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ORIGINAL PAPER
The influence of genetic polymorphisms in MDR1 gene onbreast cancer risk factors in Chinese
Yunhe Jia • Wenjing Tian • Shuai Sun •
Peng Han • Weinan Xue • Mingqi Li •
Yanlong Liu • Shixiong Jiang • Binbin Cui
Received: 16 April 2013 / Accepted: 3 May 2013 / Published online: 21 May 2013
� Springer Science+Business Media New York 2013
Abstract Breast cancer (BC) is the most common cancer
among women in the world. The human multidrug resis-
tance 1 gene (MDR1) is potentially an important gene
influencing the susceptibility to breast cancer. This study
aimed to assess the association of MDR1 genetic poly-
morphisms with the susceptibility to BC. Overall, 353 BC
patients and 360 cancer-free controls were enrolled. The
clinical characteristics were summarized by questionnaires.
The c.1564A [ T genetic polymorphism was genotyped
using created restriction site–polymerase chain reaction
method. We found that no significant differences in the
genotypic and allelic frequencies between BC patients and
cancer-free controls. Furthermore, the distribution of BC
patients’ risk factors was not significantly different among
AA, AT, and TT genotypes. Our findings indicate that the
c.1564A [ T genetic polymorphism is not significantly
associated with the susceptibility to BC in Chinese Han
populations.
Keywords Breast cancer � MDR1 gene �Single-nucleotide polymorphisms � Risk factors �Cancer susceptibility
Introduction
Breast cancer (BC) remains the most common cancer and
the leading cause highly cancer-related deaths among
women in the world [1–5]. Over the recent decades, the
incidence and mortality of BC have arosed rapidly world-
wide [5, 6]. Nowadays, several substantial progress has
been made in the treatment and diagnosis of BC [7]. An
effort has been made to detect genetic factors and risk
factors that contribute to the risk of BC development [7–
10]. There were many studies indicated that the genetic
factors have significant function in the pathogenesis of BC
[4–14]. The human multidrug resistance 1 gene (MDR1) is
potentially an important gene influencing BC susceptibil-
ity, which encodes P-glycoprotein (Pgp), a transmembrane
efflux transporter conferring resistance to natural cytotoxic
drugs and potentially toxic xenobiotics [15–17]. It has been
indicated that the MDR1 genetic polymorphisms contribute
to affect the expression and function of Pgp, thus influ-
encing various diseases susceptibility such as BC [7, 11,
13, 14, 18, 19]. Up to date, many genetic polymorphisms of
MDR1 gene have been reported, and one of these genetic
polymorphisms, the C3435T genetic polymorphism, was
found to alter Pgp function and decrease tissue protein
expression and activity [20, 21]. The potential association
between the risk factors of BC and C3435T or other genetic
polymorphisms have been investigated [7, 11, 13, 14, 19,
22, 23]. However, there are no related studies which have
reported the association between BC risk factors and
c.1564A [ T genetic polymorphism. Therefore, our study
Yunhe Jia and Wenjing Tian contributed equally to this paper.
Y. Jia (&) � S. Sun � P. Han � W. Xue � M. Li � Y. Liu �S. Jiang � B. Cui (&)
The Colorectal Cancer Center, Tumor Hospital, Harbin Medical
University, No. 150 Haping Road, Nangang District, Harbin
150086, Heilongjiang Province, People’s Republic of China
e-mail: [email protected]
B. Cui
e-mail: [email protected]
W. Tian
Department of Epidemiology, Public Health College, Harbin
Medical University, Harbin 150086, Heilongjiang Province,
People’s Republic of China
123
Med Oncol (2013) 30:601
DOI 10.1007/s12032-013-0601-0
focused on detecting the distribution of this genetic
polymorphism and determining the influence on BC
susceptibility.
