4625.full

Biology of Human Tumors
Expression of Androgen and Estrogen Signaling Components
and Stem Cell Markers to Predict Cancer Progression and
Cancer-Specific Survival in Patients with Metastatic Prostate
Cancer
Tetsuya Fujimura1
, Satoru Takahashi2
, Tomohiko Urano3,4
, Kenichi Takayama3,4
, Toru Sugihara1
,
Daisuke Obinata2
, Yuta Yamada1
, Jimpei Kumagai1
, Haruki Kume1
, Yasuyoshi Ouchi3
, Satoshi Inoue3,4
,
Lungwani-Tyson andMuungo YukioHomma
Abstract
Purpose: Genes of androgen and estrogen signaling cells and stem cell–like cells play crucial roles in
prostate cancer. This study aimed to predict clinical failure by identifying these prostate cancer-related genes.
Experimental Design: We developed models to predict clinical failure using biopsy samples from a
training set of 46 and an independent validation set of 30 patients with treatment-na€ve prostate cancer with
bone metastasis. Cancerous and stromal tissues were separately collected by laser-captured microdissection.
We analyzed the association between clinical failure and mRNA expression of the following genes androgen
receptor (AR) and its related genes (APP, FOX family, TRIM 36, Oct1, and ACSL 3), stem cell–like molecules
(Klf4, c-Myc, Oct 3/4, and Sox2), estrogen receptor (ER), Her2, PSA, and CRP.
Results: Logistic analyses to predict prostate-specific antigen (PSA) recurrence showed an area under the
curve (AUC) of 1.0 in both sets for Sox2, Her2, and CRP expression in cancer cells, AR and ERa expression in
stromal cells, and clinical parameters. We identified 10 prognostic factors for cancer-specific survival (CSS):
Oct1, TRIM36, Sox2, and c-Myc expression in cancer cells; AR, Klf4, and ERa expression in stromal cells; and
PSA, Gleason score, and extent of disease. On the basis of these factors, patients were divided into favorable-,
intermediate-, and poor-risk groups according to the number of factors present. Five-year CSS rates for the 3
groups were 90%, 32%,and 12%in thetraining setand 75%, 48%, and 0% in the validation set, respectively.
Conclusions: Expression levels of androgen- and estrogen signaling components and stem cell markers
are powerful prognostic tools. Clin Cancer Res; 20(17); 4625–35. Ó2014 AACR.
Introduction
The pioneering work of Huggins and Hodges (1) showed
that prostate cancer is sensitive to androgen deprivation
therapy (ADT); however, at least two problems are associ-
ated with ADT. First, durability of ADT varies among pros-
tate cancer patients (2). For example, the median survival
time was 13 months for patients with prostate-specific
antigen (PSA) nadirs of 4 ng/mL, 44 months for patients
with PSA nadirs of 0.2–4 ng/mL, and 75 months for patients
with PSA nadirs of 0.2 ng/mL (2). Second, ADT is initially
effective as a treatment for advanced prostate cancer, but not
when prostate cancer acquires a castration-resistant status. A
recent study proposed that stem cell–like prostate cancer
cells with a pluripotent phenotype are involved in castra-
tion-resistant prostate cancer (CRPC; ref. 3). In cases with
stem cell–like prostate cancer cells, ADT may actually stim-
ulate cancer progression (3). Therefore, evaluation of both
the durability of ADT and presence of stem cell–like cell
components in prostate needle biopsy samples before ADT
prescription is the first step in personalized medicine for
patients with metastatic prostate cancer.
Determination of the therapeutic strategy for breast can-
cer commonly depends on the expression patterns of estro-
gen receptor (ERa), progesterone receptor (PR), and Her 2
in needle biopsy samples (4). However, pretreatment diag-
nosis by estimating gene expression is not yet prevalent for
patients with prostate cancer. The growth-inhibitory effects
on prostate cancer cells are associated with the status of
steroid nuclear receptors and related genes of prostate
cancer–related molecules, such as androgen receptor (AR;
refs. 5, 6), AR-related genes (5, 6), amyloid precursor
1
Department of Urology, Graduate School of Medicine, The University of
Tokyo, Tokyo, Japan. 2
Department of Urology, Graduate School of Med-
icine, The Nihon University, Tokyo, Japan. 3
Department of Geriatric Med-
icine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
4
Department of Anti-Aging Medicine, Graduate School of Medicine, The
University of Tokyo, Tokyo, Japan.
Note: Supplementary data for this article are available at Clinical Cancer
Research Online (http://clincancerres.aacrjournals.org/).
Corresponding Author: Tetsuya Fujimura, Department of Urology, Grad-
uate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo
113-8655, Japan. Phone: 813-5800-8753; Fax: 813-5800-8917; E-mail:
fujimurat-uro@h.u-tokyo.ac.jp
doi: 10.1158/1078-0432.CCR-13-1105
Ó2014 American Association for Cancer Research.
Clinical
Cancer
Research
www.aacrjournals.org 4625
on March 10, 2020. © 2014 American Association for Cancer Research.clincancerres.aacrjournals.orgDownloaded from
Published OnlineFirst July 1, 2014; DOI: 10.1158/1078-0432.CCR-13-1105
3,4 1

