Abstract

Nowadays, vitamin D is known to have functions beyond bone formation, including inhibiting angiogenesis and promoting tumor apoptosis. CYP27B1 and group-specific component (GC), the main enzyme responsible for the degradation and transport of active vitamin D, play important role in many cancer-related cellular processes. Relationships between CYP27B1 and GC polymorphisms and cancer susceptibility have been widely investigated, whereas the results are inconsistent. We strictly searched EMBASE, PubMed, Web of Science, WanFang and CNKI electronic databases for relevant studies exploring the associations of GC (rs4588 and rs7041) and CYP27B1 (rs4646537, rs3782130) polymorphisms with cancer risks according to search strategy. Thirty-two studies published in 13 articles involving 15713 cases and 17304 controls were included. Our analyses suggested that rs4588 and rs7041 polymorphisms were significantly associated with overall cancer risk. Stratification analyses of ethnicity indicated that rs4588 polymorphism significantly increased cancer risk in Caucasians and Asians, while rs7041 polymorphism significantly increased cancer risk in Asians. When studies were stratified by cancer type, our results indicated that rs4588 significantly increased the risk of breast cancer and digestive system tumor, but not in prostate cancer and non-small cell lung cancer, while rs7041 significantly increased the risk of non-small cell lung cancer. Above associations were noteworthy findings as evaluated by false-positive report probabilities (FPRPs). There were no associations of rs4646537 and rs3782130 with overall cancer risks. Associations between CYP27B1 and GC polymorphisms and cancer risks were examined, and additional large samples are necessary to validate our results.

Introduction

Cancer remains a major global burden of public health. According to the GLOBOCAN 2018, there will be an estimated 18.1 million new cancer cases and 9.6 million deaths in 2018 worldwide [1]. Various causes involving a variety of environmental and genetic factors lead to the development of cancer, although the exact mechanism of carcinogenesis has not been fully understood.

Vitamin D is a fat-soluble vitamin that is closely related to health [2]. They have the following three characteristics: (1) they are found in some natural foods; (2) humans store 7-dehydrocholesterol from cholesterol, which can be converted into vitamin D3 after exposure to ultraviolet light; (3) proper sunbathing is enough to satisfy the body’s vitamin D need [2]. Vitamin D deficiency is a ubiquitous phenomenon. Nowadays, vitamin D is known to have functions beyond bone formation, including enhancing immune defense [3], inhibiting cell proliferation [4], inhibiting angiogenesis [5], inhibiting cell metastasis [6], and promoting tumor apoptosis [4]. In addition, vitamin D can reduce mortality in several malignancies [7]. Numerous studies have shown that vitamin D deficiency may be the reason why thousands of patients die prematurely from colon, breast, ovarian and other cancers each year [8–10].

Vitamin D is synthesized by a series of reactions catalyzed by many enzymes. CYP2R1 and CYP27A1 are 25-hydroxylase enzymes that first convert pro-vitamin D absorbed from the diet or produced in the skin after exposure to sunlight [11]. Next, CYP27B1, 1a-hydroxylase converts 25(OH)D into 1,25-dihydroxyvitamin D [1,25(OH)2D3] in the kidney [11]. Both vitamin D metabolites bind to vitamin D-binding proteins, also known as group-specific component (GC), which aid in the transport of vitamin D [11]. Genetic polymorphisms involving the vitamin D pathway may affect its activity, so if vitamin D does play a role in carcinogenesis, it may be associated with cancer.

