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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Aug 15;8(8):12226–12238.

Peroxisome proliferator-activated receptor gamma (PPARG) polymorphisms and breast cancer susceptibility: a meta-analysis

Weifeng Tang 1,*, Yuanmei Chen 2,*, Yafeng Wang 3,*, Haiyong Gu 4, Shuchen Chen 1, Mingqiang Kang 1
PMCID: PMC4612818  PMID: 26550133

Abstract

Background: Peroxisome proliferator-activated receptor gamma (PPARG), a nuclear hormone receptor, plays a critical role in the lipid and glucose homeostasis, adipocyte differentiation, as well as intracellular insulin-signaling events. Several studies have been conducted to explore the associations of PPARG polymorphisms with breast cancer (BC), yet the findings are inconsistent. Methods: Databases of Pubmed and Embase were searched until October 5, 2014. The association between PPARG polymorphisms and BC risk was determined by crude odds ratios (ORs) with their 95% confidence intervals (CIs). Results: Finally, there are nine publications involving 3,931 BC cases and 5,382 controls included in this meta-analysis. No significant association was observed between PPARG rs1801282 C>G variants and overall BC risk in all genetic comparison models. However, in a subgroup analysis by ethnicity, significant association was observed between PPARG rs1801282 C>G variants and decreased BC risk in three genetic models: GG+CG vs. CC (OR, 0.83; 95% CI, 0.71-0.96; P = 0.011), CG vs. CC (OR, 0.82; 95% CI, 0.71-0.96; P = 0.011) and G vs. C (OR, 0.85; 95% CI, 0.75-0.97; P = 0.016) in Caucasians and in a subgroup analysis by menopausal status, significantly decreased BC risk was also found in two genetic models: GG+CG vs. CC (OR, 0.79; 95% CI, 0.67-0.95; P = 0.011) and CG vs. CC (OR, 0.77; 95% CI, 0.64-0.92; P = 0.005) in post-menopause subgroup. For PPARG rs3856806 C>T, we found no significant association between PPARG rs3856806 C>T polymorphism and breast cancer. Conclusions: In summary, despite some limitations, the results suggest that PPARG rs1801282 C>G polymorphism may be a protective factor for BC in Caucasians and in post-menopause women.

Keywords: Breast cancer, polymorphism, peroxisome proliferator-activated receptor gamma, meta-analysis

Introduction

Breast cancer (BC) is the most frequently diagnosed cancer in female with an estimated 1,676,600 new BC cases and 521,900 BC related deaths in 2012 worldwide [1]. BC is a common disease that is attributed to multiple genetic and environmental risk factors. Recently, a number of candidate genes, such as BRCA1/2, TP53, BRIP1 and PALB2, have been confirmed to contribute to the risk of breast cancer [2-6]. These mutations may play a critical role in the development of BC. BRCA1/2 is a high penetrance gene [4] and 80% of individuals who carry this mutation may develop to BC. Compared to the normal population, the medium penetrance genes PALB2, CHEK2 and BRIP1 can increase 2.3-, 2.2-, and 2.0-fold risks of BC, respectively [2,3]. Some low penetrance genes FGFR2, ESR1, MAP3K1 and TOX3 have been investigated in several genome-wide association studies (GWAS) and were confirmed as candidates of BC [7-11]. Thus, in all probability, there are a crowd of low penetrance mutations in genes contributing to the remaining unexplained susceptibility of BC, which have not yet been verified.

Accumulating epidemiological evidence highlights that impaired glucose tolerance and type 2 diabetes are associated with the risk of cancer [12-15]. Peroxisome proliferator-activated receptor gamma (PPARG), a nuclear hormone receptor, acts as a critical regulator of lipid and glucose homeostasis, adipocyte differentiation, and intracellular insulin-signaling events. A number of investigations have therefore explored the hypothesis that the mutation of PPARG gene influences the development and progression of malignancy [16-21]. The PPARG single nucleotide polymorphisms (SNPs) are deemed to alter the activity of PPARG. This gene is polymorphic, and a number of SNPs have been studied, such as the rs1801282, rs3856806, rs4135247, rs1175543, rs709158 and rs2938395 polymorphisms, etc. Among them, the rs1801282 C>G and rs3856806 C>T are the most widely explored for correlation with cancer susceptibility. In a previous review, PPARG rs1801282 C>G polymorphism were correlated with protection from colorectal cancer, but with an increased susceptibility of gastric carcinoma and rs3856806 C>T polymorphism was marginally associated with the risk of cancer [22].

