Identifying microRNA/mRNA dysregulations in ovarian cancer.

Abstract

MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA).
TCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA.
We identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms.
Our findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.

Full text

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RESEARCH ARTICLEOpen Access
Identifying microRNA/mRNA dysregulations in
ovarian cancer
Gregory D Miles1,2*, Michael Seiler3, Lorna Rodriguez1, Gunaretnam Rajagopal1,5and Gyan Bhanot1,2,3,4,5
Abstract
Background: MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple
genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may
be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and
associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner,
their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct
mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation
signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways
compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA
mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA).
Methods: TCGA Microarray data was normalized and samples whose class labels (tumour or normal) were
ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and
correlated genes/microRNA differentially expressed between tumour and normal samples were identified.
TargetScan was used to identify gene targets of microRNA.
Results: We identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of
RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly
down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and
normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs
of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We
also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory
mechanisms.
Conclusions: Our findings identify novel microRNA/mRNA relationships that can be verified experimentally. We
identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA
controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific
mechanisms which may be significant for their utility as early detection biomarkers or in the development of
microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the
existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian
tumorigenesis.
Background
Ovarian cancers have a high mortality rate and few
treatment options and the failure rate is high [1]. In
spite of significant advances in detection and effort to
reduce recurrence rates, the five year survival rate has
remained relatively unchanged for over 50 years [2].
The Cancer Genome Atlas (TCGA) [3] is a public data-
base which provides multi-modal, patient matched data,
including microRNA and mRNA expression levels as
well as clinical data (survival, recurrence and treatment),
for large cohorts in several cancers, including serous
cystadenocarcinoma (the most common type of ovarian
cancer). In this paper, we apply a validated method [4] to
identify statistically significant microRNA/mRNA regula-
tions which are disrupted in serous cystadenocarcinoma
* Correspondence: milesgr@umdnj.edu
1The Cancer Institute of New Jersey, New Brunswick, NJ, USA
Full list of author information is available at the end of the article
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© 2012 Miles et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

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using patient matched data from TCGA. Although
mRNA and microRNA dysregulations in ovarian cancer
have been identified in various studies [5-15], regulatory
mRNA targets of microRNA have not been established.
Our analysis proceeds as follows:
We first identify those mRNA or microRNA which can
individually and robustly distinguish ovarian cancer sam-
ples from normal ovary samples based on expression level.
We then identify putative mRNA potentially regulated by
the microRNA by matching microRNA/mRNA using seed
sequence complementarity from TargetScan http://www.
targetscan.org). Finally, we look for a significant correla-
tion/anti-correlation signature between patient matched
microRNA/mRNA in tumor samples or normal samples.
This procedure allows us to identify microRNA/mRNA
regulations which are common (maintained) between
tumor and normal tissue as well as microRNA/mRNA
regulations that are disrupted in carcinogenesis [4].
Using several statistical tests (Students T-test with FDR
correction, unpaired t-test without FDR correction, and
Mann-Whitney test with FDR correction), K-Means
Clustering [16] and principal component analysis [17],
our analysis found 18 microRNA and 49 mRNA which
best distinguish tumour from normal. Using seed
sequence matches between all putative pairs between
these sets (from TargetScan) and Pearson correlation at
significance p < 0.05 and < 0.1 (one tailed test) in the
tumour and normal samples, respectively, we found forty
microRNA/mRNA pairs of potential interest, including
fifteen pairs anti-correlated across all tumour samples
and seven pairs anti-correlated across the normal sam-
ples. Within the anti-correlated pairs, one pair: hsa-miR-
140-3p/RAD51AP1, was anti-correlated in both tumour
and normal samples with opposite expression levels in
tumor/normal, implying the dysregulation of a direct
microRNA/mRNA mechanism. In addition, we also iden-
tified microRNA/mRNA pairs that were correlated or
anti-correlated in the tumour samples but not in the nor-
mal samples, suggesting a de novo gain of microRNA
function in tumours. Also present were microRNA/
mRNA regulations present in normal samples but not in
tumour samples, indicating a de novo loss of microRNA
function in tumours. Interestingly, there are also pairs
identified which show positive correlation in both tumour
and normal samples, implying the potential existence of
indirect pathways or intermediate regulatory mechanisms
with a possible role in ovarian tumorigenesis.
