Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumours
BMC Genomics
Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumours
Ruping Sun 0
Xuping Fu 0
Yao Li 0
Yi Xie 0
Yumin Mao 0
0 Address: State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University , Shanghai , PR China
Background: Recently, microRNAs (miRNAs) have taken centre stage in the field of human molecular oncology. Several studies have shown that miRNA profiling analyses offer new possibilities in cancer classification, diagnosis and prognosis. However, the function of miRNAs that are dysregulated in tumours remains largely a mystery. Global analysis of miRNA-target gene expression has helped illuminate the role of miRNAs in developmental gene expression programs, but such an approach has not been reported in cancer transcriptomics. Results: In this study, we globally analysed the expression patterns of miRNA target genes in prostate cancer by using several public microarray datasets. Intriguingly, we found that, in contrast to global mRNA transcript levels, putative miRNA targets showed a reduced abundance in prostate tumours relative to benign prostate tissue. Additionally, the down-regulation of these miRNA targets positively correlated with the number of types of miRNA target-sites in the 3' untranslated regions of these targets. Further investigation revealed that the globally low expression was mainly driven by the targets of 36 specific miRNAs that were reported to be up-regulated in prostate cancer by a miRNA expression profiling study. We also found that the transcript levels of miRNA targets were lower in androgen-independent prostate cancer than in androgen-dependent prostate cancer. Moreover, when the global analysis was extended to four other cancers, significant differences in transcript levels between miRNA targets and total mRNA backgrounds were found. Conclusion: Global gene expression analysis, along with further investigation, suggests that miRNA targets have a significantly reduced transcript abundance in prostate cancer, when compared with the combined pool of all mRNAs. The abnormal expression pattern of miRNA targets in human cancer could be a common feature of the human cancer transcriptome. Our study may help to shed new light on the functional roles of miRNAs in cancer transcriptomics.
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Background
MicroRNAs are endogenous, approximately 22 nt
singlestranded non-coding RNAs that negatively regulate
protein expression by binding to the 3' untranslated regions
(3' UTR) of messenger RNAs (mRNAs) and inhibiting
translation or inducing mRNA degradation or
deadenylation [1]. MiRNA genes are expressed as large precursor
RNAs, called pri-mRNAs, which may encode multiple
miRNAs in a polycistronic arrangement. These precursors
are converted into mature miRNAs of 19 to 25 nucleotides
by the RNase III enzymes, Drosha (nuclear) and Dicer
(cytosolic). MiRNAs have been identified in the genomes
of plants, animals and viruses. Human miRNAs have been
implicated in a variety of biological processes, and it is
estimated that 30% of protein coding genes are regulated
by miRNAs [2].
Recently, the potential role of miRNAs in human cancers
has been indicated by several studies, which suggest that
aberrations in miRNA expression in cancers may be
involved in tumour genesis and progression [3].
Abnormal expression of miRNAs has been found in various
cancers, and profiling of miRNAs has been shown to be a
more accurate method of classifying cancer subtypes than
profiling protein-coding genes. In the case of prostate
cancer, a highly prevalent disease in the western world,
several miRNA expression profiles have been reported [4-8].
Although some of these studies are not consistent,
potentially due to differences in samples or chip platforms, all
of them confirmed the widespread dysregulation of
miRNAs in prostate cancer. However, the disruption of
miRNA expression observed in human cancers needs to be
understood by analysing the causes and consequences of
the miRNA alterations. Thus far, the causes of miRNA
dysregulation are partially known to be the results of three
mechanisms: (1) miRNA genes tend to locate in cancer
related genomic regions; (2) miRNA expression is
epigenetically regulated in cancers; and (3) miRNA processing
genes such as Drosha and Dicer are de-regulated in cancers.
However, little is known about the consequences of
improper regulation of miRNA expression. More work
needs to be done to show whether these miRNAs have a
direct function in cancer progression or are simply
differentially modulated in tumours.
Identifying the genes targeted by miRNA is crucial for
understanding the functions of the miRNAs. Based on the
conservation of 3' UTRs, which are complementary to the
"seed" region (nucleotides 27 from the 5' end) of
miRNAs, several computational methods have been
developed to search for miRNA targets. Some of these methods
have been biologically validated and proven to be
accurate [2,9]. According to single miRNA-mRNA target pair
information, miRNAs are thought to function as either
tumour suppressors or oncogenes. For example, the let-7
family has been shown to target the RAS gene [10].
Downregulation of the let-7 has been found in lung cancer, and
this finding is correlated with a poor prognosis. Thus,
reduced expression of let-7 is predicted to promote lung
cancer progression. However, the situation is complicated
by the fact that miRNAs regulate multiple genes, and a
single mRNA can be targeted by several different miRNAs.
Therefore, the impact of changes in miRNA expression in
cancers is likely to be dependent on the cellular context.
Evidence is emerging that miRNAs can not only repress
translation of mRNAs but can also induce their
degradation, even if the mRNA target sites have only partial
complementarity to the miRNAs. For example, several studies
have revealed a correlation between the expression of
miRNAs and that of their targets through analysis of
mRNA target gene expression profiles and in situ
hybridisation [11-13]. A recent publication has reported the
impact of miRNAs on global mRNA and protein
expression and showed that the regulation of protein-coding
genes by miRNAs is quite similar at both the transcript
and protein levels [14]. Moreover, Wu et al. have reported
that miRNAs increase target mRNA decay rates by
promoting rapid deadenylation [1]. Assuming that the miRNAs
dysregulated in prostate cancer have an influence on the
expression of their targets, analysis of target gene
expression may provide clues to the functions of miRNAs in
prostate cancer.
In this study, we computationally explored the global
expression patterns of miRNA targets in human prostate
cancer using several published microarray datasets.
