Medical

Communication

Biosci. Biotech. Res. Comm. 9(4): 643-652 (2016)

A common transition in multi-drug resistance gene and

risk of breast cancer: A genetic association study with

an in silico-analysis

Davood Kheirkhah

and Mohammad Karimian

Department of Pediatrics, Kashan University of Medical Sciences, Kashan

Anatomical Sciences Research Center, Kashan University of Medical Sciences, Kashan

ABSTRACT

We have investigated the correlation of multi-drug resistance- c.3435T>C common gene transition with breast cancer

risk in Asian populations by an updated meta-analysis which followed by an in silico approach. In a meta-analysis

approach, we collected all related studies. For this purpose, we used common electronic databases such as PubMed,

Google Scholar and Science Direct. We employed bioinformatics to evaluate molecular effects of c.3435T>C transi-

tion. Our data revealed that there is no signi cant association between the polymorphism and breast cancer within

Asian population. But, strati ed meta-analysis revealed that there are a signi cant associations within T vs. C

(OR=1.40, 95%CI=1.14-1.73, P=0.002), TT vs. CC (OR=1.85, 95%CI=1.15-2.98, P=0.011), and TT vs. CC+CT (OR=1.59,

95%CI=1.06-2.37, P=0.024) genetic models. Also, bioinformatics data revealed that c.3435T>C polymorphism could

affect splicing pattern. Based on results, the c.3435T>C transition with effects on RNA splicing pattern is a risk factor

for breast cancer in Iranian populations.

KEY WORDS: BREAST CANCER, GENETIC POLYMORPHISM, RISK FACTOR, META-ANALYSIS

643

ARTICLE INFORMATION:

*Corresponding Author: mdkarimian@yahoo.com

Received 11

Nov, 2016

Accepted after revision 26

Dec, 2016

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007

Thomson Reuters ISI ESC and Crossref Indexed Journal

NAAS Journal Score 2015: 3.48 Cosmos IF : 4.006

reserved.

Online Contents Available at: http//www.bbrc.in/

INTRODUCTION

Breast cancer is the most prevalent cancer in women

all over the world, and its occurrence is rising. It is a

multifactorial disease that is caused by complicated

interactions among genetic and environmental factors,

(Jemal et al., 2006 and Nickels et al., 2013). Lately, a

growing amount of research have been focused to assess

the association among genetic factors and breast can-

cer susceptibility. Moreover, individual genetic varia-

tions, especially genetic polymorphisms in enzymes that

metabolize drugs have an important role in metabo-

lism and the fate of drugs. Also, some studies have

showed that genetic polymorphisms of the multi-drug

644 MDR1 GENE AND BREAST CANCER BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Davood Kheirkhah and Mohammad Karimian

resistance1 (MDR1; ABCB1) gene, may be correlated

with cancer risk (Turgut et al., 2007). The MDR1 gene

that encodes a membrane-bound phosphoglycoprotein

(P-gp), is positioned on chromosome 7 with 29 exons.

It functions as an ef ux pump which protects the cells

against numerous elements including amino acids, pro-

teins, organic cations, and others, (Kreile et al., 2013,

Ozdemir et al., 2013, Lee, 2016 and Isvoran et al., 2016 ).

More than 50 variants exist in the MDR1 gene.

Among these, three following single nucleotide poly-

morphisms (SNPs) are more common than others: (i)

C1236T (rs1128503), (ii) G2677T (rs2032582), and (iii)

C3435T (rs1045642; c.3435T>C). Some previous reports

reported that c.3435T>C single nucleotide polymor-

phism in MDR1 gene may contribute in breast cancer

risk (Johnatty et al., 2013 and Tazzite et al., 2016).

In this study, we have investigated the association of

aforementioned transition with breast cancer risk within

Asian populations by a meta-analysis and in silico

approach.