protein (APP; ref. 7), forkhead-box (FOX) family proteins
(8–11), octameter transcription factor (Oct1; ref. 12), and
ERs (5, 13, 14). However, the distribution and functions of
these steroid nuclear receptors vary depending on the sites
from which prostate cancer samples are obtained (13–15).
For example, the AR gene is expressed in luminal, basal, and
stromal cells. AR functions as a proliferation stimulator in
stromal cells, as a suppressor in epithelial basal cells, and as
a survival factor in epithelial luminal cells (15). Therefore,
cancer and stromal cells should be separately collected for
the analysis of prostate cancer samples.
Here, we attempted to predict PSA recurrence and cancer-
specific survival (CSS) by measuring expression of genes
related to prostate cancer in patients with bone metastasis
using laser-captured microdissection (LCM) technique.
Materials and Methods
Tissue selection and patient characteristics of the
training set
Formalin-fixed, paraffin-embedded sections of the pri-
mary tumors were obtained from 46 treatment-na€ve
consecutive patients (age, 58–87 years; mean age, 74
years) diagnosed with bone metastatic prostate cancer
between 2001 and 2009 by transrectal ultrasound–guided
biopsy. This study was approved by our institutional
ethics committee. Before treatment, serum PSA levels
were 8.6 to 13,700 ng/mL (mean, 219 ng/mL). The
sections were evaluated by two pathologists, and the
tumors were assigned to Gleason scores (GS) of 7
(n ¼ 8), 8 (n ¼ 11), 9 (n ¼ 26), and 10 (n ¼ 1). The clini-
cal primary tumor (cT) stages were 2 (n ¼ 6), 3a (n ¼ 15),
3b (n ¼ 14), and 4 (n ¼ 11). The clinical regional lymph
node (cN) stages were 0 (n ¼ 21) and 1 (n ¼ 25). To
diagnose bone metastasis, we performed bone scintigra-
phy using technetium-99m-methylene diphosphonate in
all the patients. Computed tomography was used in 3 of
46 patients to distinguish from bone degenerative
changes. On the basis of the number or extent of metas-
tases, the scans were divided into five extent of disease
(EOD) grades as follows: 0, normal or abnormal due to
benign bone disease; 1, number of bony metastases less
than 6, each of which is less than 50% the size of a
vertebral body (one lesion about the size of a vertebral
body would be counted as two lesions): 2, number of
bone metastases between 6 and 20, size of lesions as
described above; 3, number of metastases greater than 20
but fewer than a super scan; and 4, superscan or its
equivalent (i.e., more than 75% of the ribs, vertebrae, and
pelvic bones; ref. 16). EOD was 1 (n ¼ 25), 2 (n ¼ 10), 3
(n ¼ 9), and 4 (n ¼ 2).
All patients received ADT by medical or surgical cas-
tration with or without the administration of antiandro-
gen agents, bicalutamide (n ¼ 22), chlormadinone (n ¼
3), flutamide (n ¼ 3), and estramustine phosphonate
(n ¼ 2; Table 1). PSA relapse was defined by consecutive
increase in serum PSA levels to above the patient’s PSA
nadir (17). If PSA relapse occurred during initial ADT,
either new antiandrogen agent was added or switched to
another. When patients were switched to other antian-
drogen agents, antiandrogen withdrawal syndrome was
checked. From September 2008, systemic chemotherapy
by docetaxel was also administered every 3 or 4 weeks. If
prostate cancer became hormone- and chemotherapy-
refractory, patients received best supportive care. A total
of 37 patients (80%) experienced relapse. The mean time
to PSA relapse was 972 Æ 1,193 days (range, 5–4,616
days). The mean follow-up duration was 1,650 Æ 1,319
days (range, 79–5,961 days). At the end of the follow-up
period, 9 patients (19%) were alive without PSA relapse,
whereas 15 (33%) were alive with biochemical or clinical
recurrence. Twenty-two patients (48%) died of prostate
cancer during the follow-up period.
Patient characteristics of the validation set
An independent cohort of 30 patients with prostate
cancer with bone metastasis (age, 59–91 years; mean age,
68 years) between 2001 and 2011 were enrolled. Before
treatment, serum PSA levels were 5.8 to 8,428 ng/mL
(mean, 498 ng/mL). The tumors were assigned to Gleason
scores of 7 (n ¼ 4), 8 (n ¼ 4), 9 (n ¼ 16), and 10 (n ¼ 6).
The cT stages were 2 (n ¼ 4), 3a (n ¼ 9), 3b (n ¼ 8), and 4
(n ¼ 9). The cN stages were 0 (n ¼ 5) and 1 (n ¼ 25). EOD
was 1 (n ¼ 11), 2 (n ¼ 9), 3 (n ¼ 4), and 4 (n ¼ 6; ref. 16).
All patients received ADT by medical or surgical castra-
tion with or without the administration of antiandrogen
agents, bicalutamide (n ¼ 27), and estramustine phospho-
nate (n ¼ 3). A total of 25 patients (83%) experienced
relapse. The mean time to PSA relapse was 571 Æ 877 days
(range, 6–4,616 days). The mean follow-up duration was
1,143 Æ 1,226 days (range, 71–6,395 days). At the end of
the follow-up period, 5 patients (16%) were alive without
PSA relapse, whereas 13 (43%) were alive with biochemical
Translational Relevance
Androgen and estrogen signaling play crucial roles in
prostate cancer. Stem cell–like cells are also known to be
involved in prostate cancer progression. In the present
study, we investigated the expression of androgen and
estrogen signaling components and stem cell markers to
predict prostate-specific antigen (PSA) recurrence and
cancer-specific survival (CSS) in patients with metastatic
prostate cancer. Discriminant analysis using the mRNA
expression of AR, ERa, Sox2, Her2, CRP, and clinical
parameters highly predicted PSA recurrence. We identi-
fied 10 prognostic factors for CSS: Oct1, TRIM36, Sox2,
and c-Myc, AR, Klf4, and ERa expression as well as PSA,
Gleason score, and extent of disease. On the basis of
these factors, we propose a new risk classification for
CSS. Taken together, the expression pattern of androgen
and estrogen signaling components and stem cell mar-
kers predicted PSA recurrence and CSS in patients with
prostate cancer characterized by bone metastasis.
Fujimura et al.
Clin Cancer Res; 20(17) September 1, 2014 Clinical Cancer Research4626

or clinical recurrence. Twelve patients (40%) died of pros-
tate cancer during the follow-up period.
Laser-captured microdissection
Tissue sections (10 mm) were deparaffinized and rehy-
drated using graded ethanol and rinsed in diethylpyrocar-
bonate-treated water. After staining with 0.05% toluidine
blue solution (WAKO), the tissue sections were separated
into groups of cancer cells and stromal cells by laser micro-
dissection (Leica 6500). To obtain sufficient materials, we
collected 30 to 460 acini of epithelium within an area of
3,865 mm3
in average, and all of the surrounding stroma
were collected. Tissues were collected into Eppendorf cap
containing 25 mL of ISOGEN reagent (Nippon Gene) and
tissues were stored at À80
C before RNA isolation.
Quantitative reverse-transcription PCR
Total RNA was extracted using ISOGEN PB kit (Nippon
Gene). Tissue samples were incubated 15 minutes with
proteinase K and extraction buffer at 50
C then added to
anISOGEN-LS(NipponGene).BoundRNAwaspurifiedina
series of wash steps to remove cellular components. Residual
DNA was digested byincubating the eluate with DNase.RNA
quality and RNA quantity was assessed using a NanoDrop
ND-1000 spectrophotometer (Japan SCRUM Inc). The ratio
of absorbance at 260 nm and 280 nm was 1.7:2.0, and a total
of 40 ng RNA was used. First-strand cDNA was generated
using PrimeScript RT Master Mix (Takara). Quantitative
reverse-transcription PCR (qRT-PCR) was performed with
150 nmol/L primers using 7300 Real-Time PCR system
(Applied Biosystems); one cycle at 50
C for 2 minutes, one
cycleat 95
C for 10 minutes, 40 cycles at 95
C for 15seconds
and at 50
C for 1 minute, one cycle at 95
C for 15 seconds,
onecycle at 59
C for 30seconds,and one cycle at95
C for 15
seconds. mRNA expression was normalized relative to
GAPDH and the average relative expression of 4 times
examination was adopted.
Primers
The size of PCR amplicons (base pair: bp) and the
sequences of the PCR primers used are shown below:
AR (59 bp) AR forward: 50
-
GCTGCAAGGTCTTCTTCAAAAGA-30
AR reverse: 50
-GCTGGCGCACAGGTACTTCT-30
Oct1 (130 bp) Oct1 forward: 50
-
CCTGCTTTCTTTTGCGGTAG-30
Oct1 reverse: 50
- GTTCTGTTTTCGCCCAACAT-30
FOXO1 (128 bp) FOXO1 forward: 50
-
CTGCATCCATGGACAACAAC-30
FOXO1 reverse: 50
- AGGCCATTTGGAAAACTGTG-30
FOXA1 (78 bp) FOXA1 forward: 50
-
CATTGCCATCGTGTGCTTGT-30
FOXA1 reverse: 50
- CCCGTCTGGCTATACTAACACCAT-30
FOXP1 (65 bp) FOXP1 forward: 50
-
ACCGCTTCCATGGGAAAT-30
FOXP1 reverse: 50
- CCGTTCAGCTCTTCCCGTATT-30
APP (121 bp) APP forward: 50
-
GAGACACCTGGGGATGAGAA-30
APP reverse: 50
- CTTGACGTTCTGCCTCTTCC-30
ACSL3 (110 bp) ACSL3 forward: 50
-
GACACAAGGGCGCATATCTT-30
ACSL3 reverse: 50
- AGGGTGGCAAATGTTACAGC-30
TRIM36 (136 bp) TRIM36 forward: 50
-
CGTTGTTCTGCCTTTCACAA-30
TRIM36 reverse: 50
- GCAACACCAGCTGACAGAAA-30
Oct3/4 (110 bp) Oct3/4 forward: 50
-
AGTGAGAGGCAACCTGGAGA-30
Table 1. Correlation between men with and
without PSA recurrence in patients with bony
metastatic prostate cancer (n ¼ 46)
Groups
Clinical findings
PSA
recurrence
(n ¼ 37)
Without PSA
recurrence
(n ¼ 9) P
Age, y 70 Æ 8.0 72 Æ 4.3 0.20
Serum PSA (ng/mL) 1,109 Æ 2,421 1,170 Æ 2,777 0.15
Gleason score
7 7 1 0.55
8 7 4
9 22 4
10 1 0
Clinical T stage
2 4 2 0.27
3a 11 4
3b 13 1
4 9 2
Clinical N stage
0 14 5 0.61
1 23 4
EOD
1 18 7 0.28
2 8 2
3 9 0
4 2 0
MAB
No 14 3 0.58
Yes 21 6
Initial antiandrogen agent
Bicalutamide 17 5
Chlormadinone 3 0
Flutamide 2 1
Estrogens 2 0
PSA nadir
0.01 1 9 0.0001
0.01–0.1 16 0
0.1 20 0
Time to PSA nadir
after ADT (days)
424 Æ 286 419 Æ 303 0.31
Abbreviations: MAB, maximum androgen blockade with
antiandrogen agents.
Androgen and Estrogen Signaling and Stem Cell Markers in Prostate Cancer
www.aacrjournals.org Clin Cancer Res; 20(17) September 1, 2014 4627

