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Asian Pacific Journal of Cancer Prevention : APJCP logoLink to Asian Pacific Journal of Cancer Prevention : APJCP
. 2021 Aug;22(8):2323–2334. doi: 10.31557/APJCP.2021.22.8.2323

Association of PON1, LEP and LEPR Polymorphisms with Susceptibility to Breast Cancer: A Meta-Analysis

Soheila Sayad 1, Seyed Alireza Dastgheib 2, Meraj Farbod 3,*, Fatemeh Asadian 4, Mojgan Karimi-Zarchi 5,6, Seyedali Salari 7, Seyed Hossein Shaker 8, Jalal Sadeghizadeh-Yazdi 9, Hossein Neamatzadeh 10,11
PMCID: PMC8629481  PMID: 34452542

Abstract

Objective:

Breast cancer is the most common cancer in American women, except for skin cancers. In this meta-analysis, the associations of polymorphisms within paraoxonase 1 (PON1), leptin (LEP) and leptin receptor (LEPR) genes with susceptibility to breast cancer were comprehensively evaluated.

Methods:

A universal search in PubMed, Scopus, CNKI, SID, Web of Knowledge and Google Scholar was performed to identify relevant studies up to 01 May, 2021. The strength of the associations was estimated by Odds ratios (ORs) with 95% confidence intervals (95% CIs).

Results:

A total of 39 case-control studies including 7 studies with 2005 cases and 2748 controls were on PON1 rs662, 6 studies with 2,031 cases and 1,973 controls on PON1 rs854560, 12 studies with 3,444 cases and 3,583 controls on LEP rs7799039, and 14 studies with 5,330 cases and 6,188 controls on LEPR rs1137101 were selected. Pooled data showed that PON1 rs662 and rs854560 polymorphisms were associated with risk of breast cancer in overall population, but not LEP rs7799039 and LEPR rs1137101.

Conclusions:

Our pooled data revealed that the PON1 rs662 and rs854560 polymorphisms were significantly associated with an increased risk of breast cancer in the overall population. However, LEP rs7799039 and LEPR rs1137101 polymorphisms were not associated.

Key Words: Breast cancer, paraoxonase 1, leptin, leptin receptor, polymorphism

Introduction

Global facts and figures about the cancer revealed that breast cancer still key public health concern and leading cause of deaths among women globally (Jafari-Nedooshan et al., 2017; Moghimi et al., 2018). Heightened awareness of breast cancer risk in the past decades has led to an increase in the detection methods which can be used to detect the breast cancer in the early stages (Dinegde and Xuying, 2017). In the more affluent countries, mammography screening has been in place for a few decades and has successfully reduced mortality (Motamedi et al., 2012; Najminejad et al., 2020; Esmaeili et al., 2021). However, in developing countries, screening and paid little attention to fight with breast cancer is one of the lowest priorities in health policy makers (da Costa Vieira et al., 2017). Breast cancer is most likely triggered and/or promoted by multiple risk factors. The two strongest risk factors for breast cancer are gender and age (Feng et al., 2018). The etiological make-up of a heterogeneous and complex disease such as breast cancer is diverse and includes genetics and environmental factors. Breast cancer susceptibility gene 1 (BRCA1) and breast cancer susceptibility gene 2 (BRCA2) are the two major genes associated with hereditary breast and ovarian cancer (Forat-Yazdi et al., 2015; Neamatzadeh et al., 2015). However, there are more than 30 instances of SNPs identified as breast cancer susceptibility loci in the genome by GWAS (Kaklamani et al., 2011). Paraoxonase 1 (PON1), leptin (LEP) and leptin receptor (LEPR) genes are good example of a GWAS-identified locus that has been implicated in development of breast cancer (Gallicchio et al., 2007; Liu and Liu, 2011).

PON1, also called serum aromatic esterase 1, is the main means of protection of the nervous system against the neurotoxicity of organophosphates in serum (Richard et al., 2013; Mackness and Sozmen, 2020). Moreover, PON1 hydrolysis numerous exogenous and endogenous esters, such as arylesters, homocysteine thiolactone (HTL), other lactones, and cyclic carbonates (Costa et al, 2011; Seow et al., 2016). The human PON1 (MIM#602720) gene is a member of a multigene family consisting of three members including PON2 and PON3, which share ≈60% sequence identity with PON1 (Gallicchio et al., 2007; Liu and Liu, 2011). However, PON1 remains the most popular member of this family. The PON1 gene is located on chromosome 7q21.22, consisting 9 exons and spans 33.2 kb (Li et al., 1997). Of the PON1 polymorphisms, PON1 rs662 and rs854560 are most widely studied for their association with susceptibility to different cancers (Seow et al., 2016). Moreover, human LEP gene plays a critical role in energy expenditure as well as the progression of carcinogenesis (Tang et al., 2019). It is also reported that LEP may affect angiogenesis, inflammation, thrombosis, and tumor growth, invasion, and metastasis (Tang et al., 2019). It is revealed that the LEP signal may be transmitted through several signaling pathways such as JAK/STAT, MAPK, PI3K, Wnt/β-catenin, and ERK (Kavitha et al., 2013). The human LEP (MIM#164160) is located on chromosome 7q31.3, consists of three exons and spans approximately 16.4 kb (Funcke et al, 2014). It is highly polymorphic and the LEP rs7799039 G>A SNP is the most widely studied for its role in development of different human diseases (Tang et al., 2019).