Recently, more studies have focused on the association of PPARG SNPs with BC [23-30]. Some of them identified the potential correlation between PPARG SNPs with BC risk [26,28]. A meta-analyses including three investigations confirmed that PPARG rs1801282 C>G was associated with the decreased risk of BC [31]; however, the other meta-analysis suggested that PPARG SNPs were not associated with BC [30]. At present, more studies have demonstrated that PPARG SNPs may clarify the causes and events correlated with BC; nevertheless, the results remain inconclusive. Therefore, in this study, we performed an updated meta-analysis to further explore the role of the PPARG polymorphisms in BC risk.

Materials and methods

Our study is reported on the basis of the PRISMA (Preferred Reporting Items for Meta-analyses) guideline (Table S1. PRISMA checklist) [32].

Search strategy

We searched literatures from PubMed and Embase databases (published up to October 5, 2014) using the following terms ‘Peroxisome proliferator-activated receptor gamma’, ‘PPARG’ ‘PPARγ’, ‘polymorphism’, ‘mutation’, ‘variant’, ‘cancer’, ‘carcinoma’, ‘malignance’ and ‘breast’. In order to minimize potential publication bias, additional relevant studies in the citations were also manually scanned. Only the latest study with the largest samples was recruited in our study to avoid overlapping data.

Inclusion and exclusion criteria

In our meta-analysis, all studies included had to meet all the following criteria: (a) case-control or cohort studies which assessed the association of PPARG SNPs with BC risk; (b) the available frequencies of genotypes or alleles must be provided and the genotype distribution among controls complied with the Hardy-Weinberg equilibrium (HWE). The major reasons for exclusion of studies were: (a) incomplete data; (b) duplicated studies or overlapping data; (c) only relevant to BC treatment; (d) meta-analysis, review, editorial, comment or letter.

Data extraction

In a standardized form, three reviewers (W. Tang, Y. Chen and Y. Wang) independently checked and extracted the data from all included publications. The following characteristic terms were extracted: the surname of first author, the year of publication, the country of origin, the ethnicity of participants, the allele and genotype frequencies, the genotyping method, the sample size, and the evidence of HWE in controls. If different results generated, disputes were settled by consulting the third reviewer (H. Gu).

Methodological quality assessment

The quality assessment was carefully performed by three authors (W. Tang, Y. Chen and Y. Wang) according to the ‘methodological quality assessment scale’ described previously [33,34]. Scores range from 0 to 10, and if the quality scores were ≥ 6, publications were defined as ‘high quality’; otherwise, they were classified as ‘low quality’.

Statistical analysis

In our study, the pooled odds ratios (ORs) with 95% confidence intervals (CIs) were assessed for dominant model, recessive model, heterozygote comparison, homozygote comparison and allelic comparison. Stratified analyses were conducted by ethnicity, menopausal status, source of controls and sample sizes. Heterogeneity among the studies was assessed by using a χ2-test-based Q statistic test. The value of P < 0.1 showed substantial heterogeneity across the publications, then the data were pooled by using the random-effects model (DerSimonian and Laird) [35]; otherwise, the fixed-effects model was used (Mantel-Haenszel) [36]. Both one-way sensitivity analysis and “trim-and-fill” method were conducted to evaluate the stability of this meta-analysis. Potential publication bias across the studies was assessed by a funnel plots and Egger’s linear regression test. The distribution of the genotypes in control subjects was checked for HWE using a web-based χ2 test program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). All data analysis was conducted with STATA 12.0 software package (Stata Corp LP, College Station, Texas).

Results

Study characteristics

As shown in Figure 1, a total of 142 potentially relevant publications were obtained based on the search terms from PubMed and Embase databases. Finally, there were nine publications involving 3,931 BC cases and 5,382 controls included in this meta-analysis. All subjects were female. For PPARG rs1801282 C>G polymorphism, eight publications focusing on the association of this SNP with BC risk remained in the pooled analysis [23-29]. As for subjects in these studies, four were Caucasians [25,26,28,29]; two were mixed populations [23,27] and two were Asians [24,30]. As for menopausal status, three studies investigated post-menopause women [25,26,28], while five studies investigated overall adult women [23,24,27,29,30]. For PPARG rs3856806 C>T polymorphism, three studies were included [24,30,37]. Among them, all were Asians and investigated overall adult women. The detailed characteristics of the eligible studies and distribution of the PPARG polymorphisms as well as alleles are summarized in Tables 1 and 2, respectively.

