Circulating Micro-RNAs as Potential Blood-Based Markers
for Early Stage Breast Cancer Detection
Michael G. Schrauder1,2*., Reiner Strick1,2., Ru ¨diger Schulz-Wendtland3, Pamela L. Strissel1,2, Laura
Kahmann1,2, Christian R. Loehberg1,2, Michael P. Lux1,2, Sebastian M. Jud1,2, Arndt Hartmann4,
Alexander Hein1,2, Christian M. Bayer1,2, Mayada R. Bani1,2, Swetlana Richter1,2, Boris R. Adamietz3,
Evelyn Wenkel3, Claudia Rauh1,2, Matthias W. Beckmann1,2, Peter A. Fasching1,2,5
1Department of Obstetrics and Gynaecology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany, 2Comprehensive
Cancer Center Erlangen-Nuremberg, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany, 3Institute of Diagnostic Radiology, University Hospital
Erlangen, Erlangen, Germany, 4Institute of Pathology, University Hospital Erlangen, Erlangen, Germany, 5Division of Hematology and Oncology, Department of Medicine,
David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
Introduction: MicroRNAs (miRNAs, miRs) are a class of small, non-coding RNA molecules with relevance as regulators of
gene expression thereby affecting crucial processes in cancer development. MiRNAs offer great potential as biomarkers for
cancer detection due to their remarkable stability in blood and their characteristic expression in many different diseases. We
investigated whether microarray-based miRNA profiling on whole blood could discriminate between early stage breast
cancer patients and healthy controls.
Methods: We performed microarray-based miRNA profiling on whole blood of 48 early stage breast cancer patients at
diagnosis along with 57 healthy individuals as controls. This was followed by a real-time semi-quantitative Polymerase Chain
Reaction (RT-qPCR) validation in a separate cohort of 24 early stage breast cancer patients from a breast cancer screening
unit and 24 age matched controls using two differentially expressed miRNAs (miR-202, miR-718).
Results: Using the significance level of p,0.05, we found that 59 miRNAs were differentially expressed in whole blood of
early stage breast cancer patients compared to healthy controls. 13 significantly up-regulated miRNAs and 46 significantly
down-regulated miRNAs in our microarray panel of 1100 miRNAs and miRNA star sequences could be detected. A set of 240
miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a
specificity of 78.8%, and a sensitivity of 92.5%, as well as an accuracy of 85.6%. Two miRNAs were validated by RT-qPCR in an
independent cohort. The relative fold changes of the RT-qPCR validation were in line with the microarray data for both
miRNAs, and statistically significant differences in miRNA-expression were found for miR-202.
Conclusions: MiRNA profiling in whole blood has potential as a novel method for early stage breast cancer detection, but
there are still challenges that need to be addressed to establish these new biomarkers in clinical use.
Citation: Schrauder MG, Strick R, Schulz-Wendtland R, Strissel PL, Kahmann L, et al. (2012) Circulating Micro-RNAs as Potential Blood-Based Markers for Early
Stage Breast Cancer Detection. PLoS ONE 7(1): e29770. doi:10.1371/journal.pone.0029770
Editor: Jo ¨rg D. Hoheisel, Deutsches Krebsforschungszentrum, Germany
Received September 13, 2011; Accepted December 5, 2011; Published January 5, 2012
Copyright: ? 2012 Schrauder 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: This research was supported by a grant to MGS by the ELAN-Fonds of the Medical Faculty of the Friedrich-Alexander University Erlangen-Nuremberg
(reference number: 09.11.09.1) (http://www.elan.uk-erlangen.de). The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Michael.Schrauder@uk-erlangen.de
. These authors contributed equally to this work.
Breast cancer (BC) is one of the leading causes of cancer death
among women worldwide . A plethora of studies on BC
detection and treatment has been published in recent years.
Whereas most studies focus on the improvement of prognosis by
improving the therapies, one of the most promising approaches is
to detect the cancer in an early stage. Mammography and
ultrasound are currently the standard diagnostic tools which have
been proven to be successful for the detection of early stage BC,
but new minimally invasive diagnostic approaches are still urgently
needed to supplement breast imaging and to improve detection
rates and BC screening compliance.
MicroRNAs (miRNAs, miRs) are a novel class of endogenous,
non-coding, single-stranded RNAs, first described in 1993 by Lee
et al. in C. elegans . These small regulatory RNA molecules
(approximately 22 nucleotides long) post-transcriptionally inhibit
gene expression by either degrading or blocking translation of
messenger RNA (mRNA) targets . MiRNAs suppress the
translation of target mRNAs mainly by binding to their
39untranslated region (UTR), but also other mechanisms have
been described [4–7]. Depending on the degree of concordance
PLoS ONE | www.plosone.org1 January 2012 | Volume 7 | Issue 1 | e29770
between the miRNA sequence and the mRNA the negative
regulatory effect on the target mRNAs can vary from weak
repression of protein translation to complete cleavage of the
MiRNA loci are statistically over-represented at fragile genomic
regions that are commonly amplified or deleted in human cancers,
implying a connection of miRNAs with cancer initiation and
progression [9–11]. Whether miRNAs act mainly as tumor
suppressors (suppressor-miRs), promotors of tumorigenesis (onco-
miRs) or both is still widely elusive, but the global decrease in
miRNA expression in human cancers suggests that most miRNAs
may act as direct suppressor-miRs or post-transcriptional repres-
sors of known oncogenes [12–17].
The finding of a decrease of miR-15a and miR-16-1 in patients
with chronic lymphocytic leukaemia was one of the first direct
links between regulative miRNAs and cancer . Meanwhile,
miRNAs have been implicated in nearly all human cancers and
especially the relevance in BC has been shown by several groups
[19–23]. Some miRNAs were found to be up-regulated in BC
tissue compared to normal breast tissue and other miRNAs were
down-regulated, which is in accordance with the hypothesis of
miRNAs acting as onco-miRs and tumor-suppressor miRs .