Oct3/4 reverse: 50
- ACACTCGGACCACATCCTTC-30
Sox2 (95 bp) Sox2 forward: 50
-
CAAGATGCACAACTCGGAGA-30
Sox2 reverse: 50
- GCTTAGCCTCGTCGATGAAC-30
Klf4 (127 bp) Klf4 forward: 50
-
ACTCGCCTTGCTGATTGTCT-30
Klf4 reverse: 50
- AGTTAACTGGCAGGGTGGTG-30
c-Myc (123 bp) c-Myc forward: 50
-
TCAAGAGGCGAACACACAAC-30
c-Myc reverse: 50
- TAACTACCTTGGGGGCCTTT-30
CRP (107 bp) CRP forward: 50
-
TGGTCTTGACCAGCCTCTCT-30
CRP reverse: 50
- CGGTGCTTTGAGGGATACAT-30
Her2 (132 bp) Her2 forward: 50
-
ACCAAGCTCTGCTCCACACT-30
Her2 reverse: 50
- ACTGGCTGCAGTTGACACAC-30
ERb (175 bp) ERb forward: 50
-
AAGAAGATTCCCGGCTTTGT-30
ERb reverse: 50
- CTTCTACGCATTTCCCCTCA-30
Klf5 (81 bp) Klf5 forward: 50
-
CACCTCCATCCTATGCTGCT-30
Klf5 reverse: 50
- AGTTAACTGGCAGGGTGGTG-30
ERa (153 bp) ERa forward: 50
-
AGCACCCTGAAGTCTCTGGA-30
ERa reverse: 50
- GATGTGGGAGAGGATGAGGA-30
GAPDH (80 bp) GAPDH forward: 50
-
GGTGGTCTCCTCTGACTTCAACA-30
GAPDH reverse: 50
- GTGGTCGTTGAGGGCAATG-30
We have previously uncovered the AR transcriptional
network in prostate cancer cells by chromatin immuno-
precipitation (ChIP) combined with DNA microarray
(ChIP-chip) and cap analysis gene expression (CAGE)
(18). In addition, on the basis of previous studies, we
selected APP (7), FOX family proteins (FOXO1, FOXA1,
and FOXP1; refs. 9–11), tripartite molecule 36 (TRIM;
ref. 18), Oct1 (12), and ACSL 3 (18) for this study. Stem
cell–like markers [Oct3/4, Sox2, Kruppel-like factor (Klf4),
and c-Myc; refs. 19, 20) and prostate cancer–related
genes (CRP, Her2, ERb, Klf5, and ERa) were also evaluated
(21–23).
Antibodies
We performed immunohistochemistry of AR and Klf4 to
evaluate a correlation with its mRNA expression.
Because only a small amount of the biopsy samples was
available, we chose two antibodies; AR (AR441; ref. 24) and
Klf4 (ab72543; ref. 25) which had been reported to be
adequate for immunohistochemical analysis. Mouse
monoclonal antibody for AR (AR441) and rabbit polyclon-
al antibody for Klf4 (ab72543) were purchased from Dako
and Abcam, respectively.
Immunohistochemical analysis
Immunohistochemical analysis for AR and Klf4 was
performed with the streptavidin-biotin amplification meth-
od using an EnVisionþ Visualization Kit (Dako) for AR and
Klf4, as previously described (12, 26). The primary antibody
against AR and Klf4 (1:50 dilution) was applied and incu-
bated at room temperature for 1 hour. The sections were
then rinsed in PBS and incubated at room temperature with
EnVisionþ for 1 hour. The antigen–antibody complex was
visualized with 3, 30
-diaminobenzidine (DAB) solution [1
mmol/L DAB, 50 mmol/L Tris-HCl buffer (pH 7.6), and
0.006% H2O2].
Immunohistochemical assessment
The sample slides were evaluated for staining intensity
(ref. 26; none, weak, moderate, and strong) based on
labeling index (LI; ref. 27). LIs were determined by
counting the percentage of cells with positive immuno-
reactivity in 1,000 cells (27). Two pathologists (T. Fuji-
mura and S. Takahashi) independently evaluated the
tissue sections, and the average LI was used. We defined
positive immunoreactivity as showing moderate or strong
immunoreactivity.
Statistical analyses
Correlations between age, pretreatment serum PSA
levels, mRNA levels, and PSA relapse were evaluated using
the Wilcoxon test. Associations between PSA relapse,
Gleason scores, clinical stage, and antiandrogen therapy
were assessed using c2
tests. Correlations between mRNA
expression, PSA relapse, and clinical parameters were
statistically analyzed using logistic regression analyses.
Appropriate variables indicating !2 F values were selected
by stepwise method. CSS curves were plotted using the
Kaplan–Meier method and verified using the log-rank
test. Cox-hazard proportional analysis was used for esti-
mating the relationship between mRNA expression and
CSS. A correlation between the mRNA expression and LI
was evaluated using Spearman rank-correlation coeffi-
cient. JMP 9.0 software (SAS Institute) was used for
statistical analyses, and P 0.05 was considered statisti-
cally significant.
Results
Relationship between PSA relapse and
clinicopathologic data
We divided the patients into two groups according to
PSA recurrence during the follow-up period: the PSA
recurrence group (n ¼ 37) and the no-recurrence group
(n ¼ 9; Table 1). No significant correlations were found
between PSA recurrence and clinicopathologic character-
istics such as age, pretreatment serum PSA levels, Gleason
scores, clinical stage, EOD, or therapeutic regimens (Table
1). However, all patients without PSA recurrence achieved
a PSA nadir of 0.01, which is the measurement limit in
our institute, a proportion significantly higher than that
in the PSA recurrence group (P 0.0001). Time to PSA
nadir following ADT among the PSA recurrence and no-
recurrence groups was 424 Æ 286 days and 419 Æ 303
days, respectively (P ¼ 0.31). The CSS rate was signifi-
cantly worse in men with PSA recurrence than in the other
patients (P ¼ 0.0045).
Fujimura et al.

