC [39].

SUMO2 was also included in the list. In previous studies, investigators have discovered the role of desumoylation in the development of PC [40] [42]. Yang et al. reported that small ubiquitin-like modifier isoforms 1, 2 and 3 are activated in human astrocytic brain tumors and are required for glioblastoma cell survival. Therefore, we speculated that SUMO2 might play a similar role in PC.

POTEH, located on chromosomes 21, may also be a candidate for the immunotherapy of PC [43], which is recorded in GeneCards as a PC-related gene. A previous study showed that POTEH is expressed highly in PC, but is limited in benign tissues [44].

Conclusions

Overall, we identified a number of key genes related to PC by analyzing ChIP-Seq data in the present study. These genes include AR, CDK9, EGFR, NEDD4, HSPA4 and SUMO4, and about one-half of them were located in the intergenic regions of chromosomes with differential peak annotations. These genes may help enhance the understanding of PC and also provides direction for future research. Future research is needed to define their roles in detail and subsequently develop effective molecular target therapies for PC.

Authors’ contributions

YuZ and YuhZ designed and coordinated the study, participated in most of the experiments and prepared the manuscript. ZH and ZZ provided assistance with the study design, coordinated and carried out all the experiments and participated in manuscript preparation. JL and XZ provided assistance for all experiments. All authors read and approved the final manuscript.

Additional files

References

  1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: Global cancer statistics. CA Cancer J Clin 2011, 61: 69–90. 10.3322/caac.20107

    Article  PubMed  Google Scholar 

  2. Darwish-Yassine M, Berenji M, Wing D, Copeland G, Demers RY, Garlinghouse C, Fagerlin A, Newth GE, Northouse L, Holmes-Rovner M, Rovner D, Sims J, Wei JT: Evaluating long-term patient-centered outcomes following prostate cancer treatment: findings from the Michigan Prostate Cancer Survivor study. J Cancer Surviv 2014, 8: 121–130. 10.1007/s11764-013-0312-8

    Article  PubMed  Google Scholar 

  3. Grunfeld EA, Halliday A, Martin P, Drudge-Coates L: Andropause syndrome in men treated for metastatic prostate cancer: a qualitative study of the impact of symptoms. Cancer Nurs 2012, 35: 63–69. 10.1097/NCC.0b013e318211fa92

    Article  PubMed  Google Scholar 

  4. Lockett KL, Hall MC, Xu J, Zheng SL, Berwick M, Chuang SC, Clark PE, Cramer SD, Lohman K, Hu JJ: The ADPRT V762A genetic variant contributes to prostate cancer susceptibility and deficient enzyme function. Cancer Res 2004, 64: 6344–6348.

  5. Thompson IM, Ankerst DP, Chi C, Goodman PJ, Tangen CM, Lucia MS, Feng Z, Parnes HL, Coltman CA Jr: Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst 2006, 98: 529–534. 10.1093/jnci/djj131

    Article  PubMed  Google Scholar 

  6. Struewing JP, Hartge P, Wacholder S, Baker SM, Berlin M, McAdams M, Timmerman MM, Brody LC, Tucker MA: The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med 1997, 336: 1401–1408.

  7. Fraser M, Zhao H, Luoto KR, Lundin C, Coackley C, Chan N, Joshua AM, Bismar TA, Evans A, Helleday T, Bristow RG: PTEN deletion in prostate cancer cells does not associate with loss of RAD51 function: implications for radiotherapy and chemotherapy. Clin Cancer Res 2012, 18: 1015–1027.

  8. Furey TS: ChIP-Seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nat Rev Genet 2012, 13: 840–852. 10.1038/nrg3306

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  9. Park PJ: ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 2009, 10: 669–680. 10.1038/nrg2641

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  10. Schmidt D, Wilson MD, Spyrou C, Brown GD, Hadfield J, Odom DT: ChIP-seq: using high-throughput sequencing to discover protein–DNA interactions. Methods 2009, 48: 240–248. 10.1016/j.ymeth.2009.03.001

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  11. Lin B, Wang J, Hong X, Yan X, Hwang D, Cho JH, Yi D, Utleg AG, Fang X, Schones DE, Zhao K, Omenn GS, Hood L: Integrated expression profiling and ChIP-seq analyses of the growth inhibition response program of the androgen receptor. PLoS One 2009, 4: e6589. 10.1371/journal.pone.0006589

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sharma NL, Massie CE, Ramos-Montoya A, Zecchini V, Scott HE, Lamb AD, MacArthur S, Stark R, Warren AY, Mills IG, Neal DE: The androgen receptor induces a distinct transcriptional program in castration-resistant prostate cancer in man. Cancer Cell 2013, 23: 35–47. 10.1016/j.ccr.2012.11.010