Recently, genome-wide association studies (GWASs) have identified CYP27B1 and GC polymorphisms significantly associated with 25(OH)D concentrations [12]. The worldwide variation of CYP27B1 gene (Chromosome 12: 58,156,117-58,162,769 reverse strand) and of its polymorphism SNP rs4646537 (Chromosome 12:58157281 forward strand) and SNP rs3782130 (Chromosome 12:58161898 forward strand), and GC gene (Chromosome 4: 72,607,410-72,669,758 reverse strand) and of its polymorphism SNP rs4588 (Chromosome 4:72618323 forward strand) and SNP rs7041 (Chromosome 4:72618334 forward strand) were analyzed with data obtained from the public database 1000 Genomes Phase 3 Browser. According to the 1000 Genomes Project Phase 3 allele frequencies, the minor allele frequency (MAF) for rs4646537 is 4% in the combined population, the MAF for rs3782130 is 35% in the combined population, the MAF for rs4588 is 21% in the combined population, and the MAF for rs7041 is 38% in the combined population. Up to now, two common CYP27B1 polymorphisms (rs4646537, rs3782130) and two common GC polymorphisms (rs4588 and rs7041) were found to be associated with cancer risks, including breast cancer, non-small cell lung cancer, prostate cancer, hepatocellular carcinoma, esophageal cancer, gastric cancer and colorectal cancer. However, the results are inconsistent, probably because of the limited sample size. To better explore the precise relationship, we performed a meta-analysis and trial sequential analysis (TSA) to characterize the associations of GC (rs4588 and rs7041) and CYP27B1 (rs4646537, rs3782130) polymorphisms with cancer susceptibility.

Materials and methods

Literature retrieval

We strictly searched EMBASE, PubMed, Web of Science, Wan Fang and CNKI electronic databases (up to 1 December 2018) for relevant studies exploring the associations of GC (rs4588 and rs7041) and CYP27B1 (rs4646537, rs3782130) polymorphisms with cancer risks according to the search strategy (Supplementary Table S1). Four authors (Man Zhu, Zhenzhao Luo, Zheqiong Tan and Hui Wang) independently searched and screened the search.

Inclusion and exclusion criteria

Enrolled studies should meet the following inclusion criteria: (A) Human-based research; (B) Case–control/cohort studies; (C) Effective data were available to compute odds ratio (OR), 95% confidence interval (CI) and P-value; (D) Involved in the associations of GC (rs4588 and rs7041) or CYP27B1 (rs4646537, rs3782130) polymorphisms (at least one polymorphism involved) with cancer risk; (E) The control group met Hardy–Weinberg equilibrium (HWE). When P>0.05, the genetic balance of the population genes is indicated, indicating that the data are from the same Mendelian population [13]. In addition, the enrolled studies also need to meet the following exclusion criteria: (A) Case only or non-cancer subject only studies; (B) Duplicate publications; (C) Conference abstracts.

Data extraction

Two researchers (Tangwei Wu and Hui Hu) independently screened the detailed data from all enrolled studies. The following data were collected: first author name, issuing time, country, ethnicity, type of cancer, control source, genotyping method, numbers of cases and controls.

Quality assessment

Two researchers (Tangwei Wu and Hui Hu) assessed the quality of each investigation using the quality assessment criteria (Supplementary Table S2), which was derived from previously published meta-analysis of molecular association studies [14]. The quality assessment criteria cover the methodology for the ascertainment of cancer case (0–2 points), case representation (0–2 points), control representation (0–3 points), control selection (0–2 points), genotyping examination (0–2 points), conformity to HWE (0–1 point) and total sample size (0–3 points). Total scores ranged from 0 to 15, and studies with scores >9 points were classified as high quality.

Statistical analysis

Stata software (Stata, College Station, TX, U.S.A.), version 12.0, was used for statistical analysis. Associations of GC (rs4588 and rs7041) and CYP27B1 (rs4646537, rs3782130) polymorphisms with cancer risks were estimated by OR and 95% CI. Five different genetic models (dominant, recessive, homozygote, heterozygote and allele model) were used in current study. Statistical heterogeneity was counted by Cochrane Q-test and P-values, and random-effect model was used if P≤0.10 or I2 ≥ 50%, otherwise, fixed-effect model was used. Stratification analysis was performed based on ethnicity, cancer type and the detection method of genotype. Publication bias (Begg’s test and Egger’s test) analyses and sensitivity analyses were used to evaluate the reliability of current study. P<0.05 was considered statistically significant. For each significant finding, false-positive report probability (FPRP) analysis was performed using the method reported by Wacholder et al. [15]. We calculated FPRP assuming a prior probability of 0.1 as previously proposed [16]. We set 0.2 as an FPRP threshold and only result with FPRP-value <0.2 was referred as noteworthy [16].