MATERIAL AND METHODS

The literature search was done by both two authors of

this paper (DK and MK). Standard electronic databases

such as PubMed, Google Scholar, and Science Direct

were explored for eligible papers up to Dec, 2016. Key-

words and phrases which used for our search strategy

were as follow: (“multi-drug resistance” or “MDR” or

“ABCB1” or “C3435T” or “rs1045642”), and (“genetic

polymorphism” or “SNP” or “variant” or “single nucleo-

tide polymorphism”) and (“breast cancer”). Citations in

possible eligible papers were also studied as a subsidiary

source to recognize more eligible papers.

Included studies in the meta-analysis were chosen

based on the following criteria: (1) the paper evaluated

the association between the MDR1- c.3435T>C poly-

morphism and breast cancer risk; (2) The total number

of participants in the case and control groups, the geno-

type distribution, or other relevant information could be

extracted from the paper. (3) Paper published in English

language. The exclusion criteria for meta-analysis were

as follow: (1) publication was review, meta-analysis, let-

ter to the editor, or abstract; (2) the paper introduced

either vague information or did not prepare frequencies

of genotypes ; (3) the paper related to ethnicities except

than Asians.

The information from included papers were extracted

by two authors independently (DK and MK). Differences

concerning study selection and data extraction were set-

tled by a discussion. We extracted the following infor-

mation from eligible studies:  rst author’s name, publi-

cation year, country, genotyping methods, frequencies of

genotypes in case and control groups, and estimation of

Hardy-Weinberg equilibrium (HWE) in control groups.

At  rst, Hardy-Weinberg equilibrium (HWE) for

control group of each study were calculated by a Chi-

squared test. This calculation was performed by had-

2know online web server. A two-tailed p value less than

0.05 (p<0.05) was considered as a statistically signi -

cant difference. All statistical meta-analysis were done

by Comprehensive Meta Analysis program (Biostat, Inc.,

Englewood, NJ, USA) and The Open Meta Analyst soft-

ware (Tufts University, Medford, MA, USA). Pooled odds

ratios (ORs) with 95% con dence intervals (CIs) were

estimated to assess the association of MDR1- c.3435T>C

polymorphism and breast cancer risk. Tests were per-

formed under the following models: 1- Allelic (T vs. C

allele), 2- Homozygous (TT vs. CC), 3- Heterozygous (CT

vs. CC), and 4- Dominant model (TT+CT vs. CC), and 5-

Recessive model (TT vs. CT+CC). The Cochrane Q-test and

I2 score were employed to estimate heterogeneity and

apvalue more than 0.1 was considered as statistically

signi cant. Fixed-effect and random-effects model were

used in the absence and presence of heterogeneity of the

studies, respectively. Also, a strati ed meta-analysis was

performed in Iranian populations. Publication bias was

discovered by funnel plots and Egger’s test (Begg and

Mazumdar, 1994; Egger et al., 1997). Sensitivity analysis

was done to evaluate the magnitude effect of individual

studies on the overall analysis.

IN SILICO ANALYSIS

For bioinformatics analysis, the entire gene sequence of

MDR1 was deduced from NCBI database (Chromosome 7,

NC_000007.14). The location of c.3435T>C SNP on the

MDR1 gene was determined manually. Also, the loca-

tion of this SNP on RNA sequence was found according

to the procedure. Since c.3435T>C mutation is a syn-

onymous SNP, the effects of this transition on protein

structure were not evaluated in the current study. But,

this polymorphism may affect the RNA structure or/and

splicing pattern of MDR1. Therefore, we used some bio-

informatics tools to evaluate of these molecular effects

of c.3435T>C on MDR1. For this purpose, RNAsnp server

was used to evaluate the effects of c.3435T>C on the

RNA structure (http://rth.dk/resources/rnasnp/).

