Genome-Wide MicroRNA Expression Analysis of Clear
Cell Renal Cell Carcinoma by Next Generation Deep
Susanne Osanto1*, Yongjun Qin1, Henk P. Buermans2, Johannes Berkers4, Evelyne Lerut5,
Jelle J. Goeman3, Hendrik van Poppel4
1Department of Clinical Oncology, Leiden University Medical Center, Leiden, the Netherlands, 2Department of Human Genetics, Leiden University Medical Center,
Leiden, the Netherlands, 3Department of Medical Statistics, Leiden University Medical Center, Leiden, the Netherlands, 4Department of Urology, Catholic University of
Leuven, Leuven, Belgium, 5Department of Pathology, Catholic University of Leuven, Leuven, Belgium
MicroRNAs (miRNAs), non-coding RNAs regulating gene expression, are frequently aberrantly expressed in human cancers.
Next-generation deep sequencing technology enables genome-wide expression profiling of known miRNAs and discovery
of novel miRNAs at unprecedented quantitative and qualitative accuracy. Deep sequencing was performed on 11 fresh
frozen clear cell renal cell carcinoma (ccRCC) and adjacent non-tumoral renal cortex (NRC) pairs, 11 additional frozen ccRCC
tissues, and 2 ccRCC cell lines (n=35). The 22 ccRCCs patients belonged to 3 prognostic sub-groups, i.e. those without
disease recurrence, with recurrence and with metastatic disease at diagnosis. Thirty-two consecutive samples (16 ccRCC/
NRC pairs) were used for stem-loop PCR validation. Novel miRNAs were predicted using 2 distinct bioinformatic pipelines. In
total, 463 known miRNAs (expression frequency 1–150,000/million) were identified. We found that 100 miRNA were
significantly differentially expressed between ccRCC and NRC. Differential expression of 5 miRNAs was confirmed by stem-
loop PCR in the 32 ccRCC/NRC samples. With respect to RCC subgroups, 5 miRNAs discriminated between non-recurrent
versus recurrent and metastatic disease, whereas 12 uniquely distinguished non-recurrent versus metastatic disease.
Blocking overexpressed miR-210 or miR-27a in cell line SKCR-7 by transfecting specific antagomirs did not result in
significant changes in proliferation or apoptosis. Twenty-three previously unknown miRNAs were predicted in silico.
Quantitative genome-wide miRNA profiling accurately separated ccRCC from (benign) NRC. Individual differentially
expressed miRNAs may potentially serve as diagnostic or prognostic markers or future therapeutic targets in ccRCC. The
biological relevance of candidate novel miRNAs is unknown at present.
Citation: Osanto S, Qin Y, Buermans HP, Berkers J, Lerut E, et al. (2012) Genome-Wide MicroRNA Expression Analysis of Clear Cell Renal Cell Carcinoma by Next
Generation Deep Sequencing. PLoS ONE 7(6): e38298. doi:10.1371/journal.pone.0038298
Editor: Chad Creighton, Baylor College of Medicine, United States of America
Received January 12, 2012; Accepted May 3, 2012; Published June 20, 2012
Copyright: ? 2012 Osanto et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Renal clear cell carcinoma contributes to about 3% of all
human cancers . The incidence of the disease has been steadily
rising in Europe to over 30,000 new cases per year . Of the
various histological subsets, renal clear cell carcinoma (ccRCC) is
the most common subtype at diagnosis. One third of patients
present with metastases, whereas another third will develop
metastases. The majority of patients with distant metastases will
succumb to the disease despite introduction of novel effective
targeted agents to treat patients with metastatic disease [3,4].
There are no robust diagnostic markers to reliably establish the
prognosis at the time of diagnosis in an early stage of the disease.
MiRNAs regulate gene expression post-transcriptionally and
have been found to modulate crucial biological processes such as
differentiation, proliferation and apoptosis . Dysregulated
miRNAs have been reported in many human cancers [6–8].
Next-generation deep sequencing enables miRNA profiling at
unprecedented quantitative and qualitative levels. Compared to
conventional miRNA array platforms, the major advantages of
sequencing technology are massive parallel analysis of genome-
widely expressed miRNAs (miRNome), quantification of expres-
sion levels of individual miRNAs (absolute abundance), identifi-
cation of miRNA sequence variations and the discovery of novel
A number of mainly array platform-based studies recently
demonstrated that a considerable number of miRNAs are
dysregulated in ccRCC [9–17] and a few miRNA have been
reported to be functionally involved in ccRCC [18,19]. Although
the expression profiling results of the various array studies are not
consistent, the data indicate that dysregulated miRNAs may play a
pivotal role in the pathogenesis of ccRCC.
At present, there is a need for a quantitative genome-wide
miRNA expression profiling using a robust technology to provide
better insight of miRNAs dysregulation in RCC. To this end, we
performed miRNA deep sequencing in a large number of clear cell
RCC tumors and paired NRC to identify dysregulated miRNAs
that may serve as reliable diagnostic markers and potential
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Genome-wide Expression of miRNAs
From all sequenced 35 miRNA libraries, we identified a total of
463 miRNA sequences, expressed both in RCC and normal
kidney tissues (data available in the Gene Expression Omnibus
GSE37616). Of these, 284 miRNA sequences matched to both
the 3p-arm and the 5p-arm of 142 miRNA precursors. We also
included 94 miRNAs which matched only to a 3p-arm and 85
miRNAs which matched only to a 5p-arm of their miRNAs
precursors (Table S1).
