Discovery of novel hypermethylated genes in prostate cancer using genomic CpG island microarrays.
ABSTRACT Promoter and 5' end methylation regulation of tumour suppressor genes is a common feature of many cancers. Such occurrences often lead to the silencing of these key genes and thus they may contribute to the development of cancer, including prostate cancer.
In order to identify methylation changes in prostate cancer, we performed a genome-wide analysis of DNA methylation using Agilent human CpG island arrays. Using computational and gene-specific validation approaches we have identified a large number of potential epigenetic biomarkers of prostate cancer. Further validation of candidate genes on a separate cohort of low and high grade prostate cancers by quantitative MethyLight analysis has allowed us to confirm DNA hypermethylation of HOXD3 and BMP7, two genes that may play a role in the development of high grade tumours. We also show that promoter hypermethylation is responsible for downregulated expression of these genes in the DU-145 PCa cell line.
This study identifies novel epigenetic biomarkers of prostate cancer and prostate cancer progression, and provides a global assessment of DNA methylation in prostate cancer.
- SourceAvailable from: PubMed Central[Show abstract] [Hide abstract]
ABSTRACT: Epigenetic silencing mediated by CpG methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with tumor progression may identify potential prognostic markers for prostate cancer (PCa). We treated two PCa cell lines, 22Rv1 and DU-145 with the demethylating agent 5-Aza 2'-deoxycitidine (DAC) and global methylation status was analyzed by performing methylation-sensitive restriction enzyme based differential methylation hybridization strategy followed by genome-wide CpG methylation array profiling. In addition, we examined gene expression changes using a custom microarray. Gene Set Enrichment Analysis (GSEA) identified the most significantly dysregulated pathways. In addition, we assessed methylation status of candidate genes that showed reduced CpG methylation and increased gene expression after DAC treatment, in Gleason score (GS) 8 vs. GS6 patients using three independent cohorts of patients; the publically available The Cancer Genome Atlas (TCGA) dataset, and two separate patient cohorts. Our analysis, by integrating methylation and gene expression in PCa cell lines, combined with patient tumor data, identified novel potential biomarkers for PCa patients. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.Oncotarget 09/2014; 5(17):7858-69. · 6.63 Impact Factor
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ABSTRACT: Prostate cancer (PC) is the most commonly diagnosed neoplasm and the third most common cause of cancer-related death amongst men in the Western world. PC is a clinically highly heterogeneous disease, and distinction between aggressive and indolent disease is a major challenge for the management of PC. Currently, no biomarkers or prognostic tools are able to accurately predict tumor progression at the time of diagnosis. Thus, improved biomarkers for PC prognosis are urgently needed. This review focuses on the prognostic potential of DNA methylation biomarkers for PC. Epigenetic changes are hallmarks of PC and associated with malignant initiation as well as tumor progression. Moreover, DNA methylation is the most frequently studied epigenetic alteration in PC, and the prognostic potential of DNA methylation markers for PC has been demonstrated in multiple studies. The most promising methylation marker candidates identified so far include PITX2, C1orf114 (CCDC181) and the GABRE~miR-452~miR-224 locus, in addition to the three-gene signature AOX1/C1orf114/HAPLN3. Several other biomarker candidates have also been investigated, but with less stringent clinical validation and/or conflicting evidence regarding their possible prognostic value available at this time. Here, we review the current evidence for the prognostic potential of DNA methylation markers in PC.International Journal of Molecular Sciences 01/2014; 15(9):16544-16576. · 2.34 Impact Factor
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ABSTRACT: Epigenetic modifications and DNA methylation in particular, have been recognized as important mechanisms to alter gene expression in malignant cells. Here, we identified candidate genes which were upregulated after an epigenetic treatment of B-cell lymphoma cell lines (Burkitt's lymphoma, BL; Follicular lymphoma, FL; Diffuse large B-cell lymphoma, DLBCL activated B-cell like, ABC; and germinal center like, GCB) and simultaneously expressed at low levels in samples from lymphoma patients. Qualitative methylation analysis of 24 candidate genes in cell lines revealed five methylated genes (BMP7, BMPER, CDH1, DUSP4 and LRP12), which were further subjected to quantitative methylation analysis in clinical samples from 59 lymphoma patients (BL, FL, DLBCL ABC and GCB; and primary mediastinal B-cell lymphoma, PMBL). The genes LRP12 and CDH1 showed the highest methylation frequencies (94% and 92%, respectively). BMPER (58%), DUSP4 (32%) and BMP7 (22%), were also frequently methylated in patient samples. Importantly, all gene promoters were unmethylated in various control samples (CD19+ peripheral blood B cells, peripheral blood mononuclear cells and tonsils) as well as in follicular hyperplasia samples, underscoring a high specificity. The combination of LRP12 and CDH1 methylation could successfully discriminate between the vast majority of the lymphoma and control samples, emphasized by receiver operating characteristic analysis with a c-statistic of 0.999. These two genes represent promising epigenetic markers which may be suitable for monitoring of B-cell lymphoma.PLoS ONE 01/2014; 9(9):e104249. · 3.53 Impact Factor
Discovery of Novel Hypermethylated Genes in Prostate
Cancer Using Genomic CpG Island Microarrays
Ken Kron1,2., Vaijayanti Pethe1., Laurent Briollais1, Bekim Sadikovic3, Hilmi Ozcelik1, Alia Sunderji1,2,
Vasundara Venkateswaran4, Jehonathan Pinthus5, Neil Fleshner6, Theodorus van der Kwast7, Bharati
1Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada, 2Department of Laboratory Medicine and Pathobiology, University of Toronto,
Toronto, Ontario, Canada, 3Department of Pediatric Laboratory Medicine, the Hospital for Sick Children, Toronto, Ontario, Canada, 4Division of Urology, Sunnybrook
Health Sciences Centre, Toronto, Ontario, Canada, 5Department of Surgery, Division of Urology, McMaster University, Hamilton, Ontario, Canada, 6Division of Urology,
Department of Surgery, University Health Network, University of Toronto, Toronto, Ontario, Canada, 7Department of Pathology, University Health Network, University of
Toronto, Toronto, Ontario, Canada, 8Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada
Background: Promoter and 59 end methylation regulation of tumour suppressor genes is a common feature of many
cancers. Such occurrences often lead to the silencing of these key genes and thus they may contribute to the development
of cancer, including prostate cancer.
