MicroRNA Gene Expression Deregulation in Human Breast Cancer

Abstract

MicroRNAs (miRNAs) are a class of small noncoding RNAs that control gene expression by targeting mRNAs and triggering either translation repression or RNA degradation. Their aberrant expression may be involved in human diseases, including cancer. Indeed, miRNA aberrant expression has been previously found in human chronic lymphocytic leukemias, where miRNA signatures were associated with specific clinicobiological features. Here, we show that, compared with normal breast tissue, miRNAs are also aberrantly expressed in human breast cancer. The overall miRNA expression could clearly separate normal versus cancer tissues, with the most significantly deregulated miRNAs being mir-125b, mir-145, mir-21, and mir-155. Results were confirmed by microarray and Northern blot analyses. We could identify miRNAs whose expression was correlated with specific breast cancer biopathologic features, such as estrogen and progesterone receptor expression, tumor stage, vascular invasion, or proliferation index.

Full text

MicroRNA Gene Expression Deregulation in Human Breast Cancer
Marilena V. Iorio,
1
Manuela Ferracin,
2
Chang-Gong Liu,
1
Angelo Veronese,
2
Riccardo Spizzo,
2
Silvia Sabbioni,
2
Eros Magri,
2
Massimo Pedriali,
2
Muller Fabbri,
1
Manuela Campiglio,
3
Sylvie Me´nard,
3
Juan P. Palazzo,
4
Anne Rosenberg,
5
Piero Musiani,
6
Stefano Volinia,
1
Italo Nenci,
2
George A. Calin,
1
Patrizia Querzoli,
2
Massimo Negrini,
2
and Carlo M. Croce
1
1
Comprehensive Cancer Center, Ohio State University, Columbus, Ohio;
2
Dipartimento di Medicina Sperimentale e Diagnostica, e Centro
Interdipartimentale per la Ricerca sul Cancro, Universita` di Ferrara, Ferrara, Italy;
3
Molecular Targeting Unit, Department of
Experimental Oncology, Istituto Nazionale Tumori, Milan, Italy; Departments of
4
Pathology, Anatomy and Cell Biology
and
5
Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania; and
6
Ce.S.I. Aging Research Center, Chieti, Italy
Abstract
MicroRNAs (miRNAs) are a class of small noncoding RNAs that
control gene expression by targeting mRNAs and triggering
either translation repression or RNA degradation. Their
aberrant expression may be involved in human diseases,
including cancer. Indeed, miRNA ab errant expression has
been previously found in human chronic lymphocytic leuke-
mias, where miRNA signatures were associated with specific
clinicobiological features. Here, we show that, compared with
normal breast tissue, miRNAs are also aberrantly expressed in
human breast cancer. The overall miRNA expression could
clearly separate normal versus cancer tissues, with the most
significantly deregulated miRNAs being mir-125b, mir-145 ,
mir-21 , and mir-155. Results were confirmed by microarray
and Northern blot analyses. We could identify miRNAs whose
expression was correlated with specific breast cancer bio-
pathologic features, such as estrogen and progesterone
receptor expression, tumor stage, vascular invasion, or
proliferation index. (Cancer Res 2005; 65(16): 7065-70)
Introduction
MicroRNAs (miRNAs) represent a class of naturally occurring
small noncoding RNA molecules, distinct from but related to
small interfering RNAs. Mature miRNAs are 19- to 25-nucleotide-
long molecules cleaved from 70- to 100-nucleotide hairpin pre-
miRNA precursors (1). The precursor is cleaved by cytoplasmic
RNase III Dicer into f 22-nucleotide miRNA duplex: one strand
(miRNA*) of the short-lived duplex is degraded, whereas the
other strand serves as mature miRNA. In animals, single-stranded
miRNA binds through partial sequence homology to the 3V un-
translated region (3V UTR) of target mRNAs, and causes either
block of translation or, less frequently, mRNA degradation. The
discovery of this class of genes has identified a new layer of gene
regulation mechanisms, which play an important role in
development and in various cellular processes, such as differen-
tiation, cell growth, and cell death (2). Deviations from normal
pattern of expression may play a role in diseases, such as in
neurologic disorders (3).