TSA

The poor effect of systematic or random errors may increase due to sparse data, which may eventually mislead results in meta-analyses [17]. In order to get more comprehensive results, TSA (Copenhagen Trial Unit, Denmark, 2011) was utilized. In our current study, an overall type-I error of 5%, a statistical test power of 80% and a 20% relative risk reduction was set up.

Results

Screening process and characteristics of enrolled studies

A total of 342 articles were obtained based on our search strategy. After reading titles and abstracts, 34 articles conformed to our inclusion criteria. After reading full-text, 21 articles were excluded, including 10 that did not describe GC (rs4588 and rs7041) or CYP27B1 (rs4646537, rs3782130) polymorphisms and cancer susceptibility, 2 that did not meet HWE, 4 case only or non-cancer subject only articles, and 5 that not provide detailed genotyping data. Finally, 13 eligible articles including 32 studies (15713 cases and 17304 controls) were enrolled in our current meta-analysis [18–30]. Figure 1 describes the screening process.

Flow chart of the process for study identification and selection

In general, sixteen studies included Caucasian populations, fourteen studies included Asian populations and two studies included African populations. TaqMan method was used in nine studies, PCR-RFLP method was used in eighteen studies, Illumina method was used in three studies and two studies used the SNPlex assay method. Ten studies reported the effects of GC polymorphisms in breast cancer, eight reported in digestive system tumor, three in non-small cell lung cancer and two in prostate cancer. Six studies reported the effects of CYP27B1 polymorphisms in prostate cancer, two reported in non-small cell lung cancer and one in digestive system tumor. The characteristics of these studies are listed in Table 1.