Methodology/Principal Findings: In order to identify methylation changes in prostate cancer, we performed a genome-
wide analysis of DNA methylation using Agilent human CpG island arrays. Using computational and gene-specific validation
approaches we have identified a large number of potential epigenetic biomarkers of prostate cancer. Further validation of
candidate genes on a separate cohort of low and high grade prostate cancers by quantitative MethyLight analysis has
allowed us to confirm DNA hypermethylation of HOXD3 and BMP7, two genes that may play a role in the development of
high grade tumours. We also show that promoter hypermethylation is responsible for downregulated expression of these
genes in the DU-145 PCa cell line.
Conclusions/Significance: This study identifies novel epigenetic biomarkers of prostate cancer and prostate cancer
progression, and provides a global assessment of DNA methylation in prostate cancer.
Citation: Kron K, Pethe V, Briollais L, Sadikovic B, Ozcelik H, et al. (2008) Discovery of Novel Hypermethylated Genes in Prostate Cancer Using Genomic CpG Island
Microarrays. PLoS ONE 4(3): e4830. doi:10.1371/journal.pone.0004830
Editor: Mikhail V. Blagosklonny, Ordway Research Institute, United States of America
Received November 24, 2008; Accepted February 17, 2009; Published March 13, 2009
Copyright: ? 2008 Kron et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Canadian Prostate Cancer Research Initiative, National Cancer Institute of Canada (#18568). Ontario Student Opportunity Trust Fund, Scace Prostate
Cancer Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: BAPAT@lunenfeld.ca
. These authors contributed equally to this work.
Prostate cancer (PCa) is the most commonly diagnosed cancer
in men and the second leading cause of cancer associated with
deaths in the US . Studies have shown that PCa is a complex
disease impacted by genetic and non-genetic epidemiological
factors, and early diagnosis is critical in the clinical management of
the disease. A common pathological variable given during of the
prostate tumour, with higher scores reflecting poorly differentiated
carcinoma. Gleason score #6 carcinomas are considered low
grade, Gleason 7 is intermediate grade, and those with Gleason
score 8 and above are regarded as high grade (for recent review on
grading system , see ).
Epigenetic modifications have been shown to affect gene
expression patterns and often contribute to the pathogenesis of
many cancers . Examples of epigenetic histone modifications
include methylation of specific lysine residues, acetylation/
deacetylation of lysine residues, and phosphorylation of histone
tails, each having varying effects on the regulation of gene
transcription. These modifications induce abnormal gene expres-
sion patterns and thus are considered to contribute to cancer
development [5,6]. Aberrant CpG dinucleotide methylation is a
well recognized epigenetic hallmark of many cancers. Global
genomic hypomethylation is found in conjunction with localized
regions of hypermethylation, typically in CpG islands that
commonly occur in the promoters or 59 regions of gene sequences
. Promoter hypermethylation acts together with specific histone
modifications to silence genes by direct inhibition of transcription
factor biding , through binding of methyl CpG binding domain
proteins , or through interactions with histone modifying
enzymes . This epigenetic mechanism can confer a growth
advantage to cancer cells by hypermethylation of tumour
suppressor genes. Accordingly, DNA methylation events may
serve as useful biomarkers , propelling a search for both
diagnostic and prognostic indicators.
CpG island hypermethylation in PCa is a common event with
over 30 hypermethylated loci currently identified . The best
characterized of these events, GSTP1 promoter methylation,
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occurs in .90% of cancers and 70% of precursor high grade
prostatic intraepithelial neoplasia (PIN) lesions [13,14] and can
also be detected in blood and urine samples . Thus, GSTP1
methylation may serve as a useful diagnostic marker for PCa.