Among human diseases, it has been shown that miRNAs are
aberrantly expressed or mutated in cancer, suggesting that they
may play a role as a novel class of oncogenes or tumor suppressor
genes. The first evidence of involvement of miRNAs in human cancer
came from molecular studies characterizing the 13q14 deletion in
human chronic lymphocytic leukemia (CLL), which revealed that
two miRNAs, mir-15a and mir-16-1, were the only genes within the
smallest common region of deletion. The same two genes were
affected by a chromosomal translocation in a CLL patient. mir-16-1
and/or mir-15a were then found down-regulated in 50% to 60% of
human CLL (4). Following this initial finding, miRNA expression
deregulation in human cancer has been proven in other instances.
For example, miR143 and miR145 are down-regulated in colon
carcinomas (5). Let-7 is down-regulated in human lung carcinomas
and restoration of its expression induces cell growth inhibition in
lung cancer A549 cells (6). The BIC gene, which contains the miR155,
is strongly up-regulated in some Burkitt’s lymphoma and several
other types of lymphomas (7, 8). The findings that miRNAs have a
role in human cancer is further supported by the fact that >50% of
miRNA genes are located at chromosomal regions, such as fragile
sites, and regions of deletion or amplification that are genetically
altered in human cancer (9), suggesting that the relevance of miRNAs
in human cancer may be presently underestimated.
Only recently, the possibility of analyzing the entire miRNAome
has become possible by the development of microarrays containing
all known human miRNAs (10–15). The use of miRNA microarrays
made possible to confirm miR-16 deregulation in human CLL, but
also recognize miRNA expression signatures associated with well-
defined clinicopathologic features of human CLL (16). Recognition of
miRNAs that are differentially expressed between normal and tumor
samples may help to identify those that are involved in human cancer
and establish the basis to unravel their pathogenic role. Here, we
present results of a genome-wide miRNA expression profiling in a
large set of normal and tumor breast tissues demonstrating the
existence of a breast cancer–specific miRNA signature.
Materials and Methods
Breast cancer samples and cell lines. RNAs from primary tumors were
from 76 samples collected at the University of Ferrara (Italy), Istituto
Nazionale dei Tumori, Milano (Italy), and Thomas Jefferson University
(Philadelphia, PA). Clinicopathologic information was available for 58 tumor
samples. RNAs from normal samples consisted of six pools of five normal
breast tissues each and four additional single breast tissues. RNAs of human
breast cell lines were from Hs578-T, MCF7, T47D, BT20, SK-BR-3, HBL100,
HCC2218, MDA-MB-175, MDA-MB-231, MDA-MB-361, MDA-MB-435, MDA-
MB-436, MDA-MB-453, and MDA-MB-468.
Immunohistochemical analysis of breast cancer samples. Hormonal
receptors were evaluated with 6F11 antibody for estrogen receptor a and
Note: Supplementary data for this article are available at Cancer Research Online
(http://cancerres.aacrjournals.org/).
M.V. Iorio and M. Ferracine contributed equally to this work. R. Spizzo is a
recipient of an Associazione Italiana per la Ricerca sul Cancro fellowship.
Requests for reprints: Carlo M. Croce, Comprehensive Cancer Center, Ohio State
University, Room 445C, Wiseman Hall, 400 12th Avenue, Columbus, OH 43210. Phone:
614-292-3063; Fax: 614-292-3312; E-mail: Carlo. Croce@osumc.edu.
I2005 American Association for Cancer Research.
doi:10.1158/0008-5472.CAN-05-1783
www.aacrjournals.org
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Priority Report

PGR-1A6 for progesterone receptor (Ventana, Tucson, AZ). The proliferation
index was assessed with MIB1 antibody (DAKO, Copenhagen, Denmark).
ERBB2 was detected with CB11 (V entana ) and p53 protein expression was
examined with DO7 (Ventana). Staining procedures were done as described
(17). Only tumor cells with distinct nuclear immunostaining for estrogen
receptor, progesterone receptor, Mib1, and p53 were recorded as positive.
Tumor cells were considered positive for ERBB2 when they showed distinct
membrane immunoreactivity. To perform a quantitative evaluation of
biological markers, the Eureka Menarini computerized image analysis system
was used. For each tumor section, at least 20 microscopic fields of invasive
carcinoma (40

objective) were measured. The following cutoff values were
used: 10% of positive nuclear area for estrogen receptor, progesterone
receptor, c-erbB2, and p53; 13% of nuclei expressing Mib1 was introduced to
discriminate cases with high and low proliferative activity.
