RESEARCH Open Access
Circulating miR-378 and miR-451 in serum are
potential biomarkers for renal cell carcinoma
Martina Redova1,2
, Alexandr Poprach1
, Jana Nekvindova3
, Robert Iliev1
, Lenka Radova4
, Radek Lakomy1
,
Marek Svoboda1,2
, Rostislav Vyzula1
and Ondrej Slaby1,2*
Abstract
Background: There is no standard serum biomarker used for diagnosis or early detection of recurrence for renal
cell carcinoma (RCC) patients. MicroRNAs (miRNAs) are abundant and highly stable in blood serum, and have been
recently described as powerful circulating biomarkers in a wide range of solid cancers. Our aim was to identify
miRNA signature that can distinguish the blood serum of RCC patients and matched healthy controls and validate
identified miRNAs as potential biomarkers for RCC.
Methods: In the screening phase of the study, blood serum of 15 RCC patients and 12 matched healthy controls
were analyzed by use of the TaqMan Low-Density Arrays enabling parallel identification of expression levels of 667
miRNAs through qRT-PCR-based approach. In the validation phase, identified miRNAs were further evaluated on
the independent group of 90 RCC patients and 35 matched healthy controls by use of individual qRT-PCR assays
and statistically evaluated.
Results: We identified 30 miRNAs differentially expressed between serum of RCC patients and healthy controls: 19
miRNAs were up-regulated and 11 miRNAs were down-regulated in RCC patients. MiR-378, miR-451 and miR-150
were further evaluated in the independent group of patients, and two of them were successfully validated: levels
of miR-378 were increased (p = 0.0003, AUC = 0.71), miR-451 levels were decreased (p < 0.0001, AUC = 0.77) in
serum of RCC patients. Combination of miR-378 and miR-451 enable identification of RCC serum with the
sensitivity of 81%, specificity 83% and AUC = 0.86.
Conclusions: Circulating miRNAs in serum are promising biomarkers in RCC.
Keywords: Renal cell carcinoma, MicroRNA, Serum, Biomarker
Background
Renal cell carcinoma (RCC) is the most common neoplasm
of adult kidney accounting for about 3% of adult
malignancies and having the highest mortality rate at
over 40% [1,2]. Renal tumors are commonly asymptomatic
in early stages and although surgical resection
remains the best therapy for RCC, after the curative
nephrectomy, 20-40% patients will develop recurrence.
Unfortunately, there is no standard serum biomarker
used for diagnosis or early detection of recurrence for
RCC patients. Although several serum proteins that
might be useful to detect the presence of advanced or
recurrent RCC have been reported [3], none indicated
analytical sensitivity efficient enough for translation into
standard of care management for RCC patients.
MicroRNAs (miRNAs) are a novel class of naturally
occurring, short non-coding, single stranded RNAs, that
regulate gene expression at the post-transcriptional level
by binding to the untranslated region (3’UTR) of target
mRNAs and causing translational inhibition and/or
mRNA degradation [4]. Specific expression profiles of
miRNAs have been shown in a variety of cancers,
including RCC [4-6]. MiRNAs are highly stable and
abundant in plasma, serum and other body fluids. Moreover,
miRNA signatures in blood are similar in men and
women, as well as individuals of different age [7]. These
circulating miRNAs had shown great potential to serve
as a novel biomarker for non-invasive diagnosis and
* Correspondence: slaby@mou.cz
1
Masaryk Memorial Cancer Institute, Department of Comprehensive Cancer
Care, Zluty kopec 7, Brno, Czech Republic
Full list of author information is available at the end of the article
Redova et al. Journal of Translational Medicine 2012, 10:55
http://www.translational-medicine.com/content/10/1/55
© 2012 Redova 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.
prognosis of plenty kinds of diseases, such as cancer,
and even physiological conditions such as prenatal
screening. Following the release from cells, circulating
miRNAs originate from (i) microvesicles (released by
exocytosis), (ii) exosomes (formed via invagination of
the early endosome and released upon fusion of late
endosome with plasma membrane), and (iii) apoptotic
vesicles and/or senescent bodies [7-9].
