TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-β signaling.

Abstract

TMPRSS2-ERG gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although TMPRSS2-ERG fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear.
We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays.
Comparison of gene expression levels among TMPRSS2-ERG fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like CRISP3 were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in TMPRSS2-ERG fusion-positive tumors.
The TMPRSS2-ERG gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.

Full text

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RESEARCH ARTICLEOpen Access
TMPRSS2-ERG -specific transcriptional modulation
is associated with prostate cancer biomarkers and
TGF-b signaling
Jan C Brase1, Marc Johannes1, Heiko Mannsperger2, Maria Fälth1, Jennifer Metzger1, Lukasz A Kacprzyk1,
Tatjana Andrasiuk1, Stephan Gade1, Michael Meister3, Hüseyin Sirma4, Guido Sauter4, Ronald Simon4,
Thorsten Schlomm5, Tim Beißbarth6, Ulrike Korf2, Ruprecht Kuner1and Holger Sültmann1*
Abstract
Background: TMPRSS2-ERG gene fusions occur in about 50% of all prostate cancer cases and represent promising
markers for molecular subtyping. Although TMPRSS2-ERG fusion seems to be a critical event in prostate cancer, the
precise functional role in cancer development and progression is still unclear.
Methods: We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal
prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon
1.0 ST microarrays.
Results: Comparison of gene expression levels among TMPRSS2-ERG fusion-positive and negative tumors as well as
benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known
biomarkers for prostate cancer detection like CRISP3 were found to be associated with the gene fusion status. WNT
and TGF-b/BMP signaling pathways were significantly associated with genes upregulated in TMPRSS2-ERG fusion-
positive tumors.
Conclusions: The TMPRSS2-ERG gene fusion results in the modulation of transcriptional patterns and cellular
pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer
detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be
considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and
targeted therapy.
Keywords: Prostate cancer, TMPRSS2-ERG, Gene expression profiling
Background
Prostate cancer is the most frequently diagnosed malig-
nancy and still one of the leading causes of cancer related
death in men [1]. Since the discovery of a recurrent gene
fusion between the androgen responsive gene TMPRSS2
(transmembrane protease, serine 2) and ERG (v-ets ery-
throblastosis virus E26 homolog (avian)) on chromosome
21 [2], prostate cancers are molecularly divided into
“fusion-positive” and “fusion-negative” cancers. Although
the TMPRSS2-ERG fusion is a critical early and common
event in prostate cancer development and progression
[3,4], the clinical implications of the fusion are controver-
sial [5-9] and the functional consequences are unclear.
After the rearrangement, ERG expression is driven by
the androgen-responsive promoter of TMPRSS2, resulting
in a significant upregulation of the transcription factor
ERG [2,10]. Initial in vitro experiments demonstrated that
ERG overexpression leads to increased invasion via the
induction of metalloproteinase and plasminogen activator
pathway genes [11]. The molecular effects of the gene
fusion were recently found to be associated with an activa-
tion of WNT-signaling which induces epithelial-to-
mesenchymal transition (EMT) and loss of cell adhesion
* Correspondence: h.sueltmann@dkfz.de
1Unit Cancer Genome Research, Division of Molecular Genetics, German
Cancer Research Center and National Center for Tumor Diseases, Im
Neuenheimer Feld 460, 69120 Heidelberg, Germany
Full list of author information is available at the end of the article
Brase et al. BMC Cancer 2011, 11:507
http://www.biomedcentral.com/1471-2407/11/507
© 2011 Brase et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

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[12,13]. Additionally, ERG overexpression was shown to
modulate androgen receptor signaling and to initiate epi-
genetic silencing resulting in cellular dedifferentiation [14].
To study the functional consequences of TMPRSS2-
ERG fusion on the transcriptome level, we analyzed
large-scale gene expression profiles using Affymetrix
GeneChip Exon 1.0 ST microarrays. Our results demon-
strate that the TMPRSS2-ERG gene fusion leads to tran-
scriptional modulation, which is associated with widely
accepted prostate cancer biomarkers and signaling
pathways.