Interestingly, in contrast to all the mRNAs with altered
expression in prostate cancer relative to benign prostate tissue,
the transcript levels of miRNA targets were significantly
lower. Closer examination revealed a positive correlation
between the reduced abundance of miRNA targets and the
number of target-site-types in the 3' UTRs of the target
mRNAs. Remarkably, we found that the predicted targets
of the up-regulated miRNAs in prostate cancer, reported
by a miRNA profiling study, were significantly more likely
to be down-regulated in prostate tumours than the
predicted targets of all other miRNAs. We also found that the
transcript levels of miRNA targets were lower in
androgenindependent prostate cancer than in androgen-dependent
prostate cancer. Furthermore, the abnormal expression
pattern of miRNA targets could be a common feature of
the human cancer transcriptome.
Results
Globally reduced transcript levels of miRNA targets
relative to total mRNAs in prostate tumours
Using the gene expression atlas published by three
independent groups (Table 1) [15-17], and the conserved
miRNA target predictions from PicTar [9] and TargetScanS
[2], we generated a global view of the transcript levels of
miRNA targets in prostate cancer. First, the expression
values of each mRNA were compared between localised
prostate cancer and benign prostate tissue in each dataset, to
determine if the mRNA had a higher or lower expression
level in the cancer tissue. After sorting total mRNAs into
three groups (high expression, low expression and
unchanged), we calculated the Rmir, Rtotal and RR values
(see Methods). The RR value is a surrogate for an increased
or decreased abundance of miRNA targets relative to total
mRNAs. Notably, for all three datasets, RR values were less
than 1 when comparing localised prostate cancer with
benign prostate tissue (Figure 1A and [see Additional file
1]). Resampling statistical tests indicated that the
differAD and AI analysis
: Affymetrix oligo nucleotide microarray. : Normal Bone Marrow. : Acute Myeloid Leukaemia. Correlation analysis: The dataset used in the
correlation analysis between the transcript levels of individual miRNAs and those of their putative targets. AD and AI analysis: The dataset used in
the analysis of the transcript levels of miRNA targets in androgen-dependent and androgen-independent prostate cancer.
ences were significant (P < 0.05). To confirm this
observation, we used the Hyper-Geometric distribution to
evaluate the enrichment levels of miRNA targets in the
three mRNA pools and found a unique and significant
enrichment in the low expression pool across all three
datasets (Enrichment P < 0.05) [see Additional file 1].
Furthermore, the average expression ratio of miRNA targets
(0.997) was significantly lower than that of the
nonmiRNA-target genes (1.021, P < 10-100, t test, dataset 1).
These findings reveal that miRNA targets have a high
propensity to fall in the low expression mRNA group in
localised prostate cancer, an observation that is robust across
different datasets. Similar results were observed when
comparing metastatic prostate tumours or all prostate
tumours with benign tissues [see Additional file 1].
Through the rest of this study, we focused on the
comparison between localised prostate tumour and benign
prostate. To determine whether the low expression of miRNA
targets occurred in benign tissues, we randomly divided
the benign samples from dataset 1 into two classes and
undertook a benign-benign comparison. In this case,
there was no significant difference between miRNA targets
and total mRNAs (P > 0.05). The absolute Rmir and Rtotal
value variation in the three datasets might result from
differences in the chip platforms or from intrinsic differences
in the samples. Nonetheless, the fact that RR ratios were
always less than 1 for tumour when compared with
benign prostate indicates that, in contrast to total mRNA,
the transcript abundances of miRNA targets are
signifi
Given the considerable noise of the gene expression data
and miRNA target prediction, we ensured the validity of
the above observations using three approaches. Firstly,
because microarray results may vary depending on quality
of the samples, we collected the gene expression profiles
that contained a relatively large number of samples and
classified the tissues adjacent to the tumours as benign
prostate tissues for all three datasets. Secondly, to rule out
the influence of different methods on determining the
high, low and unchanged expression groups, two different
methods, a and b, were adopted for datasets 1 and 2,
where the measurements of matched samples were
provided. We also adopted three different cut-off values to
determine the mRNAs with altered expression. As shown
in Figure 1A, the global low expression of miRNA targets
did not vanish when we changed methods or cut-offs, and
the RR values actually decreased with the raised cut-offs.
For example, in dataset 1, RR = 0.79, 0.76, and 0.71 for
low, medium and high cut-offs, respectively (Method a,
PicTar targets). Finally, to determine if this observation is
robust for different miRNA target predictions, we carried
out the same analysis using two different target
predictions, which are the leading programs in this field, and
obtained similar results. Figure 1C shows that the RR
values obtained using the two predictions were highly
correlated (Pearson correlation = 0.9691). Additionally, since
FCiogmurpear1ison of transcript levels of miRNA targets between in prostate cancer and in benign tissues
Comparison of transcript levels of miRNA targets between in prostate cancer and in benign tissues. (A) The
transcript levels of miRNA targets in prostate tumours were compared with those of total mRNAs using Rmir, Rtotal and RR
values (see methods). The R values of all miRNA targets (Rmir) and total altered mRNAs (Rtotal) are represented as black and
white bars, respectively. Also plotted are the RR (Rmir/Rtotal) values (colored dots, right axis). The RR value is a surrogate for an
increased or decreased abundance of miRNA targets relative to total mRNAs. Blue color denotes RR < 1 and red denotes RR
> 1. Three gene expression microarray datasets (Dataset 1, 2 and 3) were used for this comparison. Three different cut-off
values (L: Low; M: Medium; and H: High) and two methods a and b (for dataset 1 and 2) were chosen to rule out the bias of single
method. Asterisk represents the statistical significance of each comparison (resampling statistical test, see methods). One
asterisk means P < 0.05; two asterisks, P < 0.01; three asterisks, P < 0.0002. The complete data are reported [see Additional
file 1]. (B) Analysis of the protein levels of miRNA targets using a small proteomic dataset. Black and green cycles represent the
numbers of increased and decreased proteins in prostate tumours relative to benign prostate tissues, respectively. Small cycles
represent the number of miRNA targets mapped into these two protein groups. Areas of the cycles are scaled to the proteins
number. (C) Correlation between the RR values obtained using two different miRNA target predictions.
