Introduction

Breast cancer is currently the most frequently occurring cancer and one of the leading causes of cancer-related death in the world, which has become a major public health challenge [1]. The mechanism of breast carcinogenesis is still not fully understood. It has been suggested that low-penetrance susceptibility genes combining with environmental factors may be important in the development of cancer [2]. In recent years, several common low-penetrant genes have been identified as potential breast cancer susceptibility genes. An important one is insulin-like growth factor binding protein 3 (IGFBP3), which plays an important role in cellular proliferation, differentiation, apoptosis, and mammary carcinogenesis [3–5]. Several polymorphisms were identified to influence the circulating IGFBP3 level [6, 7]. An important and frequent one is the promoter A-202C polymorphism (rs2854744), locating at position -202 from the transcription start site of IGFBP3 and resulting in a reduced promoter activity and decreased IGFBP3 level [6, 7]. This polymorphism to breast cancer risk has been a research focus in scientific community and has drawn increasing attention. Several original studies have reported the role of IGFBP3 A-202C polymorphism in breast cancer risk [8–17], but the results are inconclusive, partially because of the possible small effect of the polymorphism on breast cancer risk and the relatively small sample size in each of published studies. Therefore, we performed this meta-analysis to derive a more precise estimation of these associations.

Methods

Publication search

Medline, PubMed, Embase, and Web of Science were searched (last search was updated on Dec 20, 2009, using the search terms: “IGFBP3”, “polymorphism” and “breast”). All searched studies were retrieved, and their bibliographies were checked for other relevant publications. Review articles and bibliographies of other relevant studies identified were hand-searched to find additional eligible studies. Only published studies with full text articles were included. When more than one of the same patient population was included in several publications, only the most recent or complete study was used in this meta-analysis.

Inclusion criteria

The inclusion criteria were: (a) evaluation of the IGFBP3 A-202C polymorphism and breast cancer risk, (b) case–control studies, and (c) sufficient published data for estimating an odds ratio (OR) with 95% confidence interval (CI).

Data extraction

Information was carefully extracted from all eligible publications independently by two of the authors according to the inclusion criteria listed above. Disagreement was resolved by discussion between the two authors. If these two authors could not reach a consensus, another author was consulted to resolve the dispute and a final decision was made by the majority of the votes. The following data were collected from each study: first author’s name, publication date, ethnicity, study design, menopausal status, total number of cases and controls, and numbers of cases and controls with the IGFBP3 A-202C genotypes, respectively. Different ethnicities were categorized as Caucasian, Asian, African, and mixed. Study design was stratified to population-based studies, hospital-based studies, or nest-based studies. Menopausal status was divided to premenopausal and postmenopausal. We did not define any minimum number of patients to include in our meta-analysis.

Statistical methods

Crude ORs with 95% CIs were used to assess the strength of association between the IGFBP3 A-202C polymorphism and breast cancer risk. The pooled ORs were performed for co-dominant model (AC vs. AA, CC vs. AA), dominant model (AC + CC vs. AA), and recessive model (CC vs. AA + AC), respectively. Heterogeneity assumption was checked by the chi-square-based Q test [18]. A P value greater than 0.10 for the Q test indicates a lack of heterogeneity among studies, so the pooled OR estimate of the each study was calculated by the fixed-effects model (the Mantel–Haenszel method) [19]. Otherwise, the random-effects model (the DerSimonian and Laird method) was used [20]. Subgroup analyses were performed by ethnicity, study design, and menopausal status. Sensitivity analysis was performed to assess the stability of the results. A single study involved in the meta-analysis was deleted each time to reflect the influence of the individual data-set to the pooled ORs [21]. An estimate of potential publication bias was carried out by the funnel plot, in which the standard error of log (OR) of each study was plotted against its log (OR). An asymmetric plot suggests a possible publication bias. Funnel plot asymmetry was assessed by the method of Egger’s linear regression test, a linear regression approach to measure funnel plot asymmetry on the natural logarithm scale of the OR. The significance of the intercept was determined by the t test suggested by Egger (P < 0.05 was considered representative of statistically significant publication bias) [22]. All the statistical tests were performed with STATA version 10.0 (Stata Corporation, College Station, TX).

Results

Study characteristics

A total of 10 publications met the inclusion criteria [8–17]. In four of these studies, the ORs were presented separately according to the different subgroup [10, 11, 14, 16]. Therefore, each group in one publication was considered separately for subgroup analysis. Hence, a total of 27 studies including 33,557 cases and 45,254 controls were used in the meta-analysis. Table 1 lists the studies identified and their main characteristics. Of the 27 studies, sample sizes ranged from 177 to 14,845. There were 20 studies of Caucasians, three studies of Asians, one study of Africans, and three studies of mixed populations. Almost all of the cases were pathologically confirmed. Controls were mainly healthy populations and matched for age. Among these studies, 18 were population-based, seven were hospital-based, and two were nest-based studies.