Over the past decade, several molecular epidemiological studies have been performed to identify the association of PON1 rs662, rs854560, LEP rs7799039G>A, and LEPR rs1137101 polymorphisms with susceptibility to breast cancer, but the findings have been conflicting. Thus, we performed a systematic review and updated meta-analysis to obtain a more precise assessment of the association between PON1, LEP and LEPR polymorphisms and the risk of breast cancer.

Materials and Methods

Search strategy

This meta-analysis was reported based on the Preferred Reporting Items for Meta-analyses (PRISMA) guideline. In this meta-analysis, we carried out electronic literature retrieval in Medicine’s PubMed, Scopus, EMBASE, Web of Knowledge, Cochrane Library, Google Scholar, Scientific Information Database (SID), WanFang, VIP, Chinese Biomedical Database (CBD), Scientific Electronic Library Online (SciELO) and China National Knowledge Infrastructure (CNKI) database up to 01 May, 2021. The following keywords and terms were used to search: (‘’breast cancer’’ OR “breast tumor” OR “breast neoplasm” OR “breast malignant tumor” OR “breast carcinoma’’) AND (‘’ Paraoxonase 1’’ OR ‘’Serum Paraoxonase/Arylesterase’’ OR ‘’Serum Aryldialkylphosphatase’’ OR ‘’Aromatic Esterase’’ OR ‘’Arylesterase’’ OR ‘’A-Esterase’’ OR ‘’Esterase’’ OR ‘’PON1) AND (‘’Leptin’’ OR ‘’Obesity Factor’’ OR ‘’Obese Protein’’ OR ‘’LEP’’) AND (‘’Leptin Receptor’’ OR ‘’LEPR’’ OR ‘’OBR’’ ‘’OB Receptor’’ OR ‘’HuB219’’ OR ‘’CD295’’) AND (‘’Q192R’’ OR ‘’rs662’’ OR ‘’L55M’’ OR ‘’rs854560’’ OR ‘’LEP G2548A’’OR ‘’rs7799039’’ OR ‘’LEPR Q223R’’ OR ‘’rs1137101’’ OR ‘’LEPR Lys109Arg’’ OR ‘’rs1137100’’ OR ‘’rs1137101’’ OR ‘’c.668A>G’’ OR ‘’p.Gln223Arg’’ OR ‘’Arg223Gln’’ OR ‘’R223Q’’ OR ‘’Q223R’’ OR ‘’rs7799039’’ OR ‘’2548G/A’’) AND (‘’Gene’’ OR ‘’Genotype’’ OR ‘’Allele’’ OR ‘’Polymorphism’’ OR ‘’ Single nucleotide polymorphisms’’ OR ‘’SNP’’ OR ‘’Variation’’ OR ‘’Mutation’’). No restrictions were placed on the language, year of publication, ethnicity, and sample size. The references in included studies and reviewers were carefully checked for other potential data. When a publication involved some subgroups, it was treated separately.

Selection and Exclusion Criteria

The major selection criteria were as follows: 1) studies with case-control or cohort design; 2) studies that assessed the association of genetic variants within PON1, LEP and LEPR gene with risk of breast cancer; and (2) presented sufficient data to calculate the pooled-estimating. Accordingly, the major exclusion criteria were: 1) Studies did not evaluate the association of LEP, LEPR and PON1 polymorphisms and risk of breast cancer; 2) studies focusing on animals or in vitro; 3) Studies that did not provide usable or sufficient data for pooling; 4) case only studies or no controls; 5) linkage studies and family based studies (twins and sibling); 6) case reports, abstracts, comments, conference abstracts, editorials, reviews, meta-analysis; and 7) duplicated studies or data. When duplicated studies were published by the same author obtained from the same patient sample, only the one with the largest sample size was included in this meta-analysis.

Data extraction

Two authors independently extracted the data from each eligible study and if the extracted data was different, they would review the publication again and reached consensus. If they could not get a consistent assessment, third author would be invited to resolve the dispute and a final decision was made. The following data were extracted from each study: first author name, year of publication, country of origin, ethnicity (Asian, Caucasians, Africans and Mixed populations), numbers of cases and controls, source of control, genotype and allele frequencies, genotyping method, minor allele frequency (MAFs) and Hardy-Weinberg equilibrium (HWE) in controls.

Statistical Analysis

All of the statistical calculations were performed using Comprehensive Meta-Analysis (CMA) software version 2.0 (Biostat, USA). Two-sided P-values < 0.05 were considered statistically significant. The strength of association between genetic variants at PON1, LEP and LEPR genes and risk of breast cancer was estimated by Odds ratios (ORs) with 95% confidence intervals (95% CIs). The significance of the pooled effect size was determined by Z-test, in which P<0.05 was considered statistically significant. The associations was evaluated under all five genetic models, i.e., allele (B vs. A), homozygote (BB vs. AA), heterozygote (BA vs. AA), dominant (BB+BA vs. AA), and the recessive (BB vs. BA+AA), in which ‘’B’’ presents mutant and ‘’A’’ wild allele (Jafari-Nedooshan et al., 2019; Jafari et al., 2020). Between-study heterogeneity was estimated using a Cochran-based Q statistical test, with P-values less than 0.1 indicated the absence of indicated heterogeneity among studies. Moreover, a quantitative measure of between-study heterogeneity was tested using the I2 statistic (range of 0 to 100%), in which the heterogeneity was considered low, moderate, and high based on I2 values of 25%, 50%, and 75%, respectively. Thus, there was no heterogeneity (P > 0.1 or I2 < 50%) the fixed-effect model (Mantel-Haenszel method) was applied. There was heterogeneity (P <0.1 and I2 > 50%) the random-effect (DerSimonian-Laird method) model was used for analysis. Stratified analysis was carried out on the basis of ethnicity and source of controls. The Hardy-Weinberg equilibrium (HWE) for controls in each study was evaluated using the χ2 test and P >0.05 was considered to be consistent with HWE (Bahrami Dastgheib et al., 2020; Bahrami Shajari et al., 2020). To explore the influence of an individual study on the pooled data, sensitivity analysis was also used to confirm the stability of the results under all genetic models. Begg’s funnel plot test was used to assess possible publication bias, with P <0.05 being considered to present statistical significance.