Recently, substantial progress has been made in the high-
throughput epigenomic screening for the identification of novel
targets of DNA methylation . Subsequently, other well
characterized hypermethylated genes have been identified in
PCa including RASSF1A, CDH1, and CDKN2A, to name a few.
However, no gene studied to date has been identified as a specific
diagnostic/prognostic biomarker in PCa similar to GSTP1 [17,18].
In this study, we sought to analyze methylation on a genome
wide scale using human CpG island microarrays to uncover novel
methylatled loci within prostate cancer. Among a panel of novel
and/or differentially methylated loci that we identified, we further
characterized HOXD3 and BMP7 using a combination of
MassARRAYH EpiTYPER analysis and quantitative MethyLight
assay, and assessed expression in DU-145 PCa cells.
20 fresh frozen PCa tissue samples (10 Gleason score 6 or pure
pattern 3 (PP3), and 10 Gleason score 8 or pure pattern 4 (PP4))
obtained from prostatectomy specimens of patients with prostate
cancer diagnosed between 2001 and 2007 were collected from the
tissue bank at the University Health Network (UHN), Toronto.
Patients who had therapy prior to surgery were excluded. Another
series of specimens consisting of 39 formalin-fixed, paraffin-
embedded (FFPE) PCa samples (20 PP3 and 19 PP4) from patients
diagnosed between 2006 and 2008 were similarly collected for the
validation set. All patients consented to the donation of removed
tissue to the UHN tissue bank and samples were obtained
according to protocols approved by the Research Ethics Board
from Mount Sinai Hospital (MSH) and UHN, Toronto, ON,
Canada. PCa specimens were subjected to histological examina-
tion by an expert pathologist (TVDK) for independent confirma-
tion of the Gleason grades.
Cell lines and DNA extraction
Human PCa cell lines LNCaP (ATCC # CRL- 1740), DU-145
(ATCC # HTB-81), PC-3 (ATCC # 59500) and 22RV1 (ATCC
# CRL- 2505) were obtained from Drs. M. Zielinska, R. Bristow,
and E. Diamandis. All cells were cultured as monolayers in RPMI
1640 media (Life Technologies), and supplemented with 10% fetal
bovine serum. All cell lines were grown in humidified atmosphere
with 5% CO2at 37uC. DNA was extracted after harvesting the
cells by trypsinization followed by DNA extraction using QIAamp
DNA mini kit (Qiagen Inc, Mississauga, ON, Canada), using the
protocol recommended by the supplier.
5-Aza 29 –deoxycitidine (DAC) treatment and RT-PCR
A 250 mg/ml stock solution of 5- aza- 2-deoxycitidine (DAC)
(Sigma-Aldrich, Oakville, ON, Canada) was prepared in water
and kept at 280uC until use. DU-145 cells were plated in 6 cm
dishes and incubated in culture medium with 2 mg/ml DAC for 4
days with medium change every 2 days. Cells were harvested and
total RNA was extracted using Trizol (Invitrogen, Carlsbad, CA),
using the protocol recommended by the supplier.
Primer sequences for RT-PCR of BMP7 and HOXD3 have been
described previously [19,20] and are as follows: (BMP7 forward)
59-AGA GCA TCA ACC CCA AGT-39, (BMP7 reverse) 59-CTA
CTC AGG CCC CAG CTT-39; (HOXD3 forward) 59-AGG ATC
CTG GTC TGA ACT CAG AGC AGC AGC39, (HOXD3
reverse) 59-ACT CGA GTT CAT CCG CCG GTT CTG GAA
Fresh frozen archived tissue was snap-frozen in liquid nitrogen,
crushed, and genomic DNA was isolated using the QIAamp DNA
mini kit (Qiagen) according to the kit protocol. FFPE tissue was
sectioned (7 mm) and air-dried onto slides. Areas with a distinct
Gleason grade in H&E stained slides with at least 80% or more
neoplastic cells were marked and the corresponding areas were
identified on FFPE sections for harvesting cells. Separate
specimens with histologically confirmed normal tissue were
marked as well. The enriched cell populations from highlighted
areas were then manually microdissected and genomic DNA was
isolated using the QIAamp DNA mini kit using a modified
protocol with extended proteinase K digestion. Briefly, microdis-
sected tissue was digested in 30 mL proteinase K at 56uC
overnight, followed by an addition of 20 mL proteinase K and
digestion for one hour at 56uC the following day. The Qiagen
recommended protocol for FFPE tissue was then followed.
Differential Methylation Hybridization (DMH) and Human
CpG Island Microarrays
The differential methylation hybridization technique for
preparation of methylated amplicons was carried out as described
previously . Briefly, genomic DNA (0.2 mg) from PP3 and PP4
cases was digested with MseI. The cleaved ends were ligated with
annealed H-12/H-24 linkers, followed by further digestion with
two successive rounds of digestion with methylation-sensitive
enzymes, namely HpaII and BstUI. Linker PCR reactions were
then performed with pre-treated DNA to generate the final target
amplicons for microarray hybridization. Final amplicons were
purified using the QIAquick PCR purification kit (Qiagen)
according to the manufacturer’s protocol. The reference sample
consisted of DNA isolated from lymphocytes of six healthy men
age-matched with PCa patients. Reference samples were similarly
treated for final target generation and pooled amplicons were co-
hybridized to the test cases for individual arrays.