After the first identification of serum miRNAs in 2008 , several
studies have shown that miRNAs are present in fluids like blood,
saliva, pleural fluid and urine [25–28]. The extreme stability of
circulating miRNAs in the RNase-rich environment of the
bloodstream is the basis of their value as biomarkers, but the
mechanism underlying this stability is to a large extent still elusive.
Initial studies indicated that miRNAs are protected from
degradation by inclusion in lipid or lipoprotein complexes like
microvesicles, exosomes, or apoptotic bodies [23,29,30]. Surpris-
ing results from a very recent study revealed that the majority of
circulating miRNAs co-fractionate with protein complexes and
could be protected in the circulation by these complexes . The
identification of circulating miRNAs as potential non-invasive
biomarkers for BC and other diseases was followed by initial
studies trying to associate these markers with relapse-free survival,
overall survival and response to therapy [23–26,32–38].
Aim of the present study was to analyze the miRNA expression
patterns in whole blood of patients with early stage BC in
comparison to healthy controls using a miRNA microarray chip.
All patients of this study participated in a prospective case
control study for the molecular detection of breast cancer
(MODE-B Study). Patients presenting at our specialized breast
cancer unit with suspect breast lesions are routinely asked to
participate in this still ongoing MODE-B Study. The presented
data are results from the monocentric miRNA pilot-study.
The MODE-B Study was approved by the Ethics Committee of
the Medical Faculty of the Friedrich-Alexander University
Erlangen-Nuremberg (reference number 3937). Written informed
consent was obtained from every patient and control individual
before blood was taken.
Venous blood samples (non-fasting) (2.7 mL per patient) were
collected from cases and controls in EDTA blood tubes (Sarstedt,
Monovette EDTA K; Sarstedt AG, Germany) containing 1.6 mg
EDTA as anticoagulant and stored at 220uC until further
processing. We selected the first 48 consecutive early stage BC
patients suitable for this prospectively planed miRNA-biomarker-
This study comprises two case control studies, a discovery study
(microarray chip analysis) and a validation study (RT-qPCR of
selected miRNAs). Patients were included in the specialized breast
unit. They either referred themselves because of a newly palpable
breast lesion or were referred to the breast unit by their physicians
because of suspicious lesions for further breast diagnostics and
The independent RT-qPCR validation cohort consisted of
consecutive early stage BC patients diagnosed within the German
BC screening program. These differences in patients’ recruitment
between the microarray study cohort and the RT-qPCR
validation cohort resulted inevitably in a lower risk profile of
patients in the validation cohort (Table 1), which was intended as
ultimate test of the discriminating potential of RT-qPCR based
miRNA-profiling. The control cohort included individuals with
inconspicuous mammograms who came to our hospital for a
routine check and had no history of current or previous
malignancy. All patients and controls were of Western European
descent. The demographic and clinicopathological patients’
characteristics of all BC patients in the microarray cohort and in
the RT-qPCR validation cohort are summarized in Table 1.
Tumor staging was done according to the tumor-node-metastasis
(TNM) staging system of the American joint committee on cancer
(AJCC) and the International Union for Cancer Control .
MiRNA extraction and miRNA-microarray profiling
Total RNA extraction was performed as published previously
. After unfreezing, EDTA blood was transferred into
PAXgene Blood RNA Tubes (PreAnalytiX GmbH, Switzerland)
as described previously, and total RNA was extracted using the
miRNeasy kit (Qiagen GmbH, Hilden, Germany) with minor
modifications. RNA was eluted in water and shipped on dry ice to
be analysed on febit’s GeniomH real-time analyser (GRTA, febit
gmbh, Heidelberg, Germany) using the GeniomH Biochip miRNA
The quality and quantity of the RNA was evaluated by 260/280
ratio using NanoDrop spectrophotometry (NanoDrop ND-1000)
and Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa
Each array of the GeniomH Biochip contains 11 replicates of
1100 miRNAs and miRNA star sequences as annotated in the
Sanger database miRBase15.0 . Samples were biotinylated
using microfluidic-based enzymatic on-chip labeling of miRNAs
(MPEA) . After hybridisation for 16 h at 42uC, the biochip
was washed automatically and a program for signal enhancement
was processed with the GRTA. Results were analysed using the
GeniomH Wizard Software. For each array, the median signal
intensity was extracted from the raw data file such that for each
miRNA seven intensity values have been calculated corresponding
to each replicate copy of miRNA-Base on the array. After
background correction, median values were calculated from the
seven replicate intensity values of each miRNA. To normalise
arrays, variance stabilising normalisation (VSN) as implemented in
the R package VSN has been applied and all further analyses were
carried out using the normalised and background-subtracted
intensity values .
MiRNA MicroArray Data Analysis
The approximate normal distribution of the measured data was
verified by Shapiro–Wilk test with a median P-value of 5.9E-20.
Blood Micro-RNAs for Breast Cancer Detection
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MiRNAs with different expression levels between BC patients and
controls were identified by unpaired two-tailed parametric t-test.
P-values obtained for each individual miRNA were adjusted for
multiple testing by Benjamini–Hochberg adjustment . In
addition to the single biomarker analysis, samples were also
classified according to miRNA patterns as calculated using support
vector machines (SVMs) implemented in the R (Team, 2008)
e1071 package . In detail, different kernel (linear, polynomial,
sigmoid, radial basis function) SVMs were evaluated with the cost
parameter being sampled from 0.01 to 10 in decimal powers. The
measured miRNA profiles were classified using 100 repetitions of
standard 10-fold cross-validation and subsets were selected
according to a t-test-based filter approach. This means that in
each repeat of the cross-validation the ‘‘s’’ miRNAs with lowest P-
values were computed on the training set with ‘‘s’’ being sampled
according to the included number of miRNAs . The respective
subset was then used to train the SVM for the prediction of the test
samples, which enabled a calculation of the mean accuracy,
specificity and sensitivity for each subset size. Permutation tests
were applied to check for overtraining. In this study, the class
labels were sampled at random and classifications were carried out
using the permuted class labels . The expression profile of each
differentially expressed miRNA was used for the creation of
Receiver Operating Characteristic (ROC) curves, a graphical plot
of the true positive rate versus the false positive rate. The area
under the ROC curve (AUC) is representing the discrimination
accuracy and is shown in Table 2 and 3 for the most deregulated
miRNAs found by microarray profiling. All statistical analyses
were performed using R (Wilcoxon, 1945; R development Core
Team, 2008). The microarray data has been submitted to a public
repository (Gene Expression Omnibus, GEO) and has been
approved and assigned GEO accession numbers (GSE3109,
Validation of miRNA gene expression using real-time
semi-quantitative PCR (RT-qPCR)
Two miRNA (miR-202, miR-718) which were differentially
expressed in the microarray assays were analyzed in an
independent validation cohort by RT-qPCR. Among the most
deregulated miRNAs from our microarray experiments we
selected miRNAs for PCR-validation with purchasable and well
established primers to reduce sources of error. The miRNA
isolation and RNA quantification of the independent validation
cohort was identical to the microarray analysis. TaqMan miRNA
assays from ABI (Applied Biosystems, Foster City, CA, USA) were
purchased for miR-202 and miR-718. First, each miRNA was
Table 1. Patients’ and tumor characteristics at time of BC diagnosis.