Results

Selected Studies Characteristics

The selection process of eligible studies is presented in Figure 1. Initially, 719 studies were obtained through publication search in electronic databases and other sources. Irrelevant articles were excluded by evaluating the titles and abstracts. Therefore, 76 publications were deleted for obvious irrelevance. Finally, 39 case-control studies including 7 studies with 2005 cases and 2,748 controls were on PON1 rs662, 6 studies with 2,031 cases and 1,973 controls on PON1 rs854560, 12 studies with 3,444 cases and 3,583 controls on LEP rs7799039, and 14 studies with 5,330 cases and 6,188 controls on LEPR rs1137101 were selected. Pooled data showed that PON1 rs662 and rs854560 polymorphisms were associated with risk of breast cancer in overall population, but not LEP rs7799039 and LEPR rs1137101. Table 1 describes principal characteristics of included studies. The studies have been carried out in USA, Brazil, Italy, Malaysia, Egypt, turkey, China, Iran, Mexico, Sri Lanka, India, Tunisia, Nigeria, and Korea. Among these studies, eight studies were conducted among Asians, two studies among Caucasians and two studies Africans. Seven different genotyping methods were used: PCR, PCR-RFLP, TaqMan, SNPstream, and TOFMS. The genotype, allele and minor allele frequency (MAF) in each study for PON1 rs662, rs854560, LEP rs7799039 and LEPR rs1137101 are shown in Table 1. Moreover, the distribution of genotypes in the controls was in agreement with Hardy-Weinberg equilibrium (HWE) for all selected studies, except for one study on IL-8 -251T>A polymorphism (Table 1).

Figure 1.

Figure 1

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Flowchart of Literature Search and Selection Process

Table 1.