All microarray data generated is compliant with current
MIAME standards according to Brazma et al .
Statistical analyses were performed with the statistical package
limma of R . The principle is to fit a linear model for each
probe where the log2ratio of red channel intensity and green
channel intensity is regressed on a tumour indicator variable (I).
We performed three comparisons: Pure Pattern 3, Gleason 6 (PP3)
(I=1) vs. Reference (I=0), Pure Pattern 4, Gleason 8 (PP4) (I=1)
vs. Reference (I=0), and PP4 (I=1) vs. PP3 (I=0), to find genes
that have different methylation profiles across the two groups
compared. These comparisons are analogous to a classical two-
sample t-test analysis. Alternatively, we also used an empirical
Bayes t-test. This has the same interpretation as an ordinary t-
statistic except that the standard errors have been moderated
across genes (shrunk towards a common value) using a simple
Bayesian model. This has the effect of borrowing information from
the ensemble of genes to make the inference about each individual
gene more robust. The moderated t-statistic has an increased
number of degrees of freedom compared to the ordinary t-statistic,
reflecting the greater reliability associated with the smoothed
standard error. Our analyses were conducted after pre-processing
the data. In the first case, we used a background correction
method provided by Agilent. In the second case, we used a method
Methylation in Prostate Cancer
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implanted in limma. A convolution of normal and exponential
distributions is fitted to the foreground intensities using the
background intensities as a covariate, and the expected signal
given the observed foreground becomes the corrected intensity.
This results in a smooth monotonic transformation of the
background subtracted intensities such that all the corrected
intensities are positive. Both methods performed well on our data.
We then applied a loess normalization procedure within arrays to
remove any systematic trends which arise from the microarray
technology from the methylation measures .
Partek Data Analysis and Integration
Data from Agilent Feature Extraction software .txt were
analysed using the Partek Genomic Suite Software (PGS) using a
modification of the previously described protocols [25,26]. The
processed R and G column data from 10 PP3 and 10 PP4 were
imported into PGS. The processed R signal corresponded to the
tumour DNA and processed G signal corresponded to the normal
lymphocyte DNA. The cancer-specific signal across all probes was
normalized as a ratio to baseline using Normalize to Baseline Tool
in PGS, where baseline data corresponded to normal human
lymphocyte DNA. The data was then log2transformed using the
PGS Normalization and Scaling Tool.
Such normalized and transformed dataset was then used for
detection of cancer specific methylation profiles, and secondarily
to differentiate between PP4 and PP3-specific methylation profiles.
In order to detect significant cancer-specific enrichment/deple-
tion, we performed Hidden Markov Model (HMM) region
detection across approximately 244,000 probes with the following
parameters: minimum probes: 5, detection states: 22 & 2; ignore
state: 0, maximum probability: 0.95, genomic decay: 10,000,
sigma: 1. Such detected genomic regions were annotated to the
corresponding genes using the PGS gene annotation tool with
Affymetrix HuGene-1_0-st-v1.na24.hg18.transcript.csv file. In
addition to the directly overlapping genes, proximal genes (up
and downstream 1000 nucleotides) to the enriched/depleted
regions were also annotated.
Significant differences in enrichment between PP3 and PP4
tumours were identified by calculating the average fold difference
between the PP3 and PP4 normalized signal across all probes
using the PGS ANOVA tool, and subsequent HMM region
detection and gene annotation using the above mentioned
parameters. Such genomic regions were further filtered to include
sequences with minimum 1.3 fold enrichment, and minimum
21.3 fold depletion. The visualization of data using heat maps,
.wig files for UCSC Genome Browser, genome view files, and
corresponding data tables/lists was performed using PGS as
previously described [25,26].
MassARRAY EpiTYPER Analysis
Quantitative analysis of CpG dinucleotide methylation was
performed using a mass spectrometry approach as available by
MassARRAYH EpiTYPER analysis (Sequenom). EpiTYPER
analysis is a MALDI TOF mass spectrometry based method that
provides a quantitative view of CpG dinucleotide methylation to
single or multiple dinucleotide resolution. DNA is first bisulfite
modified, tagged with a T7 promoter, and transcribed into RNA.
This is then cleaved with RNase A and cleavage products of
different mass can be resolved by the MS instrument. Analysis was
performed by the Analytical Genetics Technology Centre
(AGTC), Princess Margaret Hospital, Toronto, ON as per
manufacturer’s instructions using a subset of fresh frozen tissue
DNA that was used for CpG island microarray analysis. Regions
analyzed by EpiTYPER corresponded to those that showed an
enriched signal in the CpG island array results. All analyses were
performed in triplicate and averages and standard errors were
Sodium Bisulfite Modification and MethyLight
Sodium bisulfite modification of genomic DNA was carried out
using the EZ DNA Methylation Gold Kit (Zymo Research Corp,
Orange, CA, USA) according to the manufacturer’s protocol using
0.8 mg of paraffin- embedded tissue DNA.