Figure 1.

Figure 1

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Flow chart shows studies included procedure for meta-analysis.

Table 1.

Characteristics of the included studies and the results of the methodological quality assessment scale

Study Publication year Ethnicity Country Source of controls Menopausal status Sample size (case/control) PPARG polymorphisms Genotype method
Park et al. 2014 Asians Korea HB Mixed status 456/461 rs1801282 and rs3856806 MALDI-TOF MS
Martinez-Nava et al. 2013 Mixed populations Mexico PB Mixed status 208/220 rs1801282 RT-PCR
Wei 2013 Asians China HB Mixed status 216/216 rs3856806 MALDI-TOF MS
Petersen et al. 2012 Caucasians Denmark PB Post-menopaused 798/798 rs1801282 TaqMan
Wu et al. 2011 Asians China HB Mixed status 291/589 rs1801282 and rs3856806 RT-PCR
Justenhoven et al. 2008 Caucasians German PB Mixed status 688/724 rs1801282 MALDI-TOF MS
Gallicchio et al. 2007 Caucasians USA PB Post-menopaused 61/933 rs1801282 TaqMan
Wang et al. 2007 Caucasians USA PB Post-menopaused 488/488 rs1801282 TaqMan
Memisoglu et al. 2002 Mixed populations USA PB Mixed status 725/953 rs1801282 PCR-RFLP
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RT-PCR: real-time PCR; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; MALDI-TOF MS: Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry.

Table 2.

Distribution of PPARG polymorphisms genotype and allele

PPARG polymorphisms Study Case genotype Control genotype Case allele Control allele HWE Quality scores
rs1801282 C>G CC CG GG CC CG GG G C G C
Martinez-Nava et al. 165 43 0 169 49 2 43 373 53 387 0.448105 8.0
Park et al. 413 40 2 412 42 1 44 866 44 866 0.948224 6.5
Wu et al. 260 29 0 546 40 0 29 549 40 1132 0.392337 7.0
Gallicchio et al. 48 7 1 689 188 18 9 103 224 1566 0.223793 8.5
Wang et al. 376 87 15 375 98 5 117 839 108 848 0.615475 6.5
Memisoglu et al. 563 148 14 752 190 11 176 1274 212 1694 0.795703 7.0
Petersen et al. 616 167 15 569 209 20 197 1399 249 1347 0.876910 7.5
Justenhoven et al. 452 135 6 462 145 15 147 1039 175 1069 0.372101 7.5
rs3856806 C>T CC CT TT CC CT TT C T C T
Park et al. 320 128 8 311 117 15 768 144 739 147 0.335483 6.5
Wei 115 69 15 122 69 9 299 99 313 87 0.848027 6.5
Wu et al. 162 110 19 328 219 40 434 148 875 299 0.675591 7.0
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HWE: Hardy-Weinberg equilibrium.

Association of PPARG rs1801282 C>G polymorphism with BC risk

In total, 3,715 BC cases and 5,166 controls from eight eligible studies were relevant to the association between PPARG rs1801282 C>G polymorphism and BC. In overall meta-analysis, we found no association between PPARG rs1801282 C>G polymorphism and BC risk: GG+CG vs. CC (OR, 0.92; 95% CI, 0.82-1.03; P = 0.132), GG vs. CG+CC (OR, 1.05; 95% CI, 0.56-1.96; P = 0.884), GG vs. CC (OR, 1.02; 95% CI, 0.54-1.93; P = 0.963), CG vs. CC (OR, 0.91; 95% CI, 0.81-1.02; P = 0.107) and G vs. C (OR, 0.95; 95% CI, 0.81-1.11; P = 0.503) (Table 3). In a subgroup analysis by ethnicity, significantly decreased BC risk was confirmed in three genetic models: GG+CG vs. CC (OR, 0.83; 95% CI, 0.71-0.96; P = 0.011), CG vs. CC (OR, 0.82; 95% CI, 0.71-0.96; P = 0.011) and G vs. C (OR, 0.85; 95% CI, 0.75-0.97; P = 0.016) in Caucasians, but not in non-Caucasians (Table 3; Figure 2). In a subgroup analysis by menopausal status, significant decreased BC risk was also found in two genetic models: GG+CG vs. CC (OR, 0.79; 95% CI, 0.67-0.95; P = 0.011) and CG vs. CC (OR, 0.77; 95% CI, 0.64-0.92; P = 0.005) in post-menopause subgroup, but not mixed status (Table 3; Figure 3).