Methods
Biological samples
Prostate tissue samples were obtained from the University
Medical Center Hamburg Eppendorf. Approval for the
study was obtained from the local ethics committee and
all patients agreed to additional tissue sampling for scienti-
fic purposes. Tissue samples from 47 prostate cancer
patients with clinical high-risk tumors were included
(Additional file 1: Table S1). None of the patients had
been treated with neo-adjuvant radio-, cytotoxic- or endo-
crine therapy. During radical prostatectomy, tissue sam-
ples from the peripheral zone of the prostate were taken
with a 6 mm punch biopsy instrument immediately after
surgical removal of the prostate from tumorous areas as
described before [15]. The punches were immersed in
RNAlater (Qiagen, Hilden, Germany) for 24 h at room
temperature and subsequently stored at -80°C. To confirm
the presence of tumor, all punches were sectioned, and
the tumor cell content was determined in every 10th sec-
tion. Only sections containing at least 70% tumor cells
were included in the study. Normal prostate tissue sam-
ples from non-suspect areas of the peripheral zone were
obtained similarly from 48 different patients with clinical
low-risk tumors who underwent radical prostatectomy.
These punches were also sectioned and inspected for the
presence of normal prostatic epithelial cells in every 10th
section. Only sections containing between 20% and 40%
normal prostatic epithelial cells were included in the study.
RNA extraction
Total RNA was extracted using the AllPrep DNA/RNA
Mini kit (Qiagen) according to the manufacturer’s
instructions. Briefly, tissue sections were homogenized in
1 ml RLT Plus buffer using TissueLyser (Qiagen). After
DNA separation, 1.5 vol. of 100% ethanol were added to
the total RNA and the mixture was purified. The quantity
and quality of the total RNA was checked using the
Nanodrop photometer (Peqlab, Erlangen, Germany) and
the Bioanalyzer (Agilent, Böblingen, Germany). Samples
with low RNA quality (RIN < 6) were excluded from
further analysis.
Expression profiling using affymetrix GeneChip exon 1.0
ST arrays
The Affymetrix (Santa Clara, USA) GeneChip Whole
Transcript Sense Target Labeling Assay was used to gen-
erate amplified and labeled sense DNA. Briefly, 1 μg of
total RNA was used for rRNA reduction. Following the
manufacturer’s instructions, cDNA was hybridized to the
Affymetrix 1.0 Human Exon ST arrays and incubated at
45°C for 16 h. The washing and staining steps were carried
out using the GeneChip Fluidics station FS 450. Slides
were scanned with the Affymetrix Gene Chip scanner
3,000 7 G system.
Validation of TMPRSS2-ERG fusion events
TMPRSS2-ERG fusion events were verified using RT-
PCR. cRNA from the Affymetrix Whole Transcript Sense
Target Labeling Assay was reversely transcribed. 10 ng of
cDNA were used for RT-PCR based validation. Initial
amplification as well as nested PCR was done using pri-
mers described by Jhavar et al. [16]. All products were
separated by agarose gel electrophoresis. Additionally,
the TMPRSS2-ERG fusion transcript was quantified in
clinical prostate samples by Taqman (Life Technologies,
Carlsbad, USA) real-time PCR using the equivalent of 10
ng as a template. Quantification was performed with 2 ×
ABSOLUTE QPCR Mix (Thermo Scientific) on the
LightCycler 480 System (Roche) using the assay
described by Mertz et al. [10]. The amounts of ERG
(Hs01554635_m1), CRISP3 (Hs00195988_m1) and
TDRD1 (Hs00229805_m1) transcripts were determined
relative to B2M (Hs99999907_m1) using the second deri-
vative maximum method of the LightCycler software
(validation results are summarized in Additional file 2:
Table S2).