Methylation levels of the two genes of interest were determined
by quantiative methylation specific PCR (MSP), the MethyLight
assay, as described previously . Primers and probes were
designed specifically for bisulfite converted, methylated DNA and
are as follows: (BMP7 forward) 59-CGT TTT TTT GGT TCG
GAT CGC-39, (BMP7 probe) 6FAM-59- GTG TCG AGA GGG
TAG GGT CGG TTT CG-39-BHQ1, (BMP7 reverse) 59-CTA
AAA CCT AAC GAA ACG TCG CG-39; (HOXD3 forward) 59-
GTT TTG GTA TTT CGG GTT TTT ATC G-39, (HOXD3
probe) 6FAM-59- AAG AGC GTT TGG GGG AGG GGG GC-
39-BHQ1, (HOXD3 reverse) 59-TAA AAC TCC TAA CTT CGC
GCT ACG-39; (Alu forward) 59-GGT TAG GTA TAG TGG
TTT ATA TTT GTA ATT TTA GTA-3, (Alu probe) 6FAM-59-
CCT ACC TTA ACC TCC C-39-MGBNFQ, (Alu reverse) 59-
ATT AAC TAA ACT AAT CTT AAA CTC CTA ACC TCA-39.
All reactions were performed on the Applied Biosystems 7500
Real Time PCR instrument. Standard curves were generated
using serial dilutions of positive control supermethylated DNA for
the gene of interest and Alu repeats. Percent methylated ratio
(PMR) for a gene was calculated using Alu repeats as reference as
follows: (gene/Alu fluorescence quantity ratio for modified
specimen DNA) / (gene/Alu ratio for supermethylated DNA) X
100%. A positive score for methylation was given if PMR for a
given tumour was $10%.
Analysis of genomic methylation
We separated the analysis of our microarray data into two
subsets. The first subset consisted of all 20 cancer specimens
compared to reference DNA. A list of genes that were identified as
significantly hypermethylated in the statistical methods performed
for the cancer versus reference dataset (PP3&PP4 versus reference
DNA) is depicted in Table 1. Interestingly, 27 of the top 100
methylated genes (ranked by individual probe fold change) from
the cancer/reference dataset are homeobox or T-box genes
(Table 2), consistent with current literature analyzing methylation
patterns in other cancers including those of the lung, breast, and
colon [28,29,30]. We also found .2 fold signal in genes previously
identified as methylated in prostate cancer such as CDKN2A
(average of 15.8 fold enrichment), RUNX3 (2.8 fold), and PTGS2
(2.9 fold). The gene showing the greatest degree of methylation
was FOXC1 with an average fold change of 60.9 versus the
reference DNA. Using PGS, which restricted analysis to multiple
probes showing enrichment, the greatest degree of methylation in
a characterized gene was HOXD9 (3.2 fold change across 8
The second subset of data compared the ten PP3 cases to the
10 PP4 cases, which we termed the progression dataset. Using a
2-fold average enrichment signal difference between the two
patterns as a cut-off, we discovered a set of 493 array probes that
are able to distinguish between PP3 and PP4 cancers. We then
filtered out multiple probes representing the same gene and
probes representing uncharacterized locations, giving a final list
of 223 individual genes. One specific probe representing the
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CAP-GLY domain containing linker protein family, member 4
(CLIP4) showed the greatest fold difference between the two
patterns (6.5). Using PGS for statistical analysis, ventral anterior
homeobox 1 (VAX1) displayed the greatest average fold difference
(2.7) over multiple probes (6 total). A representative view of genes
from PGS analysis is given in Table 1. Similar to the cancer/
reference dataset, 23 of the top 100 genes ranked by probe fold
change from the progression dataset are homeobox genes
We next selected two genes from these lists for further analysis
using a combination of methylation and expression based
techniques. Selection criteria included the biological function of
the gene, involvement in /contribution to prostate cancer, and
statistical significance from CpG microarray results.
Gene specific methylation analysis
The genes chosen for analysis were:
1. BMP7 [Bone Morphogenic Protein] (chromosome # 8p21), a
gene already implicated in PCa progression  which was
previously reported as methylated in an oligodendroglioma cell
line and gastric cancers [32,33]. We decided to further
investigate its methylation profile because of its putative
downregulation in PCa progression  and observed
methylation signal in our cancer/reference dataset (3.2 fold
enrichment), suggesting that methylation of this gene may play
a role in PCa progression.