Expression profiles of circulating miRNAs were extensively
studied in (i) colorectal cancer (CRC) where
authors found miR-29a and miR-92 plasma levels to differentiate
between CRC patients and controls with sensitivity
of 89% and specificity of 70% and also to be
associated with clinical stage [10], (ii) in ovarian cancer
where levels of miR-21, miR-92 and miR-93 were higher
in 3 patients with normal CA-125 levels, a clinical biomarker
of ovarian cancer [11], (iii) in breast cancer
where it has been described that changes in the level of
total RNA, miR-10b, miR-34a, and miR-155 correlated
with the presence of overt metastases [12], (iv) in prostate
cancer where serum miR-141 levels were found to
distinguish metastatic prostate patients from agematched
controls [13]. Such studies have clearly proved
the potential of circulating miRNAs in primary diagnostics
as well as in follow-up for the early detection of disease
progression. In case of RCC the only study was
performed and circulating miR-1233 identified, but the
analytical characteristics of this biomarker in the validation
study were not convincing [14]. In our study we
focused on the circulating miRNAs expression profiles
in the serum samples of RCC patients aiming identification
of new biomarkers for RCC.
Materials and methods
Study population
We collected serum samples from the group of patients
diagnosed for RCC and undergoing radical nephrectomy
at Masaryk Memorial Cancer Institute (MMCI; Brno,
Czech Republic) between 2003 and 2008, and matched
cancer-free blood donor volunteers recruited from the
same institute with no previous history of any cancer.
All subjects were of the same ethnicity (Caucasian).
Clinical and pathological characteristics including age,
gender, stage, and grade were recorded and they are
summarized in Table 1. RCC serum samples were collected
after signing an informed consent prior to surgery
and stored at MMCI Bank of Biological Material. The
study has been approved by the local Ethical Committee
at Masaryk Memorial Cancer Institute (MMCI; Brno,
Czech Republic).
RNA isolation
Total RNA enriched for small RNAs was isolated using
Qiagen miRNeasy Mini Kit (Qiagen, GmbH, Germany)
from 200 μL serum according to modified manufacturers’
protocol. For each sample, to the 800 μl of QIAzol
solution was added 1.25 μl 0.8 μg/μl MS2 RNA
carrier (Roche, cat. No. 10165948001). The extracted
RNA was eluted in 50 μL of preheated Elution Solution,
and concentration and purity of RNA were determined
spectrofotometrically by measuring its optical density
(A260/280 > 2.0; A260/230 > 1.8) using NanoDrop ND-
1000 Spectrophotometer (Thermo Scientific, Wilmington,
DE, USA). The samples were either stored at -80°C
or further processed.
TaqMan low density arrays - screening phase
In the screening phase, we performed TaqMan Low
Density Arrays (TLDA) analysis to identify differentially
expressed miRNAs from the two pooled serum samples
(15 ccRCC patients vs. 12 healthy controls). In brief, 35
ng of total RNA was reverse-transcribed into cDNA by
the TaqMan MicroRNA Reverse Transcription Kit and
Megaplex RT set pool A and B version 2.0 (Applied Biosystems,
CA, USA). To obtain sufficient amount of
cDNA for TLDA analysis, pre-amplification step using
TaqMan PreAmp MasterMix was added. The pre-amplified
product was loaded into TaqMan Array Human
Table 1 Patient characteristics
Exploratory phase Validation phase
RCC HC RCC HC
N 15 12 90 35
Sex
male 10 10 56 26
female 5 2 34 9
Age
median 62 61 66 63
range 53-73 54-69 36-85 51-70
Histology
ccRCC 15 73
pRCC 0 8
chRCC 0 9
Pathological stage
pT1 6 50
pT2 0 14
pT3 9 23
pT4 0 3
vascular invasion 5 16
lymph nodes metastatis 0 6
distant metastasis 3 18
Fuhrman grade
G1 3 20
G2 7 39
G3 4 23
G4 1 8
Abbreviatons: RCC, renal cell carcinoma; ccRCC, clear cell RCC; pRCC, papillary
RCC; chRCC, chromophobe RCC
Redova et al. Journal of Translational Medicine 2012, 10:55
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MicroRNA A + B Cards Set v2.0 (Applied Biosystems,
CA, USA) enabling simultaneous quantitation of 667
human miRNAs. TaqMan MicroRNA Assays and analysis
were performed on the ABI 7900HT Instrument
(Applied Biosystems, CA, USA). All reactions were performed
according to the standard manufacturers’ protocols.
