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. 2017 Mar 15;8(24):39818–39832. doi: 10.18632/oncotarget.16242

Association between the BRCA2 rs144848 polymorphism and cancer susceptibility: a meta-analysis

Qiuyan Li 1,#, Rongwei Guan 1,#, Yuandong Qiao 1, Chang Liu 1, Ning He 2, Xuelong Zhang 1, Xueyuan Jia 1, Haiming Sun 1, Jingcui Yu 3, Lidan Xu 1
PMCID: PMC5503656  PMID: 28418854

Abstract

The BRCA2 gene plays an important role in cancer carcinogenesis, and polymorphisms in this gene have been associated with cancer risk. The BRCA2 rs144848 polymorphism has been associated with several cancers, but results have been inconsistent. In the present study, a meta-analysis was performed to assess the association between the rs144848 polymorphism and cancer risk. Literature was searched from the databases of PubMed, Embase and Google Scholar before April 2016. The fixed or random effects model was used to calculate pooled odd ratios on the basis of heterogeneity. Meta-regression, sensitivity analysis, subgroup analysis and publication bias assessment were also performed using STATA 11.0 software according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009. A total of 40 relevant studies from 30 publications including 34,911 cases and 48,329 controls were included in the final meta-analysis. Among them, 22 studies focused on breast cancer, seven on ovarian cancer, five on non-Hodgkin lymphoma, and the remaining six studies examined various other cancers. The meta-analysis results showed that there were significant associations between the rs144848 polymorphism and cancer risk in all genetic models. Stratified by cancer type, the rs144848 polymorphism was associated with non-Hodgkin lymphoma. Stratified by study design, the allele model was associated with breast cancer risk in population-based studies. The meta-analysis suggests that the BRCA2 rs144848 polymorphism may play a role in cancer risk. Further well-designed studies are warranted to confirm these results.

Keywords: meta-analysis, BRCA2, cancer, polymorphism, susceptibility

INTRODUCTION

Cancer is one of the most common diseases causing considerable morbidity and mortality worldwide. Environmental and genetic factors together contribute to the development of cancers [1–4]. It has been reported that DNA damage and repair is an important factor in carcinogenesis [5–7]. BRCA2 is a well-known cancer susceptibility gene involved in the repair of double-stranded DNA breaks which functions by regulating the intracellular shuttling and activity of RAD51, another critical protein in homologous recombination [8–10]. Studies have shown that cancer carcinogenesis is related to abnormalities in DNA repair mechanisms partially caused by a change in gene function which can result from genetic polymorphisms [11, 12].

Within the last few years, many studies have focused on the association between BRCA2 gene polymorphisms and cancer risk, including breast cancer, ovarian cancer, non-Hodgkin lymphoma, prostate cancer and others [13–18]. The rs144848 is the only common non-synonymous polymorphism in exon 10 of the BRCA2 gene [19]. The change from A to C in the rs144848 polymorphism results in an asparagine-to-histidine transition (N372H) which may affect BRCA2 structure at residues 290-453, a region which has been determined to interact with the histone acetyltransferase P/CAF prior to transcriptional activation of target genes [20]. Over the past decade, many association studies have been conducted to explore the role of the rs144848 N372H polymorphism in cancer risk [13, 15, 17, 18, 21–40], but it is still inconclusive whether this polymorphism in the BRCA2 gene is associated with susceptibility to cancer. Therefore, we performed a systematic review and meta-analysis of published studies focused on the association between the rs144848 polymorphism and cancer risk. Our in-depth analysis may drive a more precise estimation of risk which could in turn help identify additional genetic targets for future therapeutic interventions.

RESULTS

Study characteristics

A flow diagram for the search strategy is shown in Figure 1. Based on the search strategy, 2,174 articles were identified in the initial search. After reading titles and abstracts, 1,788 articles were excluded and 386 articles were reviewed for full text. According to the study inclusion/exclusion criteria, 40 relevant studies from 30 publications including 34,911 cases and 48,329 controls were used for the final meta-analysis [13–15, 17, 18, 21, 23–40, 46–52]. Nine studies were medium quality and 31 studies were high quality. The main characteristics of these included studies are shown in Table 1.

