Vol. 25 no. 4 2009, pages 430–434
Computational analysis of microRNA profiles and their target
genes suggests significant involvement in breast cancer
Fuxiao Xin1, Meng Li1,2,3, Curt Balch2,4, Michael Thomson5, Meiyun Fan6, Yunlong Liu7,
Scott M. Hammond8, Sun Kim1,9,∗and Kenneth P. Nephew2,3,4,6,10,∗
1School of Informatics,2Medical Sciences, School of Medicine,3Interdisciplinary Biochemistry Program,4IU Simon
Cancer Center, Indiana University, Bloomington, IN 47405,5Department of Cancer Biology, Vanderbilt University
Medical Center, Nashville, TN 37232,6Department of Pathology and Laboratory Medicine, and the Center for Cancer
Research, University of Tennessee Health Science Center, Memphis, TN 38163,7Division of Biostatistics,School of
Medicine, Indiana University, Indianapolis, IN 46202,8Cell and Developmental Biology, School of Medicine, University
of North Carolina, Chapel Hill, NC 27599,9Center for Genomics and Bioinformatics, Indiana University, Bloomington,
IN 47404 and10Department of Cellular and Integrative Physiology, School of Medicine, Indiana University,
Indianapolis, IN 46202, USA
Received August 7, 2008; revised December 9, 2008; accepted December 12, 2008
Advance Access publication December 17, 2008
Associate Editor: Ivo Hofacker
Motivation: Recent evidence shows significant involvement of
microRNAs (miRNAs) in the initiation and progression of numerous
cancers; however, the role of these in tumor drug resistance remains
Results: By comparing global miRNA and mRNA expression
patterns, we examined the role of miRNAs in resistance to the
‘pure antiestrogen’ fulvestrant, using fulvestrant-resistant MCF7-FR
cells and their drug-sensitive parental estrogen receptor (ER)-positive
MCF7 cells. We identified 14 miRNAs downregulated in MCF7-FR
cells and then used both TargetScan and PITA to predict potential
target genes. We found a negative correlation between expression of
these miRNAs and their predicted target mRNA transcripts. In genes
regulated by multiple miRNAs or having multiple miRNA-targeting
sites, an even stronger negative correlation was found. Pathway
analyses predicted these miRNAs to regulate specific cancer-
associated signal cascades. These results suggest a significant
role for miRNA-regulated gene expression in the onset of breast
cancer antiestrogen resistance, and an improved understanding of
this phenomenon could lead to better therapies for this often fatal
Contact: firstname.lastname@example.org; email@example.com
Supplementary information: Supplementary data are available at
MicroRNAs (miRNAs) are small, non-coding RNAs that have
been shown to influence the stability and translational efficiency
of cognate mRNAs (Farh et al., 2005; Lim et al., 2005). MiRNAs
are known to control diverse biological processes, and recent studies
∗To whom correspondence should be addressed.
progression of various cancers (Cimmino et al., 2005; Eis et al.,
2005; Hayashita et al., 2005; Iorio et al., 2005; Johnson et al.,
2005; Ma et al., 2007; Takamizawa et al., 2004), allowing miRNA
expression profiles to be potentially used for cancer classification,
diagnosis and prognosis (Calin and Croce, 2006; Lu et al., 2005),
and specific miRNAs could represent therapeutic targets (Hernando,
2007; Negrini et al., 2007). In human breast cancer, miRNAs -let7i,
-125b, 145, 21, 155 and 191 are significantly downregulated, as
compared with normal breast tissue (Foekens et al., 2008; Iorio
et al., 2005). Furthermore, altered expression of specific miRNAs
could be associated with poor prognosis, e.g. let-7 (Iorio et al.,
2005), 212 (Iorio et al., 2005), 181 (Iorio et al., 2005; Volinia et al.,
2006) and 191 (Foekens et al., 2008), and miR-10b can specifically
initiate invasion and metastasis (Ma et al., 2007). The importance
of miRNAs in these advanced breast cancer phenotypes raises the
question of their further involvement in antiestrogen resistance.