Quantitative miRNAs expression data were
acquired and normalized by use of ABI 7900HT SDS
software (Applied Biosystems, CA, USA).
qRT-PCR - Validation phase
In validation phase of the study, candidate miRNAs
identified by TLDA technology were further characterized.
Complementary DNA (cDNA) was synthetised
from total RNA using miRNA-specific primers according
to the TaqMan MicroRNA Assay protocol (Applied
Biosystems). For RT reactions 10 ng of RNA sample, 50
nM of stem-loop RT primer, 1 × RT buffer, 0.25 mM
each of dNTPs, 3.33 U μl-1
MultiScribe reverse transcriptase
and 0.25 U μl-1
RNase inhibitor (all from TaqMan
MicroRNA Reverse Transcription kit, Applied
Biosystems) was used. Reaction mixtures (15 μl) were
incubated for 30 min at 16°C, 30 min at 42°C, 5 min at
85°C and then held at 4°C. qRT-PCR was performed
using the Applied Biosystems 7500 instrument. The 20μl
PCR reaction mixture included 1.33 μl of RT product,
1 × TaqMan (AmpErase UNG) Universal PCR Master
Mix and 1 μl of primer and probe mix of the TaqMan
MicroRNA Assay kit (Applied Biosystems). Reactions
were incubated in a 96-well optical plate at 50°C for 2
min, 95°C for 10 min, followed by 40 cycles at 95°C for
15 s and 60°C for 10 min. All reactions were run in
duplicate and average threshold cycle and SD values
were calculated.
Statistical methods
In both phases, analysis of the qRT-PCR data was performed
using the SDS 2.0.1 software (Applied Biosystems)
(settings: automatic baseline, threshold 0.2).
According to manufacturer’s recommendations, miR-16
has been chosen as reference for normalization of miRNAs
expression levels. The relative expression levels of
target miRNAs were determined by the equation 2-ΔCT
,
in which ΔCT were calculated as follows: ΔCT = CT miRof-interest
- CT miR-16. Relative miRNA levels were then
calculated with the RQ Manager 1.2. Normalized
expression data from screening phase of the study were
statistically evaluated in environment of statistical language
R by use of Bioconductor package and LIMMA
approach combined with hierarchical clustering (HCL)
[15]. Normalized expression data in validation phase
were statistically analyzed with MedCalc software version
11.2.1. P-values of less than 0.05 were considered
statistically significant. Statistical differences between
expression levels in RCC patients’ and healthy controls’
samples were evaluated by non-parametric Mann-Whitney
U test. Sensitivity, specificity and area under curve
(AUC) for serum microRNA levels were determined
using Receiver Operator Characteristic (ROC) analysis.
Results
Screening phase
In this biomarker discovery phase, a strategy for effective
identification of RCC-associated miRNAs in serum
was performed using qRT-PCR-based miRNAs expression
profiling arrays. We determined the miRNA expression
profile of 667 miRNAs in serum from ccRCC
patients (n = 15) using TaqMan Low Density Arrays
technology, and compared to the expression profile of
healthy individuals (n = 12); the miRNA expression
levels were normalized to miR-16, the mean expression
levels were calculated and data analyzed by use of the
microarray biostatistical approaches. We observed 30
miRNAs differentially expressed between serum of
ccRCC patients and healthy controls: 19 miRNAs were
up-regulated in ccRCC patients and 11 miRNAs were
down-regulated (Figure 1, Table 2). Among them, upregulated
miR-378 (FC = 37.6, p < 0.000001), and
down-regulated miR-150 (FC = 0.02, p < 0.000001) and
miR-451 (FC = 0.2, p < 0.000001) were proposed as
putative biomarkers to the validation phase of the study,
as significance of the difference (fold change, p-value),
previous observations and biological plausibility (based
on the PubMed hits when particular miRNA is combined
with keyword “cancer”), and miRNAs with favorable
expression levels (Ct > 30) were taken into account.