Methods
TCGA data
The Cancer Genome Atlas (TCGA) is a central bank for
multidimensional experimental cancer data, including
MicroRNA and cDNA microarray data. These data were
obtained from the Data Access Matrix within the TCGA
data portal (http://cancergenome.nih.gov/dataportal/
data/access/). cDNA microarray experiments measuring
mRNA expression were run on the Affymetrix HG-
U133A platform (22,277 probesets). microRNA experi-
ments were performed on the Agilent 8 × 15 K Human
microRNA-specific microarray V2 platform measuring
the expression of 821 microRNAs. Of the 386 TCGA
microRNA data samples (378 tumours and 8 normals)
and 294 mRNA data samples (including one cell line
experiment, which was eliminated), filters were applied
such that only samples withboth microRNA/mRNA data
were retained. When this was done, we were left with
290 samples (282 tumors, 8 normals). Outliers were
then removed as described below, leaving a final cohort
of 264 samples (258 tumors, 6 normals).
Identification of samples with matched microRNA/mRNA
data
The TCGA dataset consisted of 386 samples with micro-
RNA expression data (378 tumours and 8 normals) and
294 samples with mRNA expression data (including one
cell line experiment). Of these, we retained only samples
which had data for both microRNA and mRNA levels.
This resulted in a reduced dataset with 290 total samples
(282 tumours, 8 normals). Further removal of samples
with ambiguous class labels with respect to consensus
ensemble clustering reduced this to 258 tumour samples
and 6 normals (see below).
Normalization
The mRNA expression data was normalized using
MAS5.0 summarization. We then standard normalized
the data: The mean of the summarized intensities for
each gene across all experiments was subtracted from the
individual intensity for that same gene within a given
experiment (per-gene mean subtraction). This value was
then divided by the standard deviation of the summarized
intensities for each gene across all experiments.
We further normalized the data by performing a 75th
percentile shift using GeneSpring GX 11.0 (Agilent Tech-
nologies, Inc., Santa Clara, CA, USA). In order to compare
our data with previous literature [5], these values were
then per-gene mean subtracted and divided by the per-
gene standard deviation (similar to the mRNA normaliza-
tion) to produce normalized measurable microRNA
expression values.
Removal of samples with ambiguous class labels
An ambiguous tumour/normal sample is defined as one
that does not robustly cluster with members of its labelled
class (tumour or normal). Such samples were identified
and eliminated using both mRNA and microRNA data.
This was done by using consensus ensemble K-means
clustering, using the public software ConsensusCluster
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(http://code.google.com/p/consensus-cluster/). mRNA
expression data for the 290 samples (282 tumours and
8 normals) was clustered via unsupervised K-Means con-
sensus clustering into two classes [18]. Most of the tumour
samples clustered in a manner consistent with their labels
(tumours with tumours and normal with normal) across
the dataset. However, 20 samples clustered in an ambigu-
ous way (sometimes with normal and sometimes with
tumours) and were eliminated (Figure 1), leaving 270 sam-
ples for to be analysed for their microRNA levels.
We found that microRNA levels varied over a much
smaller range than mRNA levels. Hence a similar analysis
of the microRNA data required that uninformative micro-
RNA be removed first. We therefore eliminated micro-
RNA whose expression had little or no variation across
tumour/normal sample clusters, retaining only those
whose expression levels could significantly distinguish
tumour samples from normal samples. This was done
using three statistical tests:
a) A Students’ unpaired t-test with a Benjamini Hoch-
berg FDR multiple testing correction (p < 0.05) between
the tumour and normal samples within GeneSpring GX
11.0;
b) A Mann-Whitney test with a Benjamini Hochberg
FDR multiple testing correction (p < 0.05) between the
tumour and normal samples within GeneSpring GX 11.0;
c) A Students’ unpaired T-test without a multiple test-
ing correction (p < 0.05).
Only microRNA that passed all three tests were
retained, which resulted in a robust subset of 127 micro-
RNA (Figure 2 and Additional file 1: Table S1) whose
expression levels significantly separated tumour samples
from normal samples. Consensus ensemble K-means clus-
tering into two clusters using expression levels of these
127 microRNA was performed on the 270 samples that
remained after analysis of the mRNA data (see above).
This identified six additional samples that did not retain
consistent cluster membership across bootstrap samplings
of the data. These six samples were also removed.