Parameters Microarray cohort patients (%) RT-qPCR validation cohort patients (%)
Age 61.9 (range 34–89 years)age matched pairs
pT1a0 3 (12.5)
pT1b 12 (25.0)6 (25.0)
pT1c29 (60.4) 11 (46.0)
pT2 7 (14.6) 4 (16.5)
Lymph node involvement
pN0 39 (81.25) 23 (96.0)
pN1 (1–3 lymph nodes)9 (18.75)1 (4.0)
G1 12 (25.0)10 (42.0)
G2 19 (39.6)12 (50.0)
G3 17 (35.4)2 (8.0)
Estrogen receptor status
Positive 44 (91.7)21 (87.5)
Negative4 (8.3) 3 (12.5)
Progesterone receptor status
Positive 39 (81.25) 16 (67.0)
Negative 9 (18.75) 8 (33.0)
Positive40 (83.3)1 (4.0)
Negative 8 (16.7)23 (96.0)
, ,10 11 (22.9) 4 (16.7)
10 13 (27.1)11 (45.8)
10–20 10 (20.9) 6 (25.0)
.20 14 (29.1)3 (12.5)
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specifically reverse transcribed according to manufactures protocol
using TaqMan miRNA RT-Kit with stem-loop RT-primer and
ABI7300 (ABI). Second, each sample was analyzed in duplicates
for each specific miRNA using a RT-primer with universal master
mix II (without Uracil-N-glycosylase) on the Applied Biosystems
7300 Sequence Detection System according to manufactures
protocol. The cycle thresholds (Ct) for BC patients and their age-
matched healthy controls were calculated and normalized to miR-
16 (miR-16; ABI), which was found in the literature as the most
widely-used endogenous control miRNA for RT-qPCR. Each
analysis also contained inter- and intra-assay replicates. High Ct-
values indicated low miRNA quantity and vice versa. The
expression levels of miRNAs in BC patients relative to their age-
matched healthy controls were calculated using the comparative
cycle threshold (CT) method. The average CTvalue of the control
miR-16 for every sample was subtracted from the CTvalue for
each respective mature miRNA reaction, resulting in the DCT
value. The fold changes in miRNAs were calculated by the
equation 22DDCtwhere the comparative cycle threshold (DDCT) is
defined as the difference between DCT (Cancer) minus DCT
(Control) as described previously [23,47,48]. Differences between
miRNA expression levels among two groups were evaluated using
the t-test. We set the alpha-level to consider miRNAs as significant
to 0.05. The expression profile of miR-202 in the RT-qPCR
validation was used for the creation of a ROC curve.
Characterization of study population
In total, 153 whole blood samples of early stage BC patients and
healthy control individuals were analyzed in this study. Clinical
patient and tumor characteristics at time of BC diagnosis are
shown in Table 1. The cases and controls were age-matched in the
RT-qPCR validation cohort. Age-matching was not performed in
the discovery part of our study, but cases and controls in the
microarray cohort were in the same age group (mean age: 62y vs
Table 2. List of the ten most up-regulated miRNAs in BC patients detected by highest absolute value of fold changes.
miRNAs Median controlsMedian BC pat.Fold change T-test raw P-value T-test adj P-valueAUC
miR-4306 10.0910.83 2.080.0007 0.020.71
miR-202 4.495.212.04 0.00040.020.72
miR-4257 5.506.17 1.96 0.00170.040.65
miR-1323 5.486.13 1.92 0.00220.040.69
miR-335 7.528.16 1.89 0.00170.04 0.74
miR-4977.28 7.871.826.56E-05 0.010.75
miR-106b 13.2213.761.690.0004 0.010.72
miR-9226.98 7.491.67 0.0019 0.030.65
miR-516b5.59 6.091.64 0.0008 0.03 0.67
let7a* 5.105.40 1.350.001 0.030.65
BC pat. … breast cancer patients; adj … adjusted.
Table 3. List of the fifteen most down-regulated miRNAs in BC patients detected by highest absolute value of fold changes.
miRNAs Median controlsMedian BC pat.Fold change T-test raw P-valueT-test adj P-valueAUC
miR-7187.26 18.104.22.168E-05 0.0041 0.77
miR-14716.415.612.24 0.0002 0.01270.70
miR-18212.6711.91 2.140.0001 0.0080 0.71
miR-564 7.266.60 1.93 0.00030.0127 0.67
miR-107 12.41 11.80 1.830.001930.04140.68
miR-23557.156.55 1.82 4.91E-050.0042 0.73
miR-3186-3p6.966.37 1.816.39E-06 0.00150.75
miR-24 11.5410.94 1.81 0.00070.0235 0.65
miR-3130-3p 7.89 7.291.815.03E-050.00420.73
miR-526a7.33 6.771.76 0.00030.0127 0.72
miR-14697.176.64 1.70 0.00010.00800.68
miR-8747.53 7.01 1.672.10E-06 0.00060.74
BC pat. … breast cancer patients; adj … adjusted.
