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NS-398, Ibuprofen and COX-2 RNAi produce significantly
different gene expression profiles in prostate cancer cells
Molykutty John-Aryankalayil1, Sanjeewani T. Palayoor1, David Cerna1, Michael T.
Falduto2, Scott R. Magnuson2, and C. Norman Coleman1
1Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National
Institutes of Health, Bethesda, MD
2GenUs BioSystems Inc, Northbrook, Illinois
Abstract
Cyclooxygenase-2 (COX-2) plays a significant role in tumor development and progression.
Nonsteroidal anti-inflammatory drugs (NSAIDs) exhibit potent anticancer effects in vitro and in
vivo by COX-2 dependent and independent mechanisms. In this study, we used microarray
analysis to identify the change of expression profile regulated by a COX-2 specific NSAID
NS-398 (0.01 and 0.1mM), a non-specific NSAID ibuprofen (0.1 and 1.5mM) and RNA
interference-mediated COX-2 inhibition (COX-2 RNAi) in PC3 prostate cancer cells. A total of
3,362 differentially expressed genes with 2 fold change, and p<0.05 were identified. Low
concentrations of NSAIDs and COX-2 RNAi altered very few genes (1-3%) compared to the
higher concentration of NS-398 (17%) and ibuprofen (80%). Ingenuity Pathway Analysis (IPA)
was used for distributing the differentially expressed genes into biological networks and for
evaluation of functional significance. The top 3 networks for the both NSAIDs included functional
categories DNA replication, recombination and repair, and gastrointestinal disease. Immune
response function was specific to NS-398, and cell cycle, cellular movement were among the top
functions for ibuprofen. IPA also identified renal and urological disease as a function specific for
ibuprofen. This comprehensive study identified several COX-2 independent targets of NSAIDs
which may help explain the antitumor and radiosensitizing effects of NSAIDs. However, none of
these categories were reflected in the identified networks in PC3 cells treated with clinically
relevant low concentrations of NS-398 and ibuprofen or with COX-2 RNAi suggesting the benefit
to fingerprinting pre-clinical drug concentrations to improve their relevance to the clinical setting.
Keywords
microarray; NSAIDs; COX-2; NS-398; ibuprofen; COX-2 RNAi
Introduction
A number of preclinical and clinical studies have demonstrated that nonsteroidal anti-
inflammatory drugs (NSAIDs) are effective chemopreventive and antitumor agents either
alone or in combination with standard cancer therapies including radiotherapy (1-5). The
best characterized targets of NSAIDs are cyclooxygenase enzymes COX-1 and COX-2 (6).
COX-1 is constitutively expressed in most normal tissues and is responsible for the normal
tissue homeostasis, while COX-2 is a stress response gene induced by inflammatory
Correspondence to: Molykutty John – Aryankalayil, Radiation Oncology Branch, Center for Cancer Research, 9000 Rockville Pike,
Rm 3B 49, Bldg # 10, Bethesda, MD 20892, Tel no: 301 496 1083, Aryankalayilm@mail.nih.gov.
NIH Public Access
Author Manuscript
Mol Cancer Ther. Author manuscript; available in PMC 2010 April 29.
Published in final edited form as:
Mol Cancer Ther
. 2009 January ; 8(1): 261–273. doi:10.1158/1535-7163.MCT-08-0928.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
cytokines, oncogenes and growth factors (5,6). COX-2 is selectively overexpressed at the
site of inflammation and also in a variety of human tumors. Overexpression of COX-2 in
human tumors is associated with poor prognosis and COX-2 is considered as one of the
crucial targets for cancer therapy (2,5,7).
Nonspecific NSAIDs such as ibuprofen or indomethacin inhibit both COX-1 and COX-2.
Although they are effective chemopreventive and antitumor agents, long-term use of the
nonspecific NSAIDs can result in renal and gastric toxicity, attributed to the inhibition of
prostaglandins (PG) derived from COX-1 (8,9). COX-2 specific drugs were developed based
on the hypothesis that selective COX-2 inhibition at the site of inflammation would lead to
reduction in pain and inflammation sparing the normal tissue toxicity, and were considered
as safer alternatives to the traditional NSAIDs to use in the clinic (10). However, recent
studies revealed that the long-term use of COX-2 inhibitors led to increased risk of
cardiovascular toxicity, a risk that will likely be of greater importance for long term use as
chemoprevention agent than for short-term use as treatment modifiers (8,11).
We have a long-standing interest in the use of NSAIDs to enhance the efficiency of radiation
(1,12,13). Our novel approach of using NSAIDs to enhance the effects of radiation therapy
was based on a clinical observation that ibuprofen ameliorated acute radiation-induced
urinary symptoms in patients with prostate cancer (14). Although this observation was not
confirmed in a randomized trial, prior to undertaking the clinical trial, we studied the
efficacy of ibuprofen in vitro and in vivo to confirm that there was no tumor protection.
Indeed, we found radiation sensitization of prostate carcinoma cells by ibuprofen (1,12) and
we have since pursued the potential mechanisms of action of NSAIDs.
Although NSAIDs inhibit prostaglandin synthesis at ≤ micromolar concentrations, the
antitumor and radiosensitizing effects of NSAIDs are generally seen at higher
concentrations. At higher concentrations NSAIDs inhibit a variety of cellular processes
including signal transduction, transcription, and DNA repair; alter cell cycle distribution and
inhibit cyclins; modulate Bcl-2 family proteins and induce apoptosis (5,13,15-19). NSAIDs
also inhibit angiogenesis, an important factor necessary for tumor growth and survival (20).
Microarray studies have demonstrated alterations in genes regulating metastasis (21),
apoptosis (22,23), cell cycle (24,25), programmed cell death, cell proliferation and cell-cell
communication (26) following treatment with nonspecific and/or COX-2 specific NSAIDs.
Thus NSAIDs affect multiple cellular targets in COX-2-dependent and -independent manner
(16,19,27).
We hypothesized that the gene expression profiles would differ for treatment with COX-2
specific and non-specific NSAIDs as they would for different concentrations of NSAIDs. In
the present study we analyzed the global gene expression profile in PC3 human prostate
carcinoma cells treated with NS-398, a COX-2-specific NSAID (0.01 and 0.1mM), and
ibuprofen (0.1 and 1.5mM), a nonspecific NSAID that inhibits both COX-1 and COX-2.