Validation phase
To validate the 3 promising biomarkers identified in the
screening phase, miR-378, miR-451 and miR-150, qRTPCR
assays were developed to quantify miRNAs in
serum of RCC patients and healthy controls. The qRTPCR
was performed in the independent cohort of 90
RCC patients’ and 35 healthy controls’ serum samples.
As the difference in miR-150 expression level between
RCC and healthy controls serum did not reach statistical
significance (p = 0.222), miR-150 was excluded from
further analysis. The expression of miR-378 serum level
was significantly increased in patients with RCC compared
to healthy controls (p = 0.0003), and the expression
level of miR-451 was significantly decreased in
patients with RCC compared to healthy controls (p <
0.0001), confirming the results of screening phase (Figure
2A-B, Table 3). Receiver operating characteristics
(ROC) curve analysis revealed that the serum levels of
both miR-378 and miR-451 might serve as useful biomarkers
for differentiating serum of patients with RCC
from controls with AUC of 0.71 (95% CI, 0.62 to 0.79),
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and 0.77 (95% CI, 0.69 to 0.84), respectively (Figure 2CD).
At the cut-off value of 0.02 for relative expression of
miR-378 normalized to miR-16 levels, the sensitivity was
70% and the specificity was 60%. At the cut-off value of
2.0 for relative expression of miR-451 normalized to
miR-16 levels, the sensitivity was 81% and the specificity
was 77%. Multivariate logistic regression analysis showed
that both miR-378 and miR-451 could serve as potential
biomarkers for distinguishing between RCC and healthy
controls, and even that their combination could improve
the stratification power characterized with AUC of 0.86
and the sensitivity of 81% and specificity 83% (Figure 3).
Receiver operating characteristics (ROC) curve analysis
using (C) serum miR-451 yielded an AUC of 0.77,
sensitivity of 81%, and specificity of 77% in discriminating
RCC and (D) serum miR-378 yielded AUC of 0.71,
sensitivity of 70%, and specificity of 60% in discriminating
RCC
Discussion
Many recent studies have described miRNAs expression
profile in RCC and adjacent non-tumoral tissue
confirming the different pattern. Although significant
overlap between miRNAs identified by independent studies
exist, regarding the number and type of up-/downregulated
miRNAs, data are in part conflicting
[1,2,24,25]. Circulating miRNAs may possibly serve as a
new class of powerful and non-invasive biomarkers for
RCC patients. Regarding this hypothesis, we compared
the miRNAs expression profiles of RCC patients’ and
healthy controls’ serum samples. Based on the qRT-PCR
arrays approach we were able to describe 30 differentially
expressed miRNAs, 19 of these miRNAs were upregulated
and 11 were down-regulated. Such expression
profile is a bit controversial, as the global miRNA
down-regulation in RCC tissue samples has been repeatedly
described [1-4]. The possible relation of miRNA
levels between tissue and corresponding serum is still
not clearly understood - some researchers postulate that
circulating miRNAs may not always be directly associated
with the changes occuring in tumor tissues but
may also reflect indirect effects and could be secreted by
non-tumoral cells. Also mechanisms were described how
extracellular miRNAs can potentially interact with
Figure 1 Hierarchical clustergram iscriminating blood serum of RCC patients and healthy controls according to diferentially expressed
miRNAs (yellow color indicate serum samples of RCC patients, blue healthy controls, p < 0.0001).
Redova et al. Journal of Translational Medicine 2012, 10:55
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recipient cells via a number of different processes,
including: direct fusion, internalization or receptormediated
interactions. There are likely to be other
mechanisms, especially for vesicle-free miRNAs, but all
await further investigation to provide convincing evidence
of their involvement in inter-cellular miRNA
exchange [26,27]. However, this proposed potential of
tumor cells to actively uptake miRNAs from circulation
can partly explain the opposite trends of miRNA expression
levels in tissue compared to blood serum. Several
dysregulated miRNAs identified in our study have been
described to have altered expression levels in plasma or
serum of various cancers recently: up-regulated mir-
425* [28,29], let-7a [30] or let-7f [31].