Statistical analysis
Gene expression data from the Affymetrix cel files were
analyzed using the statistical computing environment R
http://www.cran.r-project.org. Gene expression profiles
were obtained by applying the Robust Multichip Average
(RMA) [17] implementation included in the Affymetrix
Power Tools (APT). The MIAME-compliant microarray
data were submitted to the NCBI GEO database
(GSE29079). Differentially expressed genes were deter-
mined using a moderated t-statistic [18]. All p values were
corrected for multiple testing, and genes showing a false
discovery rate (FDR [19]) ≤ 0.05 were considered as signif-
icantly deregulated. TranscriptCluster IDs of deregulated
genes were subjected to pathway exploration using the
Ingenuity Pathway Analysis software (Ingenuity, Redwood
City, CA). To assess the significant differences of single
gene levels, a two-sided Wilcoxon test was used. A p value
< 0.05 was considered as significant.
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For validation purposes, 131 prostate cancer samples
from publicly available gene expression data (Taylor et al.
[20]) were re-analyzed using the methods described
above. Fusion-positive and negative samples were
assigned according to their ERG expression levels. Sam-
ples with a considerable high ERG expression (48 sam-
ples; > 9.2) were labeled as fusion-positive, whereas
tumor samples with low ERG expression (72 samples; <
8.0) were marked as fusion-negative. 11 samples with
median ERG expression (8.0 < ERG < 9.2) were omitted
from the analyses.
Results and discussion
TMPRSS2-ERG induced transcriptional deregulation
Gene expression profiles were successfully obtained from
47 cancer and 48 normal prostate tissues taken from non-
suspect areas of the peripheral zone from clinical low-risk
tumors. A limitation for the comparison between tumor
and normal was the variability of stromal components and
clinical characteristics (Gleason scores/stages) between
tumor and normal tissues (see methods).
In a first explorative analysis, we compared the gene
expression levels in tumor and normal prostate tissues.
Considerable transcriptional deregulation was identified
in the malignant tissues: 263 genes had at least a twofold
change in expression levels (Additional file 3: Table S3).
The list of deregulated genes contained markers that had
been described in prostate cancer before (e. g. CRISP3,
AMACR and MYC). Additionally, several genes with
unknown function like TDRD1 and C20orf74 were identi-
fied as biomarker candidates for prostate cancer.
To compare the gene expression levels between
TMPRSS2-ERG fusion-positive and negative tumors, we
included only samples (n = 37) with reliable assessment
based on ERG exons/gene expression levels, nested RT-
PCR as well as quantitative RT-PCR measurements
Figure 1 TMPRSS2-ERG specific transcriptional modulations. Clustering of 48 benign (light green), 20 fusion-negative (dark green) and 17
fusion-positive (red) prostate cancer tissue samples. Clustering is based on the expression levels of 126 genes, which showed at least 2-fold
expression changes (Additional file 4: Table S4) between the TMPRSS-ERG positive and negative subgroups. Expression values are color-coded
(red = upregulation; blue = downregulation).
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(Additional file 2: Table S2). We excluded a group of
samples with median ERG expression levels, since our
initial explorative analysis demonstrated that these sam-
ples might lead to noise in subsequent statistical analyses.
Seventeen tumor samples were defined as TMPRSS2-
ERG fusion-positive and 20 samples were defined as
TMPRSS2-ERG fusion-negative. A total of 1,635 genes
was differentially expressed between TMPRSS2-ERG
positive and negative tumors (FDR < 0.05). The exclusion
of the cases with a median ERG expression level might
explain the relatively large number of significantly
deregulated genes in comparison to other published stu-
dies. Roughly 75% of the differentially expressed genes
were upregulated in patients with TMPRSS2-ERG gene
fusion, which is in line with other reports [13,21,22]. A
subset of 126 genes showed at least 2-fold expression
changes (Additional file 4: Table S4). Clustering of the
deregulated genes demonstrated that fusion-negative
tumor specimens were more closely related to normal
tissue, whereas fusion-positive samples showed distinct
transcriptional modulation (Figure 1).