Pharmacokinetic studies indicate that oral administration of ibuprofen (400mg-3200mg per
day) results in peak plasma concentrations in the range of 0.2 to 1.0mM (18,28). While
NS-398 is not in clinical use the COX-2 specific inhibitor, celocoxib, (400-800 mg)
produces peak plasma concentration in the range of 3-8μmol/L (29). Thus, our low
concentrations are close to clinically relevant molar concentrations. Although effective in
inhibiting prostaglandin synthesis, the low concentrations in general, are less cytotoxic. The
high concentrations chosen for this study are typically used in preclinical studies to
demonstrate antitumor effects of NSAIDs. While pharmacological intervention by NSAIDs
can result in alterations of COX-2 dependent and independent target genes, inhibition of
COX-2 by RNAi is expected to reveal changes in COX-2 specific target genes. We knocked
down the COX-2 gene by RNAi in order to evaluate the gene expression changes specific to
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COX-2 inhibition. In addition, we studied the effect of NS-398 and ibuprofen on selected
cellular targets in another human prostate carcinoma cell line, DU-145, which did not
express COX-2 protein.
This comprehensive microarray study revealed that 24h treatment with low concentrations
of NS-398, ibuprofen and COX-2 RNAi altered very few genes in PC3 cells. However,
treatment with high concentrations of NS-398 and ibuprofen resulted in differential
expression of several COX-2-independent targets of NSAIDs. This study highlights the need
for preclinical drug fingerprinting to compare drugs, their dosages and schedules to
understand the similarities and differences of agents even within the same class, with the
ultimate goal of potentially selecting the drug and schedule appropriate for personalized
medicine.
Materials and Methods
Cells
PC3 and DU-145 human prostate carcinoma cells were obtained from American Type
Culture Collection (Rockville, MD) and maintained in RPMI 1640 supplemented with 10%
fetal bovine serum, glutamine, and antibiotics. Tissue culture reagents were purchased from
Life Technologies, Inc. (Grand Island, NY). Microarray analysis was done on PC3 cells. In
addition, we examined the NSAID-induced changes in selected genes and proteins in
DU-145 cells.
Treatment
NS-398 was purchased from Cayman chemicals (Ann Arbor, MI), dissolved in dimethyl
sulfoxide (DMSO) and stored at -20°C. Ibuprofen (I1892; Sigma chemicals) was prepared
fresh as a 100mmols/L stock in distilled water and filter sterilized before adding to cells.
Cells were cultured to 60-70% confluence and treated with 0.01 and 0.1mmols/L NS-398
and 0.1 and 1.5mmols/L ibuprofen for 24h. Control dishes were treated with equivalent
amounts of DMSO or H2O.
Transfection of COX-2 small interfering RNA
PC3 cells were transfected with 100nmol/L siRNA targeting COX-2 (SMART pool,
M-004557-00, Dharmacon, Lafayette, CO) using oligofectamine reagent (Invitrogen,
Carlsbad, CA) as described previously (30).
Measurement of PGE2 Production
To measure the effect of NS-398 and ibuprofen on PGE2 production PC3 cells were treated
with NS-398 (1-100 μmol/L) and ibuprofen (5-100μmol/L) for 24h. At the end of 24h cells
were stimulated with 30μmol/L arachidonic acid (AA). After 15min culture supernatants
were collected, centrifuged to remove debris and stored at -70°C. To measure the PGE2
production in cells after COX-2 RNAi cells were transfected with vehicle (oligofectamine)
or COX-2 siRNA. At 24h, 48h and 72h after transfection cells were stimulated with 30μmol/
L arachidonic acid and culture supernatants were collected after 15min. Prostaglandin levels
were determined by a competitive enzyme immunoassay using PGE2 Monoclonal EIA Kit
(Cayman chemicals, Ann Arbor, MI) according to the manufacturer's instructions.
Clonogenic Cell Survival
Cells were treated with NS-398, ibuprofen or siCOX-2. After 24h cells were trypsinized,
counted, and 50-200 cells were plated in triplicates in six-well plates for clonogenic assay.
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Colonies were stained with crystal violet after 12days and colonies of >50 cells were
counted.
Cell Cycle Study
Cells were treated with NS-398, ibuprofen or siCOX-2 for 24h and fixed in 70% ethanol.
Cells were processed for cell cycle analysis using Guava Cell Cycle Reagent according to
the protocol provided by the manufacturer (Cat. No. 4700-0160). Data were collected on
Guava cytosoft (Hayward, CA) and changes in cell cycle distribution were analyzed by
Modfit program.
Microarray analysis
PC3 cells were used for microarray analysis. Total RNA was extracted from cells treated
with NS-398, ibuprofen or siCOX-2 for 24h from 3 separate biological replicates, using
QIAshredder spin column (Cat. No. 79654, Qiagen, USA). All extracted RNAs were
purified with an RNeasy mini kit (Qiagen, USA). The concentration of total RNA was
measured by spectrophotometry at OD260/280 and the quality of the total RNA sample was
assessed using an Agilent Bioanalyzer with the RNA6000 Nano Lab Chip (Agilent
Technologies). Biotin-labeled cRNA was prepared by linear amplification of the Poly (A) +
RNA population within the total RNA sample. Briefly, 2 μg of total RNA was reverse
transcribed after priming with a DNA oligonucleotide containing the T7 RNA polymerase
promoter 5′ to a d(T)24 sequence. After second-strand cDNA synthesis and purification of
double-stranded cDNA, in vitro transcription was performed using T7 RNA polymerase in
the presence of biotinylated UTP. The quantity and quality of the cRNA was assayed by
spectrophotometry and on the Agilent Bioanalyzer as indicated for total RNA analysis.
Ten μg of purified cRNA was fragmented to uniform size and applied to CodeLink Human
Whole Genome Bioarrays (Applied microarrays Inc.) in hybridization buffer. CodeLink
Human Whole Genome arrays are comprised of approximately 55,000 30-mer probes
designed to conserved exons across the transcripts of targeted genes. These probes represent
well-annotated, full length, and partial human gene sequences from major public databases.
All fragmented samples were visualized on the Agilent Bioanlyzer to verify complete
fragmentation to ∼0.1 kb size before the sample was applied to the array. Arrays were
hybridized at 37° C for 18 hrs in a shaking incubator, washed in 0.75× TNT at 46° C for 1
hr, and stained with Cy5-Streptavidin dye conjugate for 30 min. Rinsed and dried arrays
were scanned with a GenePix™ 4000B scanner (Axon Instruments) at 5μm resolution.
Statistical Analysis
CodeLink Expression Analysis software (GE Healthcare) was used to process the scanned
images from arrays (gridding and feature intensity) and the data generated for each feature
on the array was analyzed with GeneSpring software (Agilent Technologies). Raw intensity
data for each gene on every array was normalized to the median intensity of the raw values
from that array. Data for all arrays were filtered for intensity values that were above
background in at least two of any set of three replicates for any condition within each drug
treatment. To ensure that genes were reliably measured, ANOVA was used to compare the
means of each condition (n=3). Cut off ratios greater than 2.0 and less than 0.5 and a p value
<0.05 relative to the respective control group were selected for this study.