The expression level of individual miRNAs within each library
varied greatly ranging from 1 to 147,000 sequence read counts per
million. In each RCC miRNA library, a small number of miRNAs
was abundantly expressed and the same miRNAs were also most
abundantly expressed in RCC cell lines and normal kidney
The nine most abundant miRNAs (miR-21, miR-145, miR-29a,
miR-451, miR-29c, miR-23a, miR-126, miR140 and miR-125a)
contributed up to 55%, 48% and 50% of the total miRNA pool in
the 22 ccRCC tumors, 2 ccRCC cell lines and 11 normal kidney
The twenty most abundantly expressed miRNAs contributed to
more than 70% of the total miRNAs of each of these miRNA
libraries, whereas the top 50 miRNAs comprised more than 90%
of each miRNA library.
Differentially Expressed miRs in ccRCC Versus Matched
Significant differential expression of 100 miRNAs was found
between 11 ccRCC and NRC after adjustment for multiple
testing. After applying additional filtering with a stringent cut-off
of 50 read counts/million, 70 miRNAs were robustly differentially
expressed. These 70 miRNAs consisted of 29 (42%) miRNAs that
were up- and 41 (59%) that were downregulated (adjusted
P,0.05). In Figure 1, these 70 miRNAs are depicted according
to overexpression in ccRCC and grouped based on expression
levels (i.e. upregulated and downregulated miRNAs depicted in
the left, respectively right panels).
The most abundantly upregulated miRNAs were miR-21-3p,
miR-451-3p and miR-210-3p, of which miR-21 had an expression
level in RCC exceeding 140,000 read counts/million. Of the 41
downregulated miRNAs, miR125a-5p, miR-204-5p and miR-
10a/b-5p were the most abundantly expressed in the normal
kidney cortex tissues.
Fold-change of Differentially Expressed miRNAs
With regard to the fold-change in expression levels, 40
differentially expressed miRNAs showed a greater than 3-fold
change in expression (Figure 2). Amongst the miRNAs with the
greatest fold-change between ccRCC and normal kidney cortex
were the upregulated miRNAs miR-122-5p, miR-224-5p, miR-
210-5p, the downregulated miR409-3p, and the upregulated miR-
We next performed an hierarchical clustering analysis utilizing
the 100 miRNAs differentially expressed between 11 tumor/
normal tissue pairs obtained by deep-sequencing to visualize the
distinct expression profile between sequenced ccRCC samples and
ccRCC cell lines from normal kidney. The expression patterns of
the 100 miRNAs clearly separated the 11 normal kidney tissues
from the 11 corresponding ccRCC tumors, and the 11 ccRCC
tumor samples without corresponding normal tissue, and the cell
lines SKRC and MZ1257 clearly clustered with the 11 tumors,
and no outliers were obvious (Figure 3).
PCR Validation of Differential Expression of miRNAs
To validate the deep sequencing results, we performed a stem-
loop PCR utilizing 5 miRNAs selected from the 100-miRNA gene
expression signature list in 32 additional tissue samples consisting
of 16 ccRCC tumors and 16 paired NRC of individual patients.
Results of the technical duplicates were very similar. Stem-loop
PCR confirmed overexpression of miR-21, miR-122 and miR-210
and downregulation of miR-199 and miR-532 (Figure 4). In
accordance with our sequencing results, miR-122-5p and miR-
210-3p showed the largest fold-change in expression levels
between tumor and normal kidney cortex tissue in agreement
with the deep-sequencing findings.
Expression of miRNA in the ccRCC Prognostic Subgroups
To identify differentially expressed miRNAs among the three
ccRCC prognostic sub-groups across 22 patients, we conducted
differential expression analysis using the same edgeR package and
identified 21 miRNAs distinguishing the 3 sub-groups pairwise.
Comparison between the non-recurrent and recurrent sub-group
revealed significant expression differences of 9 miRNAs, miR-138-
1-5p, miR-181-2-5p, miR-181-5p, miR-182-5p, miR-29b1-3p,
miR-29b2-3p, miR193b-3p, miR-15a-3p and miR-1247-5p, five
of which also discriminated between non-recurrent and metastatic
When comparing the non-recurrent with the metastatic sub-
group, a set of 17 miRNAs showed significant expression changes,
of which 5 also discriminated between non-recurrent and
recurrent sub-group. The remaining 12 miRNAs uniquely
discriminated between metastatic and non-recurrent sub-group
(Figure 5) and their expression level was overall higher than of the
other 9 miRNAs. Of interest, we did the same analysis to identify
differentially expressed miRNAs among the three ccRCC
prognostic sub-groups based on Leibovich scores but did not find
any correlation between miRNA expression and the three
Leibovich subgroups in the group of 22 ccRCC patients (data
Survival of RCC Patients and miRNA Expression
Next, we performed Kaplan Meier survival analyses to explore
a possible association between miRNA expression levels and
patient survival. To this end, ccRCC tumors were simply
dichotomized using the median miRNA expression level as cut
off. Interestingly, dichotomization based on the median of 4
miRNAs, -miR-222-5p, miR-27a-5p, miR-125b-3p and miR-935-
3p, albeit their expression levels were rather low, suggested that
higher miRNA expression level was associated with poor overall
survival (Figure S1).