Characteristics of the Case-Control Studies Included in the Meta-Analyses

First Author Country
(Ethnicity)		 SOC Genotyping
Method		 Cases/
controls		 Cases Controls
Genotype Allele Genotype Allele
PON1 rs662 AA AG GG A G AA AG GG A G
Stevens 2006 USA (Caucasian) PB PCR-RFLP 483/483 259 182 42 700 266 238 198 47 674 292
Gallicchio 2007 Brazil (Mixed) PB PCR-RFLP 58/904 38 15 5 91 25 469 353 82 1291 517
Antognelli 2009 Italy (Caucasian) PB PCR-RFLP 547/544 484 50 13 1018 76 340 152 52 832 256
Naidu 2010 Malaya (Asian) HB PCR-RFLP 387/252 200 158 29 558 216 115 115 22 345 159
Hussein 2011 Egypt (African) PB PCR-RFLP 100/100 51 41 8 143 57 46 42 12 134 66
Kaya 2016 Turkey (Caucasian) HB TaqMan 32/35 10 11 11 31 33 5 13 17 23 47
Wu 2017 China (Asian) HB TaqMan 365/378 155 156 54 466 264 167 156 55 490 266
Agachan 2019 Turkey (Caucasian) PB PCR-RFLP 33/52 17 4 12 38 28 6 29 17 41 63
PON1 rs854560 TT TA AA T A TT TA AA T A
Stevens 2006 USA (Caucasian) PB PCR-RFLP 483/493 176 230 77 582 384 202 233 58 637 349
Antognelli 2009 Italy (Caucasian) PB PCR-RFLP 547/607 107 115 325 329 765 188 188 231 564 650
Naidu 2010 Malaya (Asian) HB PCR-RFLP 387/269 159 178 50 496 278 126 126 17 378 160
Hussein 2011 Egypt (African) PB PCR-RFLP 100/76 19 21 60 59 141 35 35 6 105 47
Wu 2017 China (Asian) HB TaqMan 483/483 284 72 9 640 90 346 30 2 722 34
Farmohammadi 2019 Iran (Asian) HB PCR-RFLP 150/150 47 65 38 159 141 66 59 25 191 109
LEP rs7799039 GG GA AA G A GG GA AA G A
Snoussi 2006 Tunisia (Caucasian) HB PCR-RFLP 308/222 37 152 119 226 390 11 99 112 37 152
Vairaktaris 2008 Greece (Caucasian) HB PCR 150/152 32 78 40 142 158 112 99 11 32 78
Teras 2009 USA (Caucasian) PB SNPstream 1077/1086 445 445 187 1335 819 442 442 202 445 445
Cleveland 2010 USA (Caucasian) PB PCR 1059/1101 226 492 341 944 1174 180 561 360 226 492
Morris 2013 Mexico (Mixed) HB PCR 130/189 22 71 37 115 145 46 95 48 22 71
Rostami 2015 Iran (Asian) HB PCR-RFLP 203/171 115 64 24 294 112 63 77 31 115 64
Mahmoudi 2015 Iran (Asian) PB PCR-RFLP 45/41 27 11 7 65 25 17 19 5 27 11
Karakus 2015 Turkey (Caucasian) PB PCR 199/185 49 105 45 203 195 47 98 40 49 105
Mohammadzadeh 2015 Iran (Asian) HB PCR-RFLP 100/100 36 55 9 127 73 52 45 3 36 55
Rodrigo 2017 Sri Lanka (Asian) PB PCR 80/80 32 43 5 107 53 53 24 3 32 43
Liu 2018 China (Asian) HB TOFMS 434/442 - 182 252 - 686 - 206 236 - 182
Geriki 2019 India (Asian) HB PCR-RFLP 93/186 15 45 33 75 111 54 75 57 15 45
LEPR rs1137101 AA AG GG A G AA AG GG A G
Snoussi 2006 Tunisia (African) NS PCR-RFLP 308/222 98 145 65 341 275 102 90 30 294 150
Woo 2006 Korea (Asian) HB PCR 45/45 0 12 33 12 78 0 8 37 8 82
Gallicchio 2007 USA (Caucasian) PB TaqMan 53/872 14 24 15 52 54 278 443 151 999 745
Han 2008 China (Asian) HB PCR 240/500 33 41 166 107 373 12 78 410 102 898
Okobia 2008 Nigeria (African) HB PCR-RFLP 209/209 46 107 56 199 219 56 107 46 219 199
Teras 2009 USA (Caucasian) PB SNP stream 648/659 128 332 181 588 694 125 314 211 564 736
Cleveland 2010 USA (Caucasian) PB PCR 1059/1098 173 521 355 867 1231 187 551 360 925 1271
Nyante 2011 USA (Caucasian) PB PCR 1972/1775 494 952 526 1940 2004 416 847 485 1679 1817
Kim 2012 Korea (Asian) HB PCR 390/447 8 88 294 104 676 6 91 350 103 791
Mohammadzadeh 2014 Iran (Asian) HB PCR-RFLP 100/100 25 56 19 106 94 54 40 6 148 52
Mahmoudi 2015 Iran (Asian) PB PCR-RFLP 45/41 19 25 1 63 27 17 18 6 52 30
Wang 2015 China (Asian) PB PCR-RFLP 150/128 20 25 105 65 235 3 19 106 25 231
Rodrigo 2017 Sri Lanka (Asian) PB PCR-RFLP 80/80 65 9 6 139 21 60 6 14 126 34
El-Hussiny 2017 Egypt (African) NS PCR-RFLP 48/79 24 15 9 63 33 22 24 2 68 28
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SOC, Source Of Controls; HB, Hospital Based; PB, Population Based; RFLP, Restriction Fragment Length Polymorphism; MAF, Minor Allele Frequency; HWE, Hardy-Weinberg Equilibrium

Quantitative Data Synthesis

PON1 rs662

Table 2 listed the main results of the meta-analysis of PON1 rs662 polymorphism and breast cancer risk. When all the eligible studies were pooled into the meta-analysis, a significant association was found between PON1 rs662 and breast cancer under all three genetic models, i.e., allele (G vs. A: OR= 0.719, 95% CI: 0.648-0.798; p≤0.001, Figure 1A), homozygote (GG vs. AA: OR= 0.542, 95% CI: 0.332-0.885; p=0.014) and dominant (GG+GA vs. AA: OR= 0.720, 95% CI: 0.330-0.864; p=0.011). When subgroup analysis by ethnicity performed the results showed that the PON1 rs662 polymorphism was associated with breast cancer risk among Caucasian women under two genetic models, i.e., homozygote (GG vs. AA: OR= 0.341, 95% CI: 0.134-0.866; p=0.024) and dominant (GG+GA vs. AA: OR= 0.317, 95% CI: 0.119-0.839; p=0.021), but not among Asians. Moreover, subgroup analysis by source of controls showed that the variant was associated with breast cancer in PB group of studies.

Table 2.

Meta-Analysis Results of Association between PON1 rs662 Polymorphism and Breast Cancer Risk