Ingenuity pathway analysis (IPA)
The functional significance of differentially expressed genes perturbed by NS 398,
ibuprofen and COX-2 RNAi was evaluated using Ingenuity Pathway analysis (IPA) software
(Ingenuity Systems Version 6.3-1402, Redwood City, CA,). Genes with a minimal 2-fold
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change and a p value <0.05 were selected for network generation and pathway analyses
implemented in IPA tools. GenBank IDs of the selected genes were uploaded into the IPA,
which were next mapped to the functional networks available in the Ingenuity Pathway
Knowledge Base. Networks are comprised of biological functions assigned to networks
using significant p values for focus gene functions compared with the whole Ingenuity
Pathway Knowledge Base. Focus genes were identified as the subset having modeled
interaction(s) with the other molecules in the database. A maximum of 35 molecules
comprised a network. Each network was given a score reflecting the negative logarithm of
the p value, based on the chance of the significant molecules falling in to the network by
random. A score of 2 implies that there is a 1 in 100 chance that the focus genes are together
in a network because of random chance. Therefore, scores of 2 or higher have at least a 99%
confidence of not being generated by random chance alone.
Western blotting
Cell extracts were prepared and proteins were separated as described previously (13).
Membranes were processed by enhanced chemiluminescence method (Santa Cruz
Biotechnology, Santa Cruz, CA). Protein bands were captured by digital CCD camera (Fuji,
LAS 3000). The membranes were stripped and reprobed for actin. Signal intensities were
quantified using Image Quant (5.2 version) software (Molecular Dynamics, Sunnyvale, CA),
normalized to their loading control actin and expressed as fold change compared with
vehicle treated controls.
Antibodies used for immunoblotting were purchased from the following sources: COX-2
goat polyclonal antibody was purchased from Cayman Chemicals (Ann Arbor, MI). NAG-1
rabbit polyclonal antibody was purchased from Upstate cell signaling solutions (New York).
Cyclin A1 and E2F2 were purchased from BD Pharmingen (San Diego, CA). Cdk2 and
ATF3 were purchased from Santa Cruz Biotechnology and Cyclin E2 was from Cell
signaling (Danvers, MA). Horseradish peroxidase-conjugated goat, mouse and rabbit
antibodies were purchased from Santa Cruz Biotechnology and anti-actin antibody was
purchased from Chemicon (Temecula, CA).
Real-Time RT-PCR
The expression of angiopoietin-like 4 (ANGPTL4) gene was validated by real-time PCR
using Taqman gene expression assays and the ABI PRISM 7500 Sequence Detection
System instrument equipped with the SDS version 1.4.0 software (Applied Biosystems,
Foster City, CA). Forward and reverse primers and probes were designed and produced by
Applied Biosystems for ANGPTL4 (Hs01101127_m1). PCR was carried out in a 50-μl
reaction volume that contained 100 ng of RNA using TaqMan One-Step RT-PCR Master
Mix (Applied Biosystems). Each sample was analyzed in duplicate, for 3 different biological
sets of RNA and 18S ribosomal RNA was used as endogenous control (Hs9999901_s1).
Negative controls were processed under the same conditions without RNA template. The
threshold cycle (CT) of the endogenous control was used to normalize target gene expression
(ΔCt) to correct for experimental variation. The relative change in gene expression (ΔΔCT)
was used for comparison of the gene expression in drug treated samples versus vehicle
control by use of a paired t test.
In addition to ANGPTL4, the mRNA expression of COX-2 (Hs00153133 m1), NAG-1 (also
known as GDF-15 and MIC-1) (Hs00171132_m1), MMP3 (Hs00153133m1) and E2F2
(Hs00918091_m1) was analyzed in DU-145 cells. Gene expression levels were normalized
to GAPDH (Hs99999905_m1) expression and data are presented as the fold change in the
target genes in treated cell normalized to the internal control gene (GAPDH) and relative to
untreated cells. The baseline mRNA levels were compared between control PC3 and
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DU-145 expression and the fold differences calculated using the ΔΔCT method as described
by Livak and Schmittgen (31).
Immunoassay
The Quantitative MMP3 immunoassay kit (R&D systems) was used for the determination of
active and pro-Matrix Metalloproteinase 3 (total MMP3) in cell culture supernatants. Cells
were treated with NS-398, ibuprofen or siCOX-2 in six-well plates in duplicate for each
condition. At 24 hours, media were collected, centrifuged to remove debris, and stored at
-70°C. Cells from each well were trypsinized and counted. MMP3 concentration was
determined by ELISA according to the manufacturer's instructions and expressed as ng of
MMP3 / 106 cells.
Data analysis
Each data point represents average ± SEM of 3 experiments. Differences between the groups
were statistically evaluated by two-tailed paired t test. A p value of <0.05 was considered
statistically significant.
Results
Inhibition of PGE2 synthesis by NS-398, ibuprofen and COX-2 RNAi
PGE2 synthesis was inhibited by both NSAIDs over the concentration range studied
(NS-398 1-100μmol/L, ibuprofen 5-100μmol/L). Treatment with 0.01 mmol/L NS-398 and
0.1 mM ibuprofen for 24h reduced AA stimulated PGE2 secretion by 70% and 60%
respectively, compared to the vehicle treated controls. Transfection of cells with COX-2
siRNA reduced PGE2 secretion by 40% at 24h and by 60% at 48h compared to the vehicle
(oligofectamine) treated cells.
Clonogenic survival
The plating efficiencies of DMSO control, H2O control and oligofectamine control were
0.69 ± 0.12, 0.65 ± 0.07 and 0.67, respectively. The surviving fractions of PC3 cells treated
with 0.01 and 0.1mM NS-398 for 24h were 1.09 ± 0.04 and 1.06 ± 0.09 respectively
compared to the DMSO control (n=3). The surviving fractions of 0.1 and 1.5mM ibuprofen
treated cells were 1.03 ± 0.3 and 0.46 ± 0.12 respectively, compared to the control (n=3).
The surviving fractions of cells transfected with COX-2 siRNA was 0.93 compared to its
vehicle control (n=2). Thus, only the high concentration of ibuprofen reduced the clonogenic
survival and by 50%.
Microarray analysis of PC3 cells
Out of the total 55,000 genes represented in the Code-link Human Whole Genome array
3,362 genes were differentially expressed by the NSAID treatments and COX-2 RNAi with
high confidence (>2 fold change, p<0.05). Less than 3% genes were altered by low
concentrations of NSAIDs or COX-2 RNAi (supplementary table S1) while high
concentrations of NS-398 and ibuprofen altered 17% and 80% genes respectively
(supplementary tables S2 and S3).