The RNAsnp software works in three methods:

Method 1 is planned to calculate the effect of SNPs

on small RNA sequences (less than 1000 nucleotides);

Method 2 is planned to expect the in uence of SNPs on

lengthy RNA sequence while the  nally Method 3 works

as a combination of method 1 and 2 and it is planned to

screen all potential structure-wrecking single nucleotide

polymorphisms in an input sequence using a brute-force

methodology. In plot summary of the RNAsnp output,

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS MDR1 GENE AND BREAST CANCER 645

Davood Kheirkhah and Mohammad Karimian

the local area which identi ed with maximum structural

alteration is highlighted giving to the p-value. If the

p-value is more than 0.2 (p> 0.2) the area is highlighted

in black that shows not signi cant structural alteration is

happened (Sabarinathan et al., 2013). The ASSP (http://

wangcomputing.com/assp/) servers was employed to

explore the impact of c.3435T>C on splice site pattern

of MDR1. ASSP is a nucleotide sequence examination

instrument for the estimation and cataloguing of splice

sites. It works based on constitutive, cryptic, skipped,

and alternative exon isoform splice sites analyses (Wang

and Marín, 2006). ASSP recognizes putative splice sites

by models which known as pre-processing models.

Finally, we used String online web server to predict the

gen-gene interaction network of MDR1.

RESULTS

After screening of studies and as shown in  gure 1, total

of 7 eligible papers were included in the meta-analysis

(Tatari et al. 2009; George et al. 2009 ; Taheri et al. 2010;

Wu et al. 2012; Ghafouri et al. 2016; Abuhaliema et al.,

2016; Sharif et al., 2016). The details of included studies

in meta-analysis are given in table 1. This meta-analysis

involved 1819 breast cancer patients and 1929 healthy

controls. There were 4 studies with Iranian populations,

and 3 remained studies are related to Indian, Chinese,

and Jordanian populations. All of seven studies used

polymerase chain reaction - restriction fragment length

polymorphism (PCR-RFLP) method for c.3435T>C SNP

genotyping. Also, the distribution of genotypes in all

of control groups were consistent to Hardy–Weinberg

criteria (Table 1).

Results of association between mentioned SNP and

risk of breast cancer are summarized in table 2. Our

data revealed that there are no signi cant associations

between MDR1 c.3435T>C and risk of breast cancer in

Asian population under all of  ve genetic models (T vs.

C: OR= 1.22, 95%CI= 0.86-1.75, P= 0.267; TT vs. CC:

OR= 1.35, 95%CI= 0.72-2.55, P= 0.353; CT vs. CC: OR=

1.00; 95%CI= 0.72-1.38; P= 0.993; CT+TT vs. CC: OR=

1.07, 95%CI= 0.73-1.56, P= 0.724; TT vs. CC+CT: OR=

1.26, 95%CI= 0.80-1.99, P= 0.323) (Figure 2). When we

performed a strati ed meta-analysis in Iranian popula-

tions, we found a signi cant association between MDR1

c.3435T>C and breast cancer risk in Iranian populations

under three genetic models (T vs. C: OR= 1.40, 95%CI=

1.14-1.73, P= 0.002; TT vs. CC: OR= 1.85, 95%CI= 1.15-

2.98, P= 0.011; TT vs. CC+CT: OR= 1.59, 95%CI= 1.06-

2.37, P= 0.024) (Figures 3 and 4).

The results of heterogeneity and publication bias

are detailed in table 3. Our data revealed that there are

true heterogeneities in overall analysis under all of  ve

genetic models (T vs. C: P

heterogeneity

< 0.001; I

=88%; TT

vs. CC: P

heterogeneity

< 0.001; I

=81%; CT vs. CC: P

heterogeneity

0.028; I

=58%; CT+TT vs. CC: P

heterogeneity

< 0.001; I

=75%;

TT vs. CC+CT: P

heterogeneity

< 0.001; I

=75%). Whereas we

observed only a signi cant heterogeneity in strati ed

analysis for Iranian population under CT vs. CC model

heterogeneity

= 0.095; I

=53%). Publication bias was deter-

mined by Egger’s test and funnel plot. As shown in table

3 there are no publication biases in this meta-analysis

in overall analysis (T vs. C: P

Egger

= 0.780; TT vs. CC: P

Eg-

FIGURE 1. Study identi cation  owchart.