repression by miRNAs also result in decreased translation,
we analysed the protein levels of several miRNA targets
using a small proteomic dataset. In this dataset, 64
proteins were reported to be dysregulated in prostate cancer,
relative to benign prostate, by employing a
high-throughput immunoblot approach [18]. After converting the
names of these proteins into RefSeq mRNA IDs (some
proteins have more than one RefSeq ID), we mapped all
miRNA targets to them. As shown in Figure 1B, the
miRNA targets showed a propensity to fall in the
decreased protein group (RR < 1), suggesting a reduced
protein level of miRNA targets in prostate tumour. Taken
together, our data indicate that miRNA target genes are
Association between transcript abundance of miRNAs and
their target mRNAs in prostate cancer
To further investigate the globally low expression of all
miRNA targets, we calculated the RR values of the targets
of 120 individual miRNAs. These miRNAs had more than
50 PicTar targets with altered expression (with an average
of 125 targets per miRNA) in comparing localised prostate
tumours with benign tissues (Method a, Medium cut-off,
Dataset 1). If we were to randomly select a set of mRNAs
same as the number of targets of each miRNA found in
dataset 1, the distribution of RR values generated by the
random sets would approximate a background
distribution. The distribution of individual miRNAs clearly
differed from the background distribution: the RR values
obtained for individual miRNAs clustered in the range of
0.4 to 0.8 (Figure 2A), suggesting that these miRNAs may
down-regulate their predicted targets in prostate tumours.
We also found that the distribution of mean expression
values of the target genes of individual miRNAs clearly
differed from the mean expression value of total mRNAs
(Figure 2B). Among the 120 individual miRNAs studied,
miR-194, miR-193 and miR-29a showed the lowest RR
values (0.414, 0.423 and 0.435). It should be mentioned
that a few target groups preferentially show a relatively
high expression level. For example, miR-133a, which was
recently reported to be down-regulated in prostate
tumours [19], had an RR value > 1 (1.194). Similarly,
miR125b, which was validated to be down-regulated in
prostate cancer by quantitative RT-PCR assays [8], had a
relatively high RR value (0.958).
We next asked whether there was an association between
the low expression of general targets and the
dysregulation of miRNAs in prostate cancer. Currently, the
literature shows that the expression of miRNAs and their targets
are expected to be inversely correlated [12]. Namely, the
low expression of miRNA targets might imply a
concurrently high expression of these miRNAs in prostate cancer.
This trend was seen in a miRNA expression profiling
study, which showed a significant up-regulation of many
miRNAs in prostate cancer [4]. As expected, when relating
the expression of miRNA targets to that of the miRNAs
using this miRNA expression profile, we found a weak
negative correlation between the differential expression
scores of individual miRNAs and the RR values of their
targets (Spearman correlation, Rs = -0.342, P = 0.025, N =
43) [see Additional file 2]. In contrast to the studies using
over-expression (or knockdown) of a single miRNA to
detect correlation between the expression of the miRNA
and its targets, cancer cells show alterations of many
miRNAs with overlapping targets, and thus, it is hard to judge
the contributions of individual miRNAs to the low
expression of their targets. To facilitate a more global view, we
divided all PicTar targets into two groups. Group I
contained 5514 targets of 39 significantly up-regulated
miRNAs in prostate cancer reported by Volinia et al. (36 of the
39 had conserved PicTar targets). Group II contained all
other predicted miRNA targets (2663 targets). After
mapping these two groups to the mRNAs with altered
expression (Medium cut-off, Method a), we found that the
predicted targets of the 36 up-regulated miRNAs were
significantly more likely to be down-regulated in prostate
tumours than the predicted targets of all other miRNAs
(Figure 3, P = 0.001, 0.007, 0.013 for Dataset 13,
respectively). In all three datasets, there was no difference in the
pTFrirgoaunstsracetrei2pctalnecverls of the target groups of individual miRNAs in
Transcript levels of the target groups of individual
miRNAs in prostate cancer. (A) Count distribution of RR
values of the targets of 120 individual miRNAs is shown
above. Count distribution of RR values of random mRNA
sets is shown below. Blue color denotes RR < 1 and red
denote RR > 1. (B) Density distribution (green line) of mean
expression values (log ratio) of the targets of individual
miRNAs clearly departed from the mean expression value of
total mRNAs (blue line) and that of non target mRNAs (red
line).
relative transcript abundance between mRNA from group
II and total mRNAs (P > 0.05). The RR values for the
putative targets of the 36 up-regulated miRNAs (group I
targets) were significantly lower than the RR values for the
group II targets in these datasets, indicating a selective
down-regulation of group I targets in prostate tumours for
all three datasets. We then constructed a gene set
containing 8177 genes (the same number of PicTar targets) by
keeping the group I genes as a seed, added randomly pick
up genes (100 times) which were not miRNA targets, and
repeated the analysis for each set. The reduced abundance
of mRNAs targeted by the 36 up-regulated miRNAs
remained significant for each random gene set (RR < 1, P
< 0.05), suggesting that the targets of these miRNAs make
a strong contribution to the low expression of general
miRNA targets.