Figure 1. Study flow diagram.

Figure 1

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Table 1. Characteristics of included studies that contributed to associations between rs144848 and cancer risk.

Study [ref] per SNP Year Race/ethnicity Sourcea Cases Controls Allele frequencies NOS assessment Cancer type
Total NN NH HH Total NN NH HH Casesb Controlsb
Healey et al. [12] 2000 Caucasian PB 234 116 99 19 266 138 115 13 0.71 0.73 7 Breast
Healey et al. [12] 2000 Caucasian PB 1667 858 664 145 1201 631 493 77 0.71 0.73 7 Breast
Healey et al. [12] 2000 Caucasian PB 450 236 180 34 228 124 94 10 0.72 0.75 7 Breast
Healey et al. [12] 2000 Caucasian PB 659 325 285 49 866 433 373 60 0.71 0.72 7 Breast
Healey et al. [12] 2000 Caucasian PB 449 270 154 25 453 277 152 24 0.77 0.78 7 Breast
Spurdle et al. [45] 2002 Caucasian PB 1397 720 548 129 775 417 308 50 0.71 0.74 7 Breast
Ishitobi et al. [22] 2003 Asian HB 149 97 47 5 144 85 56 3 0.81 0.78 7 Breast
Menzel et al. [24] 2004 Caucasian PB 211 104 91 16 912 482 361 69 0.71 0.73 7 Breast
Menzel et al. [24] 2004 Caucasian PB 94 53 35 6 152 84 57 11 0.75 0.74 7 Breast
Cox et al. [44] 2005 Caucasian Nested 1285 695 501 89 1660 884 647 129 0.74 0.73 7 Breast
Millikan et al. [25] 2005 African PB 762 564 183 15 675 510 153 12 0.86 0.87 7 Breast
Millikan et al. [25] 2005 Caucasian PB 1265 662 521 82 1135 579 467 89 0.73 0.72 7 Breast
Garcia-Closas et al. [21] 2006 Caucasian PB 3161 1617 1278 266 2701 1412 1057 232 0.71 0.72 7 Breast
Garcia-Closas et al. [21] 2006 Caucasian PB 1968 1007 826 135 2276 1239 897 140 0.72 0.74 7 Breast
Johnson et al. [47] 2007 Caucasian NA 473 233 201 39 2461 1278 993 190 0.71 0.72 6 Breast
Palli et al. [48] 2007 Caucasian PB 91 48 31 12 261 127 107 27 0.70 0.69 6 Breast
Baynes et al. [46] 2007 Caucasian PB 4537 2306 1892 339 4339 2182 1824 333 0.72 0.71 7 Breast
Seymour et al. [49] 2008 Caucasian HB 252 127 111 14 100 50 44 6 0.72 0.72 6 Breast
Dombernowsky et al. [19] 2009 Caucasian PB 1200 604 503 93 4119 2129 1677 313 0.71 0.72 6 Breast
Juwle et al. [23] 2012 Asian NA 100 68 28 4 50 39 8 3 0.82 0.86 6 Breast
Hasan et al. [11] 2013 African HB 100 38 33 29 100 33 32 35 0.55 0.49 6 Breast
Jumaah et al. [50] 2014 African NA 36 26 10 0 10 10 0 0 0.86 1.00 6 Breast
Auranen et al. [26] 2003 Caucasian PB 680 355 272 53 1546 819 629 98 0.72 0.73 7 Ovarian
Auranen et al. [26] 2003 Caucasian PB 441 222 176 43 1097 578 445 74 0.70 0.73 7 Ovarian
Wenham et al. [28] 2003 Caucasian PB 312 169 128 15 398 227 146 25 0.75 0.75 7 Ovarian
Beesley et al. [32] 2007 Caucasian PB 492 249 203 40 948 502 383 63 0.71 0.73 8 Ovarian
Beesley et al. [32] 2007 Caucasian PB 930 460 401 69 825 461 296 68 0.71 0.74 8 Ovarian
Ramus et al. [36] 2008 Mixed Nested 4174 2196 1655 323 7402 3859 2979 564 0.72 0.72 7 Ovarian
Quaye et al. [37] 2009 Caucasian PB 1459 779 569 111 2294 1200 925 169 0.73 0.72 7 Ovarian
Shen et al. [30] 2006 Mixed PB 476 250 191 35 555 301 220 34 0.73 0.74 7 NHLc
Scott et al. [33] 2007 Caucasian PB 757 387 307 63 676 375 253 48 0.71 0.74 7 NHL
Shen et al. [34] 2007 Caucasian PB 556 271 236 49 498 246 203 49 0.70 0.70 7 NHL
Hill et al. [16] 2006 Mixed PB 1116 577 441 98 926 505 361 60 0.71 0.74 7 NHL
Salagovic et al. [39] 2012 Caucasian HB 107 62 34 11 127 82 40 5 0.74 0.80 7 NHL
Hu et al. [27] 2003 Asian PB 120 69 39 12 231 126 95 10 0.74 0.75 6 Esophageal
Wu et al. [31] 2006 Caucasian PB 604 306 246 52 595 332 223 40 0.71 0.75 8 Bladder
Debniak et al. [35] 2008 Caucasian Nested 627 288 280 59 3819 1994 1580 245 0.68 0.73 6 Melanoma
Agalliu et al. [15] 2010 Caucasian PB 1269 655 498 116 1243 654 500 89 0.71 0.73 8 Prostate
Agalliu et al. [15] 2010 African PB 142 104 36 2 79 59 18 2 0.86 0.86 8 Prostate
Kotnis et al. [38] 2012 Asian HB 109 35 56 18 186 81 70 35 0.58 0.62 7 Multiple
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a Source in control, PB population-based study, HB hospital-based study