Materials and methods
Subjects
In total, 713 subjects were enrolled in this study, consisting
of 353 patients with primary BC and 360 healthy age-
matched women who had no history of any breast diseases
as the control group. There was no significant difference
regarding age and gender between case and control groups
(P [ 0.05). All subjects were unrelated Chinese Han
population. According to the previous published studies [7,
13, 24–26], the patients’ general characteristics and related
risk factors, such as age, age of first pregnancy, age of
menarche, oral contraceptives consumption, number of
pregnancies, number of abortions, duration of education,
smoking status, body mass index (BMI), cancer stage,
tumor size, node involvement, number of involved nodes,
and family history of BC were collected from question-
naires. The ethics committee of Tumor Hospital of Harbin
Medical University approved this study, and the written
informed consent was obtained from each subject.
DNA extraction and genotyping
Genomic DNA was isolated form peripheral venous blood
samples using the standard extraction method and then
stored at -80 �C until analyzed [27]. The polymerase
chain reaction (PCR) primers were designed using Primer
Premier 5.0 software. Table 1 performed the information
of primers sequences, annealing temperature, fragment
region, and size. The PCR reactions were carried out in
20 lL solution containing 50 ng template DNA, 19buffer
(Tris–HCl 100 mmol/L, pH 8.3; KCl 500 mmol/L),
0.25 lmol/L primers, 2.0 mmol/L MgCl2, 0.25 mmol/L
dNTPs, and 0.5 U Taq DNA polymerase (Promega, Mad-
ison, WI, USA). The PCR protocol was as followed: 94 �C
for 5 min, followed by 32 cycles of 94 �C for 35 s, 57.2 �C
for 35 s and 72 �C for 35 s, and a final extension at 72 �C
for 8 min. The c.1564A [ T genetic polymorphism was
genotyped using created restriction site-PCR (CRS-PCR)
method with one of the primers including a nucleotide
mismatch, which enables the use of selected restriction
enzymes for discriminating sequence variations [28–32].
Aliquots of 5 lL PCR products were digested for 10 h at
37 �C with 2 U MaeIII restriction enzyme (MBI Fermen-
tas, St. Leon-Rot, Germany). The digested products were
analyzed by electrophoresis on a 2 % agarose gel and
observed under ultraviolet light. To ensure concordance
with the CRS-PCR genotyping results, we selected about
10 % of random samples to verify using DNA sequencing
method (ABI3730xl DNA Analyzer, Applied Biosystems,
Foster City, CA).
Statistical analysis
The chi-squared (v2) test evaluated the Hardy–Weinberg
equilibrium (HWE) in allelic/genotypic distributions and
clinical characteristics. All statistical analyses were ana-
lyzed using the Statistical Package for Social Sciences
Table 1 The primer sequences, PCR, and CRS-PCR analysis for c.1564A [ T genetic polymorphism in MDR1 gene
Primer sequences Annealing
temperature (�C)
Amplification
fragment (bp)
Region Restriction
enzyme
Genotype (bp)
50-GGTTTTCTGTGGTAGAAATTTGTC-3 57.2 212 Exon15 MaeIII AA: 191, 21
50-GTTGGTTTGAACTAAGCCTCAC-30 AT: 212, 191, 21
TT: 212
PCR polymerase chain reaction
CRS-PCR created restriction site–polymerase chain reaction
Underlined nucleotides mark nucleotide mismatches enabling the use of the selected restriction enzymes for discriminating sequence variations