These two analyses on mRNA and microRNA levels
identified our final sample set of 264 samples (258
tumours and 6 normals) which are listed in Additional file
2: Table S2. We note in that our analysis showed that two
of the TCGA samples which are labelled as “normal”
(TCGA-01-0628-11 and TCGA-01-0631-11) consistently
clustered with the tumour samples, suggesting that they
might contain a significant contamination from the
tumour. These were eliminated from further analysis.
Identifying optimal microRNA and mRNA to distinguish
tumour from normal
After removal of these ambiguous samples, the Consen-
susCluster software was used to perform principal compo-
nent analysis (PCA) using expression levels of the 127
microRNAs. PCA analysis was performed 43 times, using
the six remaining normal samples and six randomly
selected tumour samples (without replacement) as input.
The first two principal component eigenvectors (PC1 and
PC2) from these 43 runs were averaged. Figure 3 shows
the PCA plot obtained on projecting the samples onto the
two averaged eigenvectors. A robust subset of 18 micro-
RNA was identified as those which appeared most often
(35 times out of 43) in these datasets as significantly able
to distinguish tumour from normal using a signal-to-noise
ratio (SNR) test using SNR > 0.5 as a cutoff. These 18
microRNAs with the most significant values of this eigen-
score across the 43 datasets were retained for further ana-
lysis. A similar procedure was used for the mRNA, once
again validating (using PCA) that the normal samples
indeed separate from the tumour samples (Figure 4).
Furthermore, upon iterating 43 times once again, the SNR
filtering provided 53 genes that were most informative in
separating tumour from normal samples in a robust man-
ner, with each gene appearing in at least 20 of the 43 lists
generated by ConsensusCluster.
Identifying mRNA targets of microRNA
TargetScan (http://www.targetscan.org) was used to obtain
putative mRNA targets for all 18 microRNAs that best dis-
tinguished tumour samples from normals. In the 18 × 53
Figure 1 Identifying ambiguous samples using mRNA levels. K-
means clustering yielded two distinct subclusters which may
represent subtypes that will be addressed in the future. The 20
samples which did not cluster well with either group were classified
as ambiguous samples and removed.
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matrix of microRNA/mRNA pairs, we retained those
where the microRNA had a seed sequence in TargetScan
which was identified as either ‘conserved’ or ‘poorly con-
served’. For each such microRNA/mRNA pair, we com-
puted the Pearson rank correlation function separately
within the normal and tumour samples. We retained
those which had a p-value significance (http://danielsoper.
com/statcalc3/calc.aspx?id = 44) < 0.5 for this statistic in
tumour samples, and < 0.1 in the normal samples.
Results
At the time of this study, the TCGA ovarian cancer data
set consisted of 282 tumour samples and 8 non-matching
normal samples with both microRNA and mRNA expres-
sion data available. The microRNA data consists of expres-
sion levels for 821 microRNA measured on an Agilent 8 ×
15 K Human miRNA-specific microarray platform. The
gene expression levels were measured on the Affymetrix
GeneChip Human Genome HG-U133A array platform
consisting of 22,277 probes. Clinical and survival data
were also available for all samples, including: age, days to
death (if applicable), days to last follow-up, days to tumour
progression, days to tumour recurrence, and age at initial
pathologic diagnosis. As described in the Methods section
(above), after normalizing, removing ambiguous samples
and finding the optimum set of microRNA and mRNA,
Figure 2 MicroRNAs that separate tumour from normal. The intersection of three separate statistical tests yielded 127 microRNA that more
significantly differentiate tumour from normal samples. The tests included an unpaired T-test (p < 0.05) with a Benjamini Hochberg FDR multiple
testing correction and an unpaired Mann-Whitney test (p < 0.05) with a Benjamini Hochberg FDR multiple testing correction from within
GeneSpring GX 11.0, and a Student’s t-test (p < 0.05). Data for these 127 microRNA were subsequently used for further clustering of the samples
and SNR analysis to uncover the top microRNA which differentiate tumour and normal samples.
Figure 3 PCA on robust microRNA data reveals distinct
separation. PCA was executed using only the expression data from
the 127 microRNA represented in Figure 1. A clear separation
between tumour and normal samples validates the effectiveness of
the methods used to generate this microRNA list.
Figure 4 PCA on mRNA data reveals clear separation. PCA was
executed using gene expression data. A clear separation between
the tumour and normal samples indicates the presence of selected
genes that robustly distinguish ovarian tumour from the normal
samples.
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we were left with 258 tumor samples, 6 normals, 18
microRNA and 53 mRNA.
