Human MutationRESEARCH ARTICLE

Common Genetic Variants in Pre-MicroRNAs WereAssociated With Increased Risk of Breast Cancerin Chinese Women

Zhibin Hu,1–3 Jie Liang,2 Zhanwei Wang,2 Tian Tian,2 Xiaoyi Zhou,2 Jiaping Chen,2,3 Ruifen Miao,2 Yan Wang,2

Xinru Wang,1 and Hongbing Shen1–3�

1Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China2Department of Epidemiology and Biostatistics, Nanjing Medical University, Nanjing, China3Cancer Center, Nanjing Medical University, Nanjing, China

Communicated by Stephen ChanockReceived 16 January 2008; accepted revised manuscript 6 May 2008.

Published online 16 July 2008 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/humu.20837

ABSTRACT: Small, noncoding RNA molecules, calledmicroRNAs (miRNAs), are thought to function as eithertumor suppressors or oncogenes. Common single-nucleotide polymorphisms (SNPs) in miRNAs maychange their property through altering miRNA expres-sion and/or maturation, and thus they may have an effecton thousands of target mRNAs, resulting in diversefunctional consequences. However, it remains largelyunknown whether miRNA SNPs may alter cancersusceptibility. We evaluated the associations of selectedfour SNPs (rs2910164, rs2292832, rs11614913, andrs3746444) in pre-miRNAs (hsa-mir-146a, hsa-mir-149,hsa-mir-196a2, and hsa-mir-499) with breast cancer riskin a case-control study of 1,009 breast cancer cases and1,093 cancer-free controls in a population of Chinesewomen and we found that hsa-mir-196a2rs11614913:T4C and hsa-mir-499 rs3746444:A4Gvariant genotypes were associated with significantlyincreased risks of breast cancer (odds ratio [OR], 1.23;95% confidence interval [CI], 1.02–1.48 forrs11614913:T4C; and OR, 1.25; 95% CI, 1.02–1.51for rs3746444:A4G in a dominant genetic model) in adose-effect manner (P for trend was 0.010 and 0.037,respectively). These findings suggest, for the first time,that common SNPs in miRNAs may contribute to breastcancer susceptibility. Further functional characterizationof miRNA SNPs and their influences on target mRNAsmay provide underlying mechanisms for the observedassociations and disease etiology.Hum Mutat 30, 79–84, 2009.& 2008 Wiley-Liss, Inc.

KEY WORDS: microRNA; polymorphism; breast can-cer; genetic susceptibility

Introduction

MicroRNAs (miRNAs) are an abundant class of smallnoncoding, single-stranded RNAs of 21 to 24 nucleotides thatform base-pairs with target mRNAs and negatively regulate theirtranslational efficiency and stability [Lee et al., 1993; Ruvkun,2001; Bartel, 2004]. In mammals, mature miRNAs exert theirregulatory effects mostly by binding to imperfect complementarysites within the 30 untranslated regions (UTRs) of their mRNAtargets and also through miRNA-directed mRNA cleavage [Limet al., 2005; Yekta et al., 2004]. Bioinformatic data indicated that asingle miRNA could bind to mRNA targets of as many as 200genes and that these targets can be diverse in functions [Kreket al., 2005; Saunders et al., 2007]. Therefore, miRNAs participatein the regulation of almost each of biological processes and areinvolved in the pathogenesis of human diseases, includingembryonic development, chromosome architecture, cell prolifera-tion, apoptosis, stress resistance, fat metabolism, and stem cellmaintenance [Zamore and Haley, 2005].