To further investigate the relationship between the
abundance of individual miRNAs and their targets in prostate
cancer, we performed a correlation analysis between the
transcript levels of individual miRNAs and those of their
putative targets using a dataset containing the expression
measurements of both miRNAs and mRNAs in ten
prostate tumours and ten corresponding surrounding
nontumour tissues [19]. Of the 137 studied miRNAs with
more than 50 targets found in the mRNA expression
profiles, 67% (92) showed negative mean Pearson
correlation coefficients with their targets, 18% (24, including
miR-125b, miR-29a, and miR-194) and 7% (9, including
let-7i, and miR-138) showed significantly stronger
negative and positive, respectively, mean correlations with
their targets than with all mRNAs (P < 0.05) [see
Additional file 3]. These results suggest that while binding of
oTFfirgaunllsrocerthi3petrlemvieRlsNoAfsthe targets of up-regulated miRNAs and
Transcript levels of the targets of up-regulated
miRNAs and of all other miRNAs. All PicTar targets were
divided into two groups. Group I contained the targets of 39
significantly up-regulated miRNAs in prostate cancer
reported by Volinia et al. (36 of the 39 had conserved PicTar
targets). Group II contained all other miRNA targets. Pink
and yellow bars represent Rmir values of group I targets and
group II targets, respectively. Black and white bars represent
R values of all miRNA targets and total mRNAs, respectively.
RR values and P values are analyzed as in Figure 1A.
miRNAs to their target sequences may largely lead to the
reduction of the transcript levels of target mRNAs, it may
sometimes lead to mRNA sequestration and cellular
accumulation of the inhibited mRNAs in prostate tumours
[19]. We also determined the global distribution of the
Pearson correlation coefficient between each miRNA of
interest and either all mRNAs or the putative targets of the
miRNA. For three miRNAs, miR-125b, miR-29a and let-7i,
the distribution of the correlation coefficients was notably
different between all mRNAs and the miRNA-target
mRNAs (Figure 4A, B, C). The density distribution curves
for targets of miR-125b and miR-29a extended toward
negative correlation coefficients, indicating that the transcript
levels of some target mRNAs may be reduced by miR-125b
and miR-29a. On the contrary, the distribution curves for
targets of let-7i extended toward positive correlation
coefficients, indicating that the overall effect of binding of
let7i to its target sequence in prostate tumours may be
mRNA sequestration. A list of differentially expressed
miRNAs (in prostate cancer) that not only showed distinct
mean correlations between with their targets and with all
mRNAs but also had concordant RR values is shown in
Figure 4D. For example, miR-29a, which was reported to
be up-regulated in prostate tumour and showed strong
negative mean correlation with its targets, had a very low
RR value (0.435). let-7i, which was reported to be
downregulated and showed strong positive mean correlation
with its targets, also had a low RR value (0.767).
miR125b, which was reported to be down-regulated and
showed strong negative mean correlation with its targets,
had a relatively high RR value (0.958). These miRNAs may
be of special interest in future prostate cancer research.
Positive correlation between the reduced abundance of
miRNA targets and the number of target-site types in their
3' UTRs in prostate cancer
MiRNA target predictions rely strongly on the sequence
characteristics of 3' UTRs, which have known functions in
the stability, localisation, and translation of mRNA [20].
Most miRNA targets contain more than one type of target
site in their 3' UTRs, implying that stringent regulation by
one miRNA is rare. For example, the target mRNAs of the
36 up-regulated miRNAs included a major fraction of all
PicTar targets (5514 of 8177) which were shared by 168
miRNAs. Interestingly, we found that the average number
of target-site types in the target mRNAs of up-regulated
miRNAs ( 10) was significantly larger than the average
number found in the target mRNAs of all other miRNAs
( 2, P < 10-100, Wilcoxon-Man-Whitney test).
It has been suggested that multiple miRNAs may act
together to regulate a target mRNA [9]. However, it is still
unclear if different miRNAs act in vivo in any kind of
synergistic style. To further study the relationship between
the reduced abundance of miRNA targets and the
regulaRFieglautrioen4ship between the transcript levels of individual miRNAs and their targets in prostate cancer
Relationship between the transcript levels of individual miRNAs and their targets in prostate cancer. Global
density distribution of the Pearson correlation coefficients between the expression of mRNAs and the expression of miR-125b
(A), miR-29a (B) and let-7i (C). The black-lined curves show the distribution of the correlation coefficients for all mRNAs and
the orange-lined curves show the correlation coefficient distribution for only PicTar target mRNAs of miR-125b, miR-29a and
let-7i. (D) A list of differential expressed miRNAs (ref: reference) that not only show distinct mean correlation coefficients
between with their targets and with all mRNAs (asterisk denotes P < 0.05) but also have concordant RR values.
tory complexity of miRNAs (the number of miRNA
targetsite types in the 3' UTRs of the targets) in prostate cancer,
we divided the PicTar targets into eight groups ( 1022
miRNA targets per group), according to the number of
miRNA target-site types the mRNAs contain, and
calculated the RR value for each group. Interestingly, a
significant positive correlation was found between the
propensity for low expression and the number of miRNA
target-site types in each group (Spearman's rank
correlation, Rs > 0.7, P < 0.05, N = 8). As shown in Figure 5A, the
propensity for low expression of the target groups
increased with the number of target-site types.