MicroRNA microarray. Total RNA isolation was done with Trizol
(Invitrogen, Carlsbad , C A) according to the instructions of the
manufacturer. RNA labeling and hybridization on miRNA microarray
chips was done as previously described (10). Briefly, 5 Ag of RNA from
each sample was biotin-labeled during reverse transcription using
random examers. Hybridization was carried out on miRNA microarray
chip (KCI version 1.0; ref. 10), which contains 368 probes, including 245
human and mouse miRNA genes, in triplicate. Hybridization signals were
detected by biotin binding of a Streptavidin–Alexa 647 conjugate using a
Perkin-Elmer ScanArray XL5K. Scanner images were quantified by the
Quantarray software (Perkin-Elmer, Wellesley, MA).
Statistical and bioinformatic analysis of microarray data. Raw data
were normalized and analyzed using the GeneSpring software version 7.2
(Silicon Genetics, Redwood City, CA). Expression data were median
centered. Statistical comparisons were done by ANOVA, using the
Benjamini and Hochberg correction for false-positive reductions.
Prognostic miRNAs for tumor versus normal class prediction were
determined by using both the Prediction Analysis of Microarrays software
(PAM; ref. 18)
7
and the Support Vector Machine (19) tool. Both
algorithms were used for cross-validation and test-set prediction. All
data were submitted using MIAMExpress to the Array Express database
(accession numbers to be received upon revision).
Northern blotting. Northern blot analysis was done as previously
described (4). RNA samples (10 mg each) were electrophoresed on 15%
acrylamide, 7 mol/L urea Criterion precasted gels (Bio-Rad, Hercules, CA)
and transferred onto Hybond-N+ membrane (Amersham Biosciences,
Piscataway, NJ). Hybridization was done at 37jC in 7% SDS/0.2 mol/L
Na
2
PO
4
(pH 7.0) for 16 hours. Membranes were washed at 42jC, twice with
2

standard saline phosphate [0.18 mol/L NaCl/10 mmol/L phosphate (pH
7.4)], 1 mmol/L EDTA (saline-sodium phosphate-EDTA, SSPE), and 0.1%
SDS and twice with 0.5

SSPE/0.1% SDS. The oligonucleotides used as
probes are the complementary sequences of the mature miRNA (miR
Registry):
8
miR21 5V-TCAACATCAGTCTGATAAGCTA-3V; miR125b1: 5V-TCA-
CAAGTTAGGGTCTCAGGGA-3V; miR145: 5V-AAGGGATTCCTGG-
GAAAACTGGAC-3V. An oligonucleotide complementary to the U6 RNA
(5V-GCAGGGGCCATGCTAATCTTCTCTGTATCG-3V) was used to normalize
Table 1. miRNAs differentially expressed between breast carcinoma and normal breast tissue
P Breast cancer Normal breast
Median Range Median Range
Normalized Min Max Normalized Min Max
let-7a-2 1.94E02 1.67 0.96 6.21 2.30 1.34 5.00
let-7a-3 4.19E02 1.26 0.81 3.79 1.58 1.02 2.91
let-7d (=7d-v1) 4.61E03 0.90 0.59 1.54 1.01 0.83 1.25
let-7f-2 6.57E03 0.84 0.51 1.58 0.92 0.76 1.03
let-7i (= let-7d-v2) 3.38E02 2.05 1.02 7.49 1.53 1.01 3.47
mir-009-1 (mir-131-1) 9.12E03 1.36 0.69 4.16 1.01 0.61 2.44
mir-010b 4.49E02 1.11 0.69 4.79 1.70 0.96 6.32
mir-021 4.67E03 1.67 0.66 26.43 1.08 0.80 2.31
mir-034 (=mir-170) 1.06E02 1.67 0.70 6.40 1.09 0.65 3.17
mir-101-1 4.15E03 0.83 0.52 1.26 0.90 0.77 1.05
mir-122a 3.43E03 2.21 0.93 8.08 1.48 1.06 3.67
mir-125a 3.28E03 1.20 0.69 2.36 1.73 1.21 3.34
mir-125b-1 2.65E02 1.30 0.55 8.85 2.87 1.45 18.38
mir-125b-2 2.33E02 1.26 0.69 6.29 2.63 1.40 16.78
mir-128b 1.60E02 1.12 0.68 7.34 1.02 0.89 1.27
mir-136 2.42E03 1.32 0.74 10.26 1.06 0.76 1.47
mir-143 7.11E03 0.87 0.68 1.33 0.96 0.81 1.17
mir-145 4.02E03 1.52 0.92 8.46 3.61 1.65 14.45
mir-149 2.75E02 1.11 0.53 1.73 1.03 0.83 1.22
mir-155 (BIC) 1.24E03 1.75 0.95 11.45 1.37 1.11 1.88
mir-191 4.26E02 5.17 1.03 37.81 3.12 1.45 14.56
mir-196-1 1.07E02 1.20 0.57 3.95 0.95 0.66 1.75