A strong link between altered miRNAs, either structurechanges or quantity of mature miRNA, and cancer risk hasbeen established, opening up a new avenue of investigation formolecular mechanisms of cancer development [Esquela-Kerscherand Slack, 2006]. Recent work has shown a global decrease ofmature miRNA expression in different kinds of human cancers[Lu et al., 2005] and that global repression of miRNA maturationpromotes cellular transformation and tumorigenesis [Kumaret al., 2007]. The loss- or gain-of-function of specificmiRNAs were also thought to be key events in the genesis ofdiverse cancers including breast cancer [Esquela-Kerscher andSlack, 2006]. For example, hsa-mir-10b was identified as aninitiator of breast cancer invasion and metastasis [Ma et al., 2007],and miRNA expression patterns were deregulated in human breastcancers [Iorio et al., 2005; Sempere et al., 2007]. Therefore,mutations, misexpression, or altered mature miRNA processingare likely to be pleiotropic and may contribute to breast cancersusceptibility.

Single-nucleotide polymorphisms (SNPs) or mutations maychange the property of miRNAs through altering miRNA

OFFICIAL JOURNAL

www.hgvs.org

& 2008 WILEY-LISS, INC.

The Supplementary Material referred to in this article can be accessed at http://

www.interscience.wiley.com/jpages/1059-7794/suppmat.

Contract grant sponsor: Innovative Key Grant of Department of Education; Grant

number: 705023; Contract grant sponsor: Program for Changjiang Scholars and

Innovative Research Team in University; Grant number: IRT0631; Contract grant

sponsor: National Key Basic Research Program Grants; Grant number:

2002CB512908.

�Correspondence to: Hongbing Shen, Professor, Department of Epidemiology and

Biostatistics, Nanjing Medical University, 140 Hanzhong Rd., Nanjing 210029, China.

E-mail: hbshen@njmu.edu.cn

expression and/or maturation [Duan et al., 2007]. miRNAsrepresent ideal candidates for cancer predisposition loci becausesmall variation in quantity may have an effect on thousands oftarget mRNAs and result in diverse functional consequences.However, the role of genetic variants in miRNAs in cancersusceptibility is largely unknown. Recently, one study describednaturally occurring polymorphisms in miRNAs by systematical insilico approaches [Saunders et al., 2007]. Because SNPs located inmature miRNA regions may directly affect both the binding totarget mRNAs and the pre-miRNA maturation process, whereasSNPs located in other regions of pre-miRNA may affect pre-miRNA maturation [Duan et al., 2007; Han et al., 2006], we choseto focus on common (minor allele frequency [MAF] 40.05) SNPsin pre-miRNAs and hypothesized that these SNPs were associatedwith breast cancer risk. To test this hypothesis, we genotyped fourselected SNPs (rs2910164, rs2292832, rs11614913, and rs3746444),which are located at the pre-miRNA regions of hsa-mir-146a, hsa-mir-149, hsa-mir-196a2, and hsa-mir-499, and we evaluated theirassociations with breast cancer risk in a case-control study ofChinese women, comprising 1,009 breast cancer cases and 1,093cancer-free controls.

Patients and Methods

Patients

The subjects included in this study were recruited to an ongoingbreast cancer study [Ma et al., 2006] that was approved by theInstitutional Review Board of Nanjing Medical University(Nanjing, China). In brief, we obtained the written informedconsent from all participants or, if a direct consent could not beobtained, from the patients’ representatives. All subjects weregenetically-unrelated ethnic Han Chinese and were from NanjingCity and surrounding regions in Jiangsu Province, China. Patientswith newly-diagnosed and histopathologically-confirmed breastcancer were consecutively recruited with an overall response rateof 91.1% (1,029/1,129) from the First Affiliated Hospital ofNanjing Medical University, the Cancer Hospital of JiangsuProvince, and the Gulou Hospital, Nanjing, China, betweenJanuary 2004 and April 2007. There were no significant differencesin terms of the age, stage, and metastasis of the disease betweenthe subjects who participated in the project and those who refused(data not shown). Exclusion criteria included reported previouscancer history, metastasized cancer from other organs, andprevious radiotherapy or chemotherapy. These cases included998 invasive, 28 ductal carcinoma in situ, and three lobularcarcinoma in situ breast cancers without restrictions of age orhistological type. Cancer-free control women, frequency-matchedto the cases on age (75 years) and residential area (urban orrural), were randomly selected from a cohort of more than 30,000participants in a community-based screening program fornoninfectious diseases conducted in the same geographical region.Each woman was interviewed face-to-face by trained interviewersusing a pretested standard questionnaire to obtain information ondemographic data, menstrual and reproductive history, environ-mental exposure history, and family history of cancer in first-degree relatives (parents, siblings, and children). Those who had ahistory of physician-diagnosed diseases such as cancer, heartdiseases, and diabetes were excluded. After the interview, eachsubject provided 5 ml of venous blood. The estrogen receptor (ER)and progesterone receptor (PR) status of breast cancers wasdetermined by immunohistochemistry examinations available inthe medical records of the hospitals.