Of the 18 microRNA that robustly distinguished
tumours from normals, nine (hsa-miR-183*, hsa-miR-
15b*, hsa-miR-15b, hsa-miR-590-5p, hsa-miR-18a, hsa-
miR-16, hsa-miR-96, hsa-miR-18b, and dmr_285) were
up-regulated and nine (hsa-miR-145*, hsa-miR-143*, hsa-
miR-34b*, hsa-miR-140-3p, hsa-miR-145, hsa-miR-139-5p,
hsa-miR-34c-3p, hsa-miR-133a, and hsa-miR-34c-5p)
were down-regulated in tumour compared to normal. K-
means clustering using only these 18 microRNA levels
confirmed that these microRNA clearly separate tumour
from normal samples (Figure 5).
Of the 53 mRNA probes which robustly separated
tumour from normal samples, 44 (BUB1, TPX2, CDC25C,
ASPM, C1orf112, KIF23, CENPA, HJURP, CCNA2, TTK,
CCNB2, C12orf48, BIRC5, RAD51AP1, RACGAP1,
MELK, KIFC1, NCAPG, EXO1, KDM2A, EHMT2, DNA2,
E2F3, C8orf30A, FAM64A, CORO1B, HEATR3, NCAPH,
PSMB2, ERCC6L, KIF15, ESPL1, RANGAP1, KIF11,
SCAMP5, NUSAP1, GINS1, ZWINT, ASF1A, an unanno-
tated probe 217205_at, and two probes each for TOP2A
and AURKA) were up-regulated and 9 (DNAH3, C6,
ADH6, SERPINA6, GCLC, DNAI1, SPARCL1, and two
probes for DNAH9) were down-regulated in tumour com-
pared to normal. K-means clustering of using only data
from these 53 mRNA confirmed that these mRNA clearly
separate tumour from normal samples (Figure 6). We note
that the sub-clusters in Figure 6 suggests the presence of
two (or more) distinct disease subtypes within the tumour
samples. We will further explore these potential disease
subclasses in a subsequent paper. Figure 7 shows a heat
map of the data projected on the 53 genes that best sepa-
rate tumour samples from normal samples, and shows
that they define a highly accurate signature for this
separation.
Of the 18 × 53 possible microRNA/mRNA pairs, 69
pairs showed seed sequence complementarity and con-
servation in TargetScan. The Pearson Rank test at p <
0.5 identified 21 significant pairs (Table 1) in the
tumour samples. Of these, fifteen pairs (including nine
mRNA) showed a significant anti-correlation signal
while six showed a significant positive correlation signal.
A similar analysis of the normal samples, at p < 0.1,
identified nineteen significant pairs (Table 2) of which
seven (with six distinct mRNA) exhibited anti-correla-
tion and twelve were positively correlated.
We found that of the fifteen microRNA/mRNA pairs
anti-correlated in tumour, one (hsa-miR-140-3p/
RAD51AP1) was anti-correlated and fourteen either
showed no correlation or were positively correlated across
the normal samples. Furthermore, of the six positively cor-
related pairs in tumours, three showed no correlation and
three showed positive correlation within the normal sam-
ples, implying a potential indirect mechanism of ovarian
tumorigenesis. Of the seven pairs anti-correlated in
Figure 5 A separate cluster for normal samples implies
significance of 18 microRNA. We K-means clustered (k = 3) all
samples using microRNA expression data exclusively from the 18
microRNA in Table 1. The results are consistent with our expectation
that these 18 microRNA best separate the cancerous samples from
normal ovarian tissue.
Figure 6 A distinct cluster containing only normal samples
implies significance of 53 mRNAs. Using SNR, 53 genes were
isolated that best separate tumour from normal samples We K-
means clustered (k = 3) all samples using expression data from
these genes. The above results are consistent with our expectations
that these 53 genes separate the cancerous samples from normal
ovarian tissue well.
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Figure 7 Heat map of 53 differentially expressed genes in tumours. Heat map of 53 genes isolated using SNR across 43 iterations.
Hierarchical clustering was used to generate the heat map, revealing oncogenic behaviour in the top subcluster and tumour-suppressor activity
within the bottom subcluster.