mir-196-2 1.16E03 1.46 0.57 5.55 1.04 0.79 1.80
mir-202 1.25E02 1.05 0.71 2.03 0.89 0.65 1.20
mir-203 4.06E07 1.12 0.50 5.69 0.86 0.71 1.04
mir-204 2.15E03 0.78 0.48 1.04 0.89 0.72 1.08
mir-206 1.42E02 2.55 1.22 6.42 1.95 1.34 3.22
mir-210 6.40E13 1.60 0.98 12.13 1.12 0.97 1.29
mir-213 1.08E02 3.72 1.42 40.83 2.47 1.35 5.91
7
http://www-stat.stanford.edu/ftibs/PAM/index.html.
8
http://www.sanger.ac.uk/Software/Rfam/mirna/.
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expression levels. Two hundred nanograms of each probe was end labeled
with 100 mCi [g-
32
P]ATP using the polynucleotide kinase (Roche, Basel,
Switzerland). Blots were stripped in boiling 0.1% SDS for 10 minutes before
rehybridization.
Results
A microRNA expression signature discriminat es between
normal and cancer breast tissues. We used a miRNA microarray
(10) to evaluate miRNA expression profiles of 10 normal and 76
neoplastic breast tissues. Each tumor sample was derived from a
single specimen; 6 of the 10 normal samples consisted of pools
made of five different normal breast tissue RNAs; hence, 34 normal
breast samples were actually examined in the study.
To identify miRNA whose expression was significantly different
between normal and tumor samples and could identify the
different nature of these breast tissues, we made use of ANOVA
and class prediction statistical tools.
To identify differentially expressed miRNAs among all the human
miRNAs spotted on the chip, the ANOVA analysis on normalized
data generated a list of differentially expressed miRNAs (at P <
0.05) between normal breasts and breast cancers (Table 1). Cluster
analysis, based on differentially expressed miRNA, generated a tree
with clear distinction between normal and cancer tissues (Fig. 1A).
To identify the smallest set of predictive miRNAs differentiating
normal versus cancer tissues, we used the Support Vector Machine
(GeneSpring software; ref. 19) and PAM (18).
9
Results from the two
types of class prediction analysis were largely overlapping (Table 2;
Fig. 1B). Among miRNAs listed in Table 2, 11 of 15 have an ANOVA
P value of <0.05.
To confirm results obtained by microarray analysis, we carried
out Northern blot analysis on some of the differentially expressed
miRNAs. We analyzed the expression of mir-125b, mir-145, and
mir-21 in human breast cancers and in breast cancer cell lines. All
Northern blots confirmed results obtained by microarray analysis,
and in many cases dif ferences seemed even stronger than that
anticipated from microarray studies (Fig. 1C).
Given that biological significance of miRNA deregulation relies
on their protein-coding gene targets, we analyzed the predicted
targets of the most significantly down-regulated and up-regulated
Figure 1. Cluster analysis and PAM prediction in breast cancer and normal breast tissues. A, tree generated by a cluster analysis showing the separation of
breast cancer from normal tissues on the basis of miRNA differentially expressed (P < 0.05) between breast cancer and normal tissue (see Supplementary Table S1).
The bar at the bottom indicates the group of cancer samples (red ) or the group of normal breast tissues (yellow). B, PAM analysis displaying the graphical
representation of the probabilities (0.0-1.0) of each sample for being a cancer or a normal tissue. All breast cancer and normal tissues were correctly predicted by
the miR signature shown in Table 1. C, Northern blot analysis of human breast carcinomas and breast cancer cell lines with probes mir-125b , mir-145 , and
mir-21. The U6 probe was used for normalization of expression levels in the different lanes.