SNPs Selection and Genotyping

For Chinese, there were a total of five common SNPs (three inmature miRNA regions: hsa-mir-146a rs2910164:C4G, hsa-mir-196-a2 rs11614913:T4C, and hsa-mir-499 rs3746444:A4G; andtwo in other regions of pre-miRNA: hsa-mir-149 rs2292832:G4Tand hsa-mir-423 rs6505162:A4C) associated with 400 knownhuman pre-miRNAs listed in the public database miRBase(Supplementary Table S1; available online at http://www.interscien-ce.wiley.com/jpages/1059-7794/suppmat) [Griffiths-Jones, 2004].We genotyped 4 of these 5 SNPs (rs2910164, rs11614913,rs3746444, and rs2292832) that may affect both the binding oftarget mRNA and the pre-miRNA maturation process by using thePCR–restriction fragment length polymorphism (RFLP) assay(Supplementary Table S2). The remaining SNP (hsa-mir-423rs6505162:C4A) that did not affect hydrogen bands orpredict secondary structure free energy was not included in thisstudy. To control for the quality of genotyping, the RFLP assaywas performed without knowing the subjects’ case and controlstatus, and the same number of cases and controls wereassayed in each 96-well PCR plate with a positive control of aDNA sample with known heterozygous genotype. Two researchassistants independently examined the gel pictures and performedthe repeated assays if they did not reach a consensus on thetested genotype. In addition, 10% of the samples were randomlyselected to for repeat RFLP assays for all four of the SNPs, and theresults were 100% concordant. PCR products of the SNPs withdifferent genotypes were selected and confirmed by directsequencing using an automated sequencer, and the results of hsa-mir-196a2 rs11614913:T4C and hsa-mir-499 rs3746444:A4Gare shown in Supplementary Fig. S1. Because DNA quality orquantity was insufficient for genotyping in 20 breast cancer casesand 14 controls, the final analyses included 1,009 cases and 1,093controls.

Statistical Analysis

Differences in demographic variables, selected variables, andfrequencies of the genotypes between the cases and controls wereevaluated by using the Student’s t-test for continuous variables orthe w2 test for categorical variables. The associations between pre-miRNA SNPs and breast cancer risk were estimated by computingthe odds ratios (ORs) and 95% confidence intervals (CIs) fromboth univariate and multivariate logistic regression analyses. Thepotential gene-environment interaction was also evaluated bylogistic regression analysis and tested by comparing the changes indeviance (�2 log likelihood) between the models of main effects,with or without the interaction term. All the statistical analyseswere performed with the Statistical Analysis System (SAS)software (v.9.1.3; SAS Institute, Cary, NC).

Results

Baseline characteristics of the cases and controls are shown inTable 1. There were no significant differences in the agedistribution between the cases and controls, suggesting that ourfrequency-matching on age was satisfactory. Early age at menarchewas a risk factor for breast cancer, as expected. Of the 1,009 cases,we obtained information about ER and PR status for 767 casesand axillary lymph node metastasis status for 856 cases from theirmedical records (Table 1). The cutoff level used to define positiveER/PR status was more than 10% positive staining of theimmunohistochemical assay.