Fulvestrant (Faslodex; ICI 182780), an advanced breast cancer
therapy belonging to a new class of antihormonal agents
known as selective estrogen receptor downregulators (SERDs)
(Howell, 2000), is FDA approved for use in postmenopausal
patients following failure of first-line endocrine therapies (such
as tamoxifen), with a markedly different mechanism of action.
Specifically, this ‘pure estrogen antagonist’ inhibits cytoplasm-to-
receptor-alpha (ERα), while also inducing ERα cytoplasmic
aggregation, immobilization to the nuclear matrix, and proteasomal
degradation (Fan et al., 2003; Long and Nephew, 2006). As a
consequence of these actions, loss of both ERα-mediated genomic
and non-genomic pathways leads to the complete suppression of
effects, most breast cancers eventually develop resistance to
© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org
Computational analysis of miRNA profiles
fulvestrant (Howell, 2001; Howell and Abram, 2005), through
poorly understood mechanisms.
To investigate possible molecular changes associated with the
development of antiestrogen resistance, we previously subjected
fulvestrant-resistant breast cancer cells to global gene expression
microarray analyses (Fan et al., 2006). From these studies, we
discovered antiestrogen resistance to be associated with autocrine-
induced proliferation likely due to dysregulated EGFR, ErbB2,
cytokines/cytokine receptors, Wnt/β-catenin, Notch and IFN-
signaling pathways. These previous discoveries in MCF7-FR cells
strongly support the value of this cell line as a model system for
studying antiestrogen resistance in human breast cancer. In the
present study, we combined global gene and miRNA expression
array data to further examine a potential role for miRNAs in the
onset of this devastating condition.
ER-positive MCF7 cells and their fulvestrant-resistant daughter MCF-FR
cells were cultured as previously described (Fan et al., 2006). For gene
expression studies, total RNAwas isolated using RNeasy Mini Kits (Qiagen,
Valencia, CA, USA), converted to cRNA, labeled and hybridized to
Affymetrix U133 Plus 2.0 arrays (Affymetrix, Santa Clara, CA, USA) by the
Indiana University Center for Medical Genomics. For microRNA isolation,
precipitation, RNA ligase-mediated labeling, and hybridized to our custom
array, according to our previously published method (Thomson et al., 2004).
Cell culture and RNA isolation
2.2Microarray analysis of miRNA and mRNA in
MCF7 and MCF7-FR cells
Genome-wide mRNA expression was assessed using Affymetrix Human
signal intensities from four replicates were used for data analysis, and genes
with signal density <300 pixels were excluded. Moderated t-statistics were
calculated by LIMMA(Bioconductor), with P<0.05 considered significant.
Up- or downregulated genes were, respectively, defined as those with at
least 2-fold increased or decreased signal intensity. A custom microarray
(Thomson et al., 2004) was used to determine miRNA expression, using
two replicates for each cell line. Clustering of miRNA expression data was
performed using CLUSTER (Eisen et al., 1998), with filtering to remove
inconsistencies between replicates. For clustering, we first log-transformed
the data and median-centered the array and genes, followed by average
linkage clustering. Clustering results were visualized by TREEVIEW
Student’s t-test was performed to evaluate the statistical significance of
the cluster selection.The results showed that out of 16 microRNAs identified
by our clustering methods and across all probes analyzed, 20 probes were
among the top 22 smallest P-values (P<0.05; Supplementary Table 1).
For comprehensive prediction of miRNA target genes, two publically
available algorithms (each using dissimilar methods of target identification)
were used, the recently described probability of interaction by target
accessibility (PITA) (Kertesz et al., 2007), and TargetScan, release
4.2 (www.targetscan.org) (Lewis et al., 2005). PITA was used on the
prediction results for all identified miRNAs in the human genome with
criteria ‘3/15 flank’ (downloaded from http://genie.weizmann.ac.il/pubs/
mir07/mir07_data.html). We further used a ??G<0 cutoff to filter the
prediction results. ??G scores are computed as the free energy gained by
MiRNA target analysis
microRNA to target binding (Kertesz et al., 2007). Only targets identified
by both TargetScan and PITA were considered to be the true targets for
each miRNA. As no target genes were identified for hsa-miR-373* (miR-
373* is not listed in those databases), TargetScan and PITAonline prediction
algorithms were used. Biological pathway analyses were performed using
Pathway-Express (Draghici et al., 2007).