Relative mRNA expression of AR, AR-related genes,
stem cell–like markers, and prostate cancer-related
genes between the PSA recurrence and no-recurrence
groups
The relative mRNA expression of AR, AR-related genes,
stem cell–like markers, and prostate cancer–related
genes is shown in Table 2. Expression of AR in both
cancer and stromal cells was significantly stronger in
men without PSA recurrence than in the other group
(P ¼ 0.0026 and 0.013, respectively). Expression of APP,
TRIM36, Klf4, c-Myc, and ERb in stromal cells from
men without PSA recurrence was significantly increased
(P ¼ 0.0018, 0.047, 0.032, 0.044, and 0.032,
respectively).
Comparison of area under the curve for clinical
parameters and gene expression in prostate needle
biopsy samples for predicting PSA recurrence
Logistic regression analyses for predicting PSA recur-
rence using age, serum PSA levels, Gleason scores, T
stage, N stage, and EOD showed relatively high area
under the curves (AUCs; 0.83; Fig. 1A); however, 11
patients (23%) were misclassified by discriminant
analyses. In contrast, the AUCs for Sox2, Her2, and CRP
mRNA expression in cancer cells; AR and ERa mRNA
expression in stromal cells; and clinical parameters
were 1.0 in men with PSA recurrence. Only 2 patients
(4%) were misclassified by discriminant analyses
(Fig. 1B).
Correlation between CSS and gene expression
We compared prognostic clinical parameters and gene
expression profiles using Cox proportional hazard anal-
yses (Table 3). Cutoff values for age, serum PSA levels, T
stage, EOD, and relative mRNA expression for each gene
were determined using receiver operating characteristic
(ROC) curves. Decreased expression of Oct1, TRIM36,
Sox2, and c-Myc in cancer cells and decreased expression
of AR, Klf4, and ERa in stromal cells were significant
prognostic factors in univariate hazard analyses (HR: 2.6,
2.9, 3.0, 2.7, 3.8, 4.1, and 2.5, respectively; P ¼ 0.031,
0.0015, 0.045, 0.022, 0.0067, 0.0014, and 0.0034,
respectively). Increased serum PSA levels (!335 ng/mL),
increased Gleason scores (!8), and high EOD (!2) were
also correlated with CSS (HR: 2.7, 3.5, and 2.9; P ¼ 0.027,
0.046, and 0.016, respectively). Multivariate analyses
were not performed because of the following reasons. To
avoid multicollinearity problem, a correlation matrix was
constructed among 10 prognostic parameters. The Spear-
man rank-correlation coefficient test showed that relative
expression of Oct1, TRIM36, c-Myc, and SOX2 in cancer
cells has significant correlation with one another. Relative
expression of AR, ERa, and KLF4 in stromal cells has also
significant correlation with one another. Therefore, we
used 10 factors to classify 3 prognostic groups because of
lacking independent variables. Furthermore, the number
of cancer-specific death (n ¼ 24) events was small relative
to 10 prognostic factors.
Risk classification according to gene expression and
correlation with CSS
According to the number of cancer-specific risk factors
described above (Oct1, TRIM36, Sox2, and c-Myc expression
in cancer cells; AR, Klf4, and ERa expression in stromal cells;
serum PSA levels ! 335 ng/mL, Gleason scores ! 8, and
EOD ! 2), we divided the patients into favorable-, inter-
mediate-, and poor-risk groups, which had 0–3, 4–7, and 8–
10 risk factors, respectively. Significant differences were
observed in CSS rates among the 3 groups (favorable vs.
intermediate, P ¼ 0.0013; favorable vs. poor; P 0.0001;
and intermediate vs. poor, P ¼ 0.0059; Fig. 1C). Five-year
CSS rates for the favorable-, intermediate-, and poor-risk
groups were 90%, 32%, and 12%, respectively.
Validation study in an independent cohort of 30
patients with bony metastatic prostate cancer
To validate the reproducibility, we performed logistic
regression analyses, discriminant analyses for predicting
PSA recurrence, and made new risk classification for CSS
in an independent cohort of 30 patients with prostate
cancer with bone metastasis. Logistic regression analyses
for predicting PSA recurrence using clinical findings showed
relatively high AUCs (0.95; Fig. 2A); however, 9 patients
(30%) were misclassified by discriminant analyses. In con-
trast, the AUCs for Sox2, Her2, and CRP mRNA expression in
cancer cells; AR and ERa mRNA expression in stromal cells;
and clinical parameters were 1.0 in men with PSA recur-
rence. Four patients (13%) were misclassified by discrim-
inant analyses (Fig. 2B). According to the above classifica-
tion, we also divided the patients into favorable-, interme-
diate-, and poor-risk groups. Clinical significance was
shown in CSS rates among the 3 groups (favorable vs.
intermediate, P ¼ 0.11; favorable vs. poor; P ¼ 0.0025; and
intermediate vs. poor, P ¼ 0.033; Fig. 2C). Five-year CSS
rates for the favorable-, intermediate-, and poor-risk groups
were 75%, 48%, and 0%, respectively.
Immunohistochemistry and correlation between
immunoreactivity and mRNA expression of AR and Klf4
Among sevenprognostic genes, weselected ARand Klf4 to
investigate the relationship between immunoreactivity and
mRNA expression of these genes. Figure 2D–I shows the
results of immunohistochemical analyses for AR and Klf4 in
prostate cancer. Immunostaining of AR was identified in the
nuclei of cancer and stromal cells (Figure 2D–F), whereas
that of Klf4 was localized in both nuclei and cytoplasm in
cancer and stromal cells (Figure 2G–I). The LIs of AR in
cancer and stromal cells were 55 Æ 41 (0–100) and 19 Æ 29
(0–100), respectively. The LIs of Klf4 in cancer and stromal
cells were 50 Æ 39 (0–100) and 12 Æ 17 (0–70), respectively
(Supplementary Fig. S1A–S1D). Two pathologists indepen-
dently evaluated the tissue sections and the average LI was
used. The SEs of LIs AR in cancer and stromal cells were 0.35
and 0.06, respectively. The SEs of LIs of Klf4 in cancer and
stromal cells were 1.25 and 0.99, respectively. The Spear-
man rank-correlation coefficient (r) between immunore-
active score and mRNA expression was shown as follows: AR