80 HUMAN MUTATION, Vol. 30, No. 1, 79–84, 2009

Genotype distributions of the four SNPs in our controlswas in agreement with that expected under the Hardy-Weinbergequilibrium (P 5 0.221 for hsa-mir-146a rs2910164:C4G, 0.160for hsa-mir-149 rs2292832:G4T, 0.207 for hsa-mir-196a2rs11614913:T4C, and 0.057 for hsa-mir-499 rs3746444:A4G).As shown in Table 2, significantly increased breast cancerrisks were found to be associated with variant genotypesof hsa-mir-196a2 rs11614913 (CC/CT) and hsa-mir-499rs3746444 (GG/AG) in a dose-effect manner (P for trend 5 0.010,0.037, respectively). Subjects carrying variant homozygousgenotypes of hsa-mir-196a2 rs11614913:T4C and hsa-mir-499rs3746444:A4G had significantly increased risks of breastcancer (OR, 1.37; 95% CI, 1.08–1.74 for rs11614913 CC;and OR, 1.75; 95% CI, 1.07–2.85 for rs3746444 GG, respectively)compared with their wild-type homozygotes. In addition, therisks were also significantly evident in the dominant geneticmodels for these two SNPs (OR, 1.23; 95% CI, 1.02–1.48 forrs11614913:T4C; and OR, 1.25; 95% CI, 1.02–1.51 forrs3746444:A4G).

In the stratified analysis with selected variables, we found thatthe increased risk associated with rs11614913 variant genotypes(CC/CT) was more pronounced in: younger subjects (OR, 1.32;95% CI, 1.03–1.70); those with an early age at menarche (OR,1.30; 95% CI, 1.04–1.63) and premenopause (OR, 1.33; 95% CI,1.01–1.75); subjects with positive ER (OR, 1.42; 95% CI,1.10–1.83) and PR (OR, 1.39; 95% CI, 1.08–1.80) status; and inpatients without axillary lymph node metastasis (OR, 1.32; 95%CI, 1.05–1.67), whereas the increased risk associated withrs3746444 variant genotypes (GG/AG) was more notable in:younger subjects (OR, 1.34; 95% CI, 1.04–1.73); those with a lateage at menarche (OR, 1.61; 95% CI, 1.13–2.27) and premenopause(OR, 1.51; 95% CI, 1.14–2.00); and in patients without axillarylymph node metastasis (OR, 1.27; 95% CI, 1.01–1.61) (Table 3).However, no evidence of gene-environment interaction was found(Table 3).

Discussion

In this case-control study of breast cancer in Chinese women,we found that variant genotypes of hsa-mir-196a2rs11614913:T4C and hsa-mir-499 rs3746444:A4G were asso-ciated with significantly increased risks of breast cancer. The studyprovides the first evidence that common SNPs in miRNAs may beused as candidate biomarkers for breast cancer susceptibility.

miRNAs are an evolutionarily-conserved and abundant class ofsmall silencing RNAs [Lee et al., 1993; Ruvkun, 2001; Bartel,2004], and some of them are expressed in limited developmentalstages or in specific tissues or cells, which suggests that they maybe involved in cell differentiation and maintenance of theproperties of different cells [Esquela-Kerscher and Slack, 2006;Lu et al., 2005]. A broad survey on miRNA expression in differentcell types provides the first support for our findings of theseassociations [Landgraf et al., 2007]: hsa-mir-146a rs2910164:C4G,hsa-mir-196a2 rs11614913:T4C, and hsa-mir-499 rs3746444:A4Gare all located in their corresponding 3p mature miRNA regions,and they may influence both the binding of target mRNAs to 3pmature miRNAs and pre-miRNA maturation of 5p and 3pmiRNAs. From the survey by Landgraf et al. [2007], 5p hsa-mir-196a2 was widely expressed in breast cancer cell lines and 3p hsa-mir-196a2 was also detectable in the MCF-7 breast adenocarcino-ma cell line. In addition, 3p hsa-mir-499 (rs3746444:A4G) wasdetected in an invasive breast cancer cell line [Landgraf et al.,2007]. Therefore, it is biologically plausible that functionalpolymorphisms in the 3p mature regions of hsa-mir-196a2 andhsa-mir-499 may play a role in the development of breast cancer.However, hsa-mir-146a (rs2910164:C4G) was not detected in allof the six breast cancer cell lines examined [Landgraf et al., 2007],suggesting that rs2910164:C4G may not play a role in thedevelopment of breast cancer. These are consistent with the resultsof our current study.