A comparison of differentially expressed genes in cells treated with low concentrations of
NS-398, ibuprofen and COX-2 RNAi showed that not a single gene was commonly up
regulated (Fig. 1A) or down regulated (Fig. 1B) among these treatments. High concentration
of both NSAIDs showed much greater effect on gene expression patterns compared to the
low concentration. Global gene expression changes were much greater with ibuprofen than
for NS-398. Venn diagrams in Fig. 1C and D (lower panel) show the comparison of
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differentially expressed genes by high concentrations of NS-398, ibuprofen and COX-2
RNAi. The number of commonly expressed genes with high concentration of the two
NSAIDs was 264, out of which 97 were up regulated (Fig. 1C) and 167 were down
regulated (Fig. 1D). MMP3 was the only one gene that was commonly up regulated by high
concentrations of the two NSAIDs and COX-2 RNAi (Fig. 1C).
Ingenuity Pathway Analysis
Genes significantly affected by NSAIDs and COX-2 RNAi (> 2 fold change and p value
<0.05) were mapped to the functional networks in the IPA data base and ranked by score.
Table 1 and 2 show up to the top 10 networks affected by NSAIDs and COX-2 RNAi.
Treatment with 0.01mM NS-398, 0.1mM ibuprofen and COX-2 RNAi perturbed very few
genes (see Venn diagram- Fig.1A, B) and there were less than 10 functional IPA networks to
classify these genes (Table 1). However, treatment with 0.1mM NS-398 and 1.5mM
ibuprofen resulted in differential expression of 562 and 2701 genes, respectively (Fig. 1C,
D). IPA showed that for 0.1mM NS-398 there were 18 networks that had more than 10 focus
molecules whereas for 1.5mM ibuprofen there were 65 networks that had more than 10
focus molecules. Table 2 describes the top 10 networks in which the genes affected by high
concentration of NSAIDs were mapped. DNA replication, recombination and repair,
gastrointestinal disease and immune response were the functional categories included in the
top 3 networks for 0.1mM NS-398. The functional categories in the top 3 networks for
1.5mM ibuprofen included DNA replication, recombination and repair, cell cycle, cellular
movement, cell growth and proliferation. Significantly, IPA analysis also revealed renal and
urological disease and gastrointestinal disease categories in the top 3 networks of high
ibuprofen.
Heat maps
Fig. 2A shows heat maps of the selected top functions cell cycle, cell proliferation, DNA
replication and DNA repair identified by IPA. The fold changes in individual genes within a
functional category for all five experimental conditions were color coded to represent the
expression patterns in the heat maps. Almost all genes included in the functional networks
DNA replication and DNA repair were down regulated by high concentrations of NS-398
and ibuprofen. Genes included in cell cycle, cell proliferation and DNA repair networks
were more affected by high concentration of ibuprofen. These heat maps clearly show that
low concentrations of NSAIDs and COX-2 RNAi had very little effect on these genes.
One of the top 3 networks for high concentration of NS-398 included immune response as a
functional category. Fig. 2B shows the fold changes in 18 immune response genes that were
differentially expressed by NS-398 and by other treatments. MMP3 was the only gene
commonly up regulated by high concentration of NS-398, ibuprofen and COX-2 RNAi. Fig.
2C shows the changes in other metallopeptidases in response to NSAIDs and COX-2 RNAi.
Confirmation of microarray data
NSAID-induced changes in gene expression of selected genes detected by microarray
analysis were confirmed by western blot analysis, ELISA or real-time RT-PCR (Fig. 3A, B,
C and D). Microarray analysis revealed ∼9-fold increase in COX-2 by 0.1mM NS-398 and
1.5mM Ibuprofen, and >4 fold reduction in COX-2 by COX-2 RNAi, which was confirmed
by western blot analysis (Fig. 3A). COX-2 gene expression and protein were not altered by
lower concentrations of the NSAIDs. NAG-1, (NAG-1 is NSAID-activated gene, also called
GDF-15 and MIC-1) was significantly up regulated with higher concentrations of both
NSAIDs. Increase in NAG-1 was confirmed by western blot analysis (Fig. 3A). ATF3
(activating transcription factor 3) gene upregulation was seen only in cells treated with
ibuprofen; however, the protein was found to be up regulated by both NSAIDs. Microarray
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analysis showed that cell cycle-related genes Cyclin E2, E2F2 transcription factor and
cyclin-dependent kinase 2 were down regulated and Cyclin A1 was up regulated by higher
concentrations of NSAIDs and this was confirmed at protein level by western blot analysis
(Fig. 3B). None of these genes, except COX-2, was altered by COX-2 RNAi. Angiopoietin-
like 4 (ANGPTL 4) gene was significantly up regulated in microarray analysis by both low
(49-fold) and high concentrations (112-fold) of ibuprofen. Real-Time RT-PCR analysis
confirmed the increase at message level. Although not statistically significant, ANGPTL 4
gene expression was also up regulated by 0.1 mM NS-398 (9-fold). The increase in
ANGPTL4 by NS-398 at the message level was confirmed by Real-time RT-PCR (Fig. 3C).
MMP3 gene expression was up regulated by 0.1mM NS-398, 1.5mM ibuprofen, and also by
COX-2 RNAi. Analysis of culture supernatants by ELISA confirmed the increase in MMP3
at protein level (Fig. 3D).
Effect of NSAIDs on cell cycle progression
Microarray data and western blot analysis showed that several genes regulating cell cycle
progression were altered by high concentrations of both NSAIDs at 24h. Lower
concentrations of NSAIDs or COX-2 RNAi had no effect on these genes or proteins (Fig.
3B). Therefore, we evaluated the effect of all 5 treatments on the DNA distribution by flow
cytometry (Table 3). Treatment of cells with high concentration of NS-398 and ibuprofen
resulted in significant G1 arrest and reduction in the percentage of cells in S phase (p<0.05).
At lower concentrations, NSAIDs showed no effect on the cell cycle distribution. Silencing
COX-2 by siRNA resulted in some increase in G2M but had no effect on G1 and S phase as
compared to the vehicle-treated cells.
Effect of NS-398 and ibuprofen on DU-145 cells
We evaluated the effects of NS-398 and ibuprofen on cell survival, cell cycle and selected
molecular targets that were validated in PC3 cells, in a second human prostate carcinoma
cell line, the DU-145 cells.
Clonogenic cell Survival
The plating efficiencies of DMSO control and H2O control were 0.44 ± 0.01, 0.50 ± 0.01
respectively. The surviving fractions of DU-145 cells treated with 0.01 and 0.1mM NS-398
for 24h were 1.10 ± 0.07 and 1.01 ± 0.04 respectively compared to the DMSO control (n=3).
The surviving fractions of 0.1 and 1.5mM ibuprofen treated cells were 1.00 ± 0.02 and 0.67
± 0.02 respectively compared to the H2O control (n=3). Thus, as in the case of PC3 cells,
24h exposure to NS-398 was not cytotoxic to DU-145 cells also. However, Ibuprofen was
less cytotoxic to DU-145 cells as compared to the PC3 cells.