Polymorphism Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
Overall G vs. A Random 91.39 ≤0.001 0.719 0.648-0.798 -6.234 ≤0.001 0.063 0.467
GG vs. AA Random 73.8 ≤0.001 0.542 0.332-0.885 -2.446 0.014 0.035 0.221
GA vs. AA Fixed 14.09 0.32 1.011 0.800-1.278 0.092 0.926 0.901 0.374
GG+GA vs. AA Random 90.55 ≤0.001 0.534 0.330-0.864 -2.554 0.011 0.107 0.428
GG vs. GA+AA Random 62.05 0.01 0.72 0.492-1.053 -1.696 0.09 0.173 0.576
Ethnicity
Caucasian G vs. A Random 94.69 ≤0.001 0.48 0.220-1.047 -1.846 0.065 1 0.694
GG vs. AA Random 82.08 0.001 0.341 0.134-0.866 -2.262 0.024 1 0.479
GA vs. AA Random 57.75 0.069 0.894 0.481-1.661 -0.355 0.723 0.734 0.486
GG+GA vs. AA Random 93.93 ≤0.001 0.317 0.119-0.839 -2.312 0.021 1 0.578
GG vs. GA+AA Random 78.94 0.003 0.594 0.278-1.269 -1.345 0.179 0.734 0.872
Asian G vs. A Fixed 41.99 0.189 0.951 0.810-1.116 -0.617 0.537 NA NA
GG vs. AA Fixed 0 0.378 0.943 0.663-1.341 -0.326 0.744 NA NA
GA vs. AA Fixed 0 0.952 1.027 0.721-1.462 0.146 0.884 NA NA
GG+GA vs. AA Fixed 50.42 0.159 0.931 0.751-1.153 -0.658 0.511 NA NA
GG vs. GA+AA Fixed 0 0.607 0.959 0.688-1.337 -0.247 0.805 NA NA
Source of Controls
HB G vs. A Fixed 54.8 0.109 0.923 0.789-1.079 -1.01 0.313 0.296 0.332
GG vs. AA Fixed 36.58 0.207 0.878 0.625-1.233 -0.75 0.453 0.296 0.105
GA vs. AA Fixed 0 0.918 1.05 0.750-1.470 0.284 0.777 0.296 0.163
GG+GA vs. AA Fixed 52.79 0.12 0.904 0.732-1.117 -0.933 0.351 0.296 0.429
GG vs. GA+AA Fixed 0 0.514 0.907 0.662-1.243 -0.609 0.543 0.296 0.007
PB G vs. A Random 93.28 ≤0.001 0.563 0.311-1.020 -1.895 0.058 0.462 0.793
GG vs. AA Random 77.27 0.001 0.445 0.215-0.920 -2.186 0.029 0.462 0.645
GA vs. AA Fixed 49.26 0.096 0.976 0.704-1.353 -0.147 0.883 0.22 0.354
GG+GA vs. AA Random 92.42 ≤0.001 0.429 0.209-0.878 -2.316 0.021 0.462 0.657
GG vs. GA+AA Random 73.1 0.005 658 0.351-1.232 -1.307 0.191 0.806 0.96
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PON1 rs854560

Table 2 listed the main results of the meta-analysis of PON1 rs854560 polymorphism and breast cancer risk. Pooled data showed that the PON1 rs854560 polymorphism was significantly associated with risk of breast cancer under all four genetic models, i.e., allele (A vs. T: OR=2.107, 95% CI: 1.401-3.167; p≤0.001), homozygote (AA vs. TT: OR= 3.214, 95% CI: 1.757-5.879; p≤0.001, Figure 2B), heterozygote (AT vs. TT: OR= 0.379, 95% CI: 0.208-0.691; p=0.002), dominant (AA+AT vs. TT: OR= 1.868, 95% CI: 1.293-2.700; p=0.001) and recessive (AA vs. AT+TT: OR= 3.067, 95% CI: 1.687-5.575; p≤0.001). Subgroup analysis by ethnicity revealed that PON1 rs854560 polymorphism was a significantly associated with breast cancer among Asian and Caucasian women.

Figure 2.

Figure 2

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Forest Plot for Association of the PON1 Polymorphisms with Breast Cancer Risk in Overall Population. A, rs662 (allele model); B, rs854560 (homozygote model)

LEPR rs1137101

Table 2 listed the main results of the meta-analysis of LEPR rs1137101 polymorphism and breast cancer risk. When all the eligible studies were pooled into the meta-analysis, no significant association was found between LEPR rs1137101 and breast cancer under all five genetic models in overall population. Subgroup analysis by ethnicity revealed that the variant was a significantly associated with breast cancer among African women under all four genetic models, i.e., allele (A vs. G: OR= 0.772, 95% CI: 1.161-1.654; p≤0.001), homozygote (AA vs. GG: OR= 0.772, 95% CI: 1.339-2.786; p≤0.001), heterozygote (AG vs. GG: OR= 0.772, 95% CI: 1.010-1.772; p=0.043), and dominant (AA+AG vs. GG: OR= 0.772, 95% CI: 1.268-2.137; p≤0.001), but not among Caucasians and Asians.

LEP rs7799039G>A

Table 2 listed the main results of the meta-analysis of LEP rs7799039G>A polymorphism and breast cancer risk. Pooled data showed that this polymorphism was not associated with risk of breast cancer under all four genetic models in overall population. Moreover, subgroup analysis by ethnicity and source of controls revealed that LEP rs7799039G>A polymorphism was not significantly associated with breast cancer.

Test of Heterogeneity and sensitivity analyses

As shown in Tables 2 and 4, there was a significant heterogeneity existed under most genetic models for PON1 rs662, rs854560, LEP rs7799039 and LEPR rs1137101 polymorphisms. Thus, stratified analyses by ethnicity and source of controls carried out to find the potential source of heterogeneity. Results showed that ethnicity and source of controls have overall effect on the heterogeneity for these polymorphisms. We carried out the sensitivity analyses to assess the robustness of the results by removing each study in turn and all the results were not essentially altered, suggesting that the results of the present meta-analysis were statistically stable.

Table 4.