Functional Assays of miR-210 and miR-27a
We subsequently performed a limited set of functional analysis
of 2 of the PCR-confirmed highly differentially expressed
miRNAs, miR-210-3p and miR-27a-3p. Following transfecting
the RCC cell line SKRC7 with miR-210 and miR-27a,
respectively, we efficiently knocked down both miRNAs as
demonstrated by PCR (data not shown). No significant change
in percentage of apoptosis or proliferation rate of SKRC7 cells was
observed at the various time points measured after knocking down
of miR-210 and miR-27a, respectively. No significant enhance-
ment of the percentage of apoptotic cells at 24 and 48 hr as
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evidenced by Annexin V+/PI+double positivity was observed
following addition of anti-miR-210-3p, anti-miR-27a-3p and
scrambled control to SKRC7 cells. The percentage of Annexin
V+/PI+double positive SKCR7 cells were as follows: 1.62 and
3.9%; 1.28 and 3.62% and 3.58 and 4.50% Annexin V+/PI+
SKCR7 cells, respectively at 24 and 48 hr for anti-miR-210-3p,
anti-miR-27a-3p and scrambled control treated SKRC7 cells. No
difference was observed in cell viability and proliferation rate
between anti-miR-210-3p, anti-miR-27a-3p, and scrambled con-
trol-treated SKRC7 cells as measured by WST-1 and counting cell
numbers at 24 to 72 hr.
Differentialy Expressed miRNAs and their Predicted
The miRecord database enabled us to list our differentially
expressed miRNAs and the various validated and confirmed
targets of such a miRNA for which experimental evidence exist in
the litterature. Of the 70 differentially expressed (3p- and 5p-)
miRNAs identified by us, targets of 44 precursor miRNAs can be
found in miRecord database and these are reported in Table 1.
Since often no distinction is made between 3p- and 5p-arms of
individual miRNAs we simplified our list of miRNAs to report the
targets of the precursor miRNA (Table 1). Using DAVID database
enabled us in silico prediction of the combined sets of upregulated
or downregulated miRNAs and their potential target networks of
genes and signaling pathways (Table 1). We subsequently retrieved
the enriched biological pathways predicted by DAVID bio-
informatics database and found that the top enriched pathways
concern apoptosis and transcription pathways (Table S2).
In addition, we applied another database miRror, which
contains in silico predicted, non-validated, as well as validated
predicted targets, to identify specific targets of the 70 differentially
expressed miRNAs, either the 3p- or 5p-, and retrieved the
enriched pathways by DAVID. These targets and predicted
pathway results are different (data not shown) and we rely on the
miRecord database validated targets (Table 1) and derived
predicted pathways using DAVID (Table S2). The limitation of
using different methods, in particular using databases containing
non-validated targets, is that different pathway predictions may
Novel miRNA Prediction
Using two independent bioinformatics pipelines, we predicted
121 candidate miRNAs from all the sequenced samples which
meet the criteria of stem-loop secondary structure of miRNA
precursor and have not been reported before to be expressed in
humans or other species. Of the 121 novel miRNA, 23 miRNAs
showed expression levels .50 read counts/million (Table S3).
None of the predicted novel miRNAs showed a significant
differential expression between ccRCC and NRC and these
candidate miRNAs have not been validated yet.
Figure 1. Differentially expressed miRNAs in ccRCC tumors. Differentially expression was analyzed between 11RCC tumors and their matched
11 normal kidney tissues. Significance was determined by adj. P value ,0.05 using edgeR package. Differentially expressed miRNAs categorized into
four categories based on level of expression. Expression levels are given as Mean 6SD. In the left panels (A-D) overexpressed miRNAs are shown, each
panel representing a 10-fold difference in expression level. In the right panels (E-H), downregulated miRNAs are depicted with a 10-fold difference in
maximal miRNA expression in normal tissue in each panel.
Figure 2. Expression fold-changes of the . .3fold differentially expressed miRNAs. The expression levels of the significant miRNA in ccRCC
were relative to that of matched normal kidney tissue, as expression fold change. Only miRNAs expression fold-change .3 are presented. The red
bars indicate fold-increase of miRNAs in ccRCC compared to normal kidney tissues, the blue bars indicated miRNAs fold increase in miRNA expression
in normal kidney versus ccRCC.
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Using state-of-the-art sequencing technology, we have quanti-
fied 463 genome-widely expressed miRNAs in ccRCC, normal
kidney and ccRCC cell lines. Overall, the miRNome of ccRCC
tumors resembled that of the ccRCC cell lines. Quantitative
differential expression analysis allowed the identification of a 100-
miRNA signature distinguishing ccRCC from normal kidney. We
identified 21 miRNAs discriminating tumors of favorable prog-
nosis patients (non-recurrent) from those with a less favorable
prognosis of which patients with metastatic disease at time of
diagnosis had the worst prognosis and all died within 1.5 years
from RCC; four other miRNAs seemed associated with disease-
specific death. Quantitative deep-sequencing technology allowed
the discovery of novel miRNAs which have not been reported
before in any species and were shown to be present in normal
kidney tissue and tumors.