Table 3.

Meta-Analysis of PPARG rs1801282 C>G polymorphism with the breast cancer risk

No. of study G vs. C GG vs. CC GG+CG vs. CC GG vs. CG+CC CG vs. CC
OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test)
Total 8 0.95 (0.81-1.11) 0.503 0.065 1.02 (0.54-1.93) 0.963 0.062 0.92 (0.82-1.03) 0.132 0.119 1.05 (0.56-1.96) 0.884 0.071 0.91 (0.81-1.02) 0.107 0.178
Ethnicity
    Asians 2 1.19 (0.86-1.64) 0.302 0.225 2.00 (0.18-22.09) 0.573 NA 1.18 (0.85-1.65) 0.327 0.192 2.00 (0.18-22.18) 0.571 NA 1.17 (0.83-1.64) 0.363 0.171
    Caucasians 4 0.85 (0.75-0.97) 0.016 0.158 0.90 (0.37-2.16) 0.809 0.038 0.83 (0.71-0.96) 0.011 0.287 0.94 (0.39-2.25) 0.890 0.038 0.82 (0.71-0.96) 0.011 0.353
    Mixed populations 2 1.05 (0.86-1.26) 0.647 0.265 1.39 (0.66-2.92) 0.389 0.184 1.03 (0.83-1.27) 0.792 0.402 1.38 (0.66-2.90) 0.394 0.190 1.01 (0.81-1.25) 0.939 0.582
Menopausal status
    Post-menopaused 3 0.85 (0.63-1.15) 0.298 0.075 1.21 (0.41-3.57) 0.723 0.062 0.79 (0.67-0.95) 0.011 0.202 1.29 (0.46-3.64) 0.631 0.074 0.77 (0.64-0.92) 0.005 0.442
    Mixed status 5 1.00 (0.88-1.15) 0.958 0.237 0.85 (0.30-2.38) 0.750 0.093 1.01 (0.87-1.01) 0.865 0.395 0.85 (0.31-2.35) 0.754 0.099 1.02 (0.88-1.18) 0.801 0.537
Source of controls
    PB 6 0.91 (0.77-1.06) 0.234 0.093 0.97 (0.49-1.93) 0.930 0.039 0.89 (0.79-1.00) 0.051 0.203 1.00 (0.51-1.98) 0.991 0.045 0.88 (0.78-0.99) 0.041 0.322
    HB 2 1.19 (0.86-1.64) 0.302 0.225 2.00 (0.18-22.09) 0.573 NA 1.18 (0.85-1.65) 0.327 0.192 2.00 (0.18-22.09) 0.571 NA 1.17 (0.83-1.64) 0.363 0.171
Sample sizes
    < 1000 5 1.02 (0.85-1.22) 0.829 0.242 1.78 (0.85-3.73) 0.125 0.316 0.98 (0.81-1.19) 0.837 0.252 1.86 (0.89-3.89) 0.102 0.332 0.94 (0.77-1.15) 0.563 0.231
    ≥ 1000 3 0.90 (0.72-1.12) 0.341 0.043 0.80 (0.37-1.73) 0.578 0.064 0.89 (0.71-1.12) 0.318 0.069 0.83 (0.39-1.74) 0.619 0.075 0.89 (0.77-1.03) 0.118 0.115
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PB: Population-based; HB: Hospital-based.

Figure 2.

Figure 2

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Forest plot of breast cancer risk associated with PPARG rs1801282 C>G polymorphism for the G vs. C (fixed effects model).

Figure 3.

Figure 3

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Forest plot of breast cancer risk associated with PPARG rs1801282 C>G polymorphism for the GG+CG vs. CC (fixed effects model).