Meta-Analysis Results of Association between LEP rs7799039 Polymorphism and Breast Cancer Risk

Polymorphism Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
Overall A vs. G Random 82.4 ≤0.001 0.98 0.761-1.263 -0.154 0.878 0.265 0.51
AA vs. GG Random 71.3 0.001 0.905 0.572-1.432 -0.428 0.669 0.386 0.405
AG vs. GG Fixed 12.18 0.335 0.993 0.857-1.150 -0.095 0.924 0.901 0.57
AA+AG vs. GG Random 84.71 ≤0.001 0.931 0.600-1.446 -0.319 0.75 0.386 0.48
AA vs. AG+GG Random 46.09 0.072 0.931 0.723-1.197 -0.56 0.576 0.71 0.506
Ethnicity
Asian A vs. G Random 89.62 ≤0.001 1.086 0.538-2.191 0.23 0.818 0.734 0.458
AA vs. GG Random 76.03 0.006 1.303 0.404-4.203 0.443 0.658 0.308 0.092
AG vs. GG Fixed 0 0.424 0.832 0.506-1.368 -0.724 0.469 1 0.294
AA+AG vs. GG Random 90.89 ≤0.001 1.051 0.393-2.810 0.099 0.921 0.734 0.609
AA vs. AG+GG Fixed 52.94 0.095 0.914 0.576-1.450 -0.382 0.702 0.308 0.068
Caucasian A vs. G Random 69.07 0.039 0.847 0.675-1.063 -1.431 0.153 1 0.815
AA vs. GG Random 71.32 0.031 0.672 0.390-1.157 -1.434 0.151 1 0.759
AG vs. GG Fixed 56.67 0.099 1.014 0.863-1.190 0.164 0.869 1 0.646
AA+AG vs. GG Fixed 64.42 0.06 0.708 0.466-1.076 -1.616 0.106 1 0.837
AA vs. AG+GG Fixed 64.81 0.058 0.903 0.776-1.052 -1.31 0.19 1 0.747
Source of Controls
HB A vs. G Random 90.52 ≤0.001 1.037 0.542-1.986 0.11 0.913 0.296 0.497
AA vs. GG Random 85.4 0.001 1.286 0.377-4.386 0.402 0.688 0.296 0.501
AG vs. GG Fixed 0 0.443 0.94 0.638-1.385 -0.313 0.754 1 0.259
AA+AG vs. GG Random 90.74 ≤0.001 1.09 0.412-2.886 0.173 0.863 0.846 0.066
AA vs. AG+GG Random 67.46 0.046 1.089 0.530-2.240 0.232 0.816 1 0.582
PB A vs. G Random 74.44 0.008 1.067 0.771-1.477 0.391 0.696 0.734 0.477
AA vs. GG Fixed 21.62 0.281 0.82 0.658-1.023 -1.761 0.078 0.308 0.175
AG vs. GG Fixed 0 0.711 0.919 0.770-1.096 -0.945 0.345 0.734 0.53
AA+AG vs. GG Random 84.25 ≤0.001 1.019 0.552-1.880 0.059 0.953 0.734 0.525
AA vs. AG+GG Fixed 0 0.842 1 0.847-1.180 -0.005 0.996 0.089 0.025
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Publication bias

The publication bias of the studies was evaluated using the funnel plot and Egger’s test. Publication bias was not seen in the funnel plot (Figure 3). No statistically significant difference was discovered in the Egger’s test for PON1 rs662, rs854560, LEP rs7799039 and LEPR rs1137101 polymorphisms, indicating low publication bias in the current meta-analysis. Moreover, funnel plots’ shape of all comparison models did not reveal any obvious evidence of asymmetry and all P values of Egger’s tests were more than 0.05, providing statistical evidence of funnel plots’ symmetry.

Figure 3.

Figure 3

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The Funnel Plots of Publication Bias for Association of the PON1, LEP and LEPR Polymorphism with Breast Cancer Risk in Overall Population. A: PON1 rs662 (allele mode); B: rs854560 (homozygote model), C: LEP rs7799039 (heterozygote model), and D: LEPR rs1137101 (dominant model)

Table 3.

Meta-Analysis Results of Association between PON1 rs854560 Polymorphism and Breast Cancer Risk