Table 1 Anti-correlated and positively correlated mRNA/microRNA pairs in the tumour and/or the normal samples
microRNAmRNA normal tumour MicroRNA Regulation in tumour vs. normal Gene Regulation in tumour vs. normal P-value
hsa-miR-140-3p RAD51AP1
hsa-miR-96
hsa-miR-96
hsa-miR-140-3p
hsa-miR-139-5p
hsa-miR-133a
hsa-miR-16
hsa-miR-140-3p C8ORF30A
hsa-miR-15b
hsa-miR-16
hsa-miR-16
hsa-miR-18a
hsa-miR-18b
hsa-miR-590-5p
hsa-miR-34c-5p
hsa-miR-16
hsa-miR-18a
hsa-miR-18b
hsa-miR-18a
hsa-miR-18b
hsa-miR-15b
--
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+
Down
Up
Up
Down
Down
Down
Up
Down
Up
Up
Up
Up
Up
Up
Down
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
0.002
0.006
0.003
0.012
0.012
0.023
0.027
0.022
0.011
0.001
3.7E-4
0.038
0.014
0.020
0.028
0.016
0.0
0.0
6.1E-06
6.2E-07
0.009
KIF23
BIRC5
RACGAP1
RACGAP1
GCLC
E2F3
none
none
none
none
none
none
none
none
none
none
none
none
+
+
+
+
+
none
none
none
Down
Up
Up
Up
Up
Up
Down
Down
Up
Up
Up
Up
Up
Up
Up
Up
C8ORF30A
C8ORF30A
SCAMP5
DNAI1
DNAI1
E2F3
E2F3
CDC25C
GINS1
GINS1
RAD51AP1
RAD51AP1
RACGAP1
a) This Table lists the 21 mRNA/microRNA regulatory pairs found in ovarian tumour samples. The microRNA shown are also differentially expressed in tumour
samples compared with normal, exhibit seed sequence complementarity in the gene shown, and are significantly anti-correlated (red) or positively correlated
(blue) with the corresponding mRNA in the tumour samples. The third and fourth columns show the type of correlation (if present) within normal and tumour
samples respectively. A “+” symbol represents a positive correlation, while “-” represents an anti-correlation, and “none” represents no significant correlation
across the samples
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normal samples, all (except miR-140-3p/RAD51AP1)
showed no correlation across the tumour samples, imply-
ing a potential de novo loss in microRNA function in
tumours. Of the twelve pairs which are positively corre-
lated in normal samples, two show anti-correlation within
the tumour samples, implying a potential de novo gain in
microRNA function in tumours. The remaining ten pairs,
which are positively correlated in normal samples, showed
either a positive correlation or no correlation in tumour
samples. These are of interest because they suggest some
novel indirect mechanisms in ovarian tumorigenesis.
Discussion
The methods we applied find potential functional relation-
ships that are good candidates for experimental validation.
Many of these microRNA/mRNA pairs impact critical cel-
lular functions that are frequently dysregulated in cancer.
For instance, RAD51AP1, a gene that functions in double-
stranded DNA repair, is also positively expressed in cervi-
cal cancer [19] and breast cancer cell lines [20], cancers
that exhibit high expression homogeneity with malignant
ovarian carcinomas. Our analysis shows that RAD51AP1 is
also up-regulated in ovarian cancers. Furthermore, hsa-
mir-140-3p, a potential regulator of RAD51AP1 according
to our anti-correlation analysis, has been previously shown
to be down-regulated in ovarian cancer [20], a finding
which we confirm. Interestingly, the anti-correlated rela-
tionship exhibited with this pair is also present in the nor-
mal samples. This suggests that if RAD51AP1 is acting as
an oncogene in ovarian tumours then targeting hsa-mir-
140-3p may be a way to target RAD51AP1.
The E2F transcription factor 3 (E2F3), a gene that is up-
regulated in serous ovarian carcinomas, regulates crucial
cell cycle and tumour suppressor genes [21-30]. We find
that this gene is over-expressed in ovarian tumours. More-
over, hsa-miR-145, a microRNA that is highly down-regu-
lated in ovarian cancer [5,8,9], shows significant expression
anti-correlation with E2F3 in normal samples, indicating
potential transcriptional repression by hsa-miR-145 in nor-
mal cells but loss of this function in tumours. Another
potential regulator of E2F3, hsa-miR-139-5p, is also pre-
dicted to target Topoisomerase IIa (TOP2A), a gene
encoding an enzyme which is involved in altering DNA
topology, including chromosome condensation, chromatid
separation and the relief of torsional stress occurring in
transcription and replication. TOP2A has also been shown
to be overexpressed in ovarian tumours [31] and is cur-
rently a common target in ovarian cancer clinical trials.