Association of PPARG rs3856806 C>T polymorphism with BC risk

A total of 963 BC cases and 1,266 controls from three publications focused on the association of PPARG rs3856806 C>T with BC were enrolled for the current study. In pooled analysis, we found no significant association between them: TT+CT vs. CC (OR, 1.03; 95% CI, 0.86-1.23; P = 0.737), TT vs. CT+CC (OR, 0.95; 95% CI, 0.63-1.43; P = 0.806), TT vs. CC (OR, 0.96; 95% CI, 0.63-1.45; P = 0.843), CT vs. CC (OR, 1.04; 95% CI, 0.87-1.26; P = 0.652) and T vs. C (OR, 1.01; 95% CI, 0.87-1.18; P = 0.849) (Table 4).

Table 4.

Meta-Analysis of PPARG rs3856806 C>T polymorphism with the breast cancer risk

Genetic comparison OR (95% CI) P Test of heterogeneity
p-Value Model
TT+CT vs. CC 1.03 (0.86-1.23) 0.737 0.853 F
TT vs. CT+CC 0.95 (0.63-1.43) 0.806 0.143 F
TT vs. CC 0.96 (0.63-1.45) 0.843 0.147 F
CT vs. CC 1.04 (0.87-1.26) 0.652 0.975 F
T vs. C 1.01 (0.87-1.18) 0.849 0.531 F
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Publication bias for PPARG rs1801282 C>G polymorphism

Funnel plots and the Egger’s linear regression test were conducted to check potential publication bias across literatures. The shape of the funnel plot appeared to be symmetrical in all comparison models supported by Egger’s test (G vs. C: Begg’s test P = 0.711, Egger’s test P = 0.780; GG vs. CC: Begg’s test P = 1.000, Egger’s test P = 0.929; GG+CG vs. CC: Begg’s test P = 1.000, Egger’s test P = 0.826; GG vs. CG+CC: Begg’s test P = 1.000, Egger’s test P = 0.925; CG vs. CC: Begg’s test P = 1.000, Egger’s test P = 0.865; Figure 4).

Figure 4.

Figure 4

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Begg’s funnel plot analysis of PPARG rs1801282 C>G polymorphism with breast cancer risk for the G vs. C (random-effects model).

Sensitivity analyses for PPARG rs1801282 C>G polymorphism

Both one-way sensitivity analysis and “trim-and-fill” method were carried out to verify the stability of this meta-analysis. The adjusted ORs and CIs of nonparametric “trim-and-fill” method were not substantially altered (G vs. C: adjusted pooled OR = 0.95, 95% CI: 0.81-1.11, P = 0.503; GG vs. CC: adjusted pooled OR = 1.02, 95% CI: 0.54-1.93, P = 0.963; GG+CG vs. CC: adjusted pooled OR = 0.92, 95% CI: 0.82-1.03, P = 0.148; GG vs. CG+CC: adjusted pooled OR = 1.05, 95% CI: 0.56-1.96, P = 0.884; CG vs. CC: adjusted pooled OR = 0.91, 95% CI: 0.81-1.03, P = 0.122; Figure 5), verifying the stability of our findings. Results of one-way sensitivity analysis were not significantly changed when any study was removed in turn, attesting the robustness of our findings (Figure 6).

Figure 5.

Figure 5

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Filled funnel plot of PPARG rs1801282 C>G polymorphism with breast cancer risk for the G vs. C (random-effects model).

Figure 6.

Figure 6

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One-way sensitivity analysis of PPARG rs1801282 C>G polymorphism with breast cancer risk for the G vs. C (random-effects model).

Tests for heterogeneity for PPARG rs1801282 C>G polymorphism

Heterogeneity between studies was summarized in Table 3. Results of subgroup analysis indicated that the investigations conducted in Caucasians, post-menopause, population-based and large sample sizes (≥ 1000) subgroups might contribute to the major heterogeneity.

Results of quality assessment

According to the ‘methodological quality assessment scale’ [33,34], quality assessment was performed in all included publications. The results indicated that all studies were “high quality” (quality scores ≥ 6; Table 2), suggesting the reliability of our findings.