In the validation phase of this study, we tested 3 candidate
miRNAs (miR-378, miR-451, miR-150) in the
independent cohort of RCC patients. The up-regulation
of miR-378 and down-regulation of miR-451 expression
levels between serum of RCC patients and healthy
controls reached statistical significance in validation
study. Analytical characteristics of miR-378 (sensitivity
of 70%, specificity of 60%) and miR-451 (sensitivity of
81%, specificity of 77%) proved that both miR-378 and
miR-451 are potent in discriminating RCC from healthy
control serum. Furthermore, combination of serum
miR-378 and miR-451 levels, yielded sensitivity of 81%
and specificity 83%, proved to be even more powerful
discrimination tool. To our knowledge, the only study
concerning circulating miRNAs in renal cell carcinoma
identified circulating miR-1233 as a potential biomarker
for RCC patients, but although they performed the validation
phase on a large cohort of RCC patients from
three different study centers, the diagnostic information
was below their expectations (AUC of 0.588, sensitivity
of 77%, specificity of 37.6%) [14].
Although our observations are promising, and miR-
378/miR-451 analytical characteristics reached values for
clinical utility, large-scale prospective studies aiming
Table 2 MiRNAs diferentially expressed between serum of ccRCC patients and healthy controls
miRNA Fold change P-value Probability, that gene is differentially expressed
hsa-miR-760 169.628 < 0.000001 100.000%
hsa-miR-381 0.002 < 0.000001 100.000%
hsa-miR-378 37.596 < 0.000001 100.000%
hsa-miR-151-3p 35.734 < 0.000001 100.000%
hsa-miR-629 45.790 < 0.000001 100.000%
hsa-miR-150 0.020 < 0.000001 100.000%
hsa-miR-219-5p 0.032 < 0.000001 99.999%
hsa-miR-26b* 41.191 < 0.000001 99.999%
hsa-miR-302b 0.057 < 0.000001 99.997%
hsa-miR-19b-1* 36.432 < 0.000001 99.994%
hsa-miR-126* 9.317 < 0.000001 99.972%
hsa-let-7f 0.011 < 0.000001 99.968%
hsa-miR-571 30.947 < 0.000001 99.978%
hsa-miR-29c* 23.842 < 0.000001 99.976%
hsa-miR-625* 13.286 < 0.000001 99.938%
hsa-let-7a 0.014 < 0.000001 99.922%
hsa-miR-134 40.828 < 0.000001 99.846%
hsa-miR-320 4.533 < 0.000001 99.811%
hsa-miR-451 0.225 < 0.000001 99.723%
hsa-miR-19a 0.261 < 0.000001 99.651%
hsa-miR-365 16.768 < 0.000001 99.642%
hsa-miR-339-3p 10.606 < 0.000001 99.524%
hsa-miR-203 79.565 < 0.000001 98.908%
hsa-miR-302c 0.044 < 0.000001 98.729%
hsa-miR-454* 11.965 < 0.000001 97.193%
hsa-miR-144* 6.425 < 0.00001 92.206%
hsa-miR-655 10.716 < 0.00001 84.080%
hsa-miR-367 0.133 < 0.00001 81.739%
hsa-miR-425* 6.666 < 0.0001 79.234%
hsa-miR-142-5p 0.136 < 0.0001 74.933%
Abbreviations: miR, microRNA/miRNA; ccRCC, clear cell renal cell carcinoma
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Figure 2 Validation phase of miR-378, miR-451 and miR-150 in an independent group of serum samples (n = 125). Scatter plots of
serum levels of (A) miR-378 and (B) miR-451 in healthy control (HC) samples (n = 35) and patients with RCC (n = 90). Expression levels of the
miRNAs (log10 scale on the y-axis) are normalized to miR-16. The line represents the mean value. Statistically significant differences were
determined using Mann-Whitney tests.