3.1 Differentially expressed miRNAs in MCF7-FR
compared with MCF7 cells
To study the role of miRNA-mediated gene regulation in fulvestrant
resistance, we compared miRNA expression in MCF7-FR versus
MCF7 cells. We then analyzed the differential miRNA expression
profiles to identify three clusters of miRNAs: (i) a downregulated
group (14 miRNAs) (Fig. 1A); (ii) an upregulated group (two
miRNAs) (Fig. 1B); and (iii) an unchanged group (data not shown).
The potential target genes of these 14 MCF7-FR-downregulated
miRNAs were then identified based on overlap between TargetScan
and PITA. For these 14 downregulated miRNAs, we hypothesized
the mRNA levels of their target genes to be upregulated (Farh
et al., 2005; Lim et al., 2005). Based on the overlap between
TargetScan and PITA target prediction, we determined the number
of potential target genes upregulated (fold change >2, P<0.05, FR
versus MCF7) and not-upregulated. Out of a total of 19886 genes
on the microarray, 3297 total target genes were found upregulated
in MCF7-FR cells, 1895 were predicted to be regulated by any of
the 14 downregulated miRNAs. Of these 1895 genes, 587 were
test). The total numbers of genes predicted as targets for each of
the 14 downregulated miRNAs are listed in Table 1, accompanied
by the actual number of genes upregulated in MCF7-FR. By
comparing the number of miRNA-upregulated genes with the total
number of genes up- and not-upregulated, we discovered that
13 of the 14 miRNAs downregulated in MCF7-FR demonstrated
significantly higher ratios of upregulated genes than predicted
Fig. 1. Clustering of miRNA array results comparing MCF7-FR with
MCF7 cells identified two separate groups having (A) downregulated or
(B) upregulated expression in MCF7-FR cells. Grey signifies lower signal
intensity, while black denotes higher signal intensity.
F.Xin et al.
Table 1. Fisher’sexacttestofmiRNAtargetgenesupregulatedinMCF7-FR
miRNANumber of targets
upregulated in FR
Number of targets
‘Number of targets upregulated in MCF7-FR cells’refers to the number of target genes
upregulated in MCF7-FR cells. ‘Number of targets not-upregulated in FR cells’ refer
to the number of target genes not-upregulated in MCF7-FR cells.
∗P<0.05 (one-tailed Fisher’s exact test).
gene expression suggests that these miRNAs play a significant role
in gene regulation during the acquisition of fulvestrant resistance.
For the upregulated miRNA cluster, of 163 genes predicted as
targets of miR-221 or miR-222, 20 were found downregulated,
compared with 1950 (of the 19886 total) MCF7-FR-downregulated
genes. However, as the number of genes downregulated by miR-221
or miR-222 was not significant (P=0.17), these miRNAs may
preferentially mediate translational repression, rather than mRNA
degradation (Bagga et al., 2005).
3.2 Genes targeted by multiple downregulated
miRNAs are more likely to be upregulated than
genes targeted by a single miRNA
To further analyze the role of miRNAs in gene regulation in
fulvestrant-resistant cells, we examined whether gene-specific
regulation correlated to the number of miRNA target sites.
Specifically, we separated the target genes of the 14 downregulated
miRNAs into two groups: (i) those regulated by only one of these
14 miRNAs; or (ii) those regulated by multiple miRNAs. A one-
tailed Fisher’s exact test was then performed to determine whether
multiple miRNA regulation would further improve the Up/Not-up
upregulated gene ratio. The results showed that possible targeting
by two or more downregulated miRNAs resulted in a significantly
higher ratio of upregulated genes, as compared with targeting by
gene regulation by multiple miRNAs acting in collaboration.