Of the 126 genes from the TMPRSS2-ERG comparison,
24 genes were also found in our initial comparison of
expression profiles between tumor and benign tissue
samples (Figure 2). For example, TDRD1 was significantly
upregulated in TMPRSS2-ERG positive tumors compared
to normal (p = 1.77*10-9, FC = 26) and fusion-negative
samples (p = 1.51*10-9, FC = 23) whereas no remarkable
difference between fusion-negative and normal prostate
tissues was found (p = 0.025, FC = 1.1; Figure 3A).
CRISP3 has been frequently suggested to be a biomarker
for prostate cancer detection and prognosis [23-26] and
showed the highest fold-change in the initial tumor-nor-
mal comparison (Additional file 3: Table S3). Inclusion of
the TMPRSS2-ERG subgroup information, however,
revealed that CRISP3 gene expression is associated with
the ERG status, since it is significantly upregulated in
TMPRSS2-ERG positive tumors compared to normal tis-
sue (p = 2.2*10-12, FC = 37, or p < 0.001, FC = 29, respec-
tively; Figure 3B). In contrast, less remarkable expression
changes of CRISP3 were found between normal and
fusion-negative tumor samples (p = 0.03, FC = 1.2; Figure
Tumor vs. Normal Fusion vs. Non-fusion
CRISP3
F5
ERGERG
TMEM45B
BAMBI
PLA1A
SH3RF1 SH3RF1
PLA2G7
MON1B
ACTG2
LEPREL1
PCP4
TDRD1
CACNA1D
MAOAMAOA
MYO6
GCNT1
PCDHB13
C4A | C4BC4A | C4B
ANKRD34B
HIST1H3B
MME
SLC22A3
ANPEP
Figure 2 Overlap of prostate cancer and TMPRSS2-ERG biomarkers. Significant genes with at least 2-fold expression changes in the tumor-
normal comparison (blue, Additional file 3: Table S3) and the TMPRSS2-ERG subgroups (yellow, Additional file 4: Table S4).
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3B). To verify these results, we performed a technical
validation in the same sample set by quantitative RT-
PCR (Additional file 5: Figure S1) and we also included
an independent validation study of recently published
prostate cancer gene expression profiles [20]. CRISP3
and TDRD1 were also found to be considerably upregu-
lated in gene fusion-positive samples compared to benign
and fusion-negative tumor samples with qPCR-based
analysis (Additional file 5: Figure S1) as well as in the
validation cohort (Additional file 6: Figure S2).
The overlap between TMPRSS2-ERG deregulated genes
and candidates for prostate cancer detection (Figure 2)
might be due to the high expression fold-changes induced
by ERG. CACNA1D, for instance, has been described as a
downstream target of ERG, but was also found to be one
of the top candidate for prostate cancer detection in the
initial tumor–normal comparison. In line with previous
reports, MYO6 was also found to be upregulated in
TMPRSS2-ERG gene fusion positive tumors [27], although
MYO6 has been reported as a marker for prostate cancer
development [28]. Therefore, the results suggest that well-
known biomarkers like CRISP3 and MYO6 might not be
related to prostate cancer development but rather to tran-
scriptional alterations induced by the overexpression of
the transcription factor ERG in fusion positive prostate
tumors. Other markers with unknown function (e.g.
TDRD1, F5) might be associated with the molecular
mechanism of TMPRSS2-ERG gene fusion, but should be
avoided as biomarkers for prostate cancer, since they are
not appropriate for the detection of TMPRSS2-ERG nega-
tive tumors.
Activation of WNT and TGF-b/BMP signaling pathways in
fusion-positive prostate cancer patients
To study the transcriptional changes induced by
TMPRSS2-ERG gene fusion, we applied Ingenuity Pathway
Analysis software to investigate overrepresented pathways
among the genes with significant upregulation in fusion-
A
TDRD1
Benign Tumor
TDRD1
RNA level
B
CRISP3 CRISP3
RNA level
Benign Tumor
Benign Fusion- Fusion+
RNA level
RNA level
Benign Fusion- Fusion+
Figure 3 Association between biomarkers for prostate cancer detection and TMPRSS2-ERG gene fusion. Gene expression of TDRD1 (A)
and CRISP3 (B) in 48 benign, 47 tumor (left side) as well as in the TMPRSS2-ERG (20 fusion-negative vs. 17 fusion-positive tumor samples; right
side).