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58y). All BC cases were histologically confirmed as early stage
invasive ductal carcinoma of the breast with a tumor size ranging
between 0.15 and 4.0 cm. Hormone receptor status (estrogen/
progesterone receptors (ER/PR)), Ki-67 status, grading and
HER2-status were available for all patients (Table 1).
Discovery of differentially expressed miRNAs in blood
samples of BC patients by miRNA microarray profiling
In the discovery setting, miRNA-microarray analyses of 48 BC
cases using the GeniomH Realtime Analyzer microarray platform
identified 59 deregulated miRNAs in whole blood of early stage
BC patients compared to healthy controls . T-test with
Benjamini–Hochberg adjustment revealed 13 significantly up-
regulated miRNAs and 46 significantly down-regulated miRNAs
in our microarray panel of 1100 miRNAs and miRNA star
sequences. We also tested different subsets of the most deregulated
miRNAs to discriminate cancer cases from controls (Figure S1).
Sensitivity, specificity and accuracy increases continuously with
increasing numbers of miRNAs used for discrimination of cancer
cases and controls. Using a subset of 30 miRNAs the values of all
three variables were over 80 percent, while we were able to reach
the optimal classification results, with an accuracy of 85.6%, a
specificity of 78.8%, and a sensitivity of 92.5% with a subset of 240
miRNAs, indicating that besides the 59 statistically significant
deregulated miRNAs, other miRNAs may also contain diagnostic
information that could improve the classification result. An
example of a classification result using these 240 miRNAs is
shown in Figure 1. Table S1 shows miRNAs with significant
expression differences between distinct tumor subgroups. The
most deregulated 25 miRNAs detected by highest absolute value
of logarithmized fold changes between BC patients and healthy
controls are shown in Table 2 (up-regulated miRNAs in BC) and
Table 3 (down-regulated miRNAs in BC).
Results of RT-qPCR validation
The expression levels of two miRNAs with purchasable and well
established primers were confirmed with a Taqman-based RT-
qPCR in an independent cohort of BC patients and controls using
individual miRNA-specific primers. MiR-202 which showed up-
regulation pattern for BC cases over controls in the microarray
analyzes and miR-718 which was identified with down-regulation
pattern were selected to be validated in an independent cohort of
24 age matched pairs of early stage BC patients and 24 healthy
controls. We were able to validate the microarray separation
pattern of the two miRNAs in the validation cohort using RT-
qPCR. The PCR results are summarized in Table 4.
RT-qPCR results were concordant with the miRNA microarray
results in terms of up- and down-regulation calculated as relative
fold changes in comparison with each age matched control and
calculated as relative fold changes between the two groups
(Table 4). A discrimination between the two groups using RT-
qPCR expression values of a single miRNA was only possible for
miR-202, but not miR-718 in our small validation cohort (Figure 2
Mammography is currently the modality of choice for screening
for early BC and possesses a sufficient sensitivity and specificity.
Specificity of screening mammography is over 95%, but sensitivity
ranges between 67% and 95% and is strongly dependent on
Figure 1. Representative example of a classification result using trained support vector machines. The graph shows the logarithm of the
quotient of the probability to be BC sample and the probability to be a control sample (log odds) of all samples analyzed with miRNA-microarrays.
1… Controls; 2…BC patient.
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several factors like age, breast density and professional experience
of the examiner [50–52]. In women with very dense breast tissue
sensitivity of screening mammograms can be as low as 40 to 50%
. Magnetic resonance imaging (MRI) of the breast and breast
ultrasound could improve cancer detection in these cases, but
these imaging techniques are not routinely used for BC screening
[52,53]. Therefore, intensive research is currently carried out to
identify new, non-invasive BC detection methods. In the discovery
setting of our study we found 13 significantly up-regulated
miRNAs and 46 significantly down-regulated miRNAs in our
microarray panel of 1100 miRNAs and miRNA star sequences
and validated our results in an independent BC cohort with RT-
qPCR. A set of 240 miRNAs yielded a specificity of 78.8%, and a
sensitivity of 92.5% in this very early stage BC cohort. We
deliberately used frozen EDTA-blood to test and establish a
miRNA-detection method which could possibly be used in larger
multicentric trials collecting frozen whole blood. The stability of
miRNAs in EDTA-blood and the possibility of miRNA-profiling
from non-frozen EDTA-blood have been shown previously by
others . After miRNA-microarray-profiling miR-202, and
miR-718 were chosen for RT-qPCR validation based on the
significant expression changes on the microarray. Moreover, the
identification of biochemical pathways that are enriched with
respect to the miRNA target genes revealed miR-202 as the
Table 4. Comparison of miRNA expression fold changes between microarray and RT-qPCR.
miRNA Relative fold change RT-qPCR T-test P-value RT-qPCR validationChanges in BC cases Fold change microarray
miR-20219.38 0.03 Up-regulation2.04
miR-7185.44 0.72 Down-regulation3.12
Figure 2. RT-qPCR validation of miR-202. Significant different expression of circulating miR-202 in whole blood of BC patients versus Controls
(up-regulation of miR-202). Data derived from RT-qPCR and presented as delta-Ct values, with higher values standing for lower miRNA-expression.
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miRNA with the highest number of significant biological pathways
and Gene Ontology categories of all known miRNAs . This
target pathway analysis indicates that miR-202, which we found to
be significantly up-regulated in our early stage BC blood samples
of the microarray and the RT-qPCR cohort, is influencing a
plethora of cancer relevant biological pathways and might be
important for BC development. Considering that miR-202 belongs
to the let-7 family of miRNAs, which are known to be involved in
self-renewal and tumorigenicity of BC cells, functional data are
available regarding the role of miR-202 and other members of this
familiy in carcinogenesis [56,57]. The let-7 family regulates
estrogen receptor alpha signaling in estrogen receptor positive
BC and is highly conserved across species in sequence and
function [56,58]. The miRNAs let-7e and miR-202 target the
same seed sequence and a recent study has shown that these
miRNAs target the proto-oncogene MYCN in vitro . Further
targets of the let-7 family of miRNAs are RAS and HMG A2 (high
mobility group A2) . The two associated miRNA families, let-7
and miR-200, have also been identified as regulators of epithelial-
to-mesenchymal transition (EMT) and dedifferentiation of cancer
cells to stem cells .