Discussion

PPARG, a member of the nuclear hormone receptor superfamily, could recognize and bind to PPARG response elements, subsequently regulate and potentially affect the transcription of target genes in the promoter region. Given the PPARG has shown pro-apoptotic, pro-differentiation and anti-proliferative properties after activation, it is deemed to have overall anti-carcinogenic effects in a number of cell types [38]. In view of these findings, the PPARG SNPs have been intensively studied for the association with BC recently. Results of one pooled analysis highlighted that the PPARG rs1801282 G allele modestly modified the susceptibility of breast cancer [31]. In contrast, the other previous meta-analysis indicated that both PPARG rs1801282 G allele and rs3856806 T allele did not affect the BC risk [30]. These seemingly conflicting findings have inspired more studies on correlation of PPARG SNPs with BC risk. In the light of these results, we summarized data for 3,931 BC cases and 5,382 controls from nine recruited publications and attempted to evaluate the risk of PPARG SNPs to BC by a comprehensive meta-analysis. Our results indicated that PPARG rs1801282 G allele might modify the susceptibility of breast cancer in Caucasians and in post-menopause women [31].

PPARG is an important transcription factor which acts as a controller in inflammatory cytokine production, insulin sensitization, lipogenesis, glucose homeostasis and cell differentiation [39]. The PPARG rs1801282 C>G polymorphism, a most common SNP in exon B of PPARG, encodes a Pro→Ala substitution at amino acid residue 12 (Pro 12 Ala) [40]. As a previous study has shown this missense substitution of rs1801282 C>G could cause less transcriptional activation of target genes in vitro [41], it may presumably affect cell differentiation and then alters the risk of BC. In combination with our results, these findings suggested that the PPARG rs1801282 C>G variants might be a protective factor for BC, probably through increasing binding capacity for certain PPARG response elements and promoting the ability of pro-apoptotic, pro-differentiation and anti-proliferative properties.

PPARG rs3856806 C>T polymorphism, another important SNP, has been suggested to have inverse associations with body weight compared to PPARG rs1801282 C>G polymorphism and relate to inflammation response [42]. It has been reported that PPARG rs3856806 C>T polymorphism is correlated with several cancer risk including colorectal cancer [43-45], follicular lymphoma [46] and ovarian carcinoma [47]. This pooled study is to explore possible association of this functional mutation (rs3856806 C>T) in the PPARG gene with BC risk. Our results indicated that PPARG rs3856806 C>T polymorphism was not associated with the risk of BC, which was consistent with the previous study [30]. This conclusion, however, should be elucidated with caution as only three moderate sample sizes studies were included, which may have insufficient power to detect a reliable correlation. In the future, more studies with large sample sizes are warranted to verify our findings.

There are some merits in our study. First of all, the sample sizes were larger as compared with previous studies. Secondly, we confirmed for the first time PPARG rs1801282 G allele was correlated with the susceptibility of breast cancer in Caucasians and in post-menopause women. Finally, the quality scores of all recruited studies were ≥ 6.5 (‘high quality’, Table 2), suggesting the reliability of our results. However, certain potential limitations that may lead to bias are also acknowledged and addressed. This meta-analysis only used published studies; certain publication bias may inevitably exist. Moreover, the included publications were performed mainly in Caucasians, only two Asians and two mixed populations were recruited, which restricted the power to detect a real influence. Hence, more large-scale studies in more diverse populations are needed. Furthermore, due to lack of genotype frequency information, we did not conduct further evaluation of potential interactions, such as age, family history, hormone replacement therapy use, oral contraceptives use, body mass index, other environmental factors and lifestyle. In consideration of the complexity of cancer etiology, these gene-environment interactions should not be ignored. Finally, the association between other important polymorphisms (e.g., PPARG rs4135247, rs1175543, rs709158 and rs2938395) and BC was seldom explored, these polymorphisms were not considered in our study.

In summary, this updated meta-analysis suggests that PPARG rs1801282 C>G variants are associated with a significantly decreased risk of BC in Caucasians and in post-menopause women. As only nine publications were included in our analysis and the evidence was relatively limited, more large and well-designed epidemiological studies with the consideration of gene-gene and gene-environment interactions are definitely demanded.

Acknowledgements

This study was supported in part by Jiangsu University Clinical Medicine Science and Technology Development Fund (JLY20140012), National Natural Science Foundation of China (81472332, 81341006), Fujian Province Natural Science Foundation (2013J01126, 2013J05116), Fujian Medical University professor fund (JS12008). The Fund of Union Hospital (2015TC-1-048 and 2015TC-2-004), Fujian Province Science and Technology Programmed Fund (2012Y0030), Fujian Medical Innovation Fund (2014-CX-15) and Union Hospital Fund (2015TC-1-048 and 2015TC-2-004).

Disclosure of conflict of interest

None.

Supporting Information

ijcem0008-12226-f7.pdf (235.2KB, pdf)

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