in cancer and stromal cells (r ¼ 0.73, P 0.0001 and r ¼
0.56, P ¼ 0.0001, respectively), Klf4 in cancer and stromal
cells (r ¼ 0.08, P ¼ 0.61 and r ¼ 0.56, P 0.0001,
respectively; Supplementary Fig. S1).
Discussion
The most common initial systemic therapy for metastatic
prostate cancerisADT;however,thedurabilityofADTvaries.
Amongpatientswithmetastaticprostatecancerwhoreceived
ADT, the median survival time was 13 months for patients
with PSA nadirs of 4 ng/mL, 44 months for patients with
PSA nadirs of 0.2–4 ng/mL, and 75 months for patients with
PSA nadirs of 0.2 ng/mL (2). Several relevant nomograms
are used to predict progression-free survival and CSS in
patients with prostate cancer (28). These nomograms are
estimatedaccording toage,serum PSAlevels,Gleason scores,
and clinical stage. Clinical parameters showed a degree of
predictive power in the present study. These clinical para-
meters do not sufficiently reflect cancer aggressiveness or
durability of treatments such as ADT, radiation, or chemo-
therapy because clinical parameters do not accurately corre-
late with prostate cancer cell behavior.
The durability of ADT may be influenced by AR and AR-
related gene profiles in prostate cancer cells. ADT has an
Table 2. Comparison of relative mRNA expression between men with PSA recurrence and without PSA
recurrence (mean Æ SD; n ¼ 46)
Gene
PSA recurrence
(n ¼ 37)
Without PSA
recurrence (n ¼ 9) P
Androgen-related genes
AR Cancer 20 Æ 47 33 Æ 24 0.0026
Stroma 11 Æ 14 38 Æ 31 0.013
Oct1 Cancer 4.7 Æ 8.0 6.6 Æ 12 0.67
Stroma 3.4 Æ 5.3 9.9 Æ 20 0.35
FOXO1 Cancer 2.6 Æ 5.1 8.3 Æ 22 0.30
Stroma 3.1 Æ 6.4 0.78 Æ 1.1 0.29
FOXA1 Cancer 0.64 Æ 1.9 0.30 Æ 0.35 0.66
Stroma 0.79 Æ 2.1 0.11 Æ 0.23 0.15
FOXP1 Cancer 0.66 Æ 1.7 0.33 Æ 0.57 0.55
Stroma 0.94 Æ 4.2 0.081 Æ 0.15 0.37
APP Cancer 10 Æ 21 21 Æ 32 0.14
Stroma 38 Æ 99 39 Æ 38 0.018
ACSL3 Cancer 4.8 Æ 12 1.6 Æ 2.0 0.71
Stroma 3.4 Æ 7.2 8.3 Æ 16 0.09
TRIM36 Cancer 0.78 Æ 1.6 0.92 Æ 1.6 0.25
Stroma 2.7 Æ 12 3.4 Æ 6.9 0.047
Stem cell–like markers
Oct3/4 Cancer 9.8 Æ 13 18 Æ 36 0.59
Stroma 9.2 Æ 12 22 Æ 32 0.31
Sox2 Cancer 0.58 Æ 1.0 0.61 Æ 1.1 0.58
Stroma 2.7 Æ 9.1 1.0 Æ 1.1 0.12
Klf4 Cancer 4.5 Æ 8.5 5.5 Æ 8.7 0.57
Stroma 9.2 Æ 23 9.4 Æ 13 0.032
c-MyC Cancer 8.3 Æ 12 15 Æ 15 0.14
Stroma 17 Æ 37 26 Æ 40 0.044
Prostate cancer–related genes
CRP Cancer 4.8 Æ 9.7 2.6 Æ 3.0 0.53
Stroma 8.3 Æ 33 3.9 Æ 5.9 0.54
Her2 Cancer 1.2 Æ 2.8 1.7 Æ 4.4 0.83
Stroma 2.8 Æ 6.1 2.1 Æ 2.5 0.53
ERb Cancer 3.2 Æ 6.5 8.0 Æ 15 0.28
Stroma 6.7 Æ 17 8.0 Æ 9.3 0.032
Klf5 Cancer 7.2 Æ 11 6.7 Æ 8.2 0.86
Stroma 25 Æ 68 12 Æ 11 0.41
ERa Cancer 0.18 Æ 0.44 0.18 Æ 0.38 0.98
Stroma 0.18 Æ 0.27 1.9 Æ 3.7 0.16
Fujimura et al.

inhibitory effect on prostate cancer; however, ADT nega-
tively selects stem cell–like prostate cancer cells, an impor-
tant component of CRPC (3, 29, 30). Germann and col-
leagues found that castration induced the expression of 4
essential transcription factors required for reprogramming,
self-renewal, and pluripotency in differentiated somatic
cells, namely Oct4, Sox2, Klf4, and NANOG (3). Therefore,
before ADT is prescribed, an accurate prediction is critical
for treatment decisions, which range from ADT alone to
initial combination therapy with other therapeutic agents
such as docetaxel, cabazitaxel, zoledronic acid, and deno-
sumab (31), that are tightly linked to prognoses. In this
respect, we determined molecular indicators of ADT stabil-
ity and cancer progression in patients with metastatic pros-
tate cancer by evaluating AR, AR-related genes, stem cell–
related genes, and prostate cancer–related genes.
Molecular diagnosis using immunohistochemistry,
FISH, and DNA, RNA, or miRNA analyses is discussed
in a recent article (32–38). The studies in which gene sets
of stem cell–like cells, micro-RNA, or cell-cycle progres-
sion markers reflect more aggressive disease are limited to
localized prostate cancer (33–35). Although several
reports have shown that certain gene expression profiles
in primary prostate cancers correlate with poor progno-
sis, no consensus has been reached regarding specific
prognostic markers (32). In addition, controversial data
may reflect contamination of samples by other tissue
elements such as aggressive tumors, normal prostate
epithelium, and normal stromal components. Therefore,
LCM techniques may influence the accuracy of the
molecular diagnosis of prostate cancer. A recent study
using frozen samples showed that low PSA/HK3 mRNA
expression in prostate cancer was associated with
increased risk of biochemical recurrence in patients with
intermediate preoperative serum PSA levels (2–10
ng/mL; ref. 36). Fresh frozen samples are preferable for
RNA analysis of prostate biopsy samples. However, a
refined LCM technique efficiently provided mRNA and
miRNA from formalin-fixed biopsy samples (37, 38). In
this study, we showed that the durability of ADT is a
cancer-specific prognostic factor in patients with meta-
static prostate cancer using paraffin-embedded needle
biopsy samples.
Gene expression profiles have been successfully used to
define breast cancer subclasses with different biologic beha-
viors and responses to therapy (4). Gene expression-based
diagnostic tests are currently in clinical use for assessing the
risk of recurrence and for predicting the benefits of adjuvant
chemotherapy in patients with localized ER-positive and
lymph node-negative breast cancers (39). In contrast to
breast cancers, in which the status of estrogen receptors in
primary tumors is commonly used to make therapeutic and
prognostic decisions, the status of AR protein expression
does not seem to be as useful as in prostate cancer (32). One
possible explanation for this is that AR expression is het-
erogeneous and changes over time (31). Therefore, mea-
surement of the expression of selected AR downstream
targets can provide information on the individual function-
al status of ARs in prostate cancer cells. We previously
defined an AR transcriptional network in prostate cancer
cells using ChIP–chip and CAGE assays (18) and also
analyzed the AR-related genes APP, Oct1, and FOXP1 in
prostate cancer (7, 11, 12). In these experiments, APP and
Oct1 were correlated with prostate cancer aggressiveness
and both were considered therapeutic targets (7, 12). Some
FOX proteins that are involved in cell growth and differen-
tiation as well as in embryogenesis and longevity are known
to be AR-related genes (8). In the current study, we showed
relationships between biochemical recurrence and AR, ERa,
Sox2, CRP, and Her2 expression and between prognoses and
expression of AR, Oct1, TRIM36, Sox2, Klf4, c-Myc, and ERa.
Functional analysis of TRIM36 proteins, a subfamily of
RING type E3 ubiquitin ligases, remains unresolved, and
further investigation of the TRIM family is required in
Figure 1. Logistic regression analyses for predicting PSA recurrence after
androgen deprivation therapy and CSS classified by the number of risk
factors in patients with bony metastatic prostate cancer (n ¼ 46). A,
correlations of age, serum PSA levels, Gleason score (GS), T stage,
N stage, and EOD with PSA recurrence after ADT. The AUC for PSA
recurrence was 0.83. B, PSA recurrence after ADT can be predicted by
analyzing mRNA expression in biopsy samples using LCM. Combined
expression of Sox2, CRP, and Her2 in cancer cells, AR and ERa in stromal
cells, and previously described clinical parameters strongly predicted
PSA recurrence (AUC ¼ 1.0). C, risk factors included decreased
expression of Oct1, TRIM36, Sox2, and c-Myc expression in cancer cells,
AR, Klf4, and ERa in stromal cells, increased serum PSA levels (!335
ng/mL), high Gleason scores (!8), and high EOD (!2). Patients in the
favorable- (n ¼ 15), intermediate- (n ¼ 18), and poor- (n ¼ 13) risk groups
had 0–3, 4–7, and 8–10 risk factors, respectively. CSS rates differed
significantly among the 3 groups (favorable vs. intermediate, P ¼ 0.0013;
favorable vs. poor, P 0.0001; and intermediate vs.
poor, P ¼ 0.0059).