Several miRNAs are known to be aberrantly expressed in
human BC tissue and have been correlated with clinical stage and
clinico-pathological variables like hormone receptor status, tumor
subtypes, as well as clinical variables like metastatic potential,
progression free survival and overall survival [19,61–65]. Tissue-
based miRNA expression profiling of the inflammatory breast
cancer (IBC) subtype observed some miRNAs to be independently
associated with the difference between IBC and non-IBC. Among
those miRNAs with increased expression in the IBC subtype was
also miR-335, which was also significantly up-regulated in our
blood-based microarray discovery study . The expression of
miR-335 was also up-regulated (3.9 fold) in colonic cancer tissues
compared to para-cancerous control tissue . On the other
hand, miR-335 has also been reported as robust inhibitor of tumor
reinitiation and was found to suppress migration, invasion, and
metastatic colonization in vivo . Another study analyzing
tissues from malignant pleural mesothelioma found miR-193-3p to
be over expressed in formalin-fixed, paraffin-embedded malignant
pleural mesothelioma tissue compared to carcinoma tissue . In
line with these findings, our microarray study identified miR-193-
3p as one of the most significantly down-regulated miRNAs in
whole blood of BC patients (2.16-fold; P=0.0001).
Blood-based miRNA-profiling is still far behind the improve-
ments in tissue-based miRNA-profiling, but offers the potential for
early, non-invasive, sensitive and specific BC detection and
screening. First reports using serum or plasma for RT-PCR or
microarray based miRNA-profiling were promising. Recently, a
serum based study using next-generation sequencing of miRNAs
for BC detection has also been reported and several miRNAs have
been identified as potential serum/plasma biomarkers in different
cancer types like lung, prostate, colon and liver cancer [70–72].
Furthermore, strategies with whole blood have been established
and likewise show favourable results in the non-invasive detection
of cancer and other diseases [73–76].
One potential advantage of the whole blood approach could be
the higher miRNA-content and the chance to measure not only
tumor secreted oncogenic miRNAs, but also the changes in the
miRNA profile following the ‘‘host-reaction’’ in the body of the
patient . On the other hand, the main concern about using
whole blood is a reduction of the testing accuracy due to the
measurement of a miRNA profile which represents only an
unspecific secondary response of blood cells during tumorigenesis
. Secondly, the high protein content of whole blood could be a
problem for RNA-extraction. Results from previous studies of our
and other groups indicated that the changes in the miRNA profile
of blood cells of patients with cancer also reflect tumor-specific
host-reactions which might be measurable in whole blood [75,76].
We would therefore expect that the whole blood approach offers
the potential to diagnose cancer at a very early stage when the
concentration of tumor-secreted miRNAs is still small, but the
reaction of the immune system in response to cancer can already
be detected by miRNA profiling in whole blood. Comparing
serum and blood cells from the same healthy individual an almost
identical miRNA profile can be found, but in cancer patients the
profiles differ . In contrast to miRNA-studies using plasma or
serum we found a contrary trend in whole blood regarding the
previous published plasma levels of let-7d*, let-7c, miR-425* and
miR-589 . Although none of these miRNAs was significantly
differentially expressed in our microarray analyses the median fold
changes between BC cases and controls were completely contrary
to the plasma expression changes published by Zhao et al. for
these four miRNAs in Caucasian Americans (n=15) . For
example, the whole blood expression levels of let-7d* in our
microarray cohort (n=48) were almost 2-fold higher in BC cases
compared to controls with a P-value of 0.009 in the unadjusted t-
test and 0.098 in the adjusted t-test.
The comparison of miRNA-profiles from whole blood and
plasma/serum is also addressed in the work of Zhao et al. .
The authors of this plasma based miRNA-profiling study in BC
were not able to reproduce the data from Heneghan et al. showing
a significantly higher expression of let-7a and miR-195 in whole
blood of BC cases compared to controls [74,79]. In our study we
found only a trend towards an up-regulation of miR-195 in whole
blood of BC cases compared to controls in the miRNA-microarray
analyses (P=0.055). Possible reasons for this discrepance are
differences in sample handling, detection methods and patient
selection (clinical stages). Moreover, a recent study showed
significant differences between cell-free and cellular blood miRNA
profiles. Using different plasma fractionation procedures for
plasma, the authors showed varying degrees of efficacy in the
removal of red and white blood cells and as a consequence
different miRNA profiles . In addition to different detection
techniques, these differences could be partially responsible for the
discrepancies between blood based miRNA profiling studies.
Figure 3. Area under the receiver operating characteristic
curve (AUC) for miR-202 based on the RT-qPCR data.
Blood Micro-RNAs for Breast Cancer Detection
PLoS ONE | www.plosone.org7 January 2012 | Volume 7 | Issue 1 | e29770
Based on this data whole blood miRNA profiling seems reasonable
to achieve integrated, standardized analyses of circulating disease
specific miRNA signatures; and is able to measure the disease
specific over-expression of hematopoietically derived miRNAs and
circulating cell-free miRNAs.
The median fold expression changes of deregulated miRNAs in
our microarray analyses were in the range of 2 to 4 fold which is in
line with previous miRNA microarray profiling studies, but fare
less than previously reported in qRT-PCR based studies. These
differences were expected and are probably due to the different
detection and analysis techniques. The relative fold changes found
in our RT-qPCR validation analyses were higher, with miR-202
and miR-718 showing a relative fold change of about 20 and over
5 between cases and age-matched controls (Table 4). The RT-
qPCR validation cohort was small with only 24 age matched pairs
and we were able to show statistically significant differences in
expression values only for one of the two analysed miRNAs (miR-
202), but in agreement with the microarray results the relative fold
changes showed the same trend for both miRNAs. This is
probably due to the small sample size, but could also indicate that
a set of miRNAs rather than a single miRNA is needed for a
reliable differentiation of cancer cases and controls.