Glutamate-cysteine ligase (GCLC), an enzyme of glu-
tathione synthesis predicted to be regulated by hsa-miR-
133a in tumours (in a de novo gain of microRNA function)
and by hsa-miR-140-3p (in a de novo loss of microRNA
Table 2 Anti-correlated and positively correlated mRNA/microRNA pairs in the tumour and/or the normal samples
microRNA mRNA normal tumour MicroRNA Regulation in tumour vs. normal Gene Regulation in tumour vs. normal P-value
hsa-miR-140-3p RAD51AP1
hsa-miR-139-5p
hsa-miR-139-5p
hsa-miR-140-3p
hsa-miR-145
hsa-miR-139-5p
hsa-miR-96
hsa-miR-590-5p
hsa-miR-34c-5p
hsa-miR-16
hsa-miR-18a
hsa-miR-18b
hsa-miR-590-5p
hsa-miR-590-5p RAD51AP1
hsa-miR-18a
hsa-miR-18a
hsa-miR-15b
hsa-miR-18a
hsa-miR-590-5p
-
-
-
-
-
-
-
- Down
Down
Down
Down
Down
Down
Up
Up
Down
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up 0.021
0.036
0.009
0.036
0.009
0.009
0.002
0.036
0.036
0.0
0.036
0.036
0.036
0.021
0.021
0.036
0.021
0.036
0.036
DNAH9
TOP2A
GCLC
E2F3
E2F3
FAM64A
E2F3
E2F3
CDC25C
GINS1
GINS1
TOP2A
None
None
None
none
none
none
-
-
+
+
+
none
none
none
none
none
none
none
Down
Up
Down
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Down
Up
Up
Up
+
+
+
+
+
+
+
+
+
+
+
+
RAD51AP1
GCLC
E2F3
NUSAP1
GINS1
b) This Table lists the 19 mRNA/microRNA regulatory pairs found in normal ovary samples. The microRNA shown are also differentially expressed in tumour
samples compared with normal, exhibit seed sequence complementarity in the gene shown, and are significantly anti-correlated (red) or positively correlated
(blue) with the corresponding mRNA in the normal samples. The third and fourth columns show the type of correlation (if present) within normal and tumour
samples respectively. A “+” symbol represents a positive correlation, while “-” represents an anti-correlation, and “none” represents no significant correlation
across the samples
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function), has been shown to be an anti-apoptotic gene
that is positively expressed in ovarian cancer cell lines
[32,33].
In addition to many of the genes above which have
normal microRNA regulation disrupted by a loss of
microRNA function (Table 1) in tumours, several genes
regulated by microRNAs with de novo gains in function
exist as well. Baculoviral inhibitor of apoptosis repeat-
containing 5 (BIRC5), or Survivin, is a protein predicted
to be regulated by hsa-miR-96 that inhibits caspase acti-
vation [34] leading to negative regulation of apoptosis
[35]. Furthermore, increased presence of this protein has
been shown to decrease apoptosis in cisplatin-sensitive
ovarian carcinoma cells [35], a finding that is consistent
with the up-regulation of this gene within the TCGA
tumour data set. Additionally, another gene found to be
up-regulated in tumours in our dataset, Rac GTPase-acti-
vating protein 1 (RACGAP1) is an enzyme whose overex-
pression has also been associated with serous ovarian
carcinoma [30].
We also found six positively correlated microRNA/
mRNA pairs with matching seed sequences in tumour and
eleven pairs in normal. We reasoned that a likely explana-
tion for this lies in an intermediate regulatory mechanism
that is probably anti-correlated with the microRNA of
interest. Consequently, it is important to note that some
of the positively correlated genes/microRNA pairs also
impact important cellular functions frequently dysregu-
lated in cancer. Also noteworthy is that three of the pairs
show positive correlation in both the tumour and normal
samples. This finding suggests that while regulation of
gene expression by these microRNA is indirect, physiolo-
gical differences between tumour and normal samples is
directly dependent on the changes in expression by these
microRNA and the downstream effects they have on the
target mRNA they are paired with. For instance, nucleolar
and spindle associated protein 1 (NUSAP1), is a nucleolar-
spindle-associated protein that plays a role in spindle
microtubule organization [36] that is positively correlated
with hsa-miR-18a in normal but not in tumour. It has also
been previously shown to be positively regulated in cervi-
cal carcinoma [19,36]. Another gene of potential interest is
cell division cycle 25 homolog C (CDC25C), a critical gene
involved in cell division that dephosphorylates cyclin B-
bound CDC2 (CDK1) and triggers entry into mitosis [37].