Polymorphism Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
Overall A vs. T Random 92.43 ≤0.001 2.107 1.401-3.167 3.582 ≤0.001 0.22 0.21
AA vs. TT Random 81.73 ≤0.001 3.214 1.757-5.879 3.789 ≤0.001 0.462 0.27
AT vs. TT Random 81.85 ≤0.001 0.379 0.208-0.691 -3.17 0.002 1 0.478
AA+AT vs. TT Random 81.83 ≤0.001 1.868 1.293-2.700 3.326 0.001 0.22 0.12
AA vs. AT+TT Random 84.64 ≤0.001 3.067 1.687-5.575 3.674 ≤0.001 0.462 0.375
Ethnicity
Asian A vs. T Random 82.54 0.003 1.785 1.150-2.772 2.581 0.01 0.296 0.248
AA vs. TT Fixed 0 0.536 2.387 1.573-3.622 4.09 ≤0.001 1 0.152
AG vs. TT Random 77.65 0.011 0.792 0.313-2.001 -0.493 0.622 1 0.751
AA+AT vs. TT Random 93.29 ≤0.001 1.212 0.469-3.132 0.397 0.691 1 0.802
AA vs. AT+TT Fixed 0 0.442 2.043 1.383-3.016 3.592 ≤0.001 0.296 0.317
Caucasian A vs. T Random 93.83 ≤0.001 1.56 0.941-2.587 1.725 0.085 NA NA
AA vs. TT Fixed 73.12 0.054 2.086 1.650-2.638 6.143 ≤0.001 NA NA
AG vs. TT Random 79.06 0.029 0.559 0.331-0.946 -2.169 0.03 NA NA
AA+AT vs. TT Random 79.34 0.028 1.491 0.987-2.253 1.897 0.058 NA NA
AA vs. AT+TT Fixed 81.38 0.02 1.878 1.134-3.109 2.45 0.014 NA NA
Source of Controls
HB A vs. T Random 82.54 0.003 1.785 1.150-2.772 2.581 0.01 0.296 0.248
AA vs. TT Random 90.35 ≤0.001 3.48 1.455-8.321 2.804 0.005 1 0.478
AG vs. TT Random 90.91 ≤0.001 0.316 0.131-0.761 -2.567 0.01 1 0.501
AA+AT vs. TT Random 93.29 ≤0.001 1.212 0.469-3.132 0.397 0.691 1 0.802
AA vs. AT+TT Random 92.04 ≤0.001 3.359 1.432-7.879 2.785 0.005 1 0.566
PB A vs. T Random 95.14 ≤0.001 2.254 1.228-4.140 2.622 0.009 1 0.482
AA vs. TT Random 90.35 ≤0.001 3.48 1.455-8.321 2.804 0.005 1 0.478
AG vs. TT Random 90.91 ≤0.001 0.316 0.131-0.761 -2.567 0.01 1 0.501
AA+AT vs. TT Random 82.19 0.004 1.84 1.137-2.976 2.483 0.013 0.296 0.413
AA vs. AT+TT Fixed 0 0.465 2.038 1.380-3.009 3.58 ≤0.001 0.296 0.33
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Table 5.

Meta-Analysis Results of Association between LEPR rs1137101 Polymorphism and Breast Cancer Risk

Polymorphism Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
Overall A vs. G Random 86.48 ≤0.001 0.943 0.780-1.139 -0.614 0.539 0.661 0.781
AA vs. GG Random 84.75 ≤0.001 0.928 0.631-1.365 -0.379 0.705 0.76 0.867
AG vs. GG Random 74.12 ≤0.001 0.991 0.763-1.289 -0.064 0.949 0.669 0.717
AA+AG vs. GG Random 83.33 ≤0.001 0.994 0.742-1.331 -0.041 0.967 0.427 0.761
AA vs. AG+GG Random 76.74 ≤0.001 0.965 0.767-1.214 -0.302 0.763 0.745 0.867
Ethnicity
Caucasian A vs. G Fixed 41.76 0.161 0.977 0.916-1.043 -0.688 0.491 0.734 0.349
AA vs. GG Fixed 41.86 0.16 0.96 0.842-1.094 -0.615 0.539 0.734 0.285
AG vs. GG Fixed 0 0.92 0.983 0.873-1.107 -0.287 0.774 1 0.202
AA+AG vs. GG Fixed 0 0.859 0.996 0.892-1.112 -0.071 0.944 1 0.273
AA vs. AG+GG Fixed 55.12 0.083 0.976 0.883-1.079 -0.468 0.639 0.734 0.431
Asian A vs. G Random 89.54 ≤0.001 0.711 0.419-1.207 -1.264 0.206 1 0.907
AA vs. GG Random 87.83 ≤0.001 0.442 0.124-1.570 -1.262 0.207 1 0.762
AG vs. GG Random 86.29 ≤0.001 0.749 0.273-2.054 -0.562 0.574 0.452 0.394
AA+AG vs. GG Random 90.55 ≤0.001 0.595 0.201-1.758 -0.939 0.347 0.452 0.397
AA vs. AG+GG Random 72.07 0.001 0.664 0.413-1.066 -1.697 0.09 0.763 0.888
African A vs. G Fixed 3.035 0.357 1.386 1.161-1.654 3.612 ≤0.001 1 0.813
AA vs. GG Fixed 2.894 0.358 1.931 1.339-2.786 3.52 ≤0.001 1 0.598
AG vs. GG Fixed 61.34 0.075 1.337 1.010-1.772 2.026 0.043 0.296 0.11
AA+AG vs. GG Fixed 0 0.423 1.647 1.268-2.137 3.747 ≤0.001 1 0.916
AA vs. AG+GG Fixed 63.26 0.066 1.845 0.997-3.415 1.949 0.051 0.296 0.199
Source of Controls
HB A vs. G Random 93.07 ≤0.001 0.931 0.504-1.721 -0.227 0.82 0.806 0.83
AA vs. GG Random 93.33 ≤0.001 0.964 0.209-4.445 -0.047 0.963 1 0.815
AG vs. GG Random 90.34 ≤0.001 0.866 0.285-2.629 -0.254 0.799 0.734 0.597
AA+AG vs. GG Random 93.72 ≤0.001 0.836 0.230-3.041 -0.272 0.786 0.734 0.651
AA vs. AG+GG Random 81.48 ≤0.001 0.963 0.567-1.635 -0.141 0.888 0.806 0.43
PB A vs. G Random 86.15 ≤0.001 0.816 0.666-0.999 -1.967 0.049 0.465 0.244
AA vs. GG Random 72.44 0.001 0.893 0.758-1.051 -1.361 0.173 0.367 0.271
AG vs. GG Fixed 8.403 0.364 0.979 0.871-1.100 -0.359 0.719 0.763 0.753
AA+AG vs. GG Fixed 40.64 0.12 0.974 0.875-1.086 -0.471 0.638 0.133 0.23
AA vs. AG+GG Random 67.54 0.005 0.882 0.699-1.112 -1.063 0.288 0.367 0.249
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Discussion