The clinicopathological characteristics of the two cohorts are
slightly different with smaller and less aggressive tumors with a
higher rate of HER2-negativity in the RT-qPCR cohort compared
to the microarray cohort. This is due to the different recruitment
strategy (hospital based versus screening cohort) and the
consecutive patient recruitment.
We used miRNA microarray technology to analyze the miRNA
expression profiles of early stage BC patients compared to healthy
controls from frozen EDTA-whole-blood and validated the results
with RT-qPCR in an independent early stage BC cohort.
Research of circulating miRNAs as blood based biomarkers is
still in its infancy. However, this study as well as other recent
studies indicate that miRNA-analyses have diagnostic and
prognostic potential and could improve early stage BC detection
in the future. There are several possible applications of miRNA
profiling conceivable in the future. Firstly, miRNA profiles could
help to reduce unnecessary breast biopsies if miRNA sets could be
identified which reliably identify BC free individuals. Secondly,
miRNA profiling could be used as a pre-screening method for
example by general practitioners to identify women with an urgent
need for breast diagnostics. Thirdly, in younger patients with
dense breast tissue a future miRNA-based BC screening could
possibly provide better sensitivity and specificity than the
mammography even without radiation exposure.
Our study detected several significant deregulated miRNAs in
frozen whole blood of early stage BC patients, which should be
analyzed further with regard to their function in breast cancer
development and progression. Moreover, large prospective clinical
studies are clearly warranted to confirm our preliminary results
and further explore the existing potential of circulating miRNAs in
serum, plasma or whole blood as diagnostic and therapeutic BC
This is a classification plot demonstrating that a multimarker
signature increases test accuracy, specificity and sensitivity
depending upon the number of miRNAs that compose the
Classification plot of microarray signatures.
MiRNAs differentially expressed in different
We thank Ms. Elisabeth Stiegler, Sonja Oeser, Silke Landrith and the febit
biomed team for their expert technical assistance.
Conceived and designed the experiments: MGS RS RS-W A. Hartmann
A. Hein MWB PAF. Performed the experiments: MGS RS PLS LK SR.
Analyzed the data: MGS RS LK CRL CMB. Contributed reagents/
materials/analysis tools: MPL SMJ MRB SR BRA EW CR. Wrote the
paper: MGS RS MWB PAF.
1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, et al. (2009) Cancer statistics, 2009.
CA Cancer J Clin 59: 225–249.
2. LeeRC,FeinbaumRL,Ambros V (1993)TheC.elegans heterochronicgenelin-4
encodes small RNAs with antisense complementarity to lin-14. Cell 75: 843–854.
3. He L, Hannon GJ (2004) MicroRNAs: small RNAs with a big role in gene
regulation. Nat Rev Genet 5: 522–531.
4. Ambros V (2001) microRNAs: tiny regulators with great potential. Cell 107:
5. Forman JJ, Legesse-Miller A, Coller HA (2008) A search for conserved
sequences in coding regions reveals that the let-7 microRNA targets Dicer within
its coding sequence. Proc Natl Acad Sci U S A 105: 14879–14884.
6. Lytle JR, Yario TA, Steitz JA (2007) Target mRNAs are repressed as efficiently
by microRNA-binding sites in the 59 UTR as in the 39 UTR. Proc Natl Acad
Sci U S A 104: 9667–9672.
7. Sun G, Rossi JJ (2011) MicroRNAs and their potential involvement in HIV
infection. Trends Pharmacol Sci.
8. Baek D, Villen J, Shin C, Camargo FD, Gygi SP, et al. (2008) The impact of
microRNAs on protein output. Nature 455: 64–71.
9. Croce CM (2009) Causes and consequences of microRNA dysregulation in
cancer. Nat Rev Genet 10: 704–714.
10. Jiang J, Lee EJ, Gusev Y, Schmittgen TD (2005) Real-time expression profiling
of microRNA precursors in human cancer cell lines. Nucleic Acids Res 33:
11. Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, et al. (2004) Human
microRNA genes are frequently located at fragile sites and genomic regions
involved in cancers. Proc Natl Acad Sci U S A 101: 2999–3004.
12. Melo SA, Moutinho C, Ropero S, Calin GA, Rossi S, et al. (2010) A genetic
defect in exportin-5 traps precursor microRNAs in the nucleus of cancer cells.
Cancer Cell 18: 303–315.
13. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, et al. (2005) MicroRNA
expression profiles classify human cancers. Nature 435: 834–838.
14. Negrini M, Nicoloso MS, Calin GA (2009) MicroRNAs and cancer–new
paradigms in molecular oncology. Curr Opin Cell Biol 21: 470–479.
15. Swarbrick A, Woods SL, Shaw A, Balakrishnan A, Phua Y, et al. (2010) miR-
380-5p represses p53 to control cellular survival and is associated with poor
outcome in MYCN-amplified neuroblastoma. Nat Med 16: 1134–1140.
16. Wang X, Tang S, Le SY, Lu R, Rader JS, et al. (2008) Aberrant expression of
oncogenic and tumor-suppressive microRNAs in cervical cancer is required for
cancer cell growth. PLoS One 3: e2557.
17. Helland A, Anglesio MS, George J, Cowin PA, Johnstone CN, et al. (2011)
Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of
aggressive high-grade serous ovarian cancers. PLoS One 6: e18064.
18. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, et al. (2002) Frequent
deletions and down-regulation of micro- RNA genes miR15 and miR16 at
13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 99:
19. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, et al. (2005) MicroRNA
gene expression deregulation in human breast cancer. Cancer Res 65:
20. O’Day E, Lal A (2010) MicroRNAs and their target gene networks in breast
cancer. Breast Cancer Res 12: 201.
21. Bockmeyer CL, Christgen M, Muller M, Fischer S, Ahrens P, et al. (2011)
MicroRNA profiles of healthy basal and luminal mammary epithelial cells are
distinct and reflected in different breast cancer subtypes. Breast Cancer Res
Treat 130: 735–745.
of lymph node involvement in breast cancer from primary tumor tissue using gene
expression profiling and miRNAs. Breast Cancer Res Treat 129: 767–776.