Of the most abundantly expressed miRNAs, miR-21, miR-451,
miR-125a and miR-204 together contributed to 27% and 18% of
total miRNAs in ccRCC and normal kidney tissue. MiR-21, the
most abundantly expressed miRNA accounting for 14% of the
total miRNA in ccRCCs and 5% in normal kidney, has been
found to be overexpressed in many human cancer and to act as an
oncogene by targeting tumor suppressor PTEN in various cancers
. Of interest, the highly overexpressed MiR-451 and miRNA-
27a have been reported to regulate multi-drug resistance (MDR)
in carcinoma cell lines , and since RCC often express the
MDR phenotype these two miRNAs might indeed be important in
contributing to the RCC phenotype.
One hundred of 463 (21%) genome-widely expressed miRNAs
showed significant expression changes in RCC. Recently, dysreg-
ulation of miRNAs in ccRCC has been reported [9–17]. Of these
studies, 7 studies made use of array-based platforms, one of a
miRNA PCR-primer array platform  whereas only Weng et al
 applied deep sequencing technology to assess whether
expression profiles in 3 ccRCC differed between stored frozen
versus fixed samples. The latter investigators found that formalin-
fixed paraffin-embedded tissue could be used to profile small
RNAs. Mostly, small numbers of ccRCC tumors were used for
expression profiling. Some studies used normal kidney as control
tissue, other studies compared miRNA expression profiles between
ccRCC and different kidney tumor histologies.
Figure 3. Unsupervised cluster analysis of the 100 differentially expressed miRNA derived from differential expression analysis
with 11 ccRCC tumors and matched 11 normal additional 11ccRRC tumors and 2 ccRCC cell lines SKRC andMZ127.
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Our data confirm the ccRCC miRNAs findings reported in the
literature [9–17] (Table 1) but there are also discrepancies with
regard to reported dysregulated miRNAs in other studies and the
dysregulated miRNAs found by us utilizing deep sequencing
technology. For instance, Nakada et al  have reported 43
miRNAs differentially expressed in ccRCC and 15 miRNAs were
also found by us. Chow et al  reported 80 differentially
expressed miRNAs of which only 15 overlapped with our 100
miRNAs. Despite the large extent of overlapping results, the
discrepancies between (combined) results from earlier array studies
and our current deep sequencing results may arise from differences
in sample preparation, different technologies and the quantitative
accuracy of the applied deep sequencing technology. However, the
direction of the differential expression of miRNAs found by us and
others did not differ except for the fact that one study did not
report the direction for all of the dysregulated miRNAs. Moreover,
in our study a highly stringent selection criterion of $95% tumor
cell content was used for miRNA library construction which may
further enhanced the sensitivity of miRNA detection.
A multitude of studies have investigated the mRNA targets of
one single miRNAs and the comprehensive, publicly available
miRecord database enabled us to identify the various currently
validated targets of the list of differentially expressed miRNAs as
identified by us. Using DAVID database enabled us in silico
prediction of the combined sets of upregulated or downregulated
miRNAs and their potential target networks of genes and signaling
pathways. Target and pathway prediction suggested involvement
of the deregulated miRNAs in a variety of biological processes
involved in malignant behavior of cells and in the transformation
of normal cells to malignant cells. To understand functional
implications of the target genes, enriched biological pathways
predicted according to DAVID bio-informatics database were
identified. Interestingly, the top enriched pathways are apoptosis
and transcriptions. The deregulated miRNAs and their top
enriched pathways were in particular involved in apoptosis and
transcription suggesting that these deregulated miRNAs may
indeed play roles in a variety of biological processes involved in
malignant behavior of cells and in the transformation of normal
cells to malignant cells. Based on this integrative approach, our
data provide an important platform for future investigations
aiming at characterizing the role of specific miRNAs in ccRCC
We tested whether upregulated miRNAs could be efficiently
knocked down by using antagomirs and this was affirmed. The
second question was, whether knocking down of upregulated
miRNAs would affect proliferation and/or apoptosis in one RCC
cell line used. Both miR-210 and miR-27a were linked to
predicted target genes which are of interest with regard to cell
proliferation and apoptosis (see Table 1).
Our exploratory experiments showed that proliferation and
apoptosis of SKRC7 cells were not affected by knocking down
miR-210 or miR-27a following transfection with specific antag-
omirs. Data in other tumor types suggested that miR-210 is indeed
involved in proliferation. Interestingly, upregulation of miR-210
was also reported in other malignancies, including breast and head
and neck cancer, and found to correlate with prognosis [22,23],
although in some studies downregulation of miR-210 was found in
cancer, e.g. breast cancer in comparison with benign breast
epithelium and esophageal cancer . Expression of miR-210
might merely reflect the hypoxia status known to be typically
present in ccRCC and serve as a surrogate marker for tumor
hypoxia because miR-210 is the most robustly induced miRNA
under hypoxia [18,22,23]. This might explain the lack of effect on
proliferation or apoptosis in our cell line miR-210 knock down
experiments, but differences in miR-210 target expression of
FGFRL1 might be another explanation.