2. HOXD3 [Homoebox transcription factor] (chromosome #
2q31-37), a gene found to be methylated in lung cancer cell
Table 1. Representative genes and average PGS fold change (across multiple probes) from the top 100 for cancer/reference and
Cancer/Reference DatasetProgression Data Set
Gene Name (abbreviation)Fold Change Gene Name (abbreviation)Fold Change
Chromosome 20 open reading frame 103 (C20orf103) 3.7 Ventral anterior homeobox 1 (VAX1) 2.7
Homeobox D9 (HOXD9) 3.2 Homeobox D3 (HOXD3) 2.3
Nuclear receptor subfamily, group A, member 2 (NR5A2) 3.1 CAP-GLY domain containing linker protein family (CLIP4) 2.1
Distal-less homeobox 5 (DLX5) 3.1 Calcium channel, voltage dependent, T-type, alpha 1G
Iroquois homeobox 1 (IRX1) 3.0 Glycoprotein V (GP5) 1.9
Spastic paraplegia 20 (SPG20) 3.0Somatostatin receptor 1 (SSTR1) 1.8
Transcription factor AP-2 alpha (TFAP2A) 2.9Methylthioadenosine phosporylase (MTAP) 1.8
Wilms tumor 1 (WT1) 2.9NK2 homeobox 2 (NKX2-2) 1.7
SIX homeobox 6 (SIX6) 2.8 Homeobox C11 (HOXC11) 1.6
Homeobox D4 (HOXD4) 2.7 Ladybird homeobox 1 (LBX1) 1.6
Transcription factor 7-like 1 (TCF7L1) 2.6Motor neuron and pancreas homeobox 1 (MNX1) 1.6
Sonic hedgehog homolog (SHH) 2.5 Glutamate receptor, metabotropic 1 (GRM1) 1.6
Protocadherin, gamma subfamily C,5 (PCDHG5) 2.4 LIM homeobox 9 (LHX9) 1.6
Methionine aminopeptidase 1D (MAP1D) 2.3 Microtubule-associated protein tau (MAPT) 1.5
Runt-related transcription factor 1 (RUNX1) 2.3 Galactosidase, beta 1-like (GLB1L) 1.5
Table 2. Representative homeobox genes showing methylation for cancer/reference and progression dataset (genes in bold
overlap with Table 1).
Cancer/Reference Dataset Progression Data Set
Gene AbbreviationGene Name Gene AbbreviationGene Name
FOXC1 Forkhead box C1
VAX1 Ventral anterior homeobox 1
SIX6 Six homeobox 6 HOXD3Homeobox D3
HHEX Hematopoietically expressed homeoboxTBX15T-box 15
HOXD9 Homeobox D9GSC Goosecoid homeobox
HOXC13Homeobox C13PROX1 prospero homeobox 1
TBX4 T-box4TBX3T-box 3
HOXD8Homeobox D8PAX2Paired box 2
IRX6Iroquois homeobox 6ALX4Aristaless-like homeobox 4
BARX2BARX homeobox 2PHOX2APaired-like homeobox 2a
DLX6Distal-less homeobox 6HOXD8 Homeobox D8
Methylation in Prostate Cancer
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Figure 1. Partek Genomics Suite Visualization. (A) BMP7 and (B) HOXD3. Line graphs in the upper panel of each show log2ratio values for each
probe, with red representing PP4 cases A–J and blue representing PP3 cases 1–10. The lower panel of each is a heat map for each probe in individual
PP4 and PP3 cases. Red arrows correspond to regions selected for EpiTYPER analysis while black arrows correspond to regions chosen for MethyLight
Methylation in Prostate Cancer
PLoS ONE | www.plosone.org5 March 2008 | Volume 4 | Issue 3 | e4830
lines and primary tumours , which showed a distinct
pattern of increasing methylation with tumour grade in our
series based on average enrichment difference (6.4), suggesting
that methylation of this gene may be involved in disease
progression as well.
Partek graphical and heatmap visualization of the microarray
data is shown for the two genes in figure 1.
EpiTYPER quantitation of CpG Methylation
The EpiTYPER analysis included a subset of cases that showed
enrichment of $3 fold or a lack of methylation signal (#2 fold) on
the microarrays. Data obtained from EpiTYPER analysis
confirmed the enrichment/methylation profiles in BMP7 and
HOXD3 that were evident from the microarray results in a set of
four microarray cases chosen for analysis (Figures 2, 3). For BMP7,
methylation of the region identified by our microarray analysis
confirmed that for samples B and 3, there was a significant level of
methylation compared to that of the reference DNA (up to 76%
for CpG dinucleotide 4 in sample B) (figures 2A,B). These samples
had an average methylation of 43% and 52% (methylated/
unmethylated ratio, given as percent), respectively, across all 35
CpGs analyzed, while samples I and 4 showed an average CpG
methylation of 14% and 17%, respectively. HOXD3 displayed a
distinct pattern of increased methylation in the PP4 cases as
compared to the PP3 cases. The analysis of fresh frozen DNA
samples F, I, 4, and 8 confirmed a differential pattern of
methylation from PP3 to PP4, at least with respect to the four
Figure 2. EpiTYPER analysis of BMP7. (A) PP3 cases 3 and 4 and (B) PP4 cases B and I. Reference lymphocyte is shown for each. Coloured bars
represent the average methylation over three replicates with standard error bars displayed.