Blood Micro-RNAs for Breast Cancer Detection
PLoS ONE | www.plosone.org8 January 2012 | Volume 7 | Issue 1 | e29770
23. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, et al. (2008) Download full-text
Circulating microRNAs as stable blood-based markers for cancer detection. Proc
Natl Acad Sci U S A 105: 10513–10518.
24. Lawrie CH, Gal S, Dunlop HM, Pushkaran B, Liggins AP, et al. (2008)
Detection of elevated levels of tumour-associated microRNAs in serum of
patients with diffuse large B-cell lymphoma. Br J Haematol 141: 672–675.
25. Park NJ, Zhou H, Elashoff D, Henson BS, Kastratovic DA, et al. (2009) Salivary
microRNA: discovery, characterization, and clinical utility for oral cancer
detection. Clin Cancer Res 15: 5473–5477.
26. Chen X, Ba Y, Ma L, Cai X, Yin Y, et al. (2008) Characterization of
microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and
other diseases. Cell Res 18: 997–1006.
27. Cortez MA, Bueso-Ramos C, Ferdin J, Lopez-Berestein G, Sood AK, et al.
(2011) MicroRNAs in body fluids-the mix of hormones and biomarkers. Nat Rev
Clin Oncol 8: 467–477.
28. Lodes MJ, Caraballo M, Suciu D, Munro S, Kumar A, et al. (2009) Detection of
cancer with serum miRNAs on an oligonucleotide microarray. PLoS One 4:
29. Kosaka N, Iguchi H, Ochiya T (2010) Circulating microRNA in body fluid: a
new potential biomarker for cancer diagnosis and prognosis. Cancer Sci 101:
30. Hunter MP, Ismail N, Zhang X, Aguda BD, Lee EJ, et al. (2008) Detection of
microRNA expression in human peripheral blood microvesicles. PLoS One 3:
31. Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, et al. (2011)
Argonaute2 complexes carry a population of circulating microRNAs indepen-
dent of vesicles in human plasma. Proc Natl Acad Sci U S A 108: 5003–5008.
32. Wang F, Zheng Z, Guo J, Ding X (2010) Correlation and quantitation of
microRNA aberrant expression in tissues and sera from patients with breast
tumor. Gynecol Oncol.
33. Zhu W, Qin W, Atasoy U, Sauter ER (2009) Circulating microRNAs in breast
cancer and healthy subjects. BMC Res Notes 2: 89.
34. Roth C, Rack B, Muller V, Janni W, Pantel K, et al. (2010) Circulating
microRNAs as blood-based markers for patients with primary and metastatic
breast cancer. Breast Cancer Res 12: R90.
35. Fassan M, Baffa R, Palazzo JP, Lloyd J, Crosariol M, et al. (2009) MicroRNA
expression profiling of male breast cancer. Breast Cancer Res 11: R58.
36. Kastl L, Brown I, Schofield AC (2011) miRNA-34a is associated with docetaxel
resistance in human breast cancer cells. Breast Cancer Res Treat;[Epub ahead
37. Rothe F, Ignatiadis M, Chaboteaux C, Haibe-Kains B, Kheddoumi N, et al.
(2011) Global microRNA expression profiling identifies MiR-210 associated with
tumor proliferation, invasion and poor clinical outcome in breast cancer. PLoS
One 6: e20980.
38. Di Stefano V, Zaccagnini G, Capogrossi MC, Martelli F (2011) microRNAs as
peripheral blood biomarkers of cardiovascular disease. Vascul Pharmacol.
39. Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the
7th edition of the AJCC cancer staging manual and the future of TNM. Ann
Surg Oncol 17: 1471–1474.
40. Beekman JM, Reischl J, Henderson D, Bauer D, Ternes R, et al. (2009)
Recovery of microarray-quality RNA from frozen EDTA blood samples.
J Pharmacol Toxicol Methods 59: 44–49.
41. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA
annotation and deep-sequencing data. Nucleic Acids Res 39: D152–157.
42. Vorwerk S, Ganter K, Cheng Y, Hoheisel J, Stahler PF, et al. (2008)
Microfluidic-based enzymatic on-chip labeling of miRNAs. N Biotechnol 25:
43. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M (2002)
Variance stabilization applied to microarray data calibration and to the
quantification of differential expression. Bioinformatics 18 Suppl 1: S96–104.
44. Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I (2001) Controlling the false
discovery rate in behavior genetics research. Behav Brain Res 125: 279–284.
45. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural
Netw 10: 988–999.
46. Keller A, Ludwig N, Comtesse N, Hildebrandt A, Meese E, et al. (2006) A
minimally invasive multiple marker approach allows highly efficient detection of
meningioma tumors. BMC Bioinformatics 7: 539.
47. Davoren PA, McNeill RE, Lowery AJ, Kerin MJ, Miller N (2008) Identification
of suitable endogenous control genes for microRNA gene expression analysis in
human breast cancer. BMC Mol Biol 9: 76.
48. Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the
comparative C(T) method. Nat Protoc 3: 1101–1108.
49. Keller A, Leidinger P, Borries A, Wendschlag A, Wucherpfennig F, et al. (2009)
miRNAs in lung cancer - studying complex fingerprints in patient’s blood cells
by microarray experiments. BMC Cancer 9: 353.
50. Sinclair N, Littenberg B, Geller B, Muss H (2011) Accuracy of screening
mammography in older women. AJR Am J Roentgenol 197: 1268–1273.
51. Miglioretti DL, Walker R, Weaver DL, Buist DS, Taplin SH, et al. (2011)
Accuracy of screening mammography varies by week of menstrual cycle.
Radiology 258: 372–379.
52. Britton P, Warwick J, Wallis MG, O’Keeffe S, Taylor K, et al. (2011) Measuring
the accuracy of diagnostic imaging in symptomatic breast patients: team and
individual performance. Br J Radiol.
53. D’Orsi CJ, Newell MS (2011) On the frontline of screening for breast cancer.
Semin Oncol 38: 119–127.