3.3Genes possessing multiple downregulated miRNA
target sites are more likely to be upregulated than
those with only one site
To investigate the effect of multiple miRNA binding sites, we
compared the ratio of upregulated genes among genes having single
target sites (356 out of 1265 genes) to the ratio of upregulated genes
Table 2. Comparisons of multiple versus single miRNA regulation, and
genes having multiple versus single target sites
Single miRNA regulation
Multiple miRNAs regulation
Single targeting site
Multiple targeting sites
‘Up’means the actual number of genes upregulated in MCF7-FR cells. ‘Not-Up’is the
total number of predicted gene targets of the 14 miRNAs; ‘Ratio’ is the ratio between
actual/predicted upregulated genes (up/total).
∗P<0.05 (one-tailed Fisher’s exact test).
among that having multiple target sites (231 out of 630 genes). The
results (Table 2) show that multiple miRNA targeting sites resulted
only one target site. This result is consistent with our findings from
among multiple miRNAs in gene regulation.
3.4Pathway analysis predicts that downregulated and
upregulated miRNAs regulate specific biological
cascades in MCF7-FR cells
To assess the possible biological impact of the 14 downregulated
miRNAs in fulvestrant-resistant cells, we performed pathway
enrichment analysis of all upregulated genes (3297 genes) and
upregulated targets of the 14 miRNAs (587 genes). We then
compared the two pathway analysis profiles using Fisher’s exact
to be enriched in 13 of the 19 pathways significantly altered in
MCF7-FR cells, including well-described signaling pathways such
as TGF-β, Wnt, MAPK signaling and mTOR. Supplementary Table
3 lists the predicted target genes corresponding to the different
Differential upregulation of a group of two microRNAs, miR-
221/222, was observed in the MCF7-FR versus MCF7 cells.
Pathway analysis for the 20 target genes predicted to be
downregulated (see above) suggests that miR221/222 target the
ErbB signaling pathway. Our gene expression microarray analysis
supports this possibility, showing significantly decreased expression
of ErbB and another member of the ErbB pathway, the cell-cycle
inhibitor p27/Kip1, in MCF7-FR cells (Fan et al., 2006 and our
Recent studies have shown numerous miRNAs to be dysregulated in
of antiestrogen-resistant breast cancer.The involvement of miRNAs
in chemotherapy resistance has been recently suggested (Blower
et al., 2008; Meng et al., 2006; Salter et al., 2008; Xia et al.,
2008; Yang et al., 2008), and to our knowledge, this is the first
report of an association between miRNAs and acquired resistance
to fulvestrant, the second-line drug given to postmenopausal women
with ER-positive, tamoxifen-resistant tumors. Our results further
suggest that miRNA-regulated gene expression can be detected at
Computational analysis of miRNA profiles
the mRNA level, a finding consistent with previous reports (Farh
et al., 2005; Lim et al., 2005). However, two recent studies further
substantiate the impact of microRNAs at the level of translation
(Baek et al., 2008; Selbach et al., 2008), and although our method
does not account for miRNA regulation by translational repression,
it does provide a computational approach for using gene expression
microarray data to study miRNA regulation, a highly feasible and
straight-forward approach, given the abundance of such studies and
the relative paucity of corresponding proteomics data.
Using a custom microarray (Thomson et al., 2004), we identified
14 downregulated but only two upregulated miRNAs in MCF7-FR
cells, consistent with a previous study showing that miRNA tend to
various cancers (let-7i, miR-181a, 191, 199b, 204, 211, 212, 216,
328, 373*, 424), of which three (miR-204, 191 and let-7i) have been
specifically linked to breast cancer (Jiang et al., 2008). Our results
further suggest that these 14 miRNAs have potential relevance
to the acquisition of fulvestrant resistance, including three miRNAs
previously unreported in breast cancer (miR-346, 638 and 768-3p).
The identification of miRNAs that are differentially expressed in
fulvestrant-resistant cell lines could serve as potential biomarkers
of fulvestrant-resistant tumors. Moreover, determination of the
target genes/pathways of these dysregulated miRNAs will further
enhance our knowledge of fulvestrant resistance and facilitate
design of new targeted therapeutic agents that might allow for the
prevention or reversal of resistance to this agent. For example,
in antiestrogen-sensitive breast cancer may allow for a greater
response to fulvestrant therapy in a subset of breast cancers. In
addition, altered expression of these miRNAs themselves could be
predictive of drug resistance development and prove to be valuable
markers in the personalized clinical management of breast cancer.