Methylation in Prostate Cancer
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cases analyzed (figure 3A, B). High grade cases F and I had an
average methylation of 72% and 43% respectively, across all 27
CpG dinucleotides analyzed, while low grade samples 4 and 8
respectively had an average methylation of 19% and 35%.
To verify methylation patterns of these genes, we validated
them in an independent series of paraffin embedded PCa cases,
with matched normal tissue from the same specimens where
available, and also assessed their methylation status in PCa cell
lines (DU-145, PC-3, 22RV1, and LNCaP) using MethyLight.
BMP7 methylation was verified in a total of 4 tumour specimens
(two PP3, two PP4) as well as two normal samples from separate
cases (figure 4A). HOXD3 methylation was present in a total of
eight specimens (two PP3, six PP4) (figure 4B). DU-145 cells were
positive for methylation of both BMP7 and HOXD3. PMR values
for DU-145 and positive cases are given in table 3.
We next treated DU-145 cells with the demethylating agent
DAC and performed semi-quantitative RT-PCR analysis using
untreated and treated cells to assess the effect of methylation on
expression on the two genes. HOXD3 expression appears to be
completely abolished in untreated DU-145 cells while BMP7 is
minimally expressed. Treatment with DAC induced HOXD3
expression and caused an increase in BMP7 mRNA levels
(Figure 5), indicating that methylation is involved in the reduced
expression of both BMP7 and HOXD3.
Figure 3. EpiTYPER analysis of HOXD3. (A) PP3 cases 4 and 8 and (B) PP4 cases F and I. Reference lymphocyte is shown for each. Coloured bars
represent the average methylation over three replicates with standard error bars displayed.
Methylation in Prostate Cancer
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We have used human CpG island microarrays to identify
methylated genes in PCa as a whole, as well as differentially
methylated in low grade, PP3 and high grade, PP4 PCa.
Intermediate Gleason score 7 tumours were not examined as they
are composed of both patterns 3 and 4, and we chose to narrow
our focus to those tumours that are composed entirely of Gleason
pattern 3 or Gleason pattern 4 in order to have enriched cell
pattern populations. We found that we were able to identify CpG
islands that are both quantitatively more methylated and
methylated at an increased frequency in PP4 tumours when
compared to PP3 tumours. This may reflect an overall shift to a
greater state of methylation within promoter CpG islands as the
tumour progresses towards a higher grade.
Most genes uncovered through our arrays have either never
been shown to be methylated in PCa or in other types of cancers.
Other previously described methylated genes in prostate cancer,
such as CDKN2A, PTGS2, and RUNX3, all showed evidence of
methylation based on fold changes and statistical significance. The
stringency of the statistical analyses that we performed could have
prevented the inclusion of these genes within our top genes of the
progression or cancer/reference dataset. Therefore, this may not
be indicative of a lack of methylation, but instead can be explained
by quantitative methylation levels. It is possible that methylation of
these genes may have occurred in fewer cells and/or in a fewer
number of CpG dinucleotides, thus producing a less robust signal
in our screen. Alternatively, grouping of cases in statistical analyses
may have filtered these genes out, since methylation in a fewer
number of specimens would create a lower average and higher
variability across these cases. We were surprised to find that the
best characterized methylation event in PCa, hypermethylation of
the GSTP1 promoter, was not captured in our array screen results.
It is possible that the method we used for target DNA preparation
in combination with the microarray platform is responsible for the
lack of detection of GSTP1 methylation signal. Sequence analysis
of GSTP1 revealed that our methylated DNA enrichment method
would produce a fragment of approximately 1900 bp, which may
affect annealing to probes of significantly smaller length (approx-
Figure 4. Amplification plots for MethyLight analysis of DU-145 cells.(A) BMP7 and (B) HOXD3 MethyLight amplification plots. The x-axis
shows the cycle number while the y-axis shows the delta Rn value. +Ctrl – supermethylated DNA.
Table 3. PMR values of positive cases for BMP7 and HOXD3
HOXD3 PMR(%) BMP7PMR(%)
ii50 iv 19.6
a 16g 15
b 30h 11.2
Figure 5. RT-PCR analysis of DU-145 cells. NTC – no template
control. 2DAC – untreated. +DAC – treated with 5-aza-2-deoxycytidine.
Methylation in Prostate Cancer
PLoS ONE | www.plosone.org8 March 2008 | Volume 4 | Issue 3 | e4830
imately 45–60 mer) or may not remain intact following
methylation sensitive digestion. Upon further investigation of
GSTP1 methylation in the same 20 cancer specimens, however, we
could detect methylation in 80% of cases using MSP (data not
We developed a list of genes comparing the total cancer dataset
versus reference, as well as separating methylation profiles for PP3
and PP4 Gleason scores. We chose to do an in-depth methylation
analysis of BMP7 and HOXD3, as these are novel targets for
methylation in PCa. They represent a subset of genes where
silencing may play a role in the development of high grade
prostate cancers based on our array results, but also based on
available functional information from current literature. There-
fore, these genes do not necessarily reflect the greatest statistical
significance or the greatest methylation fold change of either two
datasets that we produced. The genes with the greatest fold
changes in the datasets, FOXC1 and VAX1, will require future
validation in a larger series of prostate tumours.