association with prostate cancer. The correlation between
suppressed Oct1, Sox2, and c-Myc expression in cancer cells
and poor prognosis may reﬂect the sensitivity of these genes
to ADT. Recent studies determined the localization and
function of Klf4 in prostate cancer cells (40, 41). In these
studies, Klf4 was downregulated in prostate cancer cell lines
and metastatic prostate cancer tissue (41), and RNA activa-
tor-mediated overexpression of Klf4 inhibited prostate can-
cer cell growth/survival and arrested cell-cycle progression
(41). Together with the present data, Klf4 appears to exert a
powerful inhibitory effect in prostate cancer.
Recent therapeutic strategies for advanced and metastatic
prostate cancer include ADT combined with antiandrogen
agents such as bicalutamide or ﬂutamide, secondary
Table 3. Cox proportional hazard regression analysis of CSS in metastatic prostate cancer (n ¼ 46)
Gene expression
(low vs. high) Cutoff Foci HR 95% index P
AR 0.27 Cancer 2.0e-6 1.3–1.3 0.078
14 Stroma 3.8 1.4–13 0.0067
Oct1 1.5 Cancer 2.6 1.1–6.4 0.031
1.1 Stroma 1.8 0.78–4.3 0.16
FOXO1 0.027 Cancer 2.5 0.95–5.9 0.063
8.3 Stroma 1.1 0.32–6.9 0.89
FOXA1 1.3 Cancer 0.27 0.072–1.8 0.15
1.3 Stroma 0.42 0.14–1.8 0.22
FOXP1 6.4 Cancer 0.094 0.013–1.8 0.099
1.6 Stroma 0.35 0.064–6.6 0.39
APP 15 Cancer 1.8 0.59–7.6 0.33
2.3 Stroma 0.69 0.24–1.7 0.45
ACSL3 0.78 Cancer 1.5 0.65–3.8 0.33
4 Stroma 1.7 0.48–11 0.45
TRIM36 0.13 Cancer 2.9 1.2–7.3 0.015
0.93 Stroma 2.6 0.99–9.3 0.053
Oct3/4 16 Cancer 2.3 0.66–14 0.22
4.5 Stroma 1.9 0.82–4.9 0.13
Sox2 0.53 Cancer 3.0 1.0–13 0.045
0.46 Stroma 1.6 0.65–4.5 0.32
Klf4 0.65 Cancer 1.3 0.56–3.1 0.53
1.6 Stroma 4.1 1.7–11 0.0014
c-Myc 3.8 Cancer 2.7 1.2–6.8 0.022
24 Stroma 0.79 0.31–2.4 0.66
PSA 0.21 Cancer 1.8 0.71–4.2 0.21
0.40 Stroma 2.0 0.86–5.1 0.10
CRP 0.20 Cancer 1.4 0.46–3.5 0.52
0.74 Stroma 1.8 0.77–4.3 0.18
Her2 5.9 Cancer 2.1 0.44–38 0.41
14 Stroma 1.2 0.26–22 0.82
ERb 0.63 Cancer 2.3 0.97–5.4 0.059
0.60 Stroma 1.9 0.72–4.5 0.19
Klf5 2.1 Cancer 2.2 0.87–5.2 0.091
1.5 Stroma 1.0 0.34–2.6 0.95
ERa 0.042 Cancer 0.61 0.27–1.4 0.26
0 Stroma 2.5 1.1–6.1 0.0034
Age !85 vs. 85 0.49 0.17–2.1 0.30
Serum PSA !335 vs. 335 2.7 1.1–6.5 0.027
Gleason score !8 vs. 7 3.5 1.0–22 0.046
T stage !4 vs. 3 1.2 0.47–2.9 0.66
N stage 1 vs. 0 1.5 0.62–3.9 0.38
EOD !2 vs. 1 2.9 1.2–7.0 0.016
NOTE: The appropriate cutoff value of each gene was decided by ROC curve including age, serum PSA levels, T stage, and EOD.
Fujimura et al.

hormonal manipulation using adrenal testosterone inhibi-
tors, low-dose diethylstilbestrol therapy, steroid therapy,
somatostatin analog therapy, and chemotherapy (31). In
the present study, we identified patients in whom the
durability of ADT was poor or moderate on the basis of
molecular diagnoses. We also validated the models of both
PSA reccurence and CSS in an independent metastatic
prostate cancer cohort. Further investigations in relative
large cohort prove the reproducibility, molecular diagnosis
of needle biopsy samples may be generalized for strategic
interventions. Immunohistochemical analysis using biopsy
samples may be easier to perform in clinical settings than
estimating mRNA expression using LCM. Therefore,
patients predicted to have CRPC in the early phaseof disease
may be given other therapeutic interventions before the
progression of aggressive phenotypes.
This study found that genes expressed in stromal cells,
such as AR, Klf4, and ERa, were correlated with cancer
progression. Expression of these genes may contribute to
the stromal–epithelial interactions in prostate cancer pro-
gression. The stromal cells in the prostatic tissue consist of
myofibroblasts, fibroblasts, and smooth muscle cells within
a connective tissue matrix. Numerous growth factors pro-
duced by the stromal cells, including transforming growth
factors, platelet-derived growth factors, fibroblast growth
factors, and EGFs, are crucial for prostate cancer growth (42,
Figure 2. Logistic regression
analyses for predicting PSA
recurrence after ADT and CSS
classified by the number of risk
factors in an independent cohort of
patients with bony metastatic
prostate cancer (n ¼ 30) and
immunohistochemistry using anti-
AR and Klf4 antibodies. A,
correlations of age, serum PSA
levels, Gleason score (GS), T stage,
N stage, and EOD with PSA
recurrence after ADT. The AUC for
PSA recurrence was 0.95. B,
combined expression of Sox2,
CRP, and Her2 in cancer cells,
AR and ERa in stromal cells, and
previously described clinical
parameters strongly predicted PSA
recurrence (AUC ¼ 1.0). C, risk
factors included decreased
expression of Oct1, TRIM36, Sox2,
and c-Myc expression in cancer
cells, AR, Klf4, and ERa in stromal
cells, increased serum PSA levels
(!1228 ng/mL), high Gleason
scores (!8), and high EOD (!2).
Patients in the favorable- (n ¼ 7),
intermediate- (n ¼ 20), and poor-
(n ¼ 3) risk groups had 0–3, 4–7,
and 8–10 risk factors, respectively.
CSS rates differed between the 3
groups (favorable vs. intermediate,
P ¼ 0.11; favorable vs. poor,
P ¼ 0.0025; and intermediate vs.
poor, P ¼ 0.033). D and E,
immunohistochemical staining for
AR (D–F) and Klf4 (G–I) antibodies
in prostate cancer. Strong (D),
moderate (E), and weak (F) staining
of AR was identified in the nuclei of
cancer and stromal cells. Strong
(G), moderate (H), and weak (I)
immunoreactivity for Klf4 was
observed in both nuclei and
cytoplasm of cancer cells.
Moderate (G and H) and weak (I)
immunostaining for Klf4 was seen
in nuclei and cytoplasm of stromal
cells.