Table 3 MiRNAs evaluated in the validation phase of study
miRNA HC* RCC* p-value Cancer association Experimentaly validated target
miR-378 0.004
0.002-0.006
0.008
0.004-0.037
0.0003 colorectal
carcinoma [16,17], oral
squamous cell
carcinoma [18],
laryngeal
carcinoma [19]
SUFU, TUSC2,
TOB2, CYP2E1
miR-451 2.067
1.250-3.480
0.802
0.055-1.091
0.0001 renal cell carcinoma [1],
colorectal
carcinoma [20],
gastric cancer [20]
MMP2, MMP9,
BCL2
miR-150 0.011
0.009-0.016
0.008
0.005-0.020
0.2222 gastric cancer [21],
chronic myeloid
leukemia [22],
colorectal
carcinoma [23]
HTT, MYB,
EGFR2
Abbreviations: HC, healthy controls; RCC, renal cell carcinoma
*expression levels are presented as medians and interquartile range
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their evaluation in the renal benign neoplasms and early
stages of the RCC are necessary to validate them as biomarkers
for early diagnosis, and the analysis of blood
serum collections from each patient to evaluate miRNA
biomarker dynamics are needed to prove their potential
for early detection of relapse in RCC patients.
Conclusions
There is no standard serum biomarker used for diagnosis
or early detection of recurrence for renal cell carcinoma
(RCC) patients. In our study, we identified 30
miRNAs differentially expressed between serum of RCC
patients and healthy controls. MiR-378, miR-451 and
miR-150 were further evaluated in the independent
group of patients, and two of them were successfully
validated: levels of miR-378 were increased, miR-451
levels were decreased in serum of RCC patients. Combination
of miR-378 and miR-451 enable identification of
RCC serum with the sensitivity of 81%, specificity 83%
and AUC = 0.86. We believe, that circulating miRNAs
in serum are promising biomarkers in RCC.
Acknowledgements
This work was supported by grant IGA 10361-3/2009 of the Czech Ministry
of Health, Project No. MZ0MOU2005 of the Czech Ministry of Health, by The
Ministry of Education, Youth and Sports for the project BBMRI CZ
(LM2010004), and by the project “CEITEC - Central European Institute of
Technology” (CZ.1.05/1.1.00/02.0068).
Author details
1
Masaryk Memorial Cancer Institute, Department of Comprehensive Cancer
Care, Zluty kopec 7, Brno, Czech Republic. 2
Central European Institute of
Technology, Masaryk University, Kamenice 5, Brno, Czech Republic. 3
Institute
of Clinical Biochemistry and Diagnostics, Faculty of Medicine and Faculty
Hospital in Hradec Kralove, Charles University, Hradec Kralove, Czech
Republic. 4
Laboratory of Experimental Medicine, Institute of Molecular and
Translational Medicine, Faculty of Medicine and Dentistry, Palacky University
and Palacky University affiliated Hospital Olomouc, Brno, Czech Republic.
Authors’ contributions
OS, MS and RV contributed to the conception and design of the study. AP,
RL and MS were responsible for recruiting/supplying patients for the study.
JN, OS, RI and LR were all involved with the acquisition and interpretation/
analysis of study data. All the authors contributed to drafting and reviewing
the manuscript, and all the authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 27 December 2011 Accepted: 22 March 2012
Published: 22 March 2012
References
1. Yi Z, Fu Y, Zhao S, Zhang X, Ma C: Differential expression of miRNA
patterns in renal cell carcinoma and nontumorous tissues. J Cancer Res
Clin Oncol 2010, 136:855-862.
2. Chow TF, Youssef YM, Lianidou E, Romaschin AD, Honey RJ, Stewart R,
Pace KT, Yousef GM: Differential expression profiling of microRNAs and
their potential involvement in renal cell carcinoma pathogenesis. Clin
Biochem 2010, 43:150-158.
3. Ljungberg B: Prognostic markers in renal cell carcinoma. Curr Opin Urol
2007, 17:303-308.