9
http://www-stat.stanford.edu/ftibs/.
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miRNAs: miR-10b, miR125b, miR-145, miR-21, and miR-155. The
analysis was done using the three algorithms, miRanda, TargetScan,
and PicTar, commonly used to predict human miRNA gene targets
(20–22). Because any of the three approaches generates an
unpredictable number of false positives, results were intersected
to identify the genes commonly predicted by at least two of the
methods. Results are shown in Supplementary Table S1.
Biopathologic features and microRNA expression. We
analyzed results from miRNA expression profiles in breast cancer
to evaluate whether a correlation existed with various biopathologic
features associated with tumor specimens. We analyzed lobular
versus ductal histotypes, breast cancers with differential estrogen
receptor a or progesterone receptor expression, lymph nodes
metastasis, vascular invasion, proliferation index, expression of
ERBB2, and immunohistochemical detection of p53. Lobular versus
ductal and ERBB2 expression classes did not reveal any differen-
tially expressed miRNA, whereas all other comparisons revealed a
small number of differentially expressed miRNAs (P < 0.05). Tumor
grade was not analyzed because the only two grade 1 samples were a
size too small to be compared with a large number of grade 2 or 3
samples. Complete results are shown in Table 3.
Discussion
We have analyzed 76 breast cancer and 10 normal breast samples
to identify miRNAs whose expression is significantly deregulated in
cancer versus normal breast tissues. We have indeed identified 29
miRNAs whose expression is significantly deregulated (at P < 0.05)
and a smaller set of 15 miRNAs that were able to correctly predict the
nature of the sample analyzed (i.e., tumor or normal breast tissue)
with 100% accuracy. These results leave few doubts that aberrant
expression of miRNA is indeed involved in human breast cancer.
Among the differentially expressed miRNAs, miR-10b, miR-125b,
miR145, miR-21, and miR-155 emerged as the most consistently
deregulated in breast cancer. Three of them, miR-10b, miR-125b,
and miR-145, were down-regulated and the remaining two, miR-
21 and miR-155, were up-regulated, suggesting that they may
potentially act as tumor suppressor genes or oncogenes,
respectively.
It has been reported that the miR-125b, a putative homologue of
lin-4 in Caenorhabditis elegans, and the let-7 miRNAs are induced
during in vitro retinoic acid–induced differentiation of Tera-2 or
embryonic stem cells. Furthermore, high expression of human miR-
125b seems to be present in differentiated cells or tissues (23).
Here, we show that breast cancer primary tumors and cell lines
show evidence of a decreased level of miR-125b expression, sug-
gesting that lack of miR-125 may impair differentiation capabilities
of cancer cells.
At present, the lack of knowledge about bona fide miRNA gene
targets hampers a full understanding on the biological functions
deregulated by miRNA aberrant expression. To partially overcome
this limitation, we made use of presently available computational
approaches to predict gene targets (21, 22, 24). Supplementary
Table S1 shows targets that were predicted by at least two of the
methods, and shows that various cancer-associated genes are
potentially regulated by miRNAs aberrantly expressed in breast
cancer.
It may be expected that targets of down-regulated miRNAs
include oncogenes or genes encoding proteins with potential
oncogenic functions. Indeed, among putative targets, several genes
with potential oncogenic functions could be found , such as FLT1
and the v-crk homologue, the growth factor BDNF, and the
transducing factor SHC1 predicted as miR-10b targets. Among
putative targets of miR-125b, potential oncogenic functions
Table 2. Normal and tumor breast tissue class predictor miRNAs
miRNA name Median expression ANOVA* P SVM prediction
strength
c
PAM score
b
Chromosome map
Cancer Normal Cancer Normal
mir-009-1 1.36 1.01 0.0091 8.05 0.011 0.102 1q22
mir-010b 1.11 1.70 0.0449 8.70 0.032 0.299 2q31
mir-021 1.67 1.08 0.0047 10.20 0.025 0.235 17q23.2
mir-034 1.67 1.09 0.0106 8.05 0.011 0.106 1p36.22
mir-102 (mir-29b) 1.36 1.14 >0.10 8.92 0.000 0.004 1q32.2-32.3
mir-123 (mir-126 ) 0.92 1.13 0.0940 9.13 0.015 0.138 9q34
mir-125a 1.20 1.73 0.0033 8.99 0.040 0.381 19q13.4
mir-125b-1 1.30 2.87 0.0265 14.78 0.096 0.915 11q24.1
mir-125b-2 1.26 2.63 0.0233 17.62 0.106 1.006 21q11.2
mir-140-as 0.93 1.10 0.0695 11.01 0.005 0.050 16q22.1
mir-145 1.52 3.61 0.0040 12.93 0.158 1.502 5q32-33
mir-155 (BIC) 1.75 1.37 0.0012 10.92 0.003 0.030 21q21
mir-194 0.96 1.09 >0.10 11.12 0.025 0.234 1q41
mir-204 0.78 0.89 0.0022 8.10 0.015 0.144 9q21.1
mir-213 3.72 2.47 0.0108 9.44 0.023 0.220 1q31.3-q32.1
*ANOVA (Welch t test in the Genespring software package) as calculated in Table 1.