It has been shown that miRNA expression patterns may assist inthe deferential diagnosis of human breast cancer [Iorio et al., 2005;

Table 1. Distribution of Selected Variables in Breast CancerCases and Controls

Variables

Cases

N 5 1,009

(%)

Controls

N 5 1,093

(%) P value

Age, years (mean7SD) 51.60711.08 51.77711.19 0.729

Age

452 596 (59.1) 624 (57.1) 0.359

r52 413 (40.9) 469 (42.9)

Age at menarche, years

(mean7SD)

15.3071.88 15.9971.92 o0.0001

Menopausal status 0.755

Premenopausal 482 (47.8) 513 (46.9)

Postmenopausal 527 (52.2) 580 (53.1)

Estrogen receptor (ER)a

Positive 424 (55.3)

Negative 343 (44.7)

Progesterone receptor (PR)a

Positive 414 (54.0)

Negative 353 (46.0)

Axillary lymph node metastasisb

Positive 313 (36.6)

Negative 543 (63.4)

aInformation on both ER and PR was available in 767 breast cancer cases.bInformation on axillary lymph node metastasis status was available in 856 breast

cancer cases.

Table 2. Main Effects of Pre-miRNA SNPs on Breast CancerRisk

Genotypes

Cases

N 5 1,009

(%)

Controls

N 5 1,093

(%) OR (95%CI)a P Pb

hsa-mir-146a rs2910164:C4G

CC 329 (33) 362 (33) 1.00 (reference)

CG 515 (51) 551 (50) 1.05 (0.86–1.28)

GG 165 (17) 180 (17) 1.02 (0.78–1.33)

hsa-mir-149 rs2292832:G4T

GG 450 (45) 482 (44) 1.00 (reference)

GT 460 (46) 503 (46) 0.98 (0.82–1.18)

TT 99 (10) 108 (10) 0.97 (0.71–1.31)

hsa-mir-196a2 rs11614913:T4C

TT 287 (28) 358 (33) 1.00 (reference)

CT 483 (48) 517 (47) 1.17 (0.96–1.42)

CC 239 (24) 218 (20) 1.37 (1.08–1.74) 0.011 0.010

CC/CT 722 (72) 735 (67) 1.23 (1.02–1.48) 0.032

hsa-mir-499 rs3746444:A4G

AA 707 (70) 816 (75) 1.00 (reference)

AG 258 (26) 248 (23) 1.19 (0.97–1.46)

GG 44 (4) 29 (3) 1.75 (1.07–2.85) 0.025 0.037

GG/AG 302 (30) 277 (26) 1.25 (1.02–1.51) 0.028

aAdjusted by age, age at menarche, and menopausal status.bP for trend.