Two other studies suggested that miR-27a may be involved in
the development of tumor drug resistance  and we were
Figure 4. miRNA stem-loop PCR validation. miRNA expression in paired tissue samples of ccRCC and adjacent normal tissue. Small RNA
preparations were analyzed for the expression of five selected miRNAs by stem-loop PCR. The expression level of each miRNA was normalized to a
small RNA reference U6. The normalized expression values of RCC tumors were relative to the paired normal tissues, and converted expression fold
changes. All miRNAs showed statistical significance, determined by pair-wise student t test with P,0.05. Each bar represents one individual RCC
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interested in miR-27a because of its involvement in MDR/P-
glycoprotein expression in cancer cells, a typical characteristic of
The lack of effect on proliferation and/or apoptosis after
knocking down miR-210 and miR-27a in one ccRCC cell line may
indicate that the effect of these miRNAs on the endpoints chosen
do not apply to ccRCC and/or to this particular cell line only, or
that the conditions to affect proliferation and/or apoptosis require
a more intricate interplay of more factors. Discrepancies between
individual cancer cell lines and tumors may be determined by cell-
specific differences in expression levels of miRNA target genes or
other cellular c.q. exogenous factors.
Of the dysregulated miRNAs miR-122 showed the largest fold-
change in expression level in RCCs. Although expression level of
miR-122 is much lower than that of miR-21, the clear cut
expression change in RCC suggests that it may serve as a novel
biomarker to distinguish tumor from normal kidney tissue.
Notably, 5 downregulated miRNAs miR-532 and miR-362,
miR-500, miR-501, miR-502 are clustered and together encoded
in one intron of a renal specific gene voltage-gated chloride ion
channel CLCN5, and have been reported to be downregulated in
Differentially expressed miRNAs as we found in our limited
subgroup of metastatic RCC not only serve as potential prognostic
markers, but also even as possible therapeutic targets. Of the 12
miRNAs which discriminated between non-recurrent and meta-
static prognostic sub-groups, miR-221/222 has been reported in
many other types of human cancers including prostate carcinoma
. MiR-130b has been suggested to regulate expression of the
tumor suppressor gene RUNX3  whereasmiR-146a has been
shown to play an important role in oncogenic transformation of
immune cells in mice model .
The downregulated miR-204, which has been found to be also
dysregulated head and neck cancer , displayed very high
expression in NRCs with expression level of on average 5.8%
(range 1.1 to 8.2%), of the total miRNAs, but gradually decreased
following the sub-groups order of no recurrence, recurrence and
metastatic sub-groups. Of interest, miR-204, located on chromo-
some 9 in intron 6 of the potential Ca2+ channel TRPM  may
proof to be a robust classifier if validated in an independent set of
A key advantage of deep sequencing is that it is a powerful tool
allowing the discovery of novel miRNAs that cannot be detected
using array-based technology. The use of our deep sequencing
data and two additional pipelines allowed discovery of 21 novel
miRNAs which generated perfect secondary structures indicating
that deep sequencing is indeed a powerful tool to identify novel
miRNAs of as yet unknown function. Future studies are warranted
to proof that they are bonafide miRNAs.
Figure 5. miRNA expression according to deep sequencing analysis. In three RCC patient subgroups with different clinical outcomes (B-D)
and adjacent normal kidney tissues (A) derived from 11 of these 25 patients. Statistical significance of miRNA expression in each group was calculated
using R package edgeR statistics within 3 RCC subgroups A) no recurrence subgroup (n=7), C) recurrence subgroup (n=8) and D) metastatic
subgroup (n=7). The 12 miRNAs depicted were selected based on highly significant difference in expression level in metastatic RCC (D) in
comparison to RCC without recurrence (B) (adj. P value ,0.05 BH correction). miRNA expression level (read counts/million) is presented as boxplots
generated at R.