We found that the down-regulation of miRNA targets in
tumour extracts correlated with the length of the 3' UTRs
of these mRNAs (Figure 5B). It is not surprising to find
more sites just by chance if the 3' UTR is longer. To ask
whether this positive correlation is a side effect of the
variations of 3' UTR length, we divided the PicTar targets into
eight groups according to the length of their 3' UTRs and
calculated the average number of target-site types for each
group. Since genes with more miRNA sites would have
not only relatively longer 3' UTRs but also significantly
higher site densities (sites/kb) of 3' UTR sequence [12],
the longer 3' UTRs does not always contain more target
sites. This was seen in Figure 5B, where group 7 and 8
contained a similar average number of target-site types
though they have distinct average 3' UTR length. If the
reduced abundance of miRNA targets simply results from
the variation of 3' UTR length, one would expect that the
group with longest 3' UTRs would show the lowest RR
value. However, group 7, which contained the most
target-site types, but not the longest 3' UTRs, showed the
lowest RR value for most datasets (all except dataset 3,
nFCuiogmrurbreeelart5oiofntabregtewt-eseitne thyeperseduced abundance and the
Correlation between the reduced abundance and the
number of target-site types. PicTar targets were divided
into eight groups according to the number of target-site
types or the length of 3' UTRs. (A) A positive correlation
between the reduced abundance of miRNA targets and the
number of target-site types the target mRNAs contain. (B) A
positive correlation between the propensity for low
expression and the length of 3' UTRs of miRNA-target mRNAs.
Average number of miRNA target-site types in each group
divided by 3' UTR length is shown above.
which contained a relatively small number of mRNAs for
calculation). Therefore, the positive correlation cannot
simply be explained by variations in 3' UTR length, but is
more likely due to the increasing number of target-site
types.
It is well known that cancer cells epigenetically silence a
number of genes by CpG island methylation. We next
asked if the global down-regulation of miRNA targets in
prostate cancer was a side effect of gene silencing by CpG
island methylation. We found that the miRNA targets had
significantly more CpG islands (average: 1.55) than all
mRNAs (average: 1.38, P < 0.01, Wilcoxon-Man-Whitney
test). However, randomly chosen groups of
non-miRNAtarget mRNAs with the same (or more) number of CpG
islands as the targets, did not exhibit the same global
down-regulation as the miRNA targets (RR > 1, P > 0.05).
Moreover, we did not find correlations between the 3'
UTR length (or miRNA target-site types) and the number
of CpG islands (P > 0.05, spearman correlation test).
Thus, the global down-regulation of miRNA targets
cannot simply be explained by gene silencing due to CpG
islands methylation.
Reduced transcript levels of miRNA targets in
androgenindependent prostate cancer when compared with
androgen-dependent prostate cancer
The main treatment for prostate cancer is androgen
ablation or chemical castration. Despite the general success of
anti-androgen therapy, a negative outcome of this
treatment is the appearance of androgen-refractory tumours
with an eventually fatal prognosis. Thus, understanding
the molecular mechanisms of the transition of prostate
cancers from androgen dependence to independence
remains an important challenge. In this study, we
investigated the expression levels of miRNA targets in
androgendependent (AD) prostate cancer and
androgen-independent (AI) prostate cancer using a dataset previously
published by our group [21]. As shown in Figure 6, the
miRNA target genes showed lower transcript abundance
in all prostate cancer (AD+AI), AI or AD prostate cancer
than in normal prostate tissue (RR < 1, P < 0.05).
Intriguingly, we found that the transcript levels of miRNA targets
were significantly lower in AI prostate cancer than in AD
prostate cancer (RR < 1, P < 0.05). Since some miRNAs are
androgen regulated, we further investigated the transcript
levels of the target group of androgen-repressed miRNAs
(miRNAs that were down-regulated after androgen
treatment in androgen-sensitive LNCaP cells) reported by S.
Ambs et al. [19]. Using the aforementioned method, we
divided all PicTar targets into two groups. Group I
contained 1012 targets of 6 androgen-repressed miRNAs (5 of
the 6 had conserved PicTar targets). Group II contained all
other predicted miRNA targets (7165 targets). We found
that the predicted targets of the 5 androgen-repressed
miRNAs were significantly more likely to be
down-regulated in AI prostate cancer (when compared with AD
prostate cancer) than the predicted targets of all other miRNAs
(P = 0.01). This result suggests that these
androgenrepressed miRNAs may have an important influence on
the expression of their targets in AI prostate cancer.
Further, they may play an unknown function in the transition
of prostate cancers from androgen dependence to
independence.
Down-regulation of miRNA targets in other cancer types
and predicted function of these protein-coding genes in
prostate cancer
To assess the basic function of down-regulated miRNA
targets in prostate cancer, we used GO term analysis to
identify the overrepresented biological processes in the group
of down-regulated miRNA targets (referred to as
"taraTFnrigdaunasrncedrir6potgleenv-edlsepoefnmdieRnNtAprotasrtgaetetsciannacnedrrogen-independent
Transcript levels of miRNA targets in
androgen-independent and androgen-dependent prostate cancer.
The transcript levels of all miRNA targets in
androgendependent (AD) prostate cancer, androgen-independent (AI)
prostate cancer and normal prostate tissues (N) were
compared with each other. The transcript level of the targets of
androgen repressed miRNAs in AI prostate cancer was also
investigated. Black and white bars represent R values of all
miRNA targets and total mRNAs, respectively. Green bar
represents R value of PicTar target mRNAs of
androgenrepressed miRNAs. RR values and P values are analyzed as in
Figure 1A.
gets"), as well as the other reduced mRNAs that lacked
conserved miRNA sites (referred to as "antitargets"). We
used the mRNA list from dataset 1 (Method b, Medium
cut-off), which contained a relatively large number of
altered mRNAs, for this analysis. As shown in Table 2, the
enrichment level in the targets was much higher than in
the antitargets in most categories, especially "regulation of
biological process" (adjusted enrichment P value = 7E-67
for targets versus 2E-08 for antitargets) and "multicellular
organismal development" (adjusted enrichment P value =
4E-83 for targets versus 4E-19 for antitargets). For a more
in-depth pathway analysis, we performed a KEGG
pathway database query using the down-regulated targets and
the antitargets (Table 3). At an adjusted P value < 0.05, 9
pathways were found to be enriched in the
down-regulated miRNA targets, such as "regulation of action
cytoskeleton", "Wnt signalling pathway", "Focal
adhesion", "MAPK signalling pathway" and "ECM-receptor
interaction". These dysregulated pathways are implicated
in cell motility, cell proliferation, cell differentiation, cell
migration and signal transduction. In contrast, the
antitargets were not found to be enriched in any pathway.