b Major allele frequency

c non-Hodgkin lymphoma

Association between BRCA2 rs144848 polymorphism and cancer risk

As shown in Table 2, there was no heterogeneity in any genetic model. The meta-analysis results showed that there were significant associations between the rs144848 polymorphism and cancer risk in all genetic models (H allele vs. N allele, OR = 1.044, 95% CI = 1.021-1.068, p < 0.001; NH vs. NN, OR = 1.037, 95% CI = 1.006-1.069, p = 0.018; HH vs. NN, OR = 1.104, 95% CI = 1.044-1.168, p = 0.001; dominant model, OR = 1.047, 95% CI = 1.018-1.078, p = 0.002; recessive model, OR = 1.086, 95% CI = 1.028-1.146, p = 0.003; Figure 2–6).

Table 2. Summary of OR and 95%CI for association between rs144848 polymorphism and susceptibility to cancer.

Variable per SNP I2 (%) p for heterogeneity OR (95% CI) p value p for publication bias Effects model Sensitive analysis
exclude OR (95% CI) p value p for publication bias
H allele vs N allele 7.0 0.345 1.044 (1.021-1.068) <0.001a 0.045 fixed [36] 1.053 (1.028-1.080) <0.001a 0.143
NH vs NN 0.0 0.491 1.037 (1.006-1.069) 0.018a 0.147 fixed [36] 1.048 (1.014-1.082) 0.005a 0.352
HH vs NN 16.8 0.183 1.104 (1.044-1.168) 0.001a 0.066 fixed [46] 1.125 (1.060-1.194) <0.001a 0.148
Dominant model 0.0 0.470 1.047 (1.018-1.078) 0.002a 0.069 fixed [36] 1.059 (1.026-1.092) <0.001a 0.069
Recessive model 16.8 0.184 1.086 (1.028-1.146) 0.003a 0.114 fixed [46] 1.102 (1.040-1.168) 0.001a 0.214
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a Statistically significant

Figure 2. Forest plot for pooled ORs for the associations between allele model (H allele vs N allele) of rs144844 and cancer risk in the overall population.

Figure 2

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Each square is proportional to the study-specific weight.

Figure 6. Forest plot for pooled ORs for the associations between recessive model (HH vs NH+NN) of rs144844 and cancer risk in the overall population.

Figure 6

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Each square is proportional to the study-specific weight.

Figure 3. Forest plot for pooled ORs for the associations between additive model (NH vs NN) of rs144844 and cancer risk in the overall population.