Table 1
Characteristics of included studies
First author Year Country Ethnicity Cancer type Control source Genotyping method Cases (AA/AB/BB) Controls (AA/AB/BB) HWE Score 
GC (rs4588) 
McCullough 2007 U.S.A. Caucasian Breast cancer PB TaqMan 240/202/48 246/186/44 0.307 12 
Anderson 2011 Canada Caucasian Breast cancer PB PCR-RFLP 792/608/135 846/642/120 0.906 10 
Zhou-1 2012 China Asian Hepatocellular carcinoma HB PCR-RFLP 101/111/25 142/148/25 0.110 
Zhou-2 2012 China Asian Esophageal cancer HB PCR-RFLP 148/108/33 159/144/34 0.868 
Zhou-3 2012 China Asian Gastric cancer HB PCR-RFLP 74/89/29 88/92/24 0.995 
Zhou-4 2012 China Asian Colorectal cancer HB PCR-RFLP 113/100/33 182/134/15 0.117 
Reimers 2015 U.S.A. Caucasian Breast cancer PB TaqMan 456/402/82 514/393/84 0.471 10 
Deschasaux 2016 France Caucasian Breast cancer PB TaqMan 101/89/30 227/181/42 0.498 
Deschasaux 2016 France Caucasian Prostate cancer PB TaqMan 82/63/20 71/43/10 0.344 
Wu 2016 China Asian Non-small cell lung cancer PB PCR-RFLP 235/173/37 230/170/26 0.462 10 
GC (rs7041) 
McCullough 2007 U.S.A. Caucasian Breast cancer PB TaqMan 154/237/103 149/235/106 0.460 12 
Anderson 2011 Canada Caucasian Breast cancer PB PCR-RFLP 288/782/558 486/760/309 0.703 10 
Zhou-1 2012 China Asian Hepatocellular carcinoma HB PCR-RFLP 117/98/22 152/139/24 0.311 
Zhou-2 2012 China Asian Esophageal cancer HB PCR-RFLP 148/119/22 188/128/21 0.899 
Zhou-3 2012 China Asian Gastric cancer HB PCR-RFLP 99/89/16 98/86/10 0.105 
Zhou-4 2012 China Asian Colorectal cancer HB PCR-RFLP 123/107/16 171/132/28 0.724 
Kong 2014 China Asian Non-small cell lung cancer PB TaqMan 272/339/50 329/240/34 0.254 10 
Wang-1 2014 Spain Caucasian Breast cancer PB Illumina 203/402/221 216/362/201 0.050 13 
Wang-2 2014 Non-Spain Caucasian Breast cancer PB Illumina 42/61/27 73/116/35 0.320 11 
Clendenen 2015 Sweden Caucasian Breast cancer PB Illumina 265/348/121 546/658/229 0.193 
Reimers 2015 U.S.A. Caucasian Breast cancer PB TaqMan 239/470/186 311/474/193 0.609 10 
Deschasaux 2016 France Caucasian Prostate cancer PB TaqMan 19/63/45 39/76/50 0.337 
Wu 2016 China Asian Non-small cell lung cancer PB PCR-RFLP 173/225/47 175/230/61 0.281 10 
CYP27B1 (rs4646537) 
Holick 2007 U.S.A. Caucasian Prostate cancer PB SNPlex assay 546/38/0 497/43/2 0.310 14 
Holt-1 2009 U.S.A. Caucasian Prostate cancer PB PCR-RFLP 319/324/61 314/325/77 0.601 10 
Holt-2 2009 U.S.A. African Prostate cancer PB PCR-RFLP 85/28/2 50/16/1 0.826 
CYP27B1 (rs3782130) 
Holick 2007 U.S.A. Caucasian Prostate cancer PB SNPlex assay 260/251/75 260/229/61 0.327 14 
Holt-1 2009 U.S.A. Caucasian Prostate cancer PB PCR-RFLP 637/50/2 636/52/0 0.303 10 
Holt-2 2009 U.S.A. African Prostate cancer PB PCR-RFLP 97/15/2 54/8/1 0.298 
Kong 2014 China Asian Non-small cell lung cancer PB TaqMan 229/297/77 230/371/120 0.150 10 
Mahmoudi 2014 Iran Asian Colorectal cancer HB PCR-RFLP 144/125/34 180/138/36 0.216 
Wu 2016 China Asian Non-small cell lung cancer PB PCR-RFLP 194/149/83 187/163/45 0.300 10 
First author Year Country Ethnicity Cancer type Control source Genotyping method Cases (AA/AB/BB) Controls (AA/AB/BB) HWE Score 
GC (rs4588) 
McCullough 2007 U.S.A. Caucasian Breast cancer PB TaqMan 240/202/48 246/186/44 0.307 12 
Anderson 2011 Canada Caucasian Breast cancer PB PCR-RFLP 792/608/135 846/642/120 0.906 10 
Zhou-1 2012 China Asian Hepatocellular carcinoma HB PCR-RFLP 101/111/25 142/148/25 0.110 
Zhou-2 2012 China Asian Esophageal cancer HB PCR-RFLP 148/108/33 159/144/34 0.868 
Zhou-3 2012 China Asian Gastric cancer HB PCR-RFLP 74/89/29 88/92/24 0.995 
Zhou-4 2012 China Asian Colorectal cancer HB PCR-RFLP 113/100/33 182/134/15 0.117 
Reimers 2015 U.S.A. Caucasian Breast cancer PB TaqMan 456/402/82 514/393/84 0.471 10 
Deschasaux 2016 France Caucasian Breast cancer PB TaqMan 101/89/30 227/181/42 0.498 
Deschasaux 2016 France Caucasian Prostate cancer PB TaqMan 82/63/20 71/43/10 0.344 
Wu 2016 China Asian Non-small cell lung cancer PB PCR-RFLP 235/173/37 230/170/26 0.462 10 
GC (rs7041) 
McCullough 2007 U.S.A. Caucasian Breast cancer PB TaqMan 154/237/103 149/235/106 0.460 12 
Anderson 2011 Canada Caucasian Breast cancer PB PCR-RFLP 288/782/558 486/760/309 0.703 10 
Zhou-1 2012 China Asian Hepatocellular carcinoma HB PCR-RFLP 117/98/22 152/139/24 0.311 
Zhou-2 2012 China Asian Esophageal cancer HB PCR-RFLP 148/119/22 188/128/21 0.899 
Zhou-3 2012 China Asian Gastric cancer HB PCR-RFLP 99/89/16 98/86/10 0.105 
Zhou-4 2012 China Asian Colorectal cancer HB PCR-RFLP 123/107/16 171/132/28 0.724 
Kong 2014 China Asian Non-small cell lung cancer PB TaqMan 272/339/50 329/240/34 0.254 10 
Wang-1 2014 Spain Caucasian Breast cancer PB Illumina 203/402/221 216/362/201 0.050 13 
Wang-2 2014 Non-Spain Caucasian Breast cancer PB Illumina 42/61/27 73/116/35 0.320 11 
Clendenen 2015 Sweden Caucasian Breast cancer PB Illumina 265/348/121 546/658/229 0.193 
Reimers 2015 U.S.A. Caucasian Breast cancer PB TaqMan 239/470/186 311/474/193 0.609 10 
Deschasaux 2016 France Caucasian Prostate cancer PB TaqMan 19/63/45 39/76/50 0.337 
Wu 2016 China Asian Non-small cell lung cancer PB PCR-RFLP 173/225/47 175/230/61 0.281 10 
CYP27B1 (rs4646537) 
Holick 2007 U.S.A. Caucasian Prostate cancer PB SNPlex assay 546/38/0 497/43/2 0.310 14 
Holt-1 2009 U.S.A. Caucasian Prostate cancer PB PCR-RFLP 319/324/61 314/325/77 0.601 10 
Holt-2 2009 U.S.A. African Prostate cancer PB PCR-RFLP 85/28/2 50/16/1 0.826 
CYP27B1 (rs3782130) 
Holick 2007 U.S.A. Caucasian Prostate cancer PB SNPlex assay 260/251/75 260/229/61 0.327 14 
Holt-1 2009 U.S.A. Caucasian Prostate cancer PB PCR-RFLP 637/50/2 636/52/0 0.303 10 
Holt-2 2009 U.S.A. African Prostate cancer PB PCR-RFLP 97/15/2 54/8/1 0.298 
Kong 2014 China Asian Non-small cell lung cancer PB TaqMan 229/297/77 230/371/120 0.150 10 
Mahmoudi 2014 Iran Asian Colorectal cancer HB PCR-RFLP 144/125/34 180/138/36 0.216 
Wu 2016 China Asian Non-small cell lung cancer PB PCR-RFLP 194/149/83 187/163/45 0.300 10 