43). These growth factors change microenviroment around
the stromal cells (43). Clark and colleagues created a
bioengineered microenviroment using tissue recombina-
tion that involved mixing of stromal and epithelial cell
populations (43). In coculture with cancer-associated fibro-
blasts (CAF), but not nonmalignant prostatic fibroblasts,
BPH-1 cells showed a more aggressive phenotype with
increased motility and a more direct way of cell migration
(43). In addition, the secretion of growth factors is influ-
enced by steroid hormones because stromal cells express
AR, ERa, and ERb that play significant roles in prostatic
epithelial cell growth (42). For example, Risbridger and
colleagues investigated prostatic tissue recombinants estab-
lished from wild-type ERa, and its knockout (KO) mice
(44). Presence of squamous metaplasia induced by estrogen
was evaluated in each combination of tissue recombinants,
such as wt-stroma (S) þ wt-epithelia (E), aERKO-S þ
aERKO-E, wt-S þ aERKO-E, and aERKO-S þ wt-E. Squa-
mous metaplasia induced by estrogen was found only in the
wt-S þ wt-E group, which suggested that both stromal and
epithelial ERa are required to achieve full response to
estrogen in terms of developing squamous metaplasia
(44). Stromal AR also influences oncogenic epithelium cell
growth (45). In Lai and colleagues (45), the authors estab-
lished an animal model with AR deletion in stromal fibro-
muscular cells in PTEN deleted from chromosome 10
(Pten) þ/À mouse. Prostatic intraepithelial neoplasia (PIN)
was developed via changing tumor microenvironment,
such as the alteration of angiogenesis and immune cells
infiltration in the model (45). Moreover, AR degradation
enhancer, ASC-J9, suppresses PIN development via stromal
AR degradation (45). These findings suggest that modula-
tors of stromal–epithelial interactions may be useful as
future therapeutic agents.
In conclusion, we demonstrated a predictive model of
PSA recurrence and CSS in patients with prostate cancer
with bone metastasis by measuring the expression of pros-
tate cancer–related genes in needle biopsy samples. Inter-
mediate- or poor-risk patients with bony metastatic prostate
cancer would be candidates for further clinical trials in
addition to ADT.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: T. Fujimura, S. Takahashi, T. Urano, Y. Yamada,
S. Inoue, Y. Homma
Development of methodology: T. Fujimura, S. Takahashi, T. Sugihara,
Y. Yamada
Acquisition of data (provided animals, acquired and managed patients,
provided facilities, etc.): T. Fujimura, T. Sugihara
Analysis and interpretation of data (e.g., statistical analysis, biosta-
tistics, computational analysis): T. Fujimura, S. Takahashi, K. Takayama,
S. Inoue
Writing, review, and/or revision of the manuscript: T. Fujimura,
S. Takahashi, T. Urano, K. Takayama, J. Kumagai, S. Inoue, Y. Homma
Administrative, technical, or material support (i.e., reporting or
organizing data, constructing databases): T. Fujimura, K. Takayama,
D. Obinata, H. Kume, Y. Ouchi, Y. Homma
Study supervision: S. Takahashi, J. Kumagai, S. Inoue, Y. Homma
Acknowledgments
The authors thank Tomoko Yamanaka and Yasuho Saito for their tech-
nical assistance and Takuhiro Yamagichi and Tempei Miyaji for their kind
advice regarding statistical analysis.
Grant Support
This work was supported by Grants of the Cell Innovation Program from
the Ministry of Education, Culture, Sports, Science Technology, Japan (to S.
Inoue), by grants from the Japan Society for the Promotion of Science (to T.
Fujimura, S. Takahashi, and S. Inoue), by Grants-in-Aid from the MHLW,
Japan (to S. Inoue), and by the Advanced Research for Medical Products
Mining Program in Health Sciences, National Institute of Biomedical Inno-
vation, Japan (to S. Inoue).
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received April 22, 2013; revised June 2, 2014; accepted June 2, 2014;
published OnlineFirst July 1, 2014.
References
1. Huggins C, Hodges CV. Studies on prostatic cancer. The effect of
castration, of estrogen and of androgen injection on serum phospha-
tases in metastatic carcinoma of the prostate. Cancer Res 1941;
1:293–7.
2. Hussain M, Tangen CM, Higano C, Scelhammer PF, Faulkner J,
Crawford ED, et al. Absolute prostate-specific antigen value after
androgen deprivation is a strong independent predictor of survival in
new metastatic prostate cancer: data from southwest oncology group
trial 9346 (INT-0162). J Clin Oncol 2006;24:3984–90.
3. Germann M, Wetterwald A, Guzman-Ramirez N, van der Pluijm G,
Culig Z, Cecchini MG, et al. Stem-like cells with luminal progenitor
phenotype survive castration in human prostate cancer. Stem Cells
2012;30:1076–86.
4. Carlson RW, Allred DC, Anderson BO, Burstein HJ, Edge SB, Farrar
WB, et al. Metastatic breast cacer, version 1.2012: featured updates to
the NCCN guidelines. J Natl Canc Netw 2012;10:821–9.
5. Tammela T. Endocrine treatment of prostate cancer. J Steroid Bio-
chem Mol Biol 2004;92:287–95.
6. Culig Z, Steiner H, Bartsch G, Hobisch A. Mechanism of endocrine
therapy-responsive and –unresponsive prostate tumors. Endocr Relat
Cancer 2005;12:229–44.
7. Takayama K, Tsutusumi S, Suzuki T, Horie-Inoue K, Ikeda K, Kaneshiro
K, et al. Amyloid precursor protein is a primary androgen target gene
that promotes prostate cancer growth. Cancer Res 2009;69:137–42.
8. Katoh M, Igarashi M, Fukuda H, Nakagama H, Katoh M. Cancer
genetic and genomics of human FOX genes. Cancer Lett 2013;328:
198–206.
9. Zhang H, Fang J, Yao D, Wu Y, Ip C, Dong Y. Activation of FOXO1 is
critical for anticancer effect methylseleninic acid in prostate cancer
cells. Prostate 2010;70:1265–73.
10. Sahu B, Laakso M, Ovaska K, Mirtti T, Lundin J, Ranniko A, et al. Dual
role of FoxA1 in androgen receptor binding to chromatin, androgen
signaling and prostate cancer. EMBO J 2011;30:3962–76.
11. Takayama K, Horie-Inoue K, Ikeda K, Urano T, Murakami K, Haya-
shizaki Y, et al. FOXP1 is an androgen-responsive transcription factor
that negatively regulates androgen receptor signaling in prostate
cancer cells. Biochem Biophys Res Commun 2008;374:388–93.
12. Obinata D, Takayama K, Urano T, Murata T, Kumagai J, Fujimura T,
et al. Oct1 regulates cell growth of LNCaP cells and is a prognostic
factor for prostate cancer. Int J Cancer 2012;130:1021–8.
13. Kawashima H, Nakatani T. Involvement of estrogen receptors in
prostatic tissues. Int J Urol 2012;19:512–22.
Fujimura et al.