Our pathway analyses suggest involvement of miRNA in
regulation of biologically important signaling cascades, including
TGF-β (Supplementary Table 2). Significant experimental evidence
suggests that loss of growth-inhibitory response to TGF-β is
associated with breast neoplasia (Benson, 2004; Muraoka-
Cook et al., 2005). Furthermore, WNT proteins are frequently
overexpressed in breast tumors (Lin et al., 2000), and dysregulation
of this pathway is likely to have a major impact on several aspects
of breast cancer biology (Li et al., 2003; Klarmann et al., 2008).As
we previously demonstrated that fulvestrant-resistant breast cancer
cells utilize multiple growth-stimulatory pathways to establish
hormone-independence and autocrine-regulated proliferation (Fan
et al., 2006), including a role for Wnt/β-catenin and EGFR/ErbB2
signaling pathways in estrogen-independent growth, the results of
the current study suggest that miRNAs may play a significant role
in this process.
In summary, we have used experimental microarray data
and computational approaches to strongly implicate specific
miRNAs in the development of fulvestrant resistance in breast
cancer. Further elucidation of the mechanisms underlying miRNA
regulation and function in breast cancer cells could provide
novel therapeutic strategies against this destructive impediment to
successful treatment of this common malignancy.
The authors thank Dr Seung Yoon Nam for helpful discussion. The
microarray studies were carried out using the facilities of the Center
for Medical Genomics at Indiana University School of Medicine.
The Center for Medical Genomics is supported in part by the
Indiana Genomics Initiative of Indiana University, INGEN, which
is supported in part by the Lilly Endowment, Inc.
CA113001); Walther Cancer Institute Foundation, Indianapolis, IN.
NationalInstituteof Health grants (CA085289,
Conflict of Interest: none declared.
Baek,D. et al. (2008) The impact of microRNAs on protein output. Nature, 455, 64–71.
Bagga,S. et al. (2005) Regulation by let-7 and lin-4 miRNAs results in target mRNA
degradation. Cell, 122, 553–563.
Benson,J.R. (2004) Role of transforming growth factor beta in breast carcinogenesis.
Lancet Oncol., 5, 229–239.
Blower,P.E. et al. (2008) MicroRNAs modulate the chemosensitivity of tumor cells.
Mol. Cancer Ther., 7, 1–9.
Calin,G.A. and Croce,C.M. (2006) MicroRNA-cancer connection: the beginning of a
new tale. Cancer Res., 66, 7390–7394.
Cimmino,A. et al. (2005) miR-15 and miR-16 induce apoptosis by targeting BCL2.
Proc. Natl Acad. Sci. USA, 102, 13944–13949.
Draghici,S. et al. (2007) A systems biology approach for pathway level analysis.
Genome Res., 17, 1537–1545.
Eis,P.S. et al. (2005) Accumulation of miR-155 and BIC RNA in human B cell
lymphomas. Proc. Natl Acad. Sci. USA, 102, 3627–3632.
Eisen,M.B. et al. (1998) Cluster analysis and display of genome-wide expression
patterns. Proc. Natl Acad. Sci. USA, 95, 14863–14868.
Fan,M. et al. (2003) The NEDD8 pathway is required for proteasome-mediated
degradation of human estrogen receptor (ER)-alpha and essential for the
antiproliferative activity of ICI 182,780 in ERalpha-positive breast cancer cells.
Mol. Endocrinol., 17, 356–365.
Fan,M. et al. (2006) Diverse gene expression and DNA methylation profiles correlate
with differential adaptation of breast cancer cells to the antiestrogens tamoxifen and
fulvestrant. Cancer Res., 66, 11954–11966.
Farh,K.K. et al. (2005) The widespread impact of mammalian microRNAs on mRNA
repression and evolution. Science, 310, 1817–1821.
Hayashita,Y. et al. (2005) A polycistronic microRNA cluster, miR-17-92, is
overexpressed in human lung cancers and enhances cell proliferation. Cancer Res.,
and therapy. Clin. Transl. Oncol., 9, 155–160.