Figure 3

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Each square is proportional to the study-specific weight.

Figure 4. Forest plot for pooled ORs for the associations between additive model (HH vs NN) of rs144844 and cancer risk in the overall population.

Figure 4

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Each square is proportional to the study-specific weight.

Figure 5. Forest plot for pooled ORs for the associations between dominant model (NH+HH vs NN) of rs144844 and cancer risk in the overall population.

Figure 5

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Each square is proportional to the study-specific weight.

Meta-regression analysis

The following covariates were considered for meta-regression: ethnicity, study design and cancer type. The results showed that cancer type contributed to effect in the meta-analysis (H allele vs. N allele, p = 0.011; HH vs. NN, p = 0.006; dominant model, p = 0.039; recessive model, p = 0.011).

Subgroup analysis by cancer type stratification

Based on cancer type, four groups were included in the meta-analysis: breast cancer group, ovarian cancer group, non-Hodgkin lymphoma group and other cancers group. The results showed that the rs144848 polymorphism was not associated with breast cancer or ovarian cancer in any model. However, the rs144848 polymorphism was associated with non-Hodgkin lymphoma in four models (H allele vs. N allele, OR = 1.110, 95% CI = 1.023-1.205, p = 0.012; HH vs. NN, OR = 1.263, 95% CI = 1.035-1.542, p = 0.022; dominant model, OR = 1.118, 95% CI = 1.008-1.240, p = 0.035; recessive model, OR = 1.216, 95% CI = 1.002-1.476, p = 0.048) and with other cancers in all genetic models (Table 3).

Table 3. Summary of OR and 95% CI for association of rs144848 polymorphism with cancer risk by cancer type stratification.

Subgroup p for heterogeneity I2 (%) OR (95% CI) p value Effects model
N allele vs H allele
Breast cancer 0.679 0.0 1.028 (0.997-1.060) 0.075 fixed
Ovarian cancer 0.359 9.1 1.024 (0.981-1.068) 0.280 fixed
NHL 0.518 0.0 1.110 (1.023-1.205) 0.012a fixed
Others 0.658 0.0 1.158 (1.074-1.249) <0.001a fixed
NH vs NN
Breast cancer 0.890 0.0 1.029 (0.988-1.072) 0.166 fixed
Ovarian cancer 0.080 46.8 1.015 (0.959-1.074) 0.604 fixed
NHL 0.954 0.0 1.090 (0.977-1.215) 0.122 fixed
Others 0.090 47.5 1.117 (1.009-1.236) 0.033a fixed
HH vs NN
Breast cancer 0.491 0.0 1.056 (0.978-1.139) 0.162 fixed
Ovarian cancer 0.446 0.0 1.063 (0.957-1.180) 0.253 fixed
NHL 0.294 19.0 1.263 (1.035-1.542) 0.022a fixed
Others 0.653 0.0 1.439 (1.199-1.726) <0.001a fixed
Dominant model
Breast cancer 0.852 0.0 1.033 (0.994-1.074) 0.097 fixed
Ovarian cancer 0.156 35.7 1.022 (0.969-1.079) 0.420 fixed
NHL 0.855 0.0 1.118 (1.008-1.240) 0.035a fixed
Others 0.237 26.3 1.162 (1.055-1.280) 0.002a fixed
Recessive model
Breast cancer 0.477 0.0 1.044 (0.969-1.124) 0.259 fixed
Ovarian cancer 0.351 10.3 1.057 (0.954-1.170) 0.290 fixed
NHL 0.277 21.6 1.216 (1.002-1.476) 0.048a fixed
Others 0.377 6.2 1.346 (1.130-1.603) 0.001a fixed
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a Statistically significant

Association between BRCA2 rs144848 polymorphism and breast cancer risk

There were 22 breast cancer studies with different ethnicities and study designs. To assess the role of genetic background and the source of the control population in breast cancer risk, we carried out a subgroup analysis. In the analysis of genetic background, the overall population was divided into three subgroups, Caucasian, Asian, and African. The results showed that no statistically significant association was observed in any population (Table 4). In the analysis of study design, the overall population was divided into two subgroups, population-based studies and hospital-based studies. The results showed that the allele model was associated with the risk of breast cancer based on population-based studies (H allele vs. N allele, OR = 1.034, 95% CI = 1.000-1.068, p = 0.047; Table 5).