c
Support Vector Machine prediction analysis tool ( from Genespring 7.2 software package). Prediction strengths are calculated as negative natural log of
the probability to predict the observed number of samples, in one of the two classes, by chance. The higher is the score, the best is the prediction
strength.
b
Centroid scores for the two classes of the PAM (18).
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included the oncogenes YES, ETS1, TEL, and AKT3; the growth
factor receptor FGFR2; or members of the mitogen-activated signal
transduction pathway VT S58635, MAP3K10, MAP3K11,and
MAPK14. The oncogenes MYCN, FOS, YES, and FLI1; integration
site of Friend leukemia virus; cell cycle promoters such as cyclins
D2 and L1; and MAPK transduction proteins such as MAP3K3 and
MAP4K4 were predicted targets for miR-145. Interestingly, the
proto-oncogene YES and the core-binding transcription factor
CBFB were potential targets of both miR-125 and miR-145.
For the up-regulated miRNAs miR-21 and miR-155, it may be
expected that gene targets belong to the class of tumor suppressor
genes. For miR-21, the TGFB gene was predicted as target of
miR-21 by all three methods. For miR-155, potential targets
included the tumor suppressor genes SOCS1 and APC, and the
kinase WEE1, which blocks the activity of Cdc2 and prevents entry
into mitosis. The hypoxia-inducible factor HIF1A was also a pre-
dicted target. Interestingly, among predicted genes, the tripartite
motif-containing protein TRIM2, the proto-oncogene SKI, and the
RAS homologues RAB6A and RAB6C were found as potential
targets of both miR-21 and miR-155.
miRNAs were found differentially expressed in various bio-
pathologic features distinctive of human breast cancer. Some of
Table 3. Differentially expressed miRNAs associated with
invasive breast cancer biopathologic features
Median expression
No. samples 20 13 P
Feature ER+ ER
mir-26a 2.473 1.483 0.0273
mir-26b 3.751 1.932 0.0273
mir-29b 1.280 0.935 0.0188
mir-30a-5p 1.779 1.202 0.0191
mir-30b 1.810 1.184 0.0250
mir-30c 1.587 1.040 0.0191
mir-30d 2.986 1.736 0.0273
mir-185 1.568 2.296 0.0399
mir-191 6.354 2.908 0.0273
mir-206 1.811 2.373 0.0273
mir-212 2.811 3.905 0.0403
No. samples 18 14 P
Feature PR+ PR
let-7c 1.445 1.129 0.0130
mir-26a 2.451 1.673 0.0474
mir-29b 1.283 0.997 0.0194
mir-30a-5p 1.879 1.219 0.0012
mir-30b 1.898 1.220 0.0044
mir-30c 1.643 1.089 0.0047
mir-30d 3.211 1.777 0.0055
No. samples 9 22 P
Feature pT1 pT2-3
mir-9-2 0.894 0.840 0.0078
mir-15a 0.905 0.830 0.0024
mir-21 1.080 1.348 0.0040
mir-30a-s 0.944 0.875 0.0065
mir-133a-1 0.928 0.843 0.0025
mir-137 0.894 0.818 0.0100
mir-153-2 0.896 0.833 0.0096
mir-154 0.924 0.852 0.0062
mir-181a 1.024 1.225 0.0045
mir-203 0.905 1.102 0.0011
mir-213 1.915 3.197 0.0003
No. samples 16 6 P
Feature pN
0
pN
10+
let-7f-1 1.195 1.053 0.0378
let-7a-3 1.191 1.039 0.0303
let-7a-2 1.470 1.213 0.0300
mir-9-3 1.634 1.344 0.0152
No. samples 21 11 P
Feature Vascular invasion
absent
Vascular invasion
present
mir-9-3 1.059 0.988 0.0451
mir-10b 1.048 0.972 0.0210
mir-27a 1.104 0.992 0.0317
mir-29a 1.101 0.970 0.0346
mir-123 1.125 0.852 0.0161
mir-205 1.299 0.762 0.0451
No. samples 26 23 P
Feature Low PI High PI
let-7c 1.817 1.361 0.0071
let-7d 1.594 1.310 0.0073
mir-26a 2.602 1.928 0.0492
mir-26b 4.039 2.695 0.0297
mir-30a-5p 1.783 1.394 0.0257
mir-102 1.389 1.037 0.0017
mir-145 1.557 1.281 0.0136
No. samples 39 14 P
Feature p53+ p53
mir-16a 0.895 1.030 0.0026
mir-128b 0.964 1.059 0.0096
Abbreviations: ER, estrogen receptor; PR, progesterone receptor; pT,
tumor stage; pN, positive lymph nodes; low PI, low proliferation index,
MIB-1 < 20; high PI, high proliferation index, MIB-1 > 30.