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positive tumors. The top canonical pathway was “Factors
promoting cardiogenesis”, a combination of TGF-b, BMP
and WNT signaling (Additional file 7: Figure S3). Overre-
presented genes from the top canonical pathway were vali-
dated in the publicly available data set of gene expression
profiles from clinical prostate tumors [20]. Almost all of
these pathway-specific genes were also found to be signifi-
cantly upregulated in fusion-positive patients in the valida-
tion cohort (Table 1). Activated WNT signaling was
recently described to be among the most highly enriched
pathways in ERG-overexpressing tumors [13]. It is also
associated with epithelial-to-mesenchymal transition in
fusion-positive tumors [12]. In addition to WNT-signaling,
we identified a significant upregulation of the TGF-b/BMP
signaling pathways in TMPRSS2-ERG fusion-positive
patients. The interaction between the pathways seems to
be relevant in gene fusion-positive prostate cancer, since
TGF-b has been extensively discussed as the main initiator
of EMT [29] and is known to closely interact with WNT-
signaling [30].
The exact molecular mechanisms leading to an enhance-
ment of TGF-b signaling in fusion-positive tumors are so
far unclear and should be analyzed in future studies. One
explanation might be the cross-talk between androgen and
TGF-b signalling [31]. ERG has recently been described to
suppress androgen signalling [14]. Since androgen
deprivation induces TGF-b signaling in prostate cells
[32-34], it is tempting to speculate that ERG-induced sup-
pression of androgen signal transduction leads to an
increase of TGF-b pathway utilization which - in combina-
tion with WNT signalling - results in EMT and cell inva-
sion in fusion-positive tumors.
Conclusions
In conclusion, our data suggest that the TMPRSS2-ERG
gene fusion marks a molecularly distinct tumor entity
with substantial transcriptional modulation. In particular,
our gene expression data indicate a deregulation of WNT
and TGF-b/BMP signaling in fusion-positive prostate
tumors.
The inclusion of benign samples in our gene expression
analysis additionally demonstrated that well-known bio-
markers for prostate cancer like CRISP3 are associated
with the TMPRSS2-ERG fusion status. Thus, these genes
might not be primarily related to the tumor status but
rather to ERG-induced transcription remodeling. There-
fore, it would be beneficial to consider the fusion status
in the discovery and assessment of molecular biomarkers
for prostate cancer. Recently, Karnes et al. were the first
to test the performance of prostate cancer biomarkers in
TMPRSS2-ERG positive and negative subgroups [35].
Their results underlined that the prior knowledge about
the TMPRSS2-ERG fusion status will help to identify
more accurate biomarkers and to develop novel targeted
therapy strategies for prostate cancer in the future.
Additional material
Additional file 1: Table S1. Clinical information for the analyzed
prostate cancer patient cohort.
Additional file 2: Table S2. TMPRSS2-ERG gene fusion validation results:
qualitative and quantitative RT-PCR reactions were carried out as
described in materials and methods. The expression levels of ERG exons
2 and 3 were compared with the adjacent exons ("Exon walking plot”)
similar to the method described by Jhavar et al. [16]. ERG expression
levels were normalized to the median expression in benign samples.
Additional file 3: Table S3. Differentially expressed genes between 48
benign and 47 tumor samples with at least 2-fold expression changes as
determined by modified t-statistic (FDR ≤ 0.05).
Additional file 4: Table S4. Differentially expressed genes between 17
fusion-positive and 20 fusion-negative tumor samples with at least 2-fold
expression changes as determined by modified t-statistic (FDR ≤ 0.05).
Additional file 5: Figure S1. Association between prostate cancer
biomarkers and TMPRSS2-ERG gene fusion as detected by q-RT PCR. Gene
expression of TDRD1 (a) and CRISP3 (b) quantified by qRT-PCR in 48
normal and 20 fusion-negative and 17 fusion-positive tumors samples.