Table 2 The genotypic and allelic frequencies of MDR1 c.1564A [ T genetic polymorphism in the studied subjects
Groups Genotypic frequencies (%) Allelic frequencies (%) v2 P
AA AT TT A T
Case group (n = 353) 170 (48.16) 140 (39.66) 43 (12.18) 480 (67.99) 226 (32.01) 2.7876 0.2481
Control group (n = 360) 196 (54.44) 131 (36.39) 33 (9.17) 523 (72.64) 197 (27.36) 2.5734 0.2762
Total (n = 713) 366 (51.33) 271 (38.01) 76 (10.66) 1,003 (70.34) 423 (29.66) 5.6666 0.0588
v2 = 3.3933, P = 0.1833 v2 = 3.694, P = 0.0546
Page 2 of 5 Med Oncol (2013) 30:601
123
Table 3 The association between c.1564A [ T genetic polymorphism in MDR1 gene and risk factors for breast cancer in patients
Risk factors Genotype Frequency (%) Total P value v2
AA AT TT
Age (M = 0) \35 62 (47.33) 51 (38.93) 18 (13.74) 131 0.7892 0.4734
C35 108 (48.65) 89 (40.09) 25 (11.26) 222
Total 170 (48.16) 140 (39.66) 43 (12.18) 353
Stage (M = 27) Earlya 102 (50.75) 80 (39.80) 19 (9.45) 201 0.3874 1.8968
Advancedb 59 (47.20) 48 (38.40) 18 (14.40) 125
Total 161 (49.39) 128 (39.26) 37 (11.35) 326
Tumor size (M = 8) T0, T1, T2 110 (50.46) 89 (40.82) 19 (8.72) 218 0.3919 1.8737
T3, T4 61 (48.03) 49 (38.58) 17 (13.39) 127
Total 171 (49.57) 138 (40.00) 36 (10.43) 345
Node involvement (M = 11) Yes 101 (48.10) 85 (40.48) 24 (11.42) 210 0.7492 0.5776
No 68 (51.52) 48 (36.36) 16 (12.12) 132
Total 169 (49.42) 133 (38.88) 40 (11.70) 342
Number of involved nodes (M = 9) N0, N1 97 (50.00) 78 (40.21) 19 (9.79) 194 0.5104 1.3453
N2, N3 68 (45.34) 62 (41.33) 20 (13.33) 150
Total 165 (47.96) 140 (40.70) 39 (11.34) 344
Family history of breast cancer (M = 16) Yes 60 (56.08) 35 (32.71) 12 (11.21) 107 0.2351 2.8956
No 108 (46.96) 97 (42.17) 25 (10.87) 230
Total 168 (49.85) 132 (39.17) 37 (10.98) 337
Duration of education (M = 11) \9 years 58 (45.31) 57 (44.53) 13 (10.16) 128 0.5238 1.2934
C9 years 107 (50.00) 82 (38.32) 25 (11.68) 214
Total 165 (48.25) 139 (40.64) 38 (11.11) 342
Smoking (M = 0) Yes 55 (44.00) 52 (41.60) 18 (14.40) 125 0.4359 1.6607
No 115 (50.44) 88 (38.60) 25 (10.96) 228
Total 170 (48.16) 140 (39.66) 43 (12.18) 353
Age of menarche (M = 5) \14 81 (47.93) 69 (40.83) 19 (11.24) 169 0.9720 0.0568
C14 88 (49.16) 71 (39.66) 20 (11.18) 179
Total 169 (48.56) 140 (40.23) 39 (11.21) 348
Age of first pregnancy (M = 30) \25 90 (48.92) 76 (41.30) 18 (9.78) 184 0.6652 0.8153
C25 62 (44.60) 60 (43.17) 17 (12.23) 139
Total 152 (47.05) 136 (42.11) 35 (10.84) 323
Number of pregnancies (M = 24) B1 113 (50.22) 91 (40.44) 21 (9.34) 225 0.0610 5.5937
[1 50 (48.08) 35 (33.65) 19 (18.27) 104
Total 163 (49.54) 126 (38.30) 40 (12.16) 329
Number of abortions (M = 24) \1 121 (50.42) 98 (40.83) 21 (8.75) 240 0.0563 5.7527
C1 38 (42.70) 35 (39.33) 16 (17.97) 89
Total 159 (48.32) 133 (40.43) 37 (11.25) 329
Oral contraceptives consumption (M = 10) Yes 62 (53.45) 43 (37.07) 11 (9.48) 116 0.5411 1.2282
No 107 (47.14) 95 (41.85) 25 (11.01) 227
Total 169 (49.27) 138 (40.23) 36 (10.50) 343
Body mass index (M = 0) \22 kg/m2 91 (48.15) 78 (41.27) 20 (10.58) 189 0.5712 1.1200
C22 kg/m2 79 (48.17) 62 (37.80) 23 (14.03) 164
170 (48.16) 140 (39.66) 43 (12.18) 353
M missing dataa Early stages include I, IIA, and IIBb Advanced stages include IIIA, IIIB, IIIC, and IV
Med Oncol (2013) 30:601 Page 3 of 5
123
software (SPSS, Windows version release 15.0; SPSS Inc.;
Chicago, IL, USA). A P value of 0.05 was considered as
statistically significant level.