Since the low expression pattern observed here is largely
seen in the targets containing multiple target-site types,
our data may suggest that down-regulated targets in
prostate cancer consist mostly of key cellular regulators and
such regulators are themselves highly regulated at
multiple levels, including regulation by miRNAs, which may
result in the coordinate repression of the target mRNAs
involved in regulatory systems and developmental
processes.
To determine whether the global down-regulation of
miRNA targets is common in human cancers, we extended
the global analysis to four other prevalent cancers,
including three solid tumours and one leukaemia (Table 1).
Significant differences in transcript levels between miRNA
targets and total mRNAs were observed for all four cancers
[see Additional file 1]. Strikingly, the expression levels of
miRNA targets were significantly lower in breast cancer,
lung adenocarcinoma and acute myeloid leukaemia than
in corresponding normal tissues (RR < 1, P < 0.05). In
contrast, the comparison of hepatocellular carcinoma and
non-tumour liver tissue yielded an opposite relationship
between the abundance of miRNA target and total mRNAs
(RR > 1, P < 0.05). In this analysis, an RR value greater
than 1 indicated an increased abundance of miRNA
targets when compared with all mRNAs. The globally
increased abundance of miRNA targets may reflect
differ: Over-represent P value was calculated using Hyper-Geometric (HG) distribution and adjusted. P values < 1E-20 (for mRNAs overrepresentation
in each category) are in bold.
: The total number of targets or antitargets found in KEGG pathway database. : The number of targets or antitargets found in each pathway. :
Adjusted P values < 0.05 are in bold.
ent mechanisms of regulation in liver cancer. Our
extended analysis indicates that the differences in
transcript levels between miRNA targets and total mRNAs may
be a common feature in human cancers.
Discussion
Different expression levels between miRNA targets and
total mRNAs have been uncovered in the comparison
between mature tissues and embryos [22] and miRNAs
have been suggested to confer precision and robustness to
developmental processes. In this study, we initially
reported that miRNA targets expressed less on a global
scale than total mRNAs in prostate tumours, relative to
benign prostate tissues. Analysis of the protein levels of
miRNA targets suggests that the level of protein expression
of miRNA targets may also be reduced, in agreement with
a recent study which reported that the regulation of
protein-coding genes by miRNAs was quite similar on the
transcript and protein levels [14]. Moreover, our data
showed that the transcript abundance of the targets of
androgen-repressed miRNAs was significantly lower than
the abundance of the targets of all other miRNAs in
androgen-independent prostate cancer. The abnormal
expression pattern of miRNA targets was also seen in three
other cancer types, suggesting that it may be a common
feature of the human cancer transcriptome.
We also found a trend for an increased down-regulation of
mRNAs with longer 3' UTRs and more target-site types,
consistent with a recent study showing that proliferating
cells express mRNAs with shortened 3' UTRs and fewer
miRNA target sites [23]. It has been reported that for
proteins with more interacting partners, their genes tend to be
regulated by more miRNA types [24,25]. Genes with more
interactions may require more elaborate regulation at the
posttranscriptional level because unwanted output of
these proteins may lead to a more severe fitness effect.
Moreover, miRNAs have been proposed to primarily
target downstream network components such as
transcription factors [26]. Disrupted expression of the highly
regulated miRNA target genes may reflect the fact that the
regulatory network in cancer cells departs from the
normal regulatory routine presented in benign cells. The
molecular mechanisms determining the intriguing
expression patterns of miRNA targets in cancer cells presented in
this study remain to be elucidated. Based on our analysis,
there are three potential reasons discussed below.
First, the abnormal transcript abundance of miRNA
targets may indicate a significant influence of miRNAs on the
expression of their target genes in prostate tumours. This
view is supported by three observations: (1) the targets of
36 up-regulated miRNAs made a strong contribution to
the low expression of all miRNA targets; (2) there was a
weak (but significant) negative correlation between the
score of the differential expression of individual miRNAs
(published by Volinia et al.) and the RR value of the
miRNA's targets; and (3) the propensity for low
expression increased with the number of target-site types
embedded in the 3' UTRs of the miRNA targets, suggesting
the possibility of synergistic regulation by multiple
different miRNAs in prostate cancer. In general, lower transcript
level is attributed to transcription inhibition or mRNA
decay. Since miRNA target prediction relies strongly on
the characteristics of 3' UTRs, translational control by 3'
UTRs may play a role in the down-regulation of miRNA
targets in prostate cancer. It has been demonstrated that
miRNAs can promote rapid mRNA degradation by
accelerating deadenylation [1] and that miRNAs are involved
in AU-rich Element (ARE)-mediated mRNA instability
[27]. Therefore, the low expression of miRNA targets may
result from the action of miRNA-mediated mRNA decay
in prostate cancer. Up-regulation of miRNAs in prostate
tumours is common [3,4,19] and is consistent with the
known oncogenic activity of many miRNAs [28,29]. It has
been reported that Dicer and other genes involved in
miRNA processing are up-regulated in prostate cancer
[19], indicating that the prostate tumour is more efficient
than normal prostate tissue at processing miRNA
precursors into mature miRNAs. These observations support the
idea that miRNAs may be up-regulated on a global scale
in prostate cancer, consistent with the global
down-regulation of their targets.