Abbreviations: A, wild type; B, mutated type; HB, hospital-based control; PB, publication-based control.

Meta-analysis and TSA of rs7041

Nine publications including thirteen studies with 6916 cases and 7870 controls examined rrs7041 polymorphism. As shown in Table 2, we found that rs7041 polymorphism significantly increased cancer risk in four models: dominant (OR = 1.22, 95% CI = 1.03–1.44, P=0.019), recessive (OR = 1.27, 95% CI = 1.02–1.58, P=0.030), homozygote (OR = 1.41, 95% CI = 1.06–1.88, P=0.017, Figure 2A), and allele (OR = 1.17, 95% CI = 1.02–1.33, P=0.022) models. When studies were stratified by ethnicity, significant associations were found in Asians (recessive, OR = 1.40, 95% CI = 1.11–1.77, P=0.005; homozygote, OR = 1.52, 95% CI = 1.19–1.93, P=0.001; heterozygote, OR = 1.28, 95% CI = 1.00–1.63, P=0.047; Allele, OR = 1.20, 95% CI = 1.09–1.32, P=0.000). Stratification analyses of cancer type indicated that rs7041 polymorphism increased the risk of non-small cell lung cancer (recessive, OR = 1.73, 95% CI = 1.05–2.84, P=0.031, Figure 2B; homozygote, OR = 1.97, 95% CI = 1.38–2.81, P=0.000; allele, OR = 1.32, 95% CI = 1.09–1.60, P=0.004). Moreover, our data indicated that rs7041 polymorphism was also significantly associated with an increased risk of cancer in the studies with publication-based controls. The FPRP values for significant findings at different prior probability levels are shown in Supplementary Table S3. With the assumption of prior probability of 0.1, these statistically significant associations were noteworthy (FPRP-value <0.2) for overall cancer risk (dominant and allele models), Asians (recessive, homozygote and allele models), non-small cell lung cancer (homozygote and allele models) and PCR-RFLP (heterozygote model) subgroups.