Bone morphogenic proteins are secreted factors that control the
development and maintenance of bone formation and belong to
the TGFb superfamily of signalling proteins . Within PCa it
has been shown that BMP7 is significantly underexpressed in laser
microdissected cancer cells, leading to an epithelial-to-mesenchy-
mal transition . It does, however, appear to be re-expressed in
metastatic PCa foci of the bone . Our discovery of promoter
methylation in BMP7 suggests a possible mechanism through
which the initial silencing is achieved, as treatment of DU-145 cell
lines with DAC increased BMP7 expression dramatically. Previous
studies have shown methylation of BMP7 in gastric cancers and
oligodendroglioma cell lines [32,33], suggesting that silencing of
BMP7 through this mechanism is not limited to PCa alone. Of
note, BMP7 methylation was not exclusive to histologically
cancerous tissue, but was also evident in adjacent normal tissue.
This may be ascribed to the field cancerization effect whereby
methylation occurs prior to any histological cancerous change in
the cells, which has been shown to occur in prostate cancer .
Alternatively, this methylation may be primarily age-related, as
this phenomenon has also been shown before in normal prostate
tissue . Future studies are required to address these issues.
HOXD3, another novel PCa methylation target, showed a
distinct shift towards greater levels of methylation from PP3 to PP4
PCa when analyzing our CpG array results. The role that HOXD3
plays in tumourigenesis and/or progression of the disease has yet
to be identified, but activation of TGFb signalling has been shown
in A549 cells transfected with HOXD3 . Aberrations in this
pathway have been well documented in PCa and other cancers
. It is therefore possible that methylation-induced silencing of
HOXD3 is perturbing TGFb signalling, and perhaps contributing
to the development of high grade PCa. Studies using the lung
cancer cell line A549  and two melanoma cell lines (A375M,
MMIV)  suggest that overexpression of HOXD3 leads to
increased motility and invasiveness in these cancers, and is not
expressed in normal melanocytes. The overall difference in
methylation captured by our CpG array screen was recapitulated
by analyzing a separate set of PCa samples, from which we could
detect a modest increase in promoter methylation between the
PP3 and PP4 cases (2 vs. 6, respectively) using MethyLight. In
addition, we found a quantitative difference between PP3 and PP4
with EpiTYPER analysis. This difference may represent an overall
increase in neoplastic cells with hypermethylated HOXD3
promoters, contributing to an overall pattern of high Gleason
grade. It is important to note that HOXD3 is expressed at
detectable levels in normal prostate , as many homeobox
genes are regulated in a spatial and temporal manner. Using DU-
145 cells, which showed exclusive methylation of the HOXD3
promoter, we were not able to detect any expression of HOXD3.
However, gene expression was observed following DAC treat-
ment. Taking these two points together, it appears that aberrant
methylation is responsible for abnormal silencing of HOXD3 in
Although much of the Agilent CpG microarray covers promoter
and 59 regions of genes, it is not limited to CpG islands in and
around these areas. The CpG array coverage also extends into
gene bodies, downstream gene locations, and currently unchar-
acterized chromosomal regions. For this study, we chose to limit
our validation to upstream gene promoters, as these are well
characterized for their effects on silencing gene expression .
We did notice, however, significant methylation events occurring
at all three of the aforementioned genome/chromosome locations
which could have varying effects on gene transcription.
In summary, we present the discovery of two novel targets of
hypermethylation in prostate cancers. We specifically chose
HOXD3 as it represents an interesting class of genes that appear
to show a pattern of increased methylation correlating with
tumour grade progression according to the classic Gleason pattern
grading system within Gleason score 6 and 8 tumours. This
pattern may be related to the aggressive biology of high grade
tumours and thus deserve further investigation.
We would like to thank Drs. M. Zielinska, R. Bristow, and E. Diamandis
for the cell lines that were used in this study. This work was supported by
the National Cancer Institute of Canada/Canadian Prostate Cancer
Research Initiative, #18568 (B.B., T.vdK., N.F., L.B., H.O., J.P., V.V.)
and the Ontario Student Opportunity Trust Fund (K.K.). B.S. is a
recipient of the Post Doctoral Fellowship form the Terry Fox Foundation,
and the National Cancer Institute of Canada and the Restracomp
Fellowship from The Hospital for Sick Children, Toronto, Canada.
Conceived and designed the experiments: KK VP TvdK BB. Performed
the experiments: KK VP TvdK. Analyzed the data: KK VP LB BS AS BB.
Contributed reagents/materials/analysis tools: KK VP BB. Wrote the
paper: KK VP LB BS HO VV JP NEF TvdK BB.
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