14. Harkonen P, Makela SI. Role of estrogens in development of prostate
cancer. J Steroid Biochem Mol Biol 2004;92:297–305.
15. Niu Y, Chang TM, Yeh S, Ma WL, Wang YZ, Chang C. Differential
androgen receptor signals in different cells explain why androgen-dep-
rivation therapy of prostate cancer fails. Oncogene 2010;29:3593–604.
16. Soloway MS, Hardeman SW, Hickey D, Raymond J, Todd B, Soloway
S, et al. Stratification of patients with metastatic prostate cancer based
on disease on initial bone scan. Cancer 1988;61:195–202.
17. Mottet N, Bellmunt J, Bolla M, Joniau S, Mason M, Matveev V, et al.
EAU guidelines on prostate cancer. Part II: treatment of advanced,
relapsing, and castration-resistant prostate cancer. Eur Urol 2011;59:
572–83.
18. Takayama K, Tsutsumi S, Katayama S, Okayama T, Horie-Inoue K,
Ikeda K, et al. Integration of cap analysis of gene expression and
chromatin immunoprecipitation analysis on array reveals genome-
wide androgen receptor signaling in prostate cancer cells. Oncogene
2011;30:619–30.
19. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from
mouse embryonic and adult fibroblast cultures by defined factors. Cell
2006;126:663–76.
20. Schoenhals M, Kassambara A, De Vos J, Hose D, Moreaux J, Klein B.
Embryonic stem cell markers expression in cancers. Biochem Biophys
Res Commun 2009;380:157–62.
21. Saito K, Kihara K. Role of C-reactive protein in urological cancers:
a usuful biomarker for predicting outcomes. Int J Urol 2013;20:
161–71.
22. Tobiume M, Yamada Y, Nakamura K, Aoki S, Zennami K, Kato Y, et al.
Significant prognostic factor of immunohistochemical HER-2 expres-
sion using initial prostate biopsy specimens with M1b prostate cancer.
Prostate 2011;71:385–93.
23. Nakajima Y, Akaogi K, Suzuki T, Osakabe A, Yamaguchi C, Sunahara
N, et al. Estrogen regulates tumor growth through a nonclassical
pathway that includes the transcription factors ERb and KLF5. Sci
Signal 2011;4:ra 22.
24. de Matteris A, Guidi A, Di Paolo B, Franco G, Revoltella RP.
Endothelin-1 in human prostatic carcinoma treated with andro-
gen withdrawal: an immunohistochemical study. Cancer 2001;91:
1933–99.
25. Verma V, Huang B, Kallingappa PK, Oback B. Dual kinase inhibition
promotes pluripotency in finite bovine embryonic cell lines. Stem Cells
Dev 2013;1:1728–42.
26. Murata T, Takayama K, Urano T, Fujimura T, Ashikari D, Obinata D,
et al. 14-3-3z, a novel androgen-responsive gene, is upregulated in
prostate cancer and promotes prostate cancer cell proliferation and
survival. Clin Cancer Res 2012;18:5617–27.
27. Takayama K, Horie-Inoue K, Katayama K, Suzuki T, Tsutsumi S, Ikeda
K, et al. Androgen-responsive long noncoding RNA CTB1-AS pro-
motes prostate cancer. EMBO J 2013;32:1665–80.
28. Shariat SF, Kattan MW, Vickers AJ, Karakiewicz PI, Scardino PT.
Critical review of prostate cancer predictive tools. Future Oncol 2009;
5:1555–84.
29. Tang Y, Hamburger AW, Wang L, Khan MA, Hussain A. Androgen
deprivation and stem cell markers in prostate cancers. Int J Clin Exp
Pathol 2010;3:128–38.
30. Pfeiffer MJ, Smit FP, Sedelaar JP, Schalken JA. Steroidogenic
enzymes and stem cell markers are upregulated during androgen
deprivation in prostate cancer. Mol Med 2011;17:657–64.
31. Beltran H, Beer TM, Carducci MA, de Bono J, Gleave M, Hussain M, at
al. New therapies for castration-resistant prostate cancer: efficacy and
safety. Eur Urol 2011;60:279–90.
32. Choudhury AD, Eeles R, Freedland SJ, Isaacs WB, Pomerantz MM,
Schalken JA, et al. The role of genetic markers in the management of
prostate cancer. Eur Urol 2012;62:577–87.
33. Market EK, Mizuno H, Vazquez A, Levine AJ. Molecular classification of
prostate cancer using curated expression signatures. Proc Natl Acad
Sci U S A 2011;108:21276–81.
34. Martnes-Uzunova ES, Jalava SE, Dits NF, van Leenders GJ, Trapman
J, Bangma CH, et al. Diagnostic and prognostic signatures from the
small non-coding RNA transcriptome in prostate cancer. Oncogene
2012;31:978–91.
35. Cuzick J, Swanson GP, Fisher G, Brothman AR, Berney DM, Reid
JE, et al. Prognostic value of an RNA expression sig-
nature derived from cell cycle proliferation genes in patients with
prostate cancer: a retrospective study. Lancet Oncol 2011;12:
245–55.
36. Sterbis JR, Gao C, Furusato B, Chen Y, Shaheduzzaman S,
Ravindranath L, et al. Higher expression of the androgen-regulated
gene PSA/HK3 mRNA in prostate cancer tissues predicts bio-
chemical recurrence-free survival. Clin Cancer Res 2008;14:
758–63.
37. Nonn L, Vaishnav A, Gallagher L, Gann PH. mRNA and micro-RNA
expression analysis in laser-capture microdissected prostate biop-
sies: valuable tool for risk assmessment and prevention traials. Exp
Mol Pathol 2010;88:45–51
38. Rogerson L, Darby S, Jabbar T, Mathers ME, Leung HY, Robson CN,
et al. Application of transcript profiling in formalin-fixed paraffin-
embedded diagnostic prostate cancer needle biopsies. BJU Int 2008;
102:364–70.
39. Oakman C, Santarpia L, Di Leo A. Breast cancer assessment
tools and optimizing adjuvant therapy. Nat Rev Clin Oncol 2010;7:
725–32.
40. Magnen CL, Bubendorf L, Ruiz C, Zlobec I, Bachmann A, Heberer M,
et al. Klf4 transcription factor is expressed in the cytoplasm of prostate
cancer cells. Eur J Cancer 2013;49:955–63.
41. Wang J, Place RF, Huang V, Wang X, Noonan EJ, Maryar CE, et al.
Prognostic value and function of KLF4 in prostate cancer: RNAs and
vector-mediated overexpression identify KLF4 as an inhibitor of tumor
cell growth and migration. Cancer Res 2010;70:10182–91.
42. Berry PA, Maitland NJ, Collins AT. Androgen receptor signaling in
prostate: effects of stromal factors on normal and cancer stem cells.
Mol Cell Endocrinol 2008;288:30–7.
43. Clark AK, Taubenberger AV, Taylor RA, Niranjan B, Chea ZY,
Zotenko E, et al. A bioengineered microenvironment to quantita-
tively measure the tumorigenic properties of cancer-associated
fibroblasts in human prostate cancer. Biomaterials 2013;34:
4777–85.
44. Risbridger G, Wang H, Young P, Kurita T, Wang YZ, Lubahn D, et al.
Evidence that epithelial and mesenchymal estrogen receptor-alpha
mediates effects of estrogen on prostatic epithelium. Dev Biol 2001;
229:432–42.
45. Lai KP, Yamashita S, Huang CK, Yeh S, Chang C. Loss of stromal
androgen receptor leads to suppressed prostate tumorigenesis via
modulation of pro-inflammatory cytokines/chemokines. EMBO Mol
Med 2012;4:791–807.

2014;20:4625-4635. Published OnlineFirst July 1, 2014.Clin Cancer Res
Tetsuya Fujimura, Satoru Takahashi, Tomohiko Urano, et al.
Cancer
Cancer-Specific Survival in Patients with Metastatic Prostate
Stem Cell Markers to Predict Cancer Progression and
Expression of Androgen and Estrogen Signaling Components and
Updated version
10.1158/1078-0432.CCR-13-1105doi:
Access the most recent version of this article at:
Material
Supplementary
http://clincancerres.aacrjournals.org/content/suppl/2014/07/14/1078-0432.CCR-13-1105.DC1
Access the most recent supplemental material at:
Cited articles
http://clincancerres.aacrjournals.org/content/20/17/4625.full#ref-list-1
This article cites 45 articles, 12 of which you can access for free at:
Citing articles
http://clincancerres.aacrjournals.org/content/20/17/4625.full#related-urls
This article has been cited by 1 HighWire-hosted articles. Access the articles at:
E-mail alerts related to this article or journal.Sign up to receive free email-alerts
Subscriptions
Reprints and
.pubs@aacr.org
To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at
Permissions
Rightslink site.
Click on Request Permissions which will take you to the Copyright Clearance Center's (CCC)
.http://clincancerres.aacrjournals.org/content/20/17/4625
To request permission to re-use all or part of this article, use this link

4625.full

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a 4625.full

Semelhante a 4625.full (20)

Mais de University of Zambia, School of Pharmacy, Lusaka, Zambia

Mais de University of Zambia, School of Pharmacy, Lusaka, Zambia (20)

Último

Último (20)

4625.full