646 MDR1 GENE AND BREAST CANCER BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Davood Kheirkhah and Mohammad Karimian

Table 1: Characteristics of included studies in meta-analysis

Country

Genotype frequencies

PHWE Genotyping method Author (Reference)Controls Cases

CC CT TT CC CT TT

Iran 12 45 20 16 57 33 0.11 PCR–RFLP Tatari et al. 2009

India 15 32 21 8 39 39 0.67 PCR–RFLP George et al. 2009

Iran 10 27 13 10 30 14 0.55 PCR–RFLP Taheri et al. 2010

China 440 624 180 388 565 220 0.08 PCR–RFLP Wu et al. 2012

Iran 141 50 9 75 16 9 0.11 PCR–RFLP Ghafouri et al. 2016

Jordan 40 65 45 68 62 20 0.10 PCR–RFLP Abuhaliema et al., 2016

Iran 79 53 8 61 68 21 0.82 PCR-RFLP Sharif et al., 2016

HWE: Hardy–Weinberg equilibrium, PCR-RFLP: Polymerase chain reaction-restriction fragment length polymorphism. A Hardy–

Weinberg equilibrium in the control group with P <0.05 did not satisfy the Hardy-Weinberg equilibrium.

Table 2: Association results of MDR1 c.3435T>C and breast cancer risk in the meta-analysis

Group T vs. C TT vs. CC CT vs. CC CT+TT vs. CC TT vs. CC+CT

(95% CI)

POR

(95% CI)

POR

(95% CI)

POR

(95% CI)

POR

(95% CI)

Asian 1.22

(0.86-1.75)

0.267 1.35

(0.72-2.55)

0.353 1.00

(0.72-1.38)

0.993 1.07

(0.73-1.56)

0.724 1.26

(0.80-1.99)

0.323

Iranian 1.40

(1.14-1.73)

0.002 1.85

(1.15-2.98)

0.011 1.04

(0.62-1.74)

0.895 1.24

(0.92-1.68)

0.160 1.59

(1.06-2.37)

0.024

OR: odds ratio, CI: con dence interval

ger

= 0.944; CT vs. CC: P

Egger

= 0.989; CT+TT vs. CC: P

Egger

0.991; TT vs. CC+CT: P

Egger

= 0.947) and strati ed analy-

sis (T vs. C: P

Egger

= 0.125; TT vs. CC: P

Egger

= 0.296; CT

vs. CC: P

Egger

= 0.600; CT+TT vs. CC: P

Egger

= 0.592; TT vs.

CC+CT: P

Egger

= 0.604). This data were con rmed by sym-

metrical funnel plots (Figure 5). To assess the strength

of the association results, sensitivity analysis was done

by eliminating one study at a time and recalculating

the summary ORs. The summary ORs continued stable,

representing that our meta-analysis is not signi cantly

in uenced by an individual study (data not shown).

The data from ASSP online web server showed that

the c.3435T>C polymorphism alters splice site pattern

of the MDR1 gene (Table 4). The data revealed that the

score of constitutive donor splice site at location 515

is 6.381 for 3435TT genotype whereas this score for

3435CC genotype is 5.066. In addition Alt./Cryptic, con-

stitutive, and con dence scores for genotype at location

507 are 0.652, 0.334, and 0.487, respectively. Whereas

these values for 3435CC are 0.628, 0.335, and 0.434,

respectively. In addition, all of mentioned values are dif-

ferent between 3435TT and 3435CC genotypes at loca-

tion 515 (0.914, 0.057, and 0.937 for TT genotype and

0.820, 0.136, and 0.834 for CC genotype).