Table 4. Summary of OR and 95% CI for association of rs144848 polymorphism with breast cancer risk by ethnicity stratification.

Subgroup p for heterogeneity I2 (%) OR (95% CI) p value Effects model
N allele vs H allele
Caucasian 0.690 0.0 1.029 (0.997-1.061) 0.075 fixed
Asian 0.262 20.5 0.974 (0.692-1.372) 0.882 fixed
African 0.185 40.8 1.024 (0.850-1.235) 0.801 fixed
NH vs NN
Caucasian 0.970 0.0 1.028 (0.986-1.072) 0.189 random
Asian 0.050 74.0 1.133 (0.427-3.006) 0.801 random
African 0.337 8.1 1.069 (0.798-1.430) 0.656 random
HH vs NN
Caucasian 0.332 10.4 1.060 (0.981-1.146) 0.138 fixed
Asian 0.551 0.0 1.086 (0.377-3.124) 0.879 fixed
African 0.388 0.0 0.877 (0.529-1.455) 0.612 fixed
Dominant model
Caucasian 0.925 0.0 1.033 (0.993-1.075) 0.106 fixed
Asian 0.101 62.8 0.955 (0.640-1.424) 0.820 fixed
African 0.244 29.2 1.065 (0.855-1.325) 0.575 fixed
Recessive model
Caucasian 0.333 10.3 1.048 (0.972-1.130) 0.220 fixed
Asian 0.395 0.0 1.078 (0.378-3.072) 0.888 fixed
African 0.443 0.0 0.876 (0.548-1.399) 0.579 fixed
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Table 5. Summary of OR and 95% CI for association of rs144848 polymorphism with breast cancer risk by the study design stratification.

Subgroup p for heterogeneity I2 (%) OR (95% CI) p value Effects model
H allele vs N allele
PB 0.691 0.0 1.034 (1.000-1.068) 0.047a fixed
HB 0.759 0.0 0.883 (0.707-1.103) 0.273 fixed
Others 0.264 24.5 1.011 (0.923-1.108) 0.810 fixed
NH vs NN
PB 0.953 0.0 1.030 (0.986-1.076) 0.182 fixed
HB 0.684 0.0 0.864 (0.638-1.171) 0.346 fixed
Others 0.174 39.6 1.050 (0.930-1.186) 0.428 fixed
HH vs NN
PB 0.315 12.4 1.076 (0.991-1.168) 0.082 fixed
HB 0.677 0.0 0.844 (0.501-1.422) 0.525 fixed
Others 0.559 0.0 0.957 (0.763-1.200) 0.702 fixed
Dominant model
PB 0.916 0.0 1.037 (0.995-1.081) 0.085 fixed
HB 0.750 0.0 0.856 (0.642-1.141) 0.290 fixed
Others 0.195 36.2 1.035 (0.922-1.162) 0.558 fixed
Recessive model
PB 0.297 14.0 1.063 (0.982-1.151) 0.132 fixed
HB 0.625 0.0 0.867 (0.538-1.398) 0.558 fixed
Others 0.627 0.0 0.943 (0.757-1.175) 0.600 fixed
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a Statistically significant

Sensitivity analysis

To determine the degree to which an individual study affected the overall OR estimates, one-way sensitivity analysis was performed by excluding one study at a time and sequentially recalculating the overall effect. The results showed no influence on pooled ORs and 95% CIs as individual studies were excluded.

Publication bias

Publication bias was observed in only one model (H allele vs. N allele, p = 0.045; Table 2). However, there was no significant publication bias in any genetic model (p > 0.05) after sensitivity analysis. Trim and fill results showed that the adjusted risk estimate remained significant (H allele vs. N allele, OR = 1.028, 95% CI = 1.006-1.050, p = 0.014), which confirmed that the results of this meta-analysis were statistically robust.