Results
MDR1 genetic polymorphisms identification
In this study, we detected a genetic polymorphism
(c.1564A [ T) within exon15 of MDR1 gene through the
CRS-PCR method. The sequence analyses indicated that
this genetic polymorphism was a non-synonymous muta-
tion in exon15 of MDR1 gene, which caused by A ? T
mutation and resulted in threonine (Thr) to serine (Ser)
amino acid replacement (p.Thr522Ser). The PCR products
were digested with MaeIII restriction enzyme (MBI Fer-
mentas, St. Leon-Rot, Germany) and divided into three
genotypes: AA (191 and 21 bp), AT (212, 191, and 21 bp),
and TT (212 bp, Table 1).
Allelic and genotypic frequencies
The allelic and genotypic frequencies were showed in
Table 2. The allele-A and genotype AA frequencies were
maximums in the cases and controls. The allele-A fre-
quencies in BC subjects and healthy controls were 67.99
and 72.6 %, and for allele T, frequencies were 32.01 and
27.36 %. The frequencies of genotype AA, AT, and TT in
BC patients were 48.16, 39.66, and 12.18 %, while these
genotypes frequencies of healthy subjects were 54.44,
36.39, and 9.17 %, respectively. The results of chi-square
test (v2) indicated that the distributions of this genetic
polymorphism were accordance with HWE in the studied
populations (P [ 0.05, Table 2).
Association between the MDR1 genetic polymorphism
and breast cancer
The potential association between the c.1564A [ T genetic
polymorphism and risk factors of BC in patients were
performed on Table 3. Our data indicated no statistically
significant association between this genetic polymorphism
and risk factors of BC in the current study (P [ 0.05).
Discussion
BC is the most polygenic malignant solid cancers in
women and is increasing in both developed and developing
countries [1–5]. It is caused from complex interactions
between genetic factors and environmental factors, and
genotypic variation plays key functions in human
phenotypic variability of cancer susceptibility [14]. The
potential association have been analyzed with several
MDR1 genetic polymorphisms and risk factors of BC. Most
of these studies were focused on the C3435T genetic
polymorphism [7, 11, 14, 19, 22, 23]. The C3435T genetic
polymorphism is located on exon26 of MDR1 gene, and it
has been founded to alter Pgp function and decreased tissue
protein expression and activity [20, 21]. Tatari et al. [7]
observed that the C3435T genetic polymorphism was not
associated with the susceptibility to BC in Iranian popu-
lation. Turgut et al. [11] detected a 1.5-fold increased risk
for the development of BC in allele T carriers for C3435T
genetic polymorphism. In the present study, we examined
the relevance of MDR1 genetic polymorphisms in relation
to BC susceptibility, clinical, and pathological character-
istics of BC using association analysis. We firstly detected
the c.1564A [ T genetic polymorphism in exon15 of
MDR1 gene through CRS-PCR method and evaluated the
potential correlation with risk factors of BC. Results from
our study suggested that no statistically significant differ-
ences were found in the distribution of BC patients’ risk
factors among different genotypes (Table 3, P [ 0.05).
There were no significant differences in the genotypic and
allelic frequencies (v2 = 3.3933, P = 0.1833; v2 =
3.6940, P = 0.0546, Table 2) between BC patients and
cancer-free controls. It was indicated that the c.1564A [ T
genetic polymorphism was not statistical significantly
associated with BC susceptibility in the population studied.
Our data demonstrated that for the c.1564A [ T genetic
polymorphism in MDR1 gene, BC patients do not differ
from cancer-free subjects in Chinese women. Our findings
add to the growing literature that suggests that the corre-
lation between genetic polymorphisms in MDR1 gene is
complex and incompletely understood, and further larger
and more detailed studies on different populations are
essential to confirm these findings and to reach to more
reliable results.
Conflict of interest The authors declare that they have no conflicts
of interest.