Cell Cycle
Effect of NSAIDs on cell cycle in DU-145 cells is shown in Table 4. Although higher
concentration of NS-398 resulted in some increase in cells in G1, it was not statistically
significant. Significant increase in cells in G1 accompanied by reduction in cells in S and
G2M compartments was seen after treatment with high concentration of ibuprofen (Table 4).
Effect of NSAIDs on COX-2, NAG-1, and ATF3 in DU-145 cells
COX-2, NAG-1 and ATF3, some of the known targets of NSAIDs, were significantly up
regulated by high concentrations of NS-398 and ibuprofen in PC3 cells. COX-2 mRNA was
very low in DU-145 cells compared to the levels in PC3 (< 42 fold). DU-145 cells did not
express COX-2 protein nor was it induced by high concentrations of NS-398 or ibuprofen
(data not shown), although mRNA expression increased by 25 fold with 1.5 mM ibuprofen
(Table 5). Similarly, NAG-1 message was very low in DU-145 cells compared to the PC3 (<
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76 fold). NAG-1 protein was not detected in DU-145 cells, however Real Time RT PCR
analysis revealed significant increases in NAG-1 at message level by both, NS-398 and
ibuprofen (Table 5). ATF3 protein was significantly up regulated by ibuprofen but not by
NS-398 (Fig. 4). Fig. 4 shows the effect of NS-398 and ibuprofen on the selected cell cycle
regulatory proteins in DU-145 cells. Cyclin A1 was up regulated by ibuprofen and not by
NS-398. Cyclin E2 and CDK2 were inhibited by ibuprofen but only to a lesser extent by
NS-398. Real Time RT PCR data showed that NS-398 had no significant effect on E2F2
message, however, ibuprofen significantly down regulated E2F2 at message level (Table 5).
ANGPTL4
Effect of NSAIDs on Angiopoietin-like 4 (ANGPTL4) in DU-145 cells was analyzed by
Real Time RT PCR (Table 5). There was no significant increase in ANGPTL4 by NS-398 in
DU-145 cells. ANGPTL4 was significantly up regulated by low and high concentration of
ibuprofen in DU-145 cells, although to a much smaller extent compared to the PC3 cells.
MMP3
Whereas MMP3 was up regulated in PC3 cells by high concentrations of NSAIDs and
COX-2 RNAi, MMP3 was not detectable in DU-145 cell culture supernatants following
NSAID treatment (data not shown). Real Time PCR data revealed significant lower baseline
expression level of MMP3 mRNA in DU-145 (<400 fold) compared to PC3 cells, however
1.5 mM ibuprofen significantly upregulated MMP3 mRNA expression (Table 5).
Discussion
The primary purpose of this study was to better understand the similarities and differences
among drug treatments from the same general class of drugs, the COX inhibitors using drug
concentrations commonly used in preclinical studies in comparison to the drug concentration
achievable in the clinic. That the published literature includes a wide range of drugs, doses
and schedules and identifies a wide range of drug targets, makes it an challenge to reconcile
the differences and thereby to understand how a drug may work in the clinic and also to
determine potential biomarkers of drug effect. The striking observation of this study is that
the 5 treatments, low and high concentration of the COX-2 specific NSAID NS-398, the
non-specific NSAID ibuprofen, and COX-2 RNAi produce very different gene expression
profiles. siRNA is considered to be a useful means of understanding the impact of turning
off a particular gene. COX-2 siRNA produced very few genes in common with any of the
drug conditions.
In preclinical studies the antitumor effects of NSAIDs are mainly seen at higher
concentrations and at these concentrations NSAIDs target a wide variety of cellular
processes (5). In the present study, treatment of PC3 cells with higher concentrations of
NS-398 and ibuprofen resulted in an accumulation of cells in G1 with a decrease in the
number of cells in S phase. These data are in agreement with earlier studies showing similar
cell cycle perturbations in cells treated with NS-398 (50-200μM) (16,32) and ibuprofen
(1mM) (18). Interestingly, inhibition of COX-2 in OVCAR-3 cells by treatment with COX-2
siRNA did not affect cell cycle progression (32) and this is confirmed in PC3 cells. The cell
cycle perturbations by NSAIDs correlated with alterations in cell cycle regulatory genes.
The microarray data revealed that high concentrations of NS-398 and ibuprofen down
regulated several cell cycle regulatory genes including Cyclin E2, cdk2 and the transcription
factor E2F2. Some of the other cell cycle regulatory genes targeted by NSAIDs included
MCM family members, P18, geminin, aurora kinase B, E2F8 and BRCA1. In addition p21,
Mdm2, CHK2, E2F5 and GADD45 were up regulated and cdc2, cdc25, E2F1, Cyclins (A2,
B1, B2, K and F), and Wee1 homolog were down regulated specifically by high
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concentration of ibuprofen. Significantly, low concentrations of the both NSAIDs and
COX-2 RNAi had no influence on cell cycle distribution and also did not alter any of these
cell cycle regulatory genes. Although some of the previous microarray studies have reported
changes in a few cell cycle regulatory genes by NSAIDs, the present global microarray
analysis is more comprehensive and has revealed a large number of cell cycle regulatory
genes that are differential expressed by NSAIDs.