It should be noted that the global down-regulation of
miRNA targets is an overall effect that does not negate the
fact that some miRNA targets are up-regulated in prostate
tumours. A recent miRNA profiling study showed a
tumour gene signature that contains up-regulated and
down-regulated miRNAs in prostate cancer [19]. This
study also showed that binding of miRNAs to 3' UTR
sequences can lead to both degradation and accumulation
of the targeted mRNA in cancer cells. In the correlation
analysis between the expression level of individual
miRNAs and the expression level of their putative targets, we
confirmed this observation on a global scale. More
specifically, both an inverse and a positive correlation could
occur between a miRNA and its target mRNAs in prostate
cancer cells. Since a miRNA can regulate multiple targets
and a single mRNA can be targeted by several different
miRNAs, the global down-regulation of miRNA targets
may largely depend on the overall effect of miRNA
regulation. The second potential reason is that the global
downregulation of miRNA targets is an overall effect that may
depend on: (1) the reduction of mRNA expression that
may be caused by the up-regulated miRNAs (such as
miR29a); (2) the decrease of mRNA sequestration that may be
caused by the down-regulated miRNAs (such as let-7i);
and (3) the moderate up-regulation of some targets of the
down-regulated miRNAs (such as miR-125b).
Furthermore, this potential reason can explain the fact that global
up-regulation of miRNA targets was observed for
hepatocellular carcinoma, a tissue which has roughly equal
numbers of up and down-regulated miRNAs [30].
The third reason is related to a perplexing problem:
several other miRNA profiling studies showed widespread
down-regulation of miRNAs in prostate cancer [5-8], and
some of the miRNAs reported to be up-regulated by
Volinia et al. overlapped with some of the miRNAs
reported to be down-regulated by another profiling study
[7]. If most miRNAs are truly down-regulated in prostate
cancer, the global down-regulation of miRNA targets may
not be causatively linked to the expression levels of the
miRNAs themselves. As miRNA targets have relatively
long 3' UTRs (with known functions in the stability,
localisation, and translation of mRNA) and more CpG islands
(which may be methylated in cancers) than non-targets, a
third explanation is that the target genes that are
downregulated in prostate cancer are key cellular regulators and
such key regulators are themselves highly regulated at
multiple levels (transcriptionally and
post-transcriptionally), including regulation by miRNAs.
There are several explanations for the discrepancies in
miRNA profiling studies. First, Volinia et al. [4] and Ambs
et al. [19] used total RNA while other studies [5-8] used
purified small RNA samples (from 18 to 300 nt).
Purification might introduce errors into miRNA expression
comparisons. For example, there is presently no way to judge
the different proportions of miRNAs within the pool of
total RNAs [31]. If cancer and benign tissue have different
proportions of miRNA content, the validity of the analysis
based on the fundamental assumption that same amount
of miRNAs is extracted from the same amount of total
RNAs is thrown into doubt. Second, it has been suggested
that purification of the small RNA fraction could reduce
nonspecific hybridisation to longer miRNA precursors. If
there is a block in precursor miRNA processing in prostate
cancer without a corresponding decrease in transcription,
this could result in the inconsistency. A third explanation
would be the differences in samples number. Volinia et al.
and Ambs et al. analysed > 50 prostate cancer samples
while other studies did not reach this size. Cancer is a
heterogeneous disease, and the heterogeneity of tumour
samples might contribute substantially to the results. It is not
surprising that miRNA expression profiles published by
different researchers are inconsistent, because miRNA
profiling technology is still in its infancy. For example,
researchers generally adopt "tried-and-true"
methodologies from cDNA microarray technology for miRNA
expression analysis, but the relatively small number of probes
on miRNA microarrays may render these high-density
approaches ineffective.
As to the basis of our investigation, the gene expression
microarray and miRNA target prediction data have proved
to be useful for gaining biological knowledge
[13,22,2426]. Although these datasets are far from being complete
and may contain noise, it is unlikely that these flaws could
totally distort the results. Since consistent results were
seen across various datasets generated by independent
groups, the noise of microarray data and false positives in
miRNA target predictions appear to have no serious effects
on our study. Furthermore, the overall significances
inferred from thousands of mRNAs would be strong
enough to reflect real biology. The strength of our global
analysis lies in the noise reduction effect, as well as the
identification of general trends of miRNA target
expression that would not have been discovered by individual
investigation of single miRNA targets. Cancer is an
extremely complex and heterogeneous disease [32]. It
should be noted that our data did not conclusively
distinguish among the three possible mechanisms discussed
above, and the detailed molecular mechanisms
responsible for the abnormal expression of miRNA targets remain
to be thoroughly elucidated. Future experiments or large
microarray studies are needed to clarify the possible
mechanisms.
Conclusion
In conclusion, our global gene expression analysis, along
with further investigations, suggests that miRNA targets
have significantly reduced transcript abundance in
prostate cancer, when compared with the combined pool of
total mRNAs. The abnormal expression patterns of
miRNA targets could be a common feature of the human
cancer transcriptome. These observations raise the
possibility that miRNA may have global functions in human
prostate cancer. Our study may help to shed new light on
miRNA functions in cancer transcriptomics, when
unprecedented opportunities to study the regulatory control
mediated by miRNAs are given by the accumulation of
cDNA, miRNA expression and proteomic datasets.
Methods
Data collection
The gene expression microarray datasets used in this study
are listed in Table 1, including five datasets in human
prostate cancer and four in other cancer types (breast
cancer, lung adenocarcinoma, acute myeloid leukaemia and
liver cancer) [33-36]. We also used one immunobloting
proteomic dataset in prostate cancer. Gene expression
datasets were of two general types, two channel ratio data
(cDNA datasets) and single channel intensity data
(Affymetrix datasets), and were generally given in a single
matrix file format. All gene expression datasets were
normalized by the authors of these studies. Probe IDs were
converted to RefSeq mRNA IDs using ID converter [37], if
necessary. We used two complete lists of human miRNA
targets published by Lewis et al. and Krek et al. These
miRNA target prediction datasets were downloaded from
the most recently updated websites.