Howell,A. (2000) Faslodex (ICI 182780). An oestrogen receptor downregulator.
Eur. J. Cancer, 36 (Suppl. 4), S87–S88.
Howell,A. (2001) Future use of selective estrogen receptor modulators and aromatase
inhibitors. Clin. Cancer Res., 7( Suppl. 12), 4402s-10s; discussion 4411s–4412s.
Howell,A. and Abram,P. (2005) Clinical development of fulvestrant (‘Faslodex’).
Cancer Treat. Rev., 31 (Suppl. 2), S3–S9.
Iorio,M.V. et al. (2005) MicroRNA gene expression deregulation in human breast
cancer. Cancer Res., 65, 7065–7070.
Jiang,Q. et al. (2008) miR2Disease: a manually curated database for microRNA
deregulation in human disease. Nucleic Acids Res., doi:10.1093/nar/gkn714.
Johnson,S.M. et al. (2005) RAS is regulated by the let-7 microRNA family. Cell, 120,
Kertesz,M. et al. (2007) The role of site accessibility in microRNA target recognition.
Nat. Genet., 39, 1278–1284.
Klarmann,G.J. et al. (2008) Epigenetic gene silencing in the Wnt pathway in breast
cancer. Epigenetics, 3, 59–63.
F.Xin et al. Download full-text
Lewis,B.P. et al. (2005) Conserved seed pairing, often flanked by adenosines, indicates
that thousands of human genes are microRNA targets. Cell, 120, 15–20.
Li,Y. et al. (2003) Evidence that transgenes encoding components of the Wnt signaling
pathway preferentially induce mammary cancers from progenitor cells. Proc. Natl
Acad. Sci. USA, 100, 15853–15858.
Lim,L.P. et al. (2005) Microarray analysis shows that some microRNAs downregulate
large numbers of target mRNAs. Nature, 433, 769–773.
Lin,S.Y. et al. (2000) Beta-catenin, a novel prognostic marker for breast cancer: its
roles in cyclin D1 expression and cancer progression. Proc. Natl Acad. Sci. USA,
Long,X. and Nephew,K.P. (2006) Fulvestrant (ICI 182,780)-dependent interacting
Chem., 281, 9607–9615.
Lu,J. et al. (2005) MicroRNAexpression profiles classify human cancers. Nature, 435,
Ma,L. et al. (2007) Tumour invasion and metastasis initiated by microRNA-10b in
breast cancer. Nature, 449, 682–688.
McDonnell,D.P. (2005) The molecular pharmacology of estrogen receptor modulators:
implications for the treatment of breast cancer. Clin. Cancer Res., 11, 871s–877s.
Meng,F. et al. (2006) Involvement of human micro-RNA in growth and response
to chemotherapy in human cholangiocarcinoma cell lines. Gastroenterology, 130,
Muraoka-Cook,R.S. et al. (2005) Dual role of transforming growth factor beta
in mammary tumorigenesis and metastatic progression. Clin. Cancer Res., 11,
Negrini,M. et al. (2007) MicroRNAs in human cancer: from research to therapy. J. Cell
Sci., 120, 1833–1840.
Salter,K.H. et al. (2008) An integrated approach to the prediction of chemotherapeutic
response in patients with breast cancer. PLoS ONE, 3, e1908.
Selbach,M. et al. (2008) Widespread changes in protein synthesis induced by
microRNAs. Nature, 455, 58–63.
Takamizawa,J. et al. (2004) Reduced expression of the let-7 microRNAs in human
lung cancers in association with shortened postoperative survival. Cancer Res., 64,
Thomson,J.M. et al. (2004) A custom microarray platform for analysis of microRNA
gene expression. Nat. Methods, 1, 47–53.
cancer gene targets. Proc. Natl Acad. Sci. USA, 103, 2257–2261.
Xia,L. et al. (2008) miR-15b and miR-16 modulate multidrug resistance by targeting
BCL2 in human gastric cancer cells. Int. J. Cancer, 123, 372–379.
Yang,H. et al. (2008) MicroRNA expression profiling in human ovarian cancer: miR-
214 induces cell survival and cisplatin resistance by targeting PTEN. Cancer Res.,