The cell cycle response of DU-145 cells to the NSAIDs was different than the response of
PC3 cells. Whereas NS-398 and ibuprofen both induced significant G1 accumulation in PC3
cells, only ibuprofen treatment resulted in significant G1 accumulation in DU-145 cells. As
in case of PC3 cells, the cell cycle perturbations by NSAIDs in DU-145 cells correlated with
alterations in cell cycle regulatory proteins. The inhibition of Cyclin E2, CDK2 and E2F2,
and up regulation of Cyclin A1 was more pronounced in cells treated with ibuprofen than
those treated with NS-398. We recently observed a difference in cell cycle response of PC3
and DU-145 cells to another compound, PX-478, an inhibitor of hypoxia-inducible factor-1α
(33)
In addition to cell cycle regulatory genes, our PC3 microarray data revealed significant
upregulation of genes with antitumorigenic and proapoptotic activities, including NAG-1
(GDF-15, MIC-1) and ATF3, mainly at higher concentrations of the both NSAIDs. We have
previously shown that ibuprofen induced apoptotic DNA fragmentation in PC3 cells but not
in DU-145 cells (12). NAG-1 which belongs to TGF-β superfamily, is up regulated by
several NSAIDs as well as by other antitumorogenic or dietary compounds and the induction
of NAG-1 has been reported to be independent of COX-2 and p53 status (34-36). It has been
shown that the increase in NAG-1 mRNA by a panel of NSAIDs correlated with the
induction of apoptosis (34). NAG-1 is also implicated in cell growth arrest (35). Increase in
NAG-1 by sulindac sulfide in ovarian carcinoma cells was associated with suppression of
cell growth, and transfection with NAG-1 siRNA reversed the suppression of cell growth
(35). The basal expression of NAG-1 appears to be cell type dependent. It has been reported
that of the 3 established human prostate carcinoma cell lines PC3 and LNCaP cells secreted
NAG-1 (MIC-1) protein at high levels whereas DU-145 cells produced no NAG-1 (MIC-1)
protein (37). Beak et al studied NAG-1 mRNA induction by nonspecific NSAIDs and
COX-2 specific NSAIDs in COX-2 deficient HCT-16 colon carcinoma cells and found that,
in general, the nonspecific NSAIDs increased NAG-1 expression by 2- to 5-fold whereas the
COX-2 specific inhibitors did not (32). NS-398 failed to induce NAG-1 mRNA or protein in
COX-2 deficient SKOV3 ovarian and HCT-16 colon carcinoma cells (34,35). Our data
showed an increase in NAG-1 message and protein in COX-2 expressing PC3 cells treated
with 0.1mmolar NS-398 whereas in COX-2 deficient DU-145 cells the induction of NAG-1
mRNA by NS-398 was much smaller compared to the induction by ibuprofen. Interestingly,
DU-145 cells did not express COX-2 and NAG-1 proteins, nor they were up regulated in
DU-145 cells by high concentrations of NS-398 or ibuprofen. Thus the NSAID-induced up
regulation of COX-2 and NAG-1 appears to be cell type dependent and appears to differ for
COX-2 specific NSAIDs and nonspecific NSAIDs.
Antitumor and proapoptotic gene ATF3 is also reportedly activated by a wide variety of
NSAIDs, including the traditional NSAIDs sulindac sulfide and indomethacin (22). ATF3 is
known to regulate several downstream genes related to cell growth (38), and invasion (39).
In PC3 cells, although the increase in ATF3 gene expression was specific to ibuprofen,
ATF3 protein was up regulated by high concentration of NS-398 as well. In DU-145 cells
the up regulation in ATF3 protein was much greater with ibuprofen as compared to the
increase with NS-398. It appears that ATF3 is involved in the induction of NAG-1. In
HCT-116 cells polyphenolic compound epigallocatechin (ECG) induces the transcription
factor ATF3 which binds to NAG-1 promoter and transactivates NAG-1 expression(40). In
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the present study the NSAIDs-induced NAG-1 protein pattern, in general, paralleled the
ATF3 protein pattern. Increase in NAG-1 and ATF3 may contribute to the antitumorigenic
effects of ibuprofen and NS-398, as reported for other NSAIDs (22,34-36).
Up regulation in COX-2 protein by high concentrations of NSAIDs has been reported in
several earlier studies (13,30,37,38). The NSAIDs-induced COX-2 mRNA in chick embryo
fibroblast cells was reportedly truncated and non-functional (41). Simmons et al. showed
that non-specific NSAID Diclofenac-induced COX-2 has distinct COX active site and also
is more sensitive to acetaminophen than COX-2 induced by Lipopolysaccharide (LPS) (42).
In the present study NS-398 and ibuprofen both induced ∼9-fold increase in COX-2 gene
and 10-fold increase in the protein in PC3 cells, as seen in our previous studies (13,30). Low
concentrations of NSAIDs did not induce COX-2, and COX-2 RNAi as expected, reduced
COX-2 expression. The functional role of the NSAIDs-induced COX-2 is unclear. Overall,
the change in COX-2, ATF3 and NAG-1 in PC3 cells as well as in DU-145 cells appears to
occur in tandem suggesting the possibility that these targets may be interrelated. We are
currently pursuing this observation in further details.
In addition to confirming the changes in NAG-1, ATF3 and COX-2, the known targets of
NSAIDs, the present microarray data identified several new targets of NSAIDs including
angiopoietin like-4 (ANGPTL4). There was a concentration dependent significant
upregulation of ANGPTL4 gene in PC3 cells treated with NS-398 and ibuprofen and
ibuprofen was much more effective than NS-398 in inducing ANGPTL4. ANGPTL4 protein
is a circulating plasma protein, expressed in the liver, adipose tissue, and placenta (43,44). It
is implicated in regulation of angiogenesis and metastasis (45). ANGPTL4 expression is
regulated by hypoxia both in endothelial cells and in tumor cells (46,47). It has been
reported that ANGPTL4 reduces endothelial cell adhesion, and decreases cell migration and
sprouting (48). Recent studies indicate that ANGPL4, through its action on both vascular
and tumor compartments, prevents the metastatic process by inhibiting vascular activity as
well as tumor cell motility and invasiveness (49). ANGPTL4 is also an important regulator
of glucose homeostasis, insulin sensitivity and lipid metabolism (50). Interestingly, one of
the top 10 networks identified by IPA for high concentration of ibuprofen included lipid
metabolism as a functional category. PC3 Microarray data also showed changes in many
genes associated with lipid metabolism. In DU-145 cells also ibuprofen significantly
increased ANGPTL4 mRNA expression, although to a much smaller extent, compared to the
increase by ibuprofen in PC3 cells. Increase in ANGPL4 by NSAIDs is a novel observation
of this study and further work is needed to determine the significance of this finding.
Several members of extracellular matrix class were altered by NSAIDs. Of particular
interest is MMP3, which was the only common gene up regulated with high concentrations
of NS-398, ibuprofen and COX-2 RNAi. Many cytokines and growth factors increase
MMP3 expression and several signaling molecules including COX-2 derived PGE2 are
implicated in the regulation of MMP3. The reports on the effect of NSAIDs on MMP3
expression appear to be contradictory. While some studies have shown an inhibition in
cytokine-induced MMP3 by NS-398 (51), others have reported an increase in IL-1α induced
MMP3 by indomethacin and NS-398, even though both drugs inhibited IL-1α induced PGE2
(52). In patients with osteoarthritis given 1200mg/day ibuprofen for 28 days MMP3 serum
concentration was found to be increased (53). Similarly, in a clinical model of acute
inflammation, treatment with rofecoxib (50 mg daily) or ibuprofen (400 mg 4 times per day)
increased MMP3 in the oral mucosa biopsy specimens of volunteers (54). Although the
precise mechanism of the overexpression of MMP3 by NSAIDs and COX-2 RNAi in the
present study is not clear, inhibition of PGE2 could be one of the factors for this increase as
reported by others (52,54).