Genetics play an important role in development and progression breast cancer (Yazdi et al., 2015). There are more and more association studies searching susceptibility genes involved in breast cancer. To date, several variants within PON1 gene associated with susceptibility to breast cancer have been verified. rs662 and rs854560 polymorphism are the most characterized SNPs that are associated with development this disease. Our present work indicated that both rs662 and rs854560 polymorphisms at PON1 gene were associated with an increased risk of BC in the overall population. All previous meta-analysis have indicated that PON1 rs662 was associated with risk of breast cancer, but not rs854560. Two meta-analysis by Fang et al., (2012) and Saadat (2012) suggested that the PON1 rs662 is a risk factor for the development of breast cancer. Wu et al., (2017) evaluated the associations of PON1 rs662 and rs854560 polymorphisms with risk of breast cancer in 365 cases and 378 controls from the Guangxi region of southern China. Their results showed that PON1 rs854560 genetic polymorphisms may be associated with the risk of BC. However, they have found that rs662 polymorphism was not associated with breast cancer risk, or with any of the clinicopathological parameters. Pan et al., (2019) in meta-analysis reported that the PON1 rs662 is associated with decrease of breast cancer risk. Their results showed an increased risk in the Caucasian and Asian population as well as HB group and PB group. However, there was an association between rs854560 polymorphism and increased breast cancer risk. Liu et al., (2019) in a mate-analysis revealed that PON1 rs854560 polymorphism could be used to identify individual with elevated susceptibility to breast cancer. However, they have not found any positive association between PON1 rs662 polymorphism and breast cancer in polled analyses. In other meta-analysis, Zhang et al., (2015) found that PON1 rs662 polymorphism was associated with a decreased risk in breast cancer. Our meta-analysis supports the growing body of evidence that the PON1 rs662 and rs854560 polymorphisms is emerging as a RISK factor for breast cancer.

Our pooled data indicated that LEP rs7799039 variant was not associated with risk of breast cancer in overall population and ethnicity. Liu and Liu (2011) in a meta-analysis based on three studies with 2,003 cases and 1,967 controls revealed for LEP rs7799039G>A polymorphism and nine studies with 4,627 cases and 5,476 controls for LEPR rs1137101 revealed that these polymorphisms were not associated with breast cancer risk. However, Yan et al., (2016) in a meta-analysis suggests that the LEP rs7799039G>A plays an important role in breast cancer susceptibility, especially in Caucasian. Although previous meta-analyses have reported the association between rs7799039 and LEPR rs1137101 polymorphisms and susceptibility to breast cancer, the current meta-analysis was more in the number of studies included and larger in sample size, which comparatively reduced the influence of contingency on the pooled data. Therefore, our conclusions were more persuasive and accurate than previous meta-analysis.

The current meta-analysis has several limitations. Therefore, some conclusions of this study should be cautiously interpreted. First, only a small number of studies were found on PON1 polymorphisms. Further studies are still required to confirm the relationship of these polymorphisms with breast cancer in different populations, especially in African and mixed populations. Second, in this work there was a considerable heterogeneity in overall population studies. Differences of ethnicity, genotyping methods and source of controls may partially explain the significant heterogeneity. Moreover, various adjusted confounders, different study designs, and other undetected factors may also lead to the presence of heterogeneity. Finally, none of the included studies separately analyzed the relations of different confounders such as age, lifestyle, family history, hormone therapy, etc. in addition, breast cancer is a complex disease which is influenced by the environment, genetic factors, and genotype-environment interactions. Thus, these interactions in development of breast cancer should be considered.

In summary, this meta-analysis aimed to summarize association between the PON1 rs662, rs854560 LEP rs7799039 and LEPR rs1137101 polymorphisms and susceptibility to breast cancer. The pooled data revealed that rs662 and rs854560 polymorphisms were associated with risk of BC and could potentially serve as useful genetic markers for breast cancer. However, there was no association between LEP rs7799039 and LEPR rs1137101 polymorphisms and breast cancer risk. More studies among different ethnicities are required to be done to reinforce the results of the current study. Nevertheless, gene-gene or gene-environment interaction which is closely related to development of breast cancer should be considered in future studies.

Author Contribution Statement

Soheila Sayad, Meraj Farbod: conceptualization, investigation. Seyed Alireza Dastgheib, Mojgan Karimi-Zarchi: Software, original draft preparation. Seyedali Salari, Seyed Hossein Shaker: Investigation. Fatemeh Asadian: Investigation, writing. Fatemeh Asadian, Hossein Neamatzadeh: Methodology, software. Seyed Alireza Dastgheib: Formal analysis, investigation. Seyed Hossein Shaker: Project administration. Jalal Sadeghizadeh-Yazdi, Hossein Neamatzadeh: Writing, reviewing, editing

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable for this manuscript.

Availability of data and material

The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of interest/Competing interests

The authors declare that they have no conflict of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.

Articles from Asian Pacific Journal of Cancer Prevention : APJCP are provided here courtesy of West Asia Organization for Cancer Prevention

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