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Table 1. Differentially expressed miRNAs in ccRCC of which experimental evidence exist for one or more target genes.*
miRNAChange Reported elsewhere Validated targets (miRecord*)
Ref 13,17 E2F1, VEGFA, CDKN1A, E2F1, ITCH
Ref 9,13,16,17 GTF2B, SLC7A1, ALDOA, SLC7A1, AKT, SLC7A1, CCNG1, BCL2L2, in total 50 targets#
- DNMT3B, NR1I2, Rps6ka5, DNMT1
Ref 11,15,16,17AGTR1, BACH1, LDOC1, MATR3, TM6SF, AGTR1, RHOA,ETS1,MEIS1, in total 24 targets#
Ref 13,17 BCL2, H3F3B, MCL1, VEGFA, DMTF1, RAB21, CADM1, SKAP2, WT1, in total 67 targets#
Ref 17BCL2, RECK, CCNE1, MKK4
Ref 10,17 CCND1, TPPP3, BCL2, H3F3B,
- TCL1A, PLAG1, VSNL1, GRIA2, AICDA, ESR1, NLK, CDX2, GATA6
Ref 17E2F6, PTK2, MCL1
Ref 10, 13,17 PTEN, TPM1, E2F1, TGFBR2, CDK6, TIMP3, NFIB, PDCD4, FAS, FAM3, in total 37 targets#
Ref 9,11,15,16,17EFNA3, MNT, CASP8AP2
Ref 9,11,15,16,17API5, KLK10, KLK1, AP2M1
Ref 10 SP3, SP4, PHB, RUNX1, GCA, PEX7,FOXO1, FADD
Ref 15,17 NOTCH1, DLL1, BCL2, E2F3, VEGF, MYCN, SIRT1, CCND1, MYB, JAG1, in total 21 targets#
Ref 17 ABCB1, MIF
Ref 16 VEGFA,RB1, RNUX1, APP, CDKN1A
- PLAG1, CDK6, BACE1, SERBP1, CRKL, RAB1B, AGO3, CDCA4, AGO1, in total of 11 targets#
Ref 13,17 HOXA1, USF2
Ref 17 HOXD10, KLF4
Ref 16,17 LIN28, ERBB2, ERBB3, TP53, HUR, ARID3B
Ref 15,16LIN28, ERBB2, CDK6, H3F3B, CDC25A, BAK1, NTRK3, PERP, GPR160, in total 52 targets#
Ref 16 NOTCH1, EIF2C3, CAMTA1, BMPR2, SOX4, ZFP91, TP53INP1, FNDC3b, in total 12 targets#
Ref 15APC, FLAP, JAK2
Ref 9,13,16 RHOC, KRT, ROCK2
Ref 9,13,15,16SERBP1, SFPQ, HMGB1, CLOCK, TGFB2, ELMO2, WDR37, SIP1, KLHL20, in total 13 targets
Ref 13,17RARG, ADCY6, MITF, IGF-IR, WAVE
Ref 9,16LAMC2, SET
Ref 10,11,13,15,16,17 ZEB1, ZEB2, RERE, ELMO2, ERRFI1, KLHL20, FOG2, WAVE3, BAP1, in total 13 targets#
Ref 9, 11,13,15,16,17 ZEB1, ERRFL1
Ref 13 SOCS3, TP63, ABL1
Ref 15,17 ARPC1B, SPDEF, CTSC, MMP3, MMP9, BMP1, CDH11, ITGB4, SHC1, in total 19 targets#
- VEGFA, ESR1
Ref 13,16 PTEN
Ref 11 LAMB3, COL1A1, ECOP, SP1
Ref 15,16,17 NOTCH1, TNRC6A, BECN1P1, KRT7
Ref 16SOX4, PTPRN2, MERTK
Ref 13,16 YAP1, YWHAZ
Ref 11,16 RERE, ELMO2, ERRF11, ERBB2IP, KLHL20, WRD37, PTPRD, BAP1, FOG2
Ref 13,16,17 RUNX3
Of the 70 differentially expressed miRNAs with .50 read counts/million found by us only the 44 miRNA of which a validated target is present in miRecord are reported.
*Above experimentally proven targets are listed in miRecord target database version 3. miRecord does not allow appropriate distinction between 3p- ad 5p- forms of
each miRNA due to differences in techniques applied in the various reports in the literature.
#If more than 9 targets per miRNA are reported, only nine are enlisted in the Table.
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Characterization of the miRNome of clear cell renal cell
carcinomas by deep sequencing enabled to precisely quantify
expression levels of miRNA and identify dysregulated miRNAs in
RCC that may serve as novel diagnostic marker. Several of the
differentially expressed miRNAs potentially target networks of
genes and signaling pathways that may be involved in the
malignant transformation of normal kidney cells and pathophys-
iology of ccRCC. Our data provide an important platform for
future investigations aimed at characterizing the role of specific
miRNAs in ccRCC pathogenesis.
Materials and Methods
Patients and Tumors
RCC specimens were used from a total of 38 patients who
underwent an open (partial) nephrectomy for ccRCC be-
tween1997–2003 at the University Hospital of Leuven, Belgium.
Immediately after surgery, tumor and matched non-malignant
kidney tissue were (separately) stored frozen at 280uC according
to a standard procedure.
Tumor staging was performed using the 2010 TNM staging
system. None of the patients received systemic anti-cancer
treatment prior to surgery. Criteria applied to select tissues for
deep sequencing and validation by PCR were a sufficiently large
sample size based on pre-specified expected variations to allow for
firm conclusions within the limitation of costs and financial
resources, the time period of diagnosis and minimum of 5 year
follow up period of the patients, availability of ccRCC and paired
adjacent non-tumoral renal parenchyma called ‘‘nontumoral renal
cortex’’ (NRC), and a high yield of high quality RNA in all tissues
subjected to deep sequencing and to PCR validation. Central
review of tissue blocks were performed by one experienced uro-
pathologist and only blocks with .95% viable tumor (ccRCC) or
non-tumoral cortex (NRC) were included in this study.
The criteria to select normal (non-tumoral) kidney tissues were
that the normal kidney cortex tissue was from the same individual,
from the same kidney, from the same surgical specimen, and
collected under the same Standard Operation Procedure (SOP)
conditions: if possible the sampling of normal cortex was done
leaving at least 1 cm of macroscopically healthy cortex adjacent to
the tumor untouched. ccRCC are macroscopically characterized
by a sharp demarcation (fibrous pseudocapsule) between tumor
and surrounding non-tumoral tissue. This is also reflected
microscopically, as RCC are sharply demarcated from the
surrounding tissue. The non-tumoral tissue was therefore obtained
in macroscopically clear-cut benign tissue and immediately fresh-
frozen. An Hematoxylin-Eosin stained slide was then made of this
frozen tissue block. To be selected as adequate for RNA isolation,
the benign nature of the tissue had to be confirmed microscop-
ically. Significant necrosis or extensive inflammation were also
excluded out in this way. This was done by one experienced
Deep sequencing and PCR validation of tumors and NRC were
performed by members of the team, blinded for the clinical data,
without prior knowledge of the TNM stage and clinical outcome
of the patients.