Determining the mRNA groups with high and low
expression in cancers relative to normal tissues
We reviewed the samples profiled for each of the gene
expression datasets and chose the samples of classes of
interest for further analysis. For each of the two classes e.g.
cancer versus normal, the probe sets with absent calls
(Affymetrix) or missing values (cDNA) in excess of 50% of
the samples were filtered out. For each probe, we first
averaged across sample replicates, then directly compared
the median expression values between the two classes
(cancer and normal). After that, we averaged the ratios
(fold changes) for probes of the same RefSeq transcripts in
each dataset and determined whether a mRNA had a high
or low expression level (Method a). In dataset 1 and 2,
where the expression measurements of matched samples
(prostate cancers and benign prostate samples from same
patients) were provided, another method (Method b) was
used to rule out the bias of single method. We determined
if a mRNA altered for each patient by computing the
expression ratios of each patient, and only those genes
showing alterations in most (based on different cut-offs of
median ratio of paired samples) patients were considered
to be altered. In dataset 2, only the "well-measured genes"
defined by the author were included in our analysis.
Moreover, in order to rule out the bias of single cut-off (or
threshold) for identifying mRNAs with low and high
expression, we also chose three different cut-off values for
each comparison (High, Medium and Low [see
Additional file 1]). For datasets 57 of three other cancer types,
we directly downloaded the over- and under-expression
gene lists from Oncomine database [38].
Calculation of R and RR values and resampling statistical
tests
Here we modified a method from Yu et al. 2007 [22].
After obtaining the mRNAs with high and low expression
in cancers relative to normal tissues, we counted the
number of miRNA targets that fell in the high expression
group (N > Benign) and divided it by the amount of targets
in the low expression group (N < Benign). The obtained ratio
was named as Rmir (Rmir = N > Benign/N < Benign). As a control,
the same calculations for all mRNAs were undertaken and
the ratio Rtotal was obtained. The ratio of Rmir to Rtotal was
computed and termed as RR (RR = Rmir/Rtotal). The RR
value is a surrogate for an increased or decreased
abundance of miRNA targets relative to total mRNAs. An RR
value less than 1 indicates that the targets prefer a lower
expression. We undertook a resampling test to judge the
statistical significance of the global observation. In each
test, we randomly picked the same number of mRNAs as
the number of miRNA targets from total altered mRNAs.
We then calculated the ratio of high expression mRNAs to
low expression mRNAs in this random sub-pool and
termed it as Rrandom. The randomization tests were
performed 5000 times and the number of times (n) was
counted when Rrandom > Rmir. P-value was defined as n/
5000. If P-value < 0.05, we determined the expression
level of miRNA targets was significantly lower than that of
total mRNAs. We used two miRNA target predictions for
this analysis and performed correlation analysis of the RR
values obtained from each of the two target predictions.
Correlation analysis between the expression levels of
individual miRNAs and those of their putative targets
For the dataset containing expression measurements of
both miRNAs and mRNAs in ten prostate tumours and
ten corresponding surrounding non-tumour tissues [19],
we calculated the log expression ratios of paired samples
for each miRNA and each mRNA. Then the Pearson
correlation coefficients between each miRNA and each mRNA
were calculated using the expression ratios. We also
determined the global density distribution of the Pearson
correlation coefficients between the miRNA of interest and
either all mRNAs or the predicted targets of the miRNA.
Analysis of the relationship between the RR values and 3'
UTR length and the number of predicted miRNA
targetThe number of target-site types of each PicTar target
mRNA was calculated. Since the underlying distributions
of the numbers of miRNA target site types and RR values
were not normal, Spearman's rank correlation test was
used to determine the relations between these two
variables. We extracted information of 3' UTR length of each
mRNA from the UTResource database [39]. The human
CpG islands data were extracted from Human CpG Island
GO term analysis and KEGG pathway analysis
To study the function of down-regulated miRNA targets,
we used the GO Term Mapper Web server [41]. We used
default GOA slim file (a list of general GO terms) for
annotating the down-regulated mRNAs. The KEGG
database [42] batch entry allowed us to evaluate the large set
of down-regulated miRNA targets and antitargets. In order
to evaluate the enrichment level of mRNAs to a specific
biological process or pathway, we used the
Hyper-Geometric (HG) distribution to calculate the enrichment P
value. A more detailed explanation of this distribution
was previously described [43].
Authors' contributions
RS planed and designed the study, performed the data
analysis and the further analysis in bioinformatics, wrote
the main draft of the paper, and generated the figures. XF
performed the data analysis and organized this research.
YL, YX and YM organized all the research and provided
advice for preparing the manuscript. All authors read and
approved the final manuscript.
Additional material
Additional file 1
Supplementary Table 1. Comparison of miRNA target expression
between in cancers and in normal tissues. The complete dataset of
comparing the expression levels of miRNA targets between in cancers and in
RR values of their targets.
Additional file 3
Additional file 2
Supplementary Table 2. A weak negative correlation between the
differential expression scores of individual miRNAs and the RR values of their
targets. The differential expression scores of individual miRNAs and the
Supplementary Table 3. Correlation analysis between the transcript
levels of individual miRNAs and those of their putative targets using a
dataset containing the expression values of both miRNAs and mRNAs in ten
prostate tumours and ten corresponding surrounding non-tumour tissues.
The average Pearson correlation coefficients for individual miRNAs with
either target mRNAs or all mRNAs.
We are grateful to two anonymous reviewers for their thoughtful
criticisms, comments and suggestions on early version of the manuscript. We
also thank Ms. Zhaorong Ma for copyediting of the manuscript. This work
is supported by a grant 2006AA02Z324 from the National High Technology
Research and Development Program of China (863 Program). This work is
also supported by Shanghai Leading Academic Discipline Project, Project
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