Tabel 1 Main characteristics of all studies included in the meta-analysis
Full size table

Main results

Table 2 lists the main results of this meta-analysis. Overall, significantly elevated breast cancer risk was associated with IGFBP3 C allele, when all studies were pooled into the meta-analysis (CC vs. AA: OR = 1.06, 95% CI = 1.02–1.11; dominant model: OR = 1.04, 95% CI = 1.00–1.07). In the subgroup analysis by ethnicity, significantly increased risks were found for Caucasians (AC vs. AA: OR = 1.04, 95% CI = 1.00–1.08; CC vs. AA: OR = 1.05, 95% CI = 1.01–1.10; dominant model: OR = 1.04, 95% CI = 1.00–1.08) and Asians (CC vs. AA: OR = 1.35, 95% CI = 1.02–1.78; recessive model: OR = 1.38, 95% CI = 1.05–1.82). When stratified by study design, statistically significantly elevated risk was found among population-based studies (CC vs. AA: OR = 1.06, 95% CI = 1.01–1.11; dominant model: OR = 1.03, 95% CI = 1.00–1.07). In the subgroup analysis by menopausal status, no statistically significantly increased risk was found among premenopausal or postmenopausal women.

Table 2 Main results of pooled ORs in the meta-analysis
Full size table

Sensitivity analysis

A single study involved in the meta-analysis was deleted each time to reflect the influence of the individual data-set to the pooled ORs, and the corresponding pooled ORs were not materially altered (data not shown), indicating that our results were statistically robust.

Publication bias

Begg’s funnel plot and Egger’s test were performed to access the publication bias of literatures. The shape of the funnel plot did not reveal any evidence of obvious asymmetry (figures not shown). Then, the Egger’s test was used to provide statistical evidence of funnel plot symmetry. The results still did not suggest any evidence of publication bias (P = 0.71 for AC vs. AA; P = 0.63 for CC vs. AA; P = 0.92 for dominant model; and P = 0.89 for recessive model).

Discussion

The present meta-analysis, including 33,557 cases and 45,254 controls, explored the association between the IGFBP3 A-202C polymorphism and breast cancer risk. The results indicate that the IGFBP3 C allele is a low-penetrant risk factor for developing breast cancer. This finding may be biologically plausible. The A to C change at IGFBP3-202 locus resulted in a reduced promoter activity and decreased IGFBP3 level [6, 7]. Low IGFBP3 concentrations have been proven to be associated with an increased cancer risk [3, 23]. Thus, the IGFBP3 C allele is a risk factor for cancer development.

Our results indicated that significantly increased breast cancer risk in IGFBP3 C genotype carriers were found among the population-based studies but not in hospital-based studies. This reason may be that the hospital-based studies have some biases because such controls may just represent a sample of ill-defined reference population, and may not be representative of the general population very well, particularly when the genotypes under investigation were associated with the disease conditions that the hospital-based controls may have. Therefore, using a proper and representative population-based control subjects is very important to reduce biases in such genetic association studies.

Some limitations of this meta-analysis should be acknowledged. First, the controls were not uniformly defined. Although most of the controls were selected mainly from healthy populations, some had benign disease. Therefore, non-differential misclassification bias was possible because these studies may have included the control groups who have different risks of developing breast cancer. Second, in the subgroup analyses, the number of Africans was relatively small, not having enough statistical power to explore the real association. Third, our results were based on unadjusted estimates, while a more precise analysis should be conducted if individual data were available, which would allow for the adjustment by other co-variants including age, ethnicity, menopausal status, smoking status, drinking status, obesity, environmental factors, and other lifestyle. In spite of these limitations, our meta-analysis also had some advantages. First, substantial number of cases and controls were pooled from different studies, which significantly increased the statistical power of the analysis. Second, no publication bias were detected, indicating that the whole pooled results may be unbiased.

In conclusion, this meta-analysis suggests that the IGFBP3 C allele is a low-penetrant risk factor for developing breast cancer. However, it is necessary to conduct large sample studies using standardized unbiased genotyping methods, homogeneous breast cancer patients and well matched controls. Moreover, gene–gene and gene–environment interactions should also be considered in the analysis. Such studies taking these factors into account may eventually lead to our better, comprehensive understanding of the association between the IGFBP3 A-202C polymorphism and breast cancer risk.