In total, 22 RCC tumors and 11 matched non-malignant
kidney tissues and 2 RCC cell lines SKRC7 and MZ1257 were
used for miRNA deep sequencing, whereas 32 tissues consisting of
16 paired ccRCC and kidney cortex (NRC) were used for PCR
validation. The 22 RCC tumors selected for deep-sequencing
come from 22 patients with a median age of 61 years old, range,
43–79 years old, of who 7 never had a recurrence of the disease, 8
patients who had a recurrence of the disease and 7 patients
presented with distant metastases at time of diagnosis who
underwent a cytoreductive (radical) nephrectomy (Table S1).
These 22 deep sequenced RCCs were classified based on known
clinicopathological characteristics such as tumor size, Fuhrman
grade, nodal involvement, and the widely used prognostic
Leibovich score for all patients even though the nomogram is
normally only used for non-metastasized tumors . Six tumors
fell into the Leibovich favorable (i.e., 1–2 unfavorable risk factors),
6 intermediate (i.e. 3–5 unfavorable risk factors) and 10 poor
category (i.e., 6 or more unfavorable risk factors) but the Leibovich
categories did not overlap with the three subgroups classification
made based on absence or presence of tumor recurrence and
distant metastases (see below). These 22 ccRCC patients had
namely a priori been classified by the clinicians, all blinded for the
deep sequencing and PCR outcome, into patients who never
experienced disease recurrence (non-recurrent sub-group, n=7, of
whom 1 died of the disease), patients who progressed after initial
nephrectomy into a stage with clinical recurrence of the disease
(recurrent disease sub-group, n=8, of whom 2 died of RCC), and
patients who had metastatic disease with distant metastasis at time
of diagnosis and cytoreductive surgery (metastatic sub-group,
n=7) and who all died within 1.5 years from RCC.
The regulations of the Ethics Committee of the Leiden
University Medical Center (LUMC) and of the Faculty of
Medicine, University Hospital of Leuven, permit the research to
be conducted without obtaining written informed consent from the
patients. The tissue material has been anonymized and the
research with the anonymous biological material and its results are
not retraceable to the individuals concerned.
RNA was isolated from approximately twenty 50 mm cryosec-
tions prepared at 220uC. Total RNA was isolated using a Trizol
reagent (Invitrogen, Carlsbad, CA). RNA with good quality e.g.
A260/A280.1.8 was used for further experiments. Of each
sample, 2 ug total RNA was used for small RNA separation
,200 bp (mirVana miRNA Isolation Kit, Applied Biosystems,
Nieuwerkerk a/d IJssel). Next, miRNA libraries were constructed
primarily based on the SREK protocol (SOLiD Small expression
Kit, Applied Biosystems, Nieuwerkerk a/d IJssel) with modifica-
tions as reported elsewhere . Of each small RNA library, the
fraction with an estimated size corresponding to RNA of 15 nt to
30 nt was excised from the gel, representing the miRNA library.
The miRNA libraries were examined with DNA chip (BioAna-
lyzer 2100, Agilent Technologies, Palo Alto, CA, USA). Subse-
quently, the miRNA libraries were subjected to deep sequencing
using a Genome Analyzer II (Illumina Inc, San Diego, CA, USA)
according to manufacture protocol .
After the completion of sequencing, the raw sequence data were
processed through the standard Illumina pipelines for base-calling
and fastq file generation. The sequence reads were mapped to the
human reference genome GRC37 using a bioinfomatic E-miR
pipeline . According to human genome annotation, mapped
reads were classified into non-coding RNA species e.g. miRNA,
mtRNA, and rRNAs (Table S4). We defined reads as miRNA if
they mapped to the miRNA precursor. The small RNA library
contained in majority of miRNA, with a proportion ranging from
64–74% in matched normal kidney tissue, from 68–81% in RCC
MicroRNA Deep Sequencing of Renal Cell Carcinoma
PLoS ONE | www.plosone.org9 June 2012 | Volume 7 | Issue 6 | e38298
and of 77 and 74%, respectively in RCC cell lines, indicating
consistency of miRNA sample preparation (Figure S2).
miRNA expression level was quantified by relative miRNA read
counts to the total read counts of all miRNAs per library
(expressed as counts/million). miRNA differential expression
analysis was performed with Bioconductor edgeR package 1.6,
in which we used an overdispersed Poisson model  with a
common dispersion parameter, combined with the exact test. For
testing differential expression of ccRCC with matched NRC we
used a paired analysis. We also performed differential expression
analysis of deep sequenced miRNAs in the three clinical subgroups
as well as in the three groups based on the Leibovich score. The
three categories of clinical subgroups and Leibovich score were
tested in a pairwise fashion. The significant miRNAs were
determined by an adjusted P value ,0.05 based on the Benjamini
and Hochberg multiple testing correction . Fold changes were
calculated using ratios of the arithmetic mean of the normalized
miRNA counts within each group.