HUMAN MUTATION, Vol. 30, No. 1, 79–84, 2009 81

Table 3. Stratified Effects of rs11614913:T4C and rs3746444:A4G on Breast Cancer Risk

hsa-mir-196a2 rs11614913:T4C hsa-mir-499 rs3746444:A4G

Selected variables OR (95%CI)a P for interaction OR (95%CI)a P for interaction

Age (years)

r52 1.32 (1.03–1.70) 1.34 (1.04–1.73)

452 1.07 (0.80–1.43) 0.271 1.13 (0.84–1.53) 0.385

Age at menarche (years)

r16 1.30 (1.04–1.63) 1.10 (0.87–1.39)

416 1.06 (0.76–1.49) 0.374 1.61 (1.13–2.27) 0.079

Menopausal status

Premenopausal 1.33 (1.01–1.75) 1.51 (1.14–2.00)

Postmenopausal 1.09 (0.84–1.42) 0.296 1.04 (0.80–1.37) 0.061

Estrogen receptor (ER)

Positive 1.42 (1.10–1.83) 1.20 (0.93–1.55)

Negative 1.19 (0.91–1.55) 1.26 (0.96–1.65)

Progesterone receptor (PR)

Positive 1.39 (1.08–1.80) 1.24 (0.96–1.60)

Negative 1.20 (0.92–1.57) 1.19 (0.91–1.56)

Axillary lymph node metastasis

Positive 1.12 (0.85–1.48) 1.09 (0.82–1.46)

Negative 1.32 (1.05–1.67) 1.27 (1.01–1.61)

aAdjusted by age, age at menarche, and menopausal status.

Table 4. Predicted Most Probable Targets of hsa-mir-196a-2 and hsa-mir-499

MiRNAs Genesa Full name or alternative titles P value�

hsa-miR-146a-3p MST1R Macrophage-stimulating protein receptor precursor 1.90E-4

790b MNAT1 CDK-activating kinase assembly factor MAT1 7.31E-4

MUC1 Mucin-1 precursor 2.29E-3

IL6 Interleukin-6 precursor 2.33E-3

ATG7 Autophagy-related protein 7 2.42E-3

hsa-miR-146a-5p IRAK1 Interleukin-1 receptor-associated kinase 1 3.28E-07

992b GTF2H4 TFIIH basal transcription factor complex p52 subunit 7.38E-06

CETN2 Centrin-2 1.09E-05

CYP11A1 Cytochrome P450 11A1, mitochondrial precursor 1.78E-05

TRAF6 TNF receptor-associated factor 6 3.00E-05

hsa-miR-196a-3p LSP1c Lymphocyte-specific protein 1 2.73E-03

724b KIF20A Kinesin-like protein KIF20A 2.89E-03

PSMD10 26S proteasome non-ATPase regulatory subunit 10 2.92E-03

BAG3 BAG family molecular chaperone regulator 3 3.14E-03

ALOX15 Arachidonate 15-lipoxygenase 3.25E-03

hsa-miR-196a-5p HOXB8d Homeobox protein Hox-B8 1.02E-09

1075b MYC Myc proto-oncogene protein 2.04E-08

PFKFB1 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 1.00E-06

TOX3c TOX high mobility group box family member 3/TNRC9 2.14E-05

NME4 Nucleoside diphosphate kinase, mitochondrial precursor 3.40E-05

hsa-miR-499-3p GPR1 Probable G-protein coupled receptor 1 4.37E-07

1015b FAT Cadherin-related tumor suppressor homolog precursor 1.58E-05

NBN Nijmegen breakage syndrome protein 1 4.72E-05

IL1RL1 Interleukin-1 receptor-like 1 precursor 7.58E-05

BCL2L14 Apoptosis regulator Bcl- G 7.81E-05

hsa-miR-499-5p GPR1 Probable G-protein coupled receptor 1 4.37E-07

999b LECT1 Chondromodulin-1 precursor 3.79E-06

KCNN3 Small conductance calcium-activated potassium channel protein 3 8.00E-06

PSIP1 Lens epithelium-derived growth factor 1.06E-05

RBP2 Retinol-binding protein II 3.37E-05

aFor each selected miRNA, we list 10 transcripts with potential roles in cancers and the lowest P values.bAll target hits with P value o0.05 for Homo sapiens and the selected miRNAs.cThe two novel gene loci identified recently in large-scale whole-genome association studies on breast cancer susceptibility [Easton et al., 2007; Stacey et al., 2007].dPredicted targets of HOXC8, HOXB7, HOXD8, and HOXA5 in the HOX gene family were also experimentally verified [Yekta et al., 2004].�The best P-value of the selected miRNA for the transcript.