DISCUSSION

The mechanisms underlying carcinogenesis are still not fully clear, but it has been suggested that genetic and environmental factors play the most important role in the development of cancer. The BRCA2 protein can regulate homologous recombination by interacting with the RAD51 recombinase, and many studies have suggested that the rs144848 polymorphism in the BRCA2 gene is a susceptibility locus for cancers [8]. However, until now, there has been no consistent result regarding the association between the rs144848 N372H polymorphism and cancer risk. To explain these contradictory results, a meta-analysis including 34,911 cases and 48,329 controls was conducted and five genetic models were utilized to assess the association between the BRCA2 rs144848 polymorphism and the risk of cancer.

In our meta-analysis, the results showed that there was no heterogeneity in any genetic model in overall population, while associations were observed between the rs144848 polymorphism and cancer risk in all genetic models. Meta-regression analysis suggested that ethnicity and study design had no influence on overall effect, but cancer type did contribute to effect (H allele vs. N allele, p = 0.011; HH vs. NN, p = 0.006; dominant model, p = 0.039; recessive model, p = 0.011). Based on cancer type, four groups were included in the meta-analysis: breast cancer group, ovarian cancer group, non-Hodgkin lymphoma group and other cancers group. The results showed that the rs144848 polymorphism was not associated with breast cancer or ovarian cancer in any model. However, the rs144848 polymorphism was associated with non-Hodgkin lymphoma in four models, and associated with other cancers in all genetic models.

The results showed a statistically significant association in all genetic models for overall population. Due to the relatively large number of research studies on breast cancer, we also did a subgroup analysis in the breast cancer group. To assess the role of genetic background in breast cancer, we stratified the population by ethnicity and found no association in Caucasian, Asian, and African subgroups. Considering that the number of publications in Asian and African populations was small, we believe our results may not be reliable due to insufficient statistical power, so additional studies should be conducted to confirm our results. However, after subgroup analysis by study design stratification, we found that the BRCA2 rs144848 N372H polymorphism was associated with increasing the risk of breast cancer in population-based studies (H allele vs. N allele, OR = 1.034, 95% CI = 1.000-1.068, p = 0.047). One-way sensitivity analysis suggested no influence of individual studies on pooled ORs and 95% CIs.

In 2006, a study from the breast cancer association consortium summarized the common breast cancer-associated polymorphisms but failed to show a significant association between the BRCA2 rs144848 polymorphism and breast cancer [53]. In 2010, Qiu et al. found in a meta-analysis that the BRCA2 rs144848 H allele may be a low-penetrant risk factor for developing breast cancer [54]. In 2014, Xue et al. conducted a meta-analysis to assess the association between the BRCA2 rs144848 polymorphism and cancer susceptibility [55]. In contrast to Qiu et al., they did not find an association between the BRCA2 rs144848 polymorphism and breast cancer, but did observe an association with ovarian cancer. Different results from Xue et al. were then obtained in 2015 by Wang et al., who found that the rs144848 polymorphism was not associated with ovarian cancer. Compared with this latter study, we updated and added several new studies which were strictly filtered by a quality assessment. In addition, we used five genetic models to assess the role of the BRCA2 rs144848 polymorphism in our meta-analysis. Another important difference from Wang et al. was that their results were based on the risk estimates obtained without the original genotype data, whereas all studies included in our meta-analysis provided genotype data, so that our results were more precise by calculating effect directly without potential deviations and biases.

The strength of this meta-analysis is that the most current literature was included. To guarantee the quality of the meta-analysis, the Newcastle-Ottawa scale was conducted to assess the quality of included studies, and a strict procedure for data extraction was performed by two investigators according to inclusion and exclusion criteria. Furthermore, no low-quality literature was included in this meta-analysis which might possibly have influenced our results. One-way sensitivity analysis and meta-regression were also performed to increase the robustness of our conclusions. Subgroup analysis by ethnicity and the source of the control population were used to explain the effect of genetic background and study design.

There are some limitations in this meta-analysis. First, the literature search strategy was limited by language, and only published papers in English were included. Second, because we excluded literature without original data, some studies were excluded. Third, other potential interactions including environment × gene, gene × gene and some potential covariates were not considered due to insufficient information.