Table 3. Differentially expressed miRNAs associated with
invasive breast cancer biopathologic features (Cont’d)
Median expression
No. samples 21 11 P
Feature Vascular invasion
absent
Vascular invasion
present
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these findings are worth noticing. For example, mir-30s are all
down-regulated in both estrogen receptor– and progesterone
receptor–negative tumors, suggesting that expression of these
miRNAs is regulated by these hormones. Another interesting
observation is the finding that the expression of various let-7
miRNAs was down-regulated in breast cancer samples with either
lymph node metastasis or higher proliferation index, suggesting
that a reduced let-7 expression could be associated with a poor
prognosis. An association between let-7 down-regulation and poor
prognosis was previously reported in human lung cancer (6). The
finding that the let-7 family of miRNAs regulates the expression of
the RAS oncogene family provides a potential explanation for the
role of the let-7 miRNAs in human cancer (25). Two miRNA, miR-
145 and miR-21, whose expression could differentiate cancer
versus normal tissues, were also differentially expressed in
cancers with different proliferation indexes or different tumor
stage. In particular, miR-145 is progressively down-regulated from
normal breast to cancer with high proliferation index. Similarly,
but in opposite direction, miR-21 is progressively up-regulated
from normal breast to cancers with high tumor stage. These
findings suggest that deregulation of these two miRNAs may
affect critical molecular events involved in tumor progression.
Another miRNA potentially involved in cancer progression is miR-
9-3. miR-9-3 was down-regulated in breast cancers with either
high vascular invasion or presence of lymph node metastasis,
suggesting that its down-regulation was acquired in the course of
tumor progression and, in particular, during the acquisition of
cancer metastatic potential.
It has been reported that miRNA genes are frequently located in
chromosomal regions characterized by nonrandom aberrations in
human cancer, suggesting that resident miRNA expression might
be affected by these genetic abnormalities (9). miR-125b, which is
down-modulated in breast cancer, is located at chromosome
11q23-24, one of the regions most frequently deleted in breast,
ovarian, and lung tumors (26, 27). The recognition of a bona fide
tumor suppressor gene located at 11q23-24 involved in the
pathogenesis of human breast cancer is still lacking. The miR-
125b gene establishes itself as an important candidate for this role.
Results reported here increase our understanding of the
molecular basis of human breast cancer and suggest that aberrant
expression of miRNA genes may be important for the pathogenesis
of this human neoplasm.
Acknowledgments
Received 5/23/2005; revised 6/22/2005; accepted 6/24/2005.
Grant support: Associazione Italiana per la Ricerca sul Cancro; Ministero
dell’Istruzione, dell’Universita` e della Ricerca Programma Post-genoma (FIRB no.
RBNE0157EH); Ministero della Salute Italiano; Progetto CAN2005—Comitato dei
Sostenitori (M. Negrini); Program Project grants P01CA76259, P01CA81534, and
CA083698 (C.M. Croce) from the National Cancer Institute; and a Kimmel Scholar
award (G.A. Calin).
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked advertisement in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
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Cancer Research
Cancer Res 2005; 65: (16). August 15, 2005
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