Additional file 6: Figure S2. Association between prostate cancer
biomarkers and TMPRSS2-ERG gene fusion in the validation cohort. Gene
expression of TDRD1 (a) and CRISP3 (b) in 29 benign and 72 fusion-
negative and 48 fusion-positive tumor samples.
Additional file 7: Figure S3. Deregulated WNT/TGF-b/BMP signaling in
fusion-positive tumors. Top canonical pathway ("Factors promoting
cardiogenesis”) of the overrepresentation analysis using the significantly
Table 1 Deregulated WNT, TGF-b and BMP signaling in
fusion-positive tumors
Own Data Taylor et al. [20]
GeneSymbol FC
p valueFC
p value
ACVR1
1.31 0.004 1.150.005
ACVR2B
1.33< 0.001 1.11 0.007
BMP7
1.26 < 0.0011.110.010
BMPR2
1.500.0021.140.028
FZD3
1.72 < 0.001 1.280.011
FZD5
1.67 < 0.0011.32 < 0.001
FZD7
1.290.0051.120.442
FZD8
1.13 < 0.0011.14< 0.001
LRP1
1.89< 0.001 1.35 < 0.001
LRP6
1.46 < 0.0011.140.011
MAP3K7
1.42 < 0.0011.170.005
PRKCH
1.250.0021.090.074
PRKD1
1.86 < 0.0011.47 < 0.001
SMAD2
1.450.0051.180.006
SMAD5
1.50 < 0.0011.28 < 0.001
SMAD9
1.29 0.0021.20< 0.001
TCF7L2
1.33 < 0.0011.090.250
TGFBR3
1.44 < 0.0011.110.231
Deregulated genes from the top canonical pathway (“Factors promoting
cardiogenesis”) according to the overrepresentation analysis using Ingenuity
Pathway Analysis software
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upregulated genes in fusion-positive tumors. Deregulated genes of the
specific pathway are indicated in grey.
Acknowledgements
We thank Thorsten Kühlwein, Elizabeth C. Xu, Sabrina Balaguer-Puig, Maike
Wosch and Annika Bittmann for excellent technical assistance. We thank
Mark Laible for helpful discussions. This work was supported by the German
Federal Ministry of Education and Research in the framework of the Program
for Medical Genome Research (01GS0890).
Author details
1Unit Cancer Genome Research, Division of Molecular Genetics, German
Cancer Research Center and National Center for Tumor Diseases, Im
Neuenheimer Feld 460, 69120 Heidelberg, Germany.2Division of Molecular
Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
3Translational Research Unit, Thoraxklinik, University of Heidelberg,
Heidelberg, Germany.4Institute of Pathology, University Medical Center
Hamburg-Eppendorf, Hamburg, Germany.5Martini-Clinic, Prostate Cancer
Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
6Department Medical Statistics, University Medical Center Göttingen,
Göttingen, Germany.
Authors’ contributions
JCB, RK, MM carried out the large-scale gene expression analysis using
microarrays, MJ, MF, JCB analyzed the gene expression data; LAK, TA, JM
performed validation experiments; MJ, MF, SG, performed statistical analysis
and TB was responsible for the supervision of the analysis; TS, GS, RS, HSi
collected clinical samples and data; HS, JCB, HM, UK, TS, RS, GS, were
involved in the conceptual design of the study; HS was responsible for the
supervision of the project, JCB and HS wrote the manuscript. All authors
read and approved the final manuscript
Competing interests
The authors declare that they have no competing interests.
Received: 9 June 2011 Accepted: 5 December 2011
Published: 5 December 2011
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Pre-publication history
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1471-2407/11/507/prepub
doi:10.1186/1471-2407-11-507
Cite this article as: Brase et al.: TMPRSS2-ERG -specific transcriptional
modulation is associated with prostate cancer biomarkers and TGF-b
signaling. BMC Cancer 2011 11:507.
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