To evaluate the effects of c.3435T>C SNP on mRNA

structure of MDR1, we used the RNAsnp server. For this

purpose, we entered the entire coding sequence of the

gene into RNAsnp main window. Then we entered the

code of SNP in a section of server entitled “SNP details”.

The data from RNAsnp revealed that c.3435T>C could

not signi cantly affect RNA structure of MDR1 gene

(Folding Window=3235-3635, Local region=3434-3483,

distance=0.0965, p-value=0.2983). Also, we found that

the minimum free energy of the optimal secondary

structure of global TT genotype sequence is -113.70 kcal/

mol, while this is -114.60 kcal/mol for CC genotype.

DISCUSSION

Breast cancer is known as the most common malignancy

among women in both developing and developed coun-

tries. This disorder is a rising issue that in uences about

12.5% of women during their life. The rate of this malig-

nancy is on the growth (Slamon et al., 1987; Soleimani

et al., 2016). The occurrence of breast cancer in Asian

women is in overall lower than other ethnicity. But, all

health statistics showed that this malignancy has been

quickly growing in current decades in Asia (Matsuno et

al., 2007).

The reasons of breast cancer are weakly recognized.

Some factors including family history, age, lifestyle,

geographical variation, and genetic factors may increase

the risk of breast cancer. Genetic variations are widely

considered as important risk factors for breast cancer

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS MDR1 GENE AND BREAST CANCER 647

Davood Kheirkhah and Mohammad Karimian

FIGURE 2. Forest plot for Asian meta-analysis. A) CT vs. CC model; B) CT+TT vs. CC model; C) TT vs.

CC+CT model; D) TT vs. CC model.

Davood Kheirkhah and Mohammad Karimian

648 MDR1 GENE AND BREAST CANCER BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

FIGURE 3. Forest plot for Iranian populations. A) T vs. C model; B) TT vs. CC model; C) TT vs. CC+CT

model.

susceptibility in exact populations (Haimov-Kochman et

al., 2002). Recognition of some crucial single nucleotide

polymorphisms, which impacts the gene expression and

protein function, it might be suitable to predict suscep-

tibility of breast cancer (Lian et al., 2012).

In this study, we investigate the association of MDR1

c.3435T>C polymorphism and breast cancer risk in Asian

population by a meta-analysis which followed by a bio-

informatics approach. For example Tatari et al. (2009)

reported a signi cant association between MDR1-3435T

allele and risk of breast cancer, whereas Taheri et al.

(2010) didn’t discover any association. Therefor meta-

analysis as an in uential statistical instrument can help

researchers to  nd out more precise conclusions. Our

meta-analysis revealed that there are no signi cant

association between mentioned SNP and breast cancer

risk in overall Asian analysis. But, strati ed meta-anal-

ysis in Iranian population showed a signi cant associa-

tion between MDR1 c.3435T>C transition and risk of

breast cancer in some genetic model. Also, after strati-

fying analysis we found that the heterogeneity become

disappear. Therefore, it may be a source of heterogeneity

among the studies. The different results in various stud-

ies may be due to environmental factors, genetic back-

grounds and lifestyles.

Some mechanisms explain the role of MDR1 gene in

breast cancer risk. MDR1 gene also known as MDR1 is

a member of the MDR1 superfamily that expresses P-gp

protein, which is an ATP-dependent ef ux pump that

permits the human cells to remove poisons and carcino-

genic agents (Kreile et al., 2013). Some studies proposed

that c.3435T>C transition may impact the risk of some

cancers such as breast carcinoma (Ikeda et al., 2015).

Really, this synonymous transition (Ile1145Ile) effects

Davood Kheirkhah and Mohammad Karimian

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS MDR1 GENE AND BREAST CANCER 649

FIGURE 5. Funnel plot for Asian meta-analysis. A) CT vs. CC model; B) TT vs. CC model; C) CT+TT

vs. CC model; D) TT vs. CC+CT model.

FIGURE 4. Forest plot for Iranian populations. A) CT vs. CC model; B) CT+TT vs. CC model.