82 HUMAN MUTATION, Vol. 30, No. 1, 79–84, 2009

Sempere et al., 2007]. For example, hsa-miR-196a2 was highlyexpressed in breast cancer compared with their normal tissues [Iorioet al., 2005]. To identify cancer-related mRNA targets for hsa-miR-196a2, hsa-mir-499, and hsa-mir-146a (3p and 5p were separated),we searched in the miRBase database [Griffiths-Jones et al., 2006]and selected five transcripts with potential roles in cancers with thelowest P values for each of the miRNAs. As shown in Table 4, thetranscripts of the genes participating in diverse cancer-relatedpathways could be targeted by hsa-miR-196a, hsa-mir-499, and hsa-mir-146a; and a few of these transcripts have been experimentallyverified [Yekta et al., 2004]. Interestingly, studies have shown that thecleavage of mRNAs of HOX gene clusters was partly hsa-miR-196a–directed [Yekta et al., 2004] and HOXD10 was recentlyidentified as a gene target for the initiation of breast cancer invasionand metastasis [Ma et al., 2007]. Furthermore, LSP1 and TOX3(TNRC9) mRNAs were targeted by hsa-miR-196a-3p and hsa-miR-196a-5p, respectively, and these two genes were also identified asnovel breast cancer susceptibility markers in large-scale whole-genome association studies [Easton et al., 2007; Stacey et al., 2007].The roles of LSP1 and TOX3 in breast cancer development remain tobe investigated, although the expression of TOX3 was implicated inbreast cancer metastasis [Smid et al., 2006]. However, it is asignificant finding that rs11614913:T4C, a genetic variant locatedin hsa-mir-196a2’s 3p mature miRNA regions, was associated withincreased risk of breast cancer.

As TOX3 and HOXD10 were implicated in breast cancermetastasis [Ma et al., 2007; Smid et al., 2006] and genetic variantsin TOX3 confer more notable susceptibility to ER/PR–positivebreast cancer [Stacey et al., 2007], the results of our stratifiedassociation analyses of ER/PR by axillary lymph node metastasisstatus related to hsa-mir-196a2 rs11614913:T4C are biologicallyplausible. However, the lack of significant interactions suggestedthat the sample size of our study may be insufficient. Althoughonly four SNPs were included in our analyses, the P value for themain effect of hsa-mir-499 rs3746444 was not significant afterBonferroni corrections (threshold P 5 0.0125). Therefore, replica-tion studies of this finding with diverse ethnic groups andfunctional characterizations of the pre-miRNA SNPs and theirtarget mRNAs in breast cancer are warranted.

In summary, we identified two SNPs in hsa-miR-196a2 and hsa-mir-499 (rs11614913:T4C and rs3746444:A4G), which serve asformerly unknown structural requirements for the processing of 5pmiRNA and/or target binding to 3p miRNAs and may affect breastcancer risk. The estimated total number of human miRNAs rangesfrom over 1,000 to tens of thousands [Miranda et al., 2006], andresearchers are just beginning to understand miRNA expressionpatterns and functions in normal or malignant human cells.Therefore, further characterization of SNPs of miRNAs wouldimprove our understanding of miRNA biogenesis and the potentialcontribution of these SNPs to cancer etiology and progression.

Acknowledgments

We thank our collaborators in the Department of General Surgery, Jiangsu

Cancer Hospital (Jinhai Tang, Jianwei Qin); the Department of General

Surgery (Shui Wang), The First Affiliated Hospital of Nanjing Medical

University; and the Department of General Surgery, Nanjing Gulou

Hospital (Xuechen Wang). We also thank Qingyi Wei of The University of

Texas M.D. Anderson Cancer Center for his scientific editing.

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