54. Heneghan HM, Miller N, Kerin MJ (2010) MiRNAs as biomarkers and
therapeutic targets in cancer. Curr Opin Pharmacol.
55. Backes C, Meese E, Lenhof HP, Keller A (2010) A dictionary on microRNAs
and their putative target pathways. Nucleic Acids Res 38: 4476–4486.
56. Roush S, Slack FJ (2008) The let-7 family of microRNAs. Trends Cell Biol 18:
57. Yu F, Yao H, Zhu P, Zhang X, Pan Q, et al. (2007) let-7 regulates self renewal
and tumorigenicity of breast cancer cells. Cell 131: 1109–1123.
58. Zhao Y, Deng C, Wang J, Xiao J, Gatalica Z, et al. (2011) Let-7 family miRNAs
regulate estrogen receptor alpha signaling in estrogen receptor positive breast
cancer. Breast Cancer Res Treat 127: 69–80.
59. Buechner J, Tomte E, Haug BH, Henriksen JR, Lokke C, et al. (2011) Tumour-
suppressor microRNAs let-7 and mir-101 target the proto-oncogene MYCN and
inhibit cell proliferation in MYCN-amplified neuroblastoma. Br J Cancer 105:
60. Peter ME (2009) Let-7 and miR-200 microRNAs: guardians against pluripo-
tency and cancer progression. Cell Cycle 8: 843–852.
61. Blenkiron C, Goldstein LD, Thorne NP, Spiteri I, Chin SF, et al. (2007)
MicroRNA expression profiling of human breast cancer identifies new markers
of tumor subtype. Genome Biol 8: R214.
62. Lowery AJ, Miller N, Devaney A, McNeill RE, Davoren PA, et al. (2009)
MicroRNA signatures predict oestrogen receptor, progesterone receptor and
HER2/neu receptor status in breast cancer. Breast Cancer Res 11: R27.
63. Yan LX, Huang XF, Shao Q, Huang MY, Deng L, et al. (2008) MicroRNA
miR-21 overexpression in human breast cancer is associated with advanced
clinical stage, lymph node metastasis and patient poor prognosis. RNA 14:
64. Kondo N, Toyama T, Sugiura H, Fujii Y, Yamashita H (2008) miR-206
Expression is down-regulated in estrogen receptor alpha-positive human breast
cancer. Cancer Res 68: 5004–5008.
65. Foekens JA, Sieuwerts AM, Smid M, Look MP, de Weerd V, et al. (2008) Four
miRNAs associated with aggressiveness of lymph node-negative, estrogen
receptor-positive human breast cancer. Proc Natl Acad Sci U S A 105:
66. Van der Auwera I, Limame R, van Dam P, Vermeulen PB, Dirix LY, et al.
(2010) Integrated miRNA and mRNA expression profiling of the inflammatory
breast cancer subtype. Br J Cancer 103: 532–541.
67. Wang YX, Zhang XY, Zhang BF, Yang CQ, Chen XM, et al. (2010) Initial
study of microRNA expression profiles of colonic cancer without lymph node
metastasis. J Dig Dis 11: 50–54.
68. Png KJ, Yoshida M, Zhang XH, Shu W, Lee H, et al. (2011) MicroRNA-335
inhibits tumor reinitiation and is silenced through genetic and epigenetic
mechanisms in human breast cancer. Genes Dev 25: 226–231.
69. Benjamin H, Lebanony D, Rosenwald S, Cohen L, Gibori H, et al. (2010) A
Diagnostic Assay Based on MicroRNA Expression Accurately Identifies
Malignant Pleural Mesothelioma. J Mol Diagn.
70. Wu Q, Lu Z, Li H, Lu J, Guo L, et al. (2011) Next-generation sequencing of
microRNAs for breast cancer detection. J Biomed Biotechnol 2011: 597145.
71. Boeri M, Verri C, Conte D, Roz L, Modena P, et al. (2011) MicroRNA
signatures in tissues and plasma predict development and prognosis of computed
tomography detected lung cancer. Proc Natl Acad Sci U S A 108: 3713–3718.
72. Foss KM, Sima C, Ugolini D, Neri M, Allen KE, et al. (2011) miR-1254 and
miR-574-5p: serum-based microRNA biomarkers for early-stage non-small cell
lung cancer. J Thorac Oncol 6: 482–488.
73. Heneghan HM, Miller N, Lowery AJ, Sweeney KJ, Newell J, et al. (2010)
Circulating microRNAs as novel minimally invasive biomarkers for breast
cancer. Ann Surg 251: 499–505.
74. Zhao H, Shen J, Medico L, Wang D, Ambrosone CB, et al. (2010) A pilot study
of circulating miRNAs as potential biomarkers of early stage breast cancer. PLoS
One 5: e13735.
75. Leidinger P, Keller A, Borries A, Reichrath J, Rass K, et al. (2010) High-
throughput miRNA profiling of human melanoma blood samples. BMC Cancer
76. Hausler SF, Keller A, Chandran PA, Ziegler K, Zipp K, et al. (2010) Whole
blood-derived miRNA profiles as potential new tools for ovarian cancer
screening. Br J Cancer 103: 693–700.
77. Heneghan HM, Miller N, Kerin MJ (2011) Circulating microRNAs: promising
breast cancer Biomarkers. Breast Cancer Res 13: 402.
78. Heneghan HM, Miller N, Kerin MJ (2010) Systemic microRNAs: novel
biomarkers for colorectal and other cancers? Gut 59: 1002–1004; author reply
79. Heneghan HM, Miller N, Kelly R, Newell J, Kerin MJ (2010) Systemic miRNA-
195 differentiates breast cancer from other malignancies and is a potential
biomarker for detecting noninvasive and early stage disease. Oncologist 15:
80. Duttagupta R, Jiang R, Gollub J, Getts RC, Jones KW (2011) Impact of cellular
miRNAs on circulating miRNA biomarker signatures. PLoS One 6: e20769.
Blood Micro-RNAs for Breast Cancer Detection
PLoS ONE | www.plosone.org9 January 2012 | Volume 7 | Issue 1 | e29770