Table 3: Results of heterogeneity and publication bias for the association of MDR1 c.3435T>C and breast cancer risk

Group T vs. C TT vs. CC CT vs. CC CT+TT vs. CC TT vs. CC+CT

Ph I2 Pe Ph I2 Pe Ph I2 Pe Ph I2 Pe Ph I2 Pe

Asian < 0.001 88% 0.780 < 0.001 81% 0.944 0.028 58% 0.989 < 0.001 75% 0.991 < 0.001 75% 0.947

Iranian 0.140 45% 0.125 0.335 12% 0.296 0.095 53% 0.600 0.117 49% 0.592 0.351 8% 0.604

Ph: Pheterogeneity (p< 0.1) was considered as a signi cant difference. Pe: PEgger (p< 0.05) was considered as a signi cant difference

650 MDR1 GENE AND BREAST CANCER BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Davood Kheirkhah and Mohammad Karimian

stability of protein (Fung et al., 2014) and leads to apop-

tosis modi cation or cellular damage witch these are

important for development of cancer (Johnstone et al.,

2000).

Our previous in silico studies revealed that SNPs may

affect gene expression (Jamali et al., 2016) mRNA struc-

ture (Karimian et al., 2015; Rayganet al., 2016) and pro-

tein structure and function (Karimian and Hosseinzadeh

Colagar, 2016a; Nikzad et al., 2015; Karimian and Hos-

seinzadeh Colagar, 2016b).

The SNP which was studied in this study is a syn-

onymous polymorphism, and then it has no effect on

the peptide sequence of the protein. But, it could impact

the mRNA structure or splicing pattern. So it is pos-

sible to apply bioinformatics tools to assess the harmful

properties of MDR1- c.3435T>C polymorphism on the

splicing pattern and mRNA structure. So that we used

in silico tools to evaluate these effects. Our data from

in silico analysis revealed that c.3435T>C could not

affect the mRNA structure. Though c.3435T>C substi-

tution leads to a decrease in minimum free energy for

mutant type but this change is not signi cant. Therefore,

it is expected that pathogenicity of c.3435T>C may arise

from its effect on RNA splicing.

There are some limitations in our meta-analysis that

should be mentioned. For instance, lack of original data,

such as smoking, BMI, age, and etc. may affect the accu-

racy of the association of the SNP and risk of breast

cancer. Also, this meta-analysis did not cover a large

number of Asian countries. In conclusion, the present

study suggests that the MDR1- c.3435T>C transition

might be correlated with risk breast cancer in Iranian

population. But, studies with larger sample size and con-

sidered to gene-environment and gene-gene interactions

are essential to approve our results.

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Table 4: ASSP prediction results for 3435TT and 3435CC genotypes

Positio

(bp)

Putative

splice site

Sequence Score

Intron

Activations

Con dence

Alt./

Cryptic

Constitutive

A) 3435TT Genotype

458 Alt. isoform/

cryptic acceptor

ttgactgcagCATTGCTGAG 6.509 0.586 0.696 0.300 0.569

507 Alt. isoform/

cryptic acceptor

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515 Alt. isoform/

cryptic donor

GGAAGAGATTgtgagggcag 6.381 0.486 0.914 0.057 0.937

569 Constitutive

donor

ACTGCCTAATgtaagtctct 13.099 0.429 0.103 0.852 0.879

B) 3435CC Genotype

458 Alt. isoform/

cryptic acceptor

ttgactgcagCATTGCTGAG 6.509 0.586 0.696 0.300 0.569

507 Alt. isoform/

cryptic acceptor

ggtgtcacagGAAGAGATCG 2.614 0.543 0.628 0.355 0.434

515 Alt. isoform/

cryptic donor

GGAAGAGATCgtgagggcag 5.066 0.486 0.820 0.136 0.834

569 Constitutive

donor

ACTGCCTAATgtaagtctct 13.099 0.429 0.103 0.852 0.879

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