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The present microarray data elucidates some of the differences in the cellular effects of
NS-398 and ibuprofen that are seen in high concentration experiments. Ibuprofen was more
cytotoxic compared to NS-398 and 24h drug treatment reduced the plating efficiency of
ibuprofen-treated cells to ∼50% whereas NS-398 was not toxic. At high concentrations both
drugs induced cell cycle perturbations although ibuprofen was more effective. The
microarray data showed that at 24h, treatment with high concentration of NS-398 and
ibuprofen resulted in alterations in 17% and 80% of the 3,362 genes. Ibuprofen was much
more effective in down regulating genes related to cell cycle, proliferation and DNA repair.
In addition the microarray data and the IPA analysis showed that ibuprofen affected genes
regulating signal transduction, gene expression, cellular function and maintenance, drug
metabolism, molecular transport, lipid and carbohydrate metabolism, more effectively
compared to NS-398. This may account for the higher ibuprofen toxicity.
The use of expression profiling has been proposed to (a) profile the molecular pathway of a
tumor to guide drug choice (55), (b) to guide the use of chemotherapeutic drugs and
combinations by predicting sensitivity and resistance (56), and (c) to predict how one drug
will behave compared to others (57). This present study adds an additional dimension in that
it is important to profile and thereby “fingerprint” drugs and drug regimens even within
drugs of the same class and, if gene silencing is used to predict how a drug is working, this
must also be fingerprinted to compare it to pharmacologic inhibition. The choice of cell lines
to use for fingerprinting could be tumor type specific or a few “standard” cell lines could be
employed with the purpose of providing an comparison of drug effect with the wide range of
concentrations used in mechanistic studies, some of which may have minimal relevance to
the clinical application.
While more work is required, drug fingerprinting could facilitate matching the drug, drug
dose and schedule with the tumor profile, optimizing the drug prior to clinical application
and facilitating understanding the drug effect in the patient. Further work in our laboratory is
“fingerprinting” radiation therapy doses and schedules. As analytical tools are more fully
developed and validated, we hypothesize that it might be possible to compare expression
profile of the tumor, the drug induced changes and the radiation induced changes to pre-
select effective combined modality treatment combinations, including potentially predicting
normal tissue injury. While the in vitro and in vivo expression profiles differ (58,59), it may
be that the in vitro data using well defined cells and tissue arrays may allow such predictions
to be made on a limited set of genes, as is now being used to predict clinical outcome in the
clinic.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
The authors gratefully acknowledge and thank Dr. David Goldstein and the CCR for support, and Dr. Joanna Shih,
Biomedical Research Branch, NCI, for consultation and help with statistical analysis. This research was supported
by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
ABBREVIETIONS
IPA Ingenuity Pathway Analysis
PGE2 Prostaglandin Endoperoxidase2
MMP3 Matrix Metallo Protease-3
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ANGPTL4 Angiopoeitin-like 4
NAG-1 NSAID Activated Gene-1
ATF3 Activating Transcription Factor-3
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Fig. 1.
Venn diagrams showing the number of overlapping genes in comparison of 0.01mM
NS-398, 0.1mM ibuprofen and siCOX-2 (A, B); and 0.1mM NS-398, 1.5mM ibuprofen and
siCOX-2 (C, D). A and C: - up regulated genes, B and D: - down regulated genes. The
number of differentially expressed genes with more than 2-fold changes for each treatment
was 0.01mM NS-398 (14 genes), 0.1mM NS-398 (562 genes), 0.1mM ibuprofen (54 genes)
and 1.5mM ibuprofen (2701 genes) and siCOX-2 (31 genes).
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Fig. 2.
Differentially expressed genes exhibiting greater than 2-fold changes and p<0.05 were
classified in to functional networks using IPA software (Table 1 and 2). (A) Heat maps of
Cell cycle, Cell proliferation, DNA replication and DNA repair categories which were
among the top 3 networks for the high concentration of NS-398 and ibuprofen. Heat maps
represent the fold change in each individual gene in these categories for each experimental
condition- 0.01mol/L NS-398 (L NS), 0.1mM NS-398 (H NS), 0.1mM ibuprofen (L IBU),
1.5mM ibuprofen (H IBU), and siCOX-2. Yellow in to dark orange indicates up-regulated
genes; Blue indicates down-regulated genes. (B) Immune response function was identified
as one of the top function for high NS-398 by IPA analysis. Heat map shows the expression
patterns of 18 immune response genes differentially expressed (>2-fold, p<0.05) by high
concentration NS-398 and by other experimental conditions. (C) Heat map of 11
metallopeptidases genes (>2-fold, p<0.05) altered by NSAIDs and COX-2 RNAi in PC3
cells.
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Fig. 3.
Validation of selected genes. (A) Western blot analysis confirming changes in COX-2,
NAG-1 and ATF3 protein levels in PC3 cells after 24h treatment with NS-398 (0.01mM and
0.1mM), ibuprofen (0.1mM and 1.5mM) and COX-2 siRNA. Δ= fold change normalized to
control or vehicle. Fold change by microarray and fold change by densitometry are
indicated. (B) Western blot analysis demonstrating alterations in cell cycle regulatory
proteins Cyclin E2, E2F2, Cdk-2 and Cyclin A1 in PC3 cells treated with NS-398 (0.01mM
and 0.1mM), ibuprofen (0.1mM and 1.5mM) and COX-2 siRNA. Δ= fold change
normalized to control or vehicle. Fold change by microarray and fold change by
densitometry are indicated. (C) Real-Time RT- PCR analysis of angiopoietin-like4
(ANGPTL4) mRNA in PC3 cells treated with NS-398 (0.01mM and 0.1mM), ibuprofen
(0.1mM and 1.5mM) and COX-2 siRNA. Each data point represents Average ± SEM of 3
separate experiments. * p<0.05, ** p<0.01. (D) MMP3 levels in culture supernatants from
PC3 cells after 24h treatment with NS-398 (0.01mM and 0.1mM), ibuprofen (0.1mM and
1.5mM) and COX-2 siRNA were determined by ELISA and normalized to control or
vehicle. Fold change is expressed as nanogram MMP3/106 cells. Each data point represents
Average ± SEM of 3 separate experiments. * p<0.01, ** p<0.001
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Fig. 4.
Western blot analysis demonstrating alterations in ATF3, and cell cycle regulatory proteins
Cyclin E2, Cdk-2 and Cyclin A1 in DU-145 cells treated with NS-398 (0.01mM and
0.1mM) and ibuprofen (0.1mM and 1.5mM). Δ= fold change normalized to control. Data
shown are representative of 3 separate experiments.
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Table 1
Functions associated with the top 10 networks for genes whose expression was affected by low dose NSAIDs or SiCOX2 treatment in PC3
Cells
Genes significantly affected in PC3 cells by NSAIDs and COX-2 RNAi (>2-fold change, p<0.05) were classified in to functional networks using IPA
software. Table 1 shows the networks and the associated functional categories identified by IPA for NS-398 (0.01mM), ibuprofen (0.1mM) and COX-2
siRNA. Score refers to the statistical significance and focus molecules indicate the number of genes that could be mapped to molecules, out of a possible
35 molecules in each network.