References
1. Aapro MS. Adjuvant therapy of primary breast cancer: a review
of key findings from the 7th international conference, St. Gallen,
February 2001. Oncologist. 2001;6:376–85.
2. Mousavi SM, Montazeri A, Mohagheghi MA, Jarrahi AM,
Harirchi I, Najafi M, Ebrahimi M. Breast cancer in Iran: an
epidemiological review. Breast J. 2007;13:383–91.
3. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM.
Estimates of worldwide burden of cancer in 2008: GLOBOCAN
2008. Int J Cancer. 2010;127:2893–917.
4. Kusinska R, Gorniak P, Pastorczak A, Fendler W, Potemski P,
Mlynarski W, Kordek R. Influence of genomic variation in FTO
Page 4 of 5 Med Oncol (2013) 30:601
123
at 16q12.2, MC4R at 18q22 and NRXN3 at 14q31 genes on
breast cancer risk. Mol Biol Rep. 2012;39:2915–9.
5. Chen G, Quan S, Hu Q, Wang L, Xia X, Wu J. Lack of associ-
ation between MDR1 C3435T polymorphism and chemotherapy
response in advanced breast cancer patients: evidence from cur-
rent studies. Mol Biol Rep. 2012;39:5161–8.
6. Fan L, Zheng Y, Yu KD, Liu GY, Wu J, Lu JS, Shen KW, Shen
ZZ, Shao ZM. Breast cancer in a transitional society over
18 years: trends and present status in Shanghai, China. Breast
Cancer Res Treat. 2009;117:409–16.
7. Tatari F, Salek R, Mosaffa F, Khedri A, Behravan J. Association
of C3435T single-nucleotide polymorphism of MDR1 gene with
breast cancer in an Iranian population. DNA Cell Biol.
2009;28:259–63.
8. Lohrisch C, Piccart M. An overview of HER2. Semin Oncol.
2001;28:3–11.
9. Larkin A, O’Driscoll L, Kennedy S, Purcell R, Moran E, Crown
J, Parkinson M, Clynes M. Investigation of MRP-1 protein and
MDR-1 P-glycoprotein expression in invasive breast cancer: a
prognostic study. Int J Cancer. 2004;112:286–94.
10. Hoffmann J, Sommer A. Steroid hormone receptors as targets for
the therapy of breast and prostate cancer–recent advances,
mechanisms of resistance, and new approaches. J Steroid Bio-
chem Mol Biol. 2005;93:191–200.
11. Turgut S, Yaren A, Kursunluoglu R, Turgut G. MDR1 C3435T
polymorphism in patients with breast cancer. Arch Med Res.
2007;38:539–44.
12. Hussien YM, Gharib AF, Awad HA, Karam RA, Elsawy WH.
Impact of DNA repair genes polymorphism (XPD and XRCC1)
on the risk of breast cancer in Egyptian female patients. Mol Biol
Rep. 2012;39:1895–901.
13. Fang Y, Zhao Q, Ma G, Han Y, Lou N. Investigation on MDR1
gene polymorphisms and its relationship with breast cancer risk
factors in Chinese women. Med Oncol. 2013;30:375.
14. Cizmarikova M, Wagnerova M, Schonova L, Habalova V, Kohut
A, Linkova A, Sarissky M, Mojzis J, Mirossay L, Mirossay A.
MDR1 (C3435T) polymorphism: relation to the risk of breast
cancer and therapeutic outcome. Pharmacogenomics J. 2010;10:
62–9.
15. Gervasini G, Carrillo JA, Garcia M, San Jose C, Cabanillas A,
Benitez J. Adenosine triphosphate-binding cassette B1 (ABCB1)
(multidrug resistance 1) G2677T/A gene polymorphism is asso-
ciated with high risk of lung cancer. Cancer. 2006;107:2850–7.
16. Bodor M, Kelly EJ, Ho RJ. Characterization of the human MDR1
gene. AAPS J. 2005;7:E1–5.
17. Borst P, Elferink RO. Mammalian ABC transporters in health and
disease. Annu Rev Biochem. 2002;71:537–92.