In conclusion, our meta-analysis determined that the BRCA2 rs144848 polymorphism was associated with non-Hodgkin lymphoma, and indicated that the rs144848 H allele of the BRCA2 gene may be a low-penetrate risk factor enhancing carcinogenesis in breast cancer. Further well-designed studies are warranted to clarify the mechanism and increase comprehensive understanding of the role of the BRCA2 rs144848 polymorphism in cancer.

MATERIALS AND METHODS

Publication research

Studies were retrieved by searching PubMed, Embase and Google Scholar following the guidelines in Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 [41]. The last search was updated on April 2016 with the terms “cancer”, “tumor”, “BRCA”, “polymorphism”, “genetic”, “variant”, “rs144848” and “N372H”. References in potential articles were also included in order to find more relevant studies.

Inclusion criteria

All articles were reviewed by two investigators independently. Studies were included in the meta-analysis if they met the following criteria: (1) Studies were case-control or cohort studies; (2) articles were original studies of human participants; (3) genotype distributions were available; (4) studies were published in English; and (5) articles were association studies between rs144848 polymorphism and cancer risk. If studies were drawn from the same population, only the study with the largest sample size or with a sufficient quantity of useful data was included. If an article reported the results from different studies, each study was treated as a separate comparison in our meta-analysis.

Quality score assessment

The Newcastle-Ottawa scale was used to assess the quality of studies [42]. Three items including selection, comparability and exposure were used to calculate the score of studies with a maximum score of nine. Any disagreements were adjusted by a third reviewer. A total score of three or lower, four to six and seven or greater was considered to indicate low, medium and high quality studies, respectively.

Data extraction

Data were extracted from included studies using a standardized form. For each study, the following information was extracted: (1) name of first author, (2) year of publication, (3) ethnicity of population, (4) source of control population and (5) sample size and genotype distribution. Ethnicity was categorized as Caucasian, Asian or African, and the study design was categorized as population-based study, hospital-based study or nested study.

Statistical analysis

The odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were calculated to assess the association between the rs144848 polymorphism and cancer risk. Five models were used in this meta-analysis: (1) H allele vs. N allele, (2) NH vs. NN, (3) HH vs. NN, (4) dominant model, (NH+HH vs. NN), and (5) recessive model, (HH vs. NH+NN). Statistical analysis was performed using STATA 11.0 (Stata Corporation, College Station, TX, USA). The chi-square test was conducted to evaluate if the studies deviated from Hardy-Weinberg equilibrium, and the threshold for disequilibrium was p < 0.05. Cochran's Q test and I2 statistic test were performed to assess heterogeneity across individual studies (p < 0.10 and I2 > 50% suggested heterogeneity). The fixed-effects model (the Mantel-Haenszel method) was used to estimate the pooled OR if I2 < 50%; otherwise, the random-effects model (the DerSimonian and Laird method) was used [43]. A value of p < 0.05 was accepted as the significance threshold for each genetic model.

Subgroup analysis was conducted based on ethnicity (Caucasian, Asian and African) and study design (population-based and hospital-based). If heterogeneity was present, meta-regression was conducted to explore the source of heterogeneity. One-way sensitivity analysis was used to assess the influence of the individual study set to the pooled ORs by sequential exclusion.

A funnel plot was performed to estimate the potential publication bias using Begg's test, in which the standard error of log (OR) was plotted against its log (OR) [44]. Egger's liner regression test was also used to evaluate publication bias with quantitative analysis as a supplement to the funnel plot [45]. The trim and fill method was used to adjust pooled ORs and 95% CIs if bias was detected.

PRISMA CHECKLIST

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China [81102278], the China Postdoctoral Science Foundation [20100481019], the Postdoctoral Science Special Foundation of Heilongjiang Province, China [LBH-TZ1208], the Postdoctoral Science Research Foundation of Heilongjiang Province, China [LBH-Q13128], and Wu lien-teh Youth Science Foundation of Harbin Medical University [WLD-QN1405].

Footnotes

CONFLICTS OF INTEREST

We declared that there is no duality of interest associated with this manuscript.

Submission declaration

Submission of the article implies that the work described has not been published previously.

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