    Article  PubMed  CAS  Google Scholar 

  13. Yu J, Yu J, Mani RS, Cao Q, Brenner CJ, Cao X, Wang X, Wu L, Li J, Hu M, Gong Y, Cheng H, Laxman B, Vellaichamy A, Shankar S, Li Y, Dhanasekaran SM, Morey R, Barrette T, Lonigro RJ, Tomlins SA, Varambally S, Qin ZS, Chinnaiyan AM: An integrated network of androgen receptor, polycomb, and TMPRSS2-ERG gene fusions in prostate cancer progression. Cancer Cell 2010, 17: 443–454. 10.1016/j.ccr.2010.03.018

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  14. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM: Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457: 910–914. A published erratum appears in Nature 2013, 499: 504 10.1038/nature07762

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Kann MG: Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief Bioinform 2007, 8: 333–346. 10.1093/bib/bbm031

    Article  PubMed  CAS  Google Scholar 

  16. Kar G, Gursoy A, Keskin O: Human cancer protein-protein interaction network: a structural perspective. PLoS Comput Biol 2009, 5: e1000601. 10.1371/journal.pcbi.1000601

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bert SA, Robinson MD, Strbenac D, Statham AL, Song JZ, Hulf T, Sutherland RL, Coolen MW, Stirzaker C, Clark SJ: Regional activation of the cancer genome by long-range epigenetic remodeling. Cancer Cell 2013, 23: 9–22. 10.1016/j.ccr.2012.11.006

    Article  PubMed  CAS  Google Scholar 

  18. Blankenberg D, Gordon A, Von Kuster G, Coraor N, Taylor J, Nekrutenko A: Manipulation of FASTQ data with Galaxy. Bioinformatics 2010, 26: 1783–1785. 10.1093/bioinformatics/btq281

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  19. Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009, 10: R25. 10.1186/gb-2009-10-3-r25

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nussbaum C, Myers RM, Brown M, Li W, Liu XS: Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008, 9: R137. 10.1186/gb-2008-9-9-r137

    Article  PubMed  PubMed Central  Google Scholar 

  21. Feng J, Liu T, Zhang Y: Using MACS to identify peaks from ChIP-Seq data. Curr Protoc Bioinformatics 2011, 34: 2.14:2.14.1–2.14.14.

    Google Scholar 

  22. Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A, Stark C, Nixon J, Ramage L, Kolas N, O'Donnell L, Reguly T, Breitkreutz A, Sellam A, Chen D, Chang C, Rust J, Livstone M, Oughtred R, Dolinski K, Tyers M: The BioGRID interaction database: 2013 update. Nucleic Acids Res 2013, 41(Database issue):D816-D823. 10.1093/nar/gks1158

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  23. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13: 2498–2504. 10.1101/gr.1239303

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  24. Kim TH, Abdullaev ZK, Smith AD, Ching KA, Loukinov DI, Green RD, Zhang MQ, Lobanenkov VV, Ren B: Analysis of the vertebrate insulator protein CTCF binding sites in the human genome. Cell 2007, 128: 1231–1245. 10.1016/j.cell.2006.12.048

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Taslim C, Chen Z, Huang K, Huang THM, Wang Q, Lin S: Integrated analysis identifies a class of androgen-responsive genes regulated by short combinatorial long-range mechanism facilitated by CTCF. Nucleic Acids Res 2012, 40: 4754–4764. 10.1093/nar/gks139

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  26. Lonergan PE, Tindall DJ: Androgen receptor signaling in prostate cancer development and progression. J Carcinog 2011, 10: 20. 10.4103/1477-3163.83937

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  27. Kashat M, Azzouz L, Sarkar SH, Kong D, Li Y, Sarkar FH: Inactivation of AR and Notch-1 signaling by miR-34a attenuates prostate cancer aggressiveness. Am J Transl Res 2012, 4: 432–442.

    PubMed  CAS  PubMed Central  Google Scholar 

  28. Lok DJA, Van Der Meer P, de la Porte PW B-A, Lipsic E, Van Wijngaarden J, Hillege HL, van Veldhuisen DJ: Prognostic value of galectin-3, a novel marker of fibrosis, in patients with chronic heart failure: data from the DEAL-HF study. Clin Res Cardiol 2010, 99: 323–328. 10.1007/s00392-010-0125-y

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  29. Shore SM, Byers SA, Maury W, Price DH: Identification of a novel isoform of Cdk9. Gene 2003, 307: 175–182. 10.1016/S0378-1119(03)00466-9

    Article  PubMed  CAS  Google Scholar 

  30. Gordon V, Bhadel S, Wunderlich W, Zhang J, Ficarro SB, Mollah SA, Shabanowitz J, Hunt DF, Xenarios I, Hahn WC, Conaway M, Carey MF, Gioeli D: CDK9 regulates AR promoter selectivity and cell growth through serine 81 phosphorylation. Mol Endocrinol 2010, 24: 2267–2280. 10.1210/me.2010-0238

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  31. Mohapatra S, Chu B, Zhao X, Cheng J, Pledger WJ: Combination inhibition of Cdk9 and AKT induces apoptosis of metastatic prostate cancer cells [Abstract 5399].; Presented at the 2007 annual meeting of the American Association for Cancer Research (AACR), April 2007; Los Angeles, CA, USA.