4. Slaby O, Jancovicova J, Lakomy R, Svoboda M, Poprach A, Fabian P, Kren L,
Michalek J, Vyzula R: Expression of miRNA-106b in conventional renal cell
carcinoma is a potential marker for prediction of early metastasis after
nephrectomy. J Exp Clin Cancer Res 2010, 29:90.
5. Slaby O, Svoboda M, Michalek J, Vyzula R: MicroRNAs in colorectal cancer:
translation of molecular biology into clinical application. Mol Cancer
2009, 8:102.
6. Redova M, Svoboda M, Slaby O: MicroRNAs and their target gene
networks in renal cell carcinoma. Biochem Biophys Res Commun 2010,
405:153-156.
7. Taylor DD, Gercel-Taylor C: MicroRNA signatures of tumor-derived
exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol
2008, 110:13-21.
8. Esquela-Kerscher A, Slack FJ: Oncomirs - microRNAs with a role in cancer.
Nat Rev Cancer 2006, 6:259-269.
9. Lawrie CH, Gal S, Dunlop HM, Pushkaran B, Liggins AP, Pulford K,
Banham AH, Pezzella F, Boultwood J, Wainscoat JS, Hatton CS, Harris AL:
Figure 3 ROC curve for combination of serum miR-378 and miR-451 yielded AUC of 0.86, the sensitivity of 81% and specificity of 83%
in discriminating RCC.
Redova et al. Journal of Translational Medicine 2012, 10:55
http://www.translational-medicine.com/content/10/1/55
Page 7 of 8
Detection of elevated levels of tumour-associated microRNAs in serum
of patients with diffuse large B-cell lymphoma. Br J Haematol 2008,
141:672-675.
10. Huang Z, Huang D, Ni S, Peng Z, Sheng W, Du X: Plasma microRNAs are
promising novel biomarkers for early detection of colorectal cancer. Int J
Cancer 2010, 127:118-126.
11. Resnick KE, Alder H, Hagan JP, Richardson DL, Croce CM, Cohn DE: The
detection of differentially expressed microRNAs from the serum of
ovarian cancer patients using a novel real-time PCR platform. Gynecol
Oncol 2009, 112:55-59.
12. Roth C, Rack B, Muller V, Janni W, Pantel K, Schwarzenbach H: Circulating
microRNAs as blood-based markers for patients with primary and
metastatic breast cancer. Breast Cancer Res 2010, 12:R90.
13. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, PogosovaAgadjanyan
EL, Peterson A, Noteboom J, O’Briant KC, Allen A, Lin DW,
Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL,
Nelson PS, Martin DB, Tewari M: Circulating microRNAs as stable bloodbased
markers for cancer detection. Proc Natl Acad Sci USA 2008,
105:10513-10518.
14. Wulfken LM, Moritz R, Ohlmann C, Holdenrieder S, Jung V, Becker F,
Herrmann E, Walgenbach-Brunagel G, von Ruecker A, Muller SC, Ellinger J:
MicroRNAs in renal cell carcinoma: diagnostic implications of serum
miR-1233 levels. PLoS One 2011, 6:e25787.
15. Reimers M, Carey VJ: Bioconductor: an open source framework for
bioinformatics and computational biology. Methods Enzymol 2006,
411:119-134.
16. Ng EK, Chong WW, Jin H, Lam EK, Shin VY, Yu J, Poon TC, Ng SS, Sung JJ:
Differential expression of microRNAs in plasma of patients with
colorectal cancer: a potential marker for colorectal cancer screening. Gut
2009, 58:1375-1381.
17. Mosakhani N, Sarhadi VK, Borze I, Karjalainen-Lindsberg ML, Sundstrom J,
Ristamaki R, Osterlund P, Knuutila S: MicroRNA profiling differentiates
colorectal cancer according to KRAS status. Genes Chromosomes Cancer
2012, 51:1-9.
18. Scapoli L, Palmieri A, Lo Muzio L, Pezzetti F, Rubini C, Girardi A, Farinella F,
Mazzotta M, Carinci F: MicroRNA expression profiling of oral carcinoma
identifies new markers of tumor progression. Int J Immunopathol
Pharmacol 2010, 23:1229-1234.