Networks Score Focus Molecules Top Functions
0.01 mM NS-398
1 14 5 Protein Synthesis, Cellular Growth and Proliferation, Cell Cycle
2 3 1 Cellular Development, Developmental Disorder, Digestive System Development and Function
0.1 mM IBU
1 44 18 Cellular Development, Cellular Growth and Proliferation, Cancer
2 26 12 Cell Cycle, Hepatic System Development and Function, Gene Expression
3 3 1 Hematological Disease, Infectious Disease, Cardiovascular Disease
4 3 1 Cell Morphology, Cell-To-Cell Signaling and Interaction, Nervous System Development and Function
5 3 1
6 3 1
7 2 1 Cardiovascular Disease, Organismal Injury & Abnormalities, Reproductive System Development and Function
SiCOX2
1 38 14 Reproductive System Disease, Cellular Movement, Cardiovascular System Development and Function
2 3 1 Molecular Transport, Small Molecule Biochemistry, Amino Acid Metabolism
3 3 1 Gene Expression
4 3 1 Small Molecule Biochemistry
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Table 2
Functions associated with the top 10 networks for genes whose expression was affected by high dose NSAIDs in PC3 Cells
Top 10 networks and the associated functional categories of the genes significantly altered by high NS-398 (0.1mM) and high ibuprofen (1.5mM),
identified by IPA.
Networks Score Focus Molecules Top Functions
0.1 mM NS-398
1 57 32 DNA Replication, Recombination, and Repair, Cancer, Gastrointestinal Disease
2 52 30 DNA Replication, Recombination, and Repair, Viral Function, Cancer
3 42 26 Cancer, Immune Response, Connective Tissue Disorders
4 35 23 DNA Replication, Recombination, and Repair, Cell Cycle, Cellular Assembly and Organization
5 27 19 Cancer, Cellular Growth and Proliferation, Hematological Disease
6 25 18 Neurological Disease, Tissue Morphology, Cancer
7 23 17 Gene Expression, Viral Function, RNA Post-Transcriptional Modification
8 23 17 Gene Expression, Cellular Growth and Proliferation, Cell Death
9 21 16 Cellular Assembly and Organization, Cellular Function and Maintenance, Cancer
10 21 16 Carbohydrate Metabolism, Lipid Metabolism, Small Molecule Biochemistry
1.5 mM IBU
1 44 35 DNA Replication, Recombination, and Repair, Cell Cycle, Cellular Movement
2 42 34 Cancer, Cellular Growth and Proliferation, Renal and Urological Disease
3 42 34 Cancer, Cell Cycle, Gastrointestinal Disease
4 39 33 Cell-To-Cell Signaling and Interaction, Cellular Movement, Cellular Assembly and Organization
5 39 33 Connective Tissue Disorders, Immunological Disease, Inflammatory Disease
6 37 32 Cell Cycle, DNA Replication, Recombination, and Repair, Cellular Assembly and Organization
7 35 31 Lipid Metabolism, Small Molecule Biochemistry, Hematological Disease
8 33 30 DNA Replication, Recombination, and Repair, Cell Cycle, Cancer
9 33 30 Cancer, Cell Death, Cellular Movement
10 33 30 Embryonic Development, Nervous System Development and Function, Organ Development
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Table 3
Cell cycle distribution of PC3 cells following treatment
Cell cycle distribution of PC3 cells following treatment with NS-398, ibuprofen and COX-2 siRNA. PC3 cells
were treated for 24h and percentages of cells in different cell cycle compartments were determined by flow
cytometry. The data represents Average ± SEM of 3 separate experiments.
Treatment G1 S G2M
DMSO control 39.9 ± 0.6 43.5 ± 0.5 16.6 ± 0.3
0.01mM NS-398 38.6 ± 0.4 43.4 ± 1.3 18.0 ± 0.9
0.1mM NS-398 61.2 ± 3.1** 21.9 ± 1.8** 17.0 ± 4.1
Control 36.8 ± 3.6 45.6 ± 3.9 17.6 ± 2.4
0.1mM ibuprofen 40.2 ± 1.1 43.6 ± 1.8 16.2 ± 1.7
1.5mM ibuprofen 78.9 ± 2.1** 14.0 ± 2.9*** 7.1 ± 0.9*
Vehicle control 35.8 ± 3.5 44.7 ± 2.1 19.5 ± 1.6
siCOX-2 34.0 ± 3.6 42.1 ± 0.8 23.9 ± 2.9*
*p <0.05,
**p <0.01 and
***p <0.001
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Table 4
Cell cycle distribution of DU-145 cells following treatment
Cell cycle distribution of DU-145 cells following treatment with NS-398 and ibuprofen. DU-145 cells were
treated for 24h and percentages of cells in different cell cycle compartments were determined by flow
cytometry. The data represents Average ± SEM of 3 separate experiments.
Treatment G1 S G2M
DMSO control 53.3 + 0.9 24.2 + 0.1 22.2 + 0.9
0.01mM NS-398 51.9 + 0.6 25.5 + 0.4 22.4 + 0.5
0.1mM NS-398 58.7 + 1.7 22.2 + 1.4 18.6 + 1.0*
Control 51.9 + 1.7 24.3 + 1.5 23.5 + 1.1
0.1mM ibuprofen 50.4 + 1.3 25.0 + 1.4*24.5 + 0.4
1.5mM ibuprofen 60.4 + 0.9*20.5 + 0.8*19.1 + 1.3*
*p <0.05
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Table 5
Changes in gene expression by NSAIDs in DU-145 cells
Real-Time RT- PCR analysis of GDF 15, angiopoietin-like4 (ANGPTL4) and E2F2 mRNA in DU-145 cells treated with NS-398 (0.01mM and 0.1mM)
and ibuprofen (0.1mM and 1.5mM). Each data point represents Average ± SEM of 3 separate experiments.
Treatment COX-2 NAG-1 ANGPTL4 E2 F2 MMP 3
DMSO Control 1 1 1 1 1
0.01mM NS-398 1.1 ± 0.0** 1.0 ± 0.1 1.2 ± 0.1 1.3 ± 0.1*1.1 ± 0.1
0.1mM NS-398 2.5 ± 0.2*6.7± 0.4** 2.0 ± 0.3 0.9 ± 0.1 1.9 ± 0.2*
Control 1 1 1 1 1
0.1mM ibuprofen 1.3 ± 0.1*0.7 ± 0.2 6.2 ± 0.9*1.0 ± 0.3 1.9 ± 0.2
1.5mM ibuprofen 25.0 ± 0.9** 106.1 ± 10.1*39.5 ± 3.7*0.4 ± 0.1*149.4 ± 0.1*
*p<0.05 and
**p<0.005
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