  32. Herbst RS: Review of epidermal growth factor receptor biology. Int J Radiat Oncol Biol Phys 2004, 59(2 Suppl):S21-S26. 10.1016/j.ijrobp.2003.11.041

    Article  Google Scholar 

  33. Marks RA, Zhang S, Montironi R, McCarthy RP, MacLennan GT, Lopez-Beltran A, Jiang Z, Zhou H, Zheng S, Davidson DD, Baldridge LA, Cheng L: Epidermal growth factor receptor (EGFR) expression in prostatic adenocarcinoma after hormonal therapy: a fluorescence in situ hybridization and immunohistochemical analysis. Prostate 2008, 68: 919–923. 10.1002/pros.20715

    Article  PubMed  CAS  Google Scholar 

  34. Guérin O, Fischel JL, Ferrero JM, Bozec A, Milano G: EGFR targeting in hormone-refractory prostate cancer: current appraisal and prospects for treatment. Pharmaceuticals 2010, 3: 2238–2247. 10.3390/ph3072238

    Article  PubMed Central  Google Scholar 

  35. Wang X, Trotman LC, Koppie T, Alimonti A, Chen Z, Gao Z, Wang J, Erdjument-Bromage H, Tempst P, Cordon-Cardo C, Pandolfi PP, Jiang X: NEDD4–1 is a proto-oncogenic ubiquitin ligase for PTEN. Cell 2007, 128: 129–139. 10.1016/j.cell.2006.11.039

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  36. Farooqi AA, Mansoor Q, Rana A, Mashhadi TM, Imran M, Naqi SA, Zia-ur-Rehman , Bhatti S: SMURF and NEDD4 interference offers therapeutic potential in chaperoning genome integrity. J Exp Integr Med 2011, 1: 43–50. 10.5455/jeim.030111.or.002

    Article  Google Scholar 

  37. Cardile V, Scifo C, Russo A, Falsaperla M, Morgia G, Motta M, Renis M, Imbriani E, Silvestre G: Involvement of HSP70 in resveratrol-induced apoptosis of human prostate cancer. Anticancer Res 2003, 23(6C):4921–4926.

    PubMed  CAS  Google Scholar 

  38. Nylandsted J, Brand K, Jäättelä M: Heat shock protein 70 is required for the survival of cancer cells. Ann N Y Acad Sci 2000, 926: 122–125. 10.1111/j.1749-6632.2000.tb05605.x

    Article  PubMed  CAS  Google Scholar 

  39. Kottke T, Sanchez-Perez L, Diaz RM, Thompson J, Chong H, Harrington K, Calderwood SK, Pulido J, Georgopoulos N, Selby P, Melcher A, Vile R: Induction of hsp70-mediated Th17 autoimmunity can be exploited as immunotherapy for metastatic prostate cancer. Cancer Res 2007, 67: 11970–11979. 10.1158/0008-5472.CAN-07-2259

    Article  PubMed  CAS  Google Scholar 

  40. Cheng J, Bawa T, Lee P, Gong L, Yeh ET: Role of desumoylation in the development of prostate cancer. Neoplasia 2006, 8: 667–676. 10.1593/neo.06445

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  41. Bawa-Khalfe T, Cheng J, Wang Z, Yeh ET: Induction of the SUMO-specific protease 1 transcription by the androgen receptor in prostate cancer cells. J Biol Chem 2007, 282: 37341–37349. 10.1074/jbc.M706978200

    Article  PubMed  CAS  Google Scholar 

  42. Wu F, Mo YY: Ubiquitin-like protein modifications in prostate and breast cancer. Front Biosci 2007, 12: 700–711. 10.2741/2094

    Article  PubMed  CAS  Google Scholar 

  43. Bera TK, Zimonjic DB, Popescu NC, Sathyanarayana BK, Kumar V, Lee B, Pastan I: POTE , a highly homologous gene family located on numerous chromosomes and expressed in prostate, ovary, testis, placenta, and prostate cancer. Proc Natl Acad Sci U S A 2002, 99: 16975–16980. A published erratum appears in Proc Natl Acad Sci U S A 2003, 100: 1462.

  44. Bera TK, Huynh N, Maeda H, Sathyanarayana BK, Lee B, Pastan I: Five POTE paralogs and their splice variants are expressed in human prostate and encode proteins of different lengths. Gene 2004, 337: 45–53. 10.1016/j.gene.2004.05.009

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China

    Yu Zhang & Yuhai Zhang

  2. Department of Urology, Beijing You An Hospital, Capital Medical University, Beijing, 100069, China

    Yu Zhang, Zhen Huang, Zhiqiang Zhu, Jianwei Liu & Xin Zheng

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Zhang, Y., Huang, Z., Zhu, Z. et al. Network analysis of ChIP-Seq data reveals key genes in prostate cancer. Eur J Med Res 19, 47 (2014). https://doi.org/10.1186/s40001-014-0047-7

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