19. Wang P, Fu T, Wang X, Zhu W: Primary, study of miRNA expression
patterns in laryngeal carcinoma by microarray. Lin Chung Er Bi Yan Hou
Tou Jing Wai Ke Za Zhi 2010, 24:535-538.
20. Bandres E, Bitarte N, Arias F, Agorreta J, Fortes P, Agirre X, Zarate R, DiazGonzalez
JA, Ramirez N, Sola JJ, Jimenez P, Rodriguez J, Garcia-Foncillas J:
microRNA-451 regulates macrophage migration inhibitory factor
production and proliferation of gastrointestinal cancer cells. Clin Cancer
Res 2009, 15:2281-2290.
21. Wu Q, Jin H, Yang Z, Luo G, Lu Y, Li K, Ren G, Su T, Pan Y, Feng B, Xue Z,
Wang X, Fan D: MiR-150 promotes gastric cancer proliferation by
negatively regulating the pro-apoptotic gene EGR2. Biochem Biophys Res
Commun 2010, 392:340-345.
22. Flamant S, Ritchie W, Guilhot J, Holst J, Bonnet ML, Chomel JC, Guilhot F,
Turhan AG, Rasko JE: Micro-RNA response to imatinib mesylate in
patients with chronic myeloid leukemia. Haematologica 2010,
95:1325-1333.
23. Ma Y, Zhang P, Wang F, Zhang H, Yang J, Peng J, Liu W, Qin H: miR-150 as
a potential biomarker associated with prognosis and therapeutic
outcome in colorectal cancer. Gut 2011, doi:10.1136/gutjnl-2011-301122.
24. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R,
Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A,
Vecchione A, Negrini M, Harris CC, Croce CM: A microRNA expression
signature of human solid tumors defines cancer gene targets. Proc Natl
Acad Sci USA 2006, 103:2257-2261.
25. Youssef YM, White NM, Grigull J, Krizova A, Samy C, Mejia-Guerrero S,
Evans A, Yousef GM: Accurate molecular classification of kidney cancer
subtypes using microRNA signature. Eur Urol 2011, 59:721-730.
26. Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, Chen Y, Xu L, Zen K, Zhang C,
Shen H: Serum microRNA signatures identified in a genome-wide serum
microRNA expression profiling predict survival of non-small-cell lung
cancer. J Clin Oncol 2010, 28:1721-1726.
27. Reid G, Kirschner MB, van Zandwijk N: Circulating microRNAs: Association
with disease and potential use as biomarkers. Crit Rev Oncol Hematol
2011, 80:193-208.
28. Grinchuk OV, Jenjaroenpun P, Orlov YL, Zhou J, Kuznetsov VA: Integrative
analysis of the human cis-antisense gene pairs, miRNAs and their
transcription regulation patterns. Nucleic Acids Res 2010, 38:534-547.
29. Zhao H, Shen J, Medico L, Wang D, Ambrosone CB, Liu S: A pilot study of
circulating miRNAs as potential biomarkers of early stage breast cancer.
PLoS One 2010, 5:e13735.
30. Tsujiura M, Ichikawa D, Komatsu S, Shiozaki A, Takeshita H, Kosuga T,
Konishi H, Morimura R, Deguchi K, Fujiwara H, Okamoto K, Otsuji E:
Circulating microRNAs in plasma of patients with gastric cancers. Br J
Cancer 2010, 102:1174-1179.
31. Silva J, Garcia V, Zaballos A, Provencio M, Lombardia L, Almonacid L,
Garcia JM, Dominguez G, Pena C, Diaz R, Herrera M, Varela A, Bonilla F:
Vesicle-related microRNAs in plasma of nonsmall cell lung cancer
patients and correlation with survival. Eur Respir J 2011, 37:617-623.
doi:10.1186/1479-5876-10-55
Cite this article as: Redova et al.: Circulating miR-378 and miR-451 in
serum are potential biomarkers for renal cell carcinoma. Journal of
Translational Medicine 2012 10:55.
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