The significant miRNAs with log2 transformed expression levels
were subjected to hierarchical cluster analysis in R with Euclidean
distance and complete linkage.
We performed stem-loop PCR to validate the results of the deep
sequencing analysis for five selected differentially expressed
miRNAs. Total RNA was extracted from RCC tumors and
matched controls from additional 16 patients. Of each sample,
150 ng total RNA was used for miRNA cDNA synthesis
(Megaplex Pool A 2.0, Applied Biosystems, Nieuwerkerk a/d
IJssel). Stem-loop PCR (Taqman MicroRNA Assays, Applied
Biosystems Nieuwerkerk a/d IJssel) was performed on a Light-
Cycler 480 (Roche diagnostics, Almere, Netherlands) with a
thermal program of 95 C/20 s, 60 C/40 s for 40 cycles and of
each sample a technical duplicate was performed. The miRNA
expression level was normalized to reference RNA RNU44. Fold
expression change was determined based on the delta Ct method
In the PCR validation set, there was a similar distribution with
regard to clinical subgroups as in the deep-sequencing set of 22
Kaplan-Meier Survival Analysis
For each miRNA sequenced, the 22 RCC patients were divided
into 2 groups according to expression level $ median (high level)
or , median (low level). The relationship between cancer specific
death and miRNAs expression level was analyzed by Kaplan
Meier analysis. A Cox regression model was applied using square
root transformed counts. P,0.05 was considered significant, and
no correction for multiple testing was applied.
Anti-miRNA inhibitor (100 nM) of either miR-210-3p or miR-
27a-3p, respectively, or scrambled Negative Control (100nM,
Applied Biosystems) with fluorescently labeled oligonucleotides
(100 nM, BLOCK-iT, Invitrogen) was transfected into a renal cell
line SKRC7 by Lipofectamine 2000 (Invitrogen). All experiments
were performed in duplicate. In all experiments, the transfection
efficiency was 90% based on percentage of BLOCK-it fluores-
cence positive CKRC7 cells. Tumor cell apoptosis (Annexin V
staining) was evaluated 24 hr and 48 hr after transfection as
previously described . Proliferation rate was assessed at 24, 48
and 72 hr post transfection with either antagomir or scrambled
control by assessing cell viability (WST-1 reagent) and counting
the number of viable cells using trypan blue dye.
Novel miRNA Prediction
For novel miRNA identification the HHMMiR  and
microPred  tools were used. Sequences from all tumor and
normal samples that were not annotated to any of the non-coding
Ensembl transcripts were extracted from the data files. Sequence
regions with length between 16 and 24 nucleotides and with
expression of at least 5 sequence reads were selected for further
analysis. For each of the remaining candidate regions the 40 nt
upstream and downstream flanking sequences were retrieved via
the Ensembl perl API. The novel microRNAs predicted by both
HHMMiR and microPred tools were presented.
miRNA Target Prediction and Enriched Pathway Analysis
miRNA target genes were retrieved from the comprehensive,
publicly available miRecord database (www.mirecord.org). For
each of the differentially expressed miRNA, only experimentally
proven target genes were selected. Based on the target genes, the
enriched pathways were predicted at bioinformatics DAVID
In addition, miRNA target genes were retrieved from the
publicly available miRror database (http://www.proto.cs.huji.ac.
il/mirror/), target genes selected for each of the 70 individual,
differentially expressed miRNAs and by linking to the DAVID
database, enriched pathways were predicted.
survival. Kaplan-Meier survival plots were generated using
Cox regression model. P,0.05 was considered significant, and no
correction for multiple testing was applied (PDF file).
miRNA expression and ccRCC patients
Human Genome and subsequently classified into vari-
ant RNA species. The total read counts of each RNA species
was expressed as percentage (Mean6SD) of the total read counts
per sequenced library (PDF file).
The sequenced RNA reads were mapped to
sequenced miRNAs in each sequenced sample. The
expression level is presented as read count/million (DOC file).
Clinical information and normalized reads of
DAVID database. The experimentally proven targets were
retrieved from miRecord database and subsequently used for
enriched pathways prediction analysis (XLS file).
Enriched biological pathways predicted using
two independent HMMiR and microPred. These are novel
miRNAs not previously reported in any species. The novel
miRNA candidate with expression levels exceeding 50 read
counts/million in all sequenced samples are presented (XLS file).
Novel miRNA candidates were predicted using
RNA libraries (XLS file).
Quality and composition of sequenced small
MicroRNA Deep Sequencing of Renal Cell Carcinoma
PLoS ONE | www.plosone.org10June 2012 | Volume 7 | Issue 6 | e38298
Author Contributions Download full-text
Conceived and designed the experiments: SO YQ. Performed the
experiments: YQ. Analyzed the data: YQ HPB JJG. Contributed
reagents/materials/analysis tools: JB EL HvP SO. Wrote the paper: YQ
SO JB EL HvP.
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PLoS ONE | www.plosone.org11 June 2012 | Volume 7 | Issue 6 | e38298