18. Jamroziak K, Mlynarski W, Balcerczak E, Mistygacz M, Trel-
inska J, Mirowski M, Bodalski J, Robak T. Functional C3435T
polymorphism of MDR1 gene: an impact on genetic suscepti-
bility and clinical outcome of childhood acute lymphoblastic
leukemia. Eur J Haematol. 2004;72:314–21.
19. George J, Dharanipragada K, Krishnamachari S, Chandrasekaran
A, Sam SS, Sunder E. A single-nucleotide polymorphism in the
MDR1 gene as a predictor of response to neoadjuvant chemo-
therapy in breast cancer. Clin Breast Cancer. 2009;9:161–5.
20. Hoffmeyer S, Burk O, von Richter O, Arnold HP, Brockmoller J,
Johne A, Cascorbi I, Gerloff T, Roots I, Eichelbaum M, Brink-
mann U. Functional polymorphisms of the human multidrug-
resistance gene: multiple sequence variations and correlation of
one allele with P-glycoprotein expression and activity in vivo.
Proc Natl Acad Sci USA. 2000;97:3473–8.
21. Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM,
Ambudkar SV, Gottesman MM. A ‘‘silent’’ polymorphism in the
MDR1 gene changes substrate specificity. Science. 2007;315:
525–8.
22. Sheng X, Zhang L, Tong N, Luo D, Wang M, Xu M, Zhang Z.
MDR1 C3435T polymorphism and cancer risk: a meta-analysis
based on 39 case-control studies. Mol Biol Rep. 2012;39:7237–49.
23. Wang J, Wang B, Bi J, Li K, Di J. MDR1 gene C3435T poly-
morphism and cancer risk: a meta-analysis of 34 case-control
studies. J Cancer Res Clin Oncol. 2012;138:979–89.
24. Ambrosone CB, Moysich KB, Furberg H, Freudenheim JL,
Bowman ED, Ahmed S, Graham S, Vena JE, Shields PG. CYP17
genetic polymorphism, breast cancer, and breast cancer risk
factors. Breast Cancer Res. 2003;5:R45–51.
25. Chang JH, Gertig DM, Chen X, Dite GS, Jenkins MA, Milne RL,
Southey MC, McCredie MR, Giles GG, Chenevix-Trench G,
Hopper JL, Spurdle AB. CYP17 genetic polymorphism, breast
cancer, and breast cancer risk factors: Australian Breast Cancer
Family Study. Breast Cancer Res. 2005;7:R513–21.
26. Shin MH, Lee KM, Yang JH, Nam SJ, Kim JW, Yoo KY, Park
SK, Noh DY, Ahn SH, Kim B, Kang D. Genetic polymorphism of
CYP17 and breast cancer risk in Korean women. Exp Mol Med.
2005;37:11–7.
27. Daly AK, Steen VM, Fairbrother KS, Idle JR. CYP2D6 multi-
allelism. Method Enzymol. 1996;272:199–210.
28. Haliassos A, Chomel JC, Tesson L, Baudis M, Kruh J, Kaplan JC,
Kitzis A. Modification of enzymatically amplified DNA for the
detection of point mutations. Nucleic Acids Res. 1989;17:3606.
29. Yuan ZR, Li J, Li JY, Gao X, Xu SZ. SNPs identification and its
correlation analysis with milk somatic cell score in bovine MBL1
gene. Mol Biol Rep. 2013;40:7–12.
30. Yuan ZR, Li JY, Li J, Zhang LP, Gao X, Gao HJ, Xu SZ.
Investigation on BRCA1 SNPs and its effects on mastitis in
Chinese commercial cattle. Gene. 2012;505:190–4.
31. Yuan ZR, Li JY, Li J, Gao X, Xu SZ. Effects of DGAT1 gene on
meat and carcass fatness quality in Chinese commercial cattle.
Mol Biol Rep. 2013;40:1947–54.
32. Zhao CJ, Li N, Deng XM. The establishment of method for
identifying SNP genotype by CRS-PCR. Yi Chuan. 2003;25:
327–9.
Med Oncol (2013) 30:601 Page 5 of 5
123