It might also have a role in suppressing p53-induced
growth arrest and has been shown to be overexpressed in
ovarian cancer cell lines [27,38]. Finally, GINS complex
subunit 1 (GINS1), which was shown to be significantly
positively correlated with hsa-miR-18a and hsa-miR-18b
in both tumour and normal samples (and positively corre-
lated with hsa-miR590-5p in normal samples), represents
a key component of the GINS complex that is essential for
initiation of DNA replication and is positively regulated in
serous ovarian carcinoma [39]. Interestingly, hsa-miR-18a,
which possesses a significant positive correlation with
GINS1 (along with NUSAP1) has shown to be highly up-
regulated in serous ovarian carcinoma both previously
[10] and within our analysis.
Hsa-miR-16, mentioned previously to be anti-corre-
lated in tumours with several potential mRNA targets
including E2F3, C8ORF30A, and SCAMP5, is signifi-
cantly positively correlated with CDC25C. Should seed
sequence complementarity and conservation between
these positively correlated pairs be purely coincidental,
the relationships between the pairs of positively corre-
lated entities suggest an elaborate mechanism by which
these oncogenes/tumour suppressors operate. Further
wet-lab validation may show this to be a significant ovar-
ian cancer biomarker. This finding may further elucidate
a broader cancer pathway involving CDC25C and some
of its target tumour suppressor and cell cycle genes.
Conclusions
Using microRNA/mRNA expression data for 258 tumor
samples and six normal samples from TCGA, we have
uncovered forty mRNA/microRNA regulation which
meet the following criteria: a) These pairs have seed
sequence complementarity b) They are able to clearly
differentiate ovarian tumours from normal ovary by
their expression levels; c) They exhibit a strong anti-cor-
relation or correlation signature within either the
tumour samples, the normal samples, or both.
The novelty of our finding is that we have significantly
reduced the space of possible dysregulated microRNA/
mRNA pairs (based on seed sequence complementarity
alone) to a robust subset which may potential represent
functional relationships. Such a possibility can be tested
experimentally on ovarian cancer cell lines knock down
and/or knock in assays. If in addition, some of these
dysregulations are associated with survival pathways for
the tumor or with resistance to therapy, it opens up the
possibility of patient specific targeted therapy.
Availability of supporting data
All expression data is available for download at The
Cancer Genome Atlas Data Portal (http://tcga-data.nci.
nih.gov/tcga/tcgaHome2.jsp).
Additional material
Additional file 1: Table S1 MicroRNAs separating tumour from
normal samples. 127 microRNAs are significantly differentially expressed
and robustly separate tumour samples from normal samples.
Additional file 2: Table S2 Samples used for analysis. This table
identifies the 258 tumor samples and 6 normal samples with matched
microRNA/mRNA data from the TCGA sample set which were retained
for analysis ambiguous samples were removed using consensus
ensemble clustering (see Methods for details).
Miles et al. BMC Research Notes 2012, 5:164
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Acknowledgements
We thank NIH/NCI for allowing us access to the TCGA datasets. Without this
access, the present work would not have been possible.
Author details
1The Cancer Institute of New Jersey, New Brunswick, NJ, USA.2Department
of Bioinformatics and Systems Biology, Boston University, Boston, MA, USA.
3BioMaPS Institute, Rutgers University, Busch Campus, Piscataway, NJ, USA.
4Department of Molecular Biology & Biochemistry; Department of Physics,
Rutgers University, Piscataway, NJ, USA.5Institute for Advanced Study,
Simons Center for Systems Biology, Princeton, NJ, USA.
Authors’ contributions
GM obtained the data, performed most of the analysis, implemented the
methods, interpreted the results, and wrote the manuscript, MS performed
portions of the analysis, suggested alternative interpretations/analyses to
perform, GB, LR, GR helped design and supervise the study. All authors have
read and approved the final manuscript. The authors declare that they have
no conflict of interest with any of the contents of this manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 11 November 2011 Accepted: 27 March 2012
Published: 27 March 2012
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doi:10.1186/1756-0500-5-164
Cite this article as: Miles et al.: Identifying microRNA/mRNA
dysregulations in ovarian cancer. BMC Research Notes 2012 5:164.
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