Meta-analysis for the association between rs7041 polymorphism and cancer risk

Figure 2
Meta-analysis for the association between rs7041 polymorphism and cancer risk

(A) Overall comparison (homozygote model); (B) stratification analysis by cancer type (recessive model).

Figure 2
Meta-analysis for the association between rs7041 polymorphism and cancer risk

(A) Overall comparison (homozygote model); (B) stratification analysis by cancer type (recessive model).

Table 2
Meta-analysis of associations between the rs7041 polymorphism and cancer risk
Model Overall and Stratification analyses Number of studies Number of cases/controls OR (95% CI) P-value Random/Fixed effect model P for heterogeneity I2 (%) 
Dominant Overall 13 6916/7870 1.22 (1.03, 1.44) 0.019 Random 0.000 80.0 
 Caucasian 4834/5624 1.25 (0.96, 1.63) 0.092 Random 0.000 87.3 
 Asian 2082/2246 1.19 (0.98, 1.45) 0.077 Random 0.030 59.7 
 Breast cancer 4707/5459 1.21 (0.92, 1.60) 0.179 Random 0.000 89.2 
 Digestive system tumor 976/1177 1.08 (0.91, 1.28) 0.364 Fixed 0.811 
 Non-small cell lung cancer 1106/1069 1.38 (0.89, 2.14) 0.150 Random 0.012 84.1 
 Prostate cancer 127/165 1.76 (0.96, 3.22) 0.067 Fixed 
 PB 5940/6693 1.28 (1.04, 1.59) 0.023 Random 0.000 85.2 
 HB 976/1177 1.08 (0.91, 1.28) 0.364 Fixed 0.811 
 PCR-RFLP 3049/3198 1.23 (0.90, 1.68) 0.198 Random 0.000 86.2 
 TaqMan 2177/2236 1.29 (0.95, 1.75) 0.102 Random 0.002 79.3 
 Illumina 1690/2436 1.11 (0.97, 1.28) 0.119 Fixed 0.796 
 High quality (>9) 5079/5095 1.28 (0.99, 1.64) 0.057 Random 0.000 87.5 
 Low quality (≤9) 1837/2775 1.11 (0.98, 1.25) 0.104 Fixed 0.653 
Recessive Overall 13 6916/7870 1.27 (1.02, 1.58) 0.030 Random 0.000 79.2 
 Caucasian 4834/5624 1.21 (0.91, 1.62) 0.192 Random 0.000 88.0 
 Asian 2082/2246 1.40 (1.11, 1.77) 0.005 Fixed 0.179 35.2 
 Breast cancer 4707/5459 1.21 (0.88, 1.66) 0.248 Random 0.000 90.0 
 Digestive system tumor 976/1177 1.16 (0.84, 1.61) 0.377 Fixed 0.356 7.4 
 Non-small cell lung cancer 1106/1069 1.73 (1.05, 2.84) 0.031 Random 0.153 51.1 
 Prostate cancer 127/165 1.26 (0.77, 2.07) 0.354 Fixed 
 PB 5940/6693 1.30 (1.01, 1.68) 0.045 Random 0.000 85.1 
 HB 976/1177 1.16 (0.84, 1.61) 0.377 Fixed 0.356 7.4 
 PCR-RFLP 3049/3198 1.55 (1.10, 2.19) 0.013 Random 0.017 63.8 
 TaqMan 2177/2236 1.06 (0.90, 1.24) 0.497 Fixed 0.498 
 Illumina 1690/2436 1.07 (0.91, 1.25) 0.400 Fixed 0.589 
 High quality (>9) 5079/5095 1.35 (0.99, 1.85) 0.055 Random 0.000 87.6 
 Low quality (≤9) 1837/2775 1.10 (0.92, 1.32) 0.298 Fixed 0.571 
Homozygote Overall 13 6916/7870 1.41 (1.06, 1.88) 0.017 Random 0.000 84.5 
 Caucasian 4834/5624 1.38 (0.92, 2.07) 0.124 Random 0.000 91.5 
 Asian 2082/2246 1.52 (1.19, 1.93) 0.001 Fixed 0.203 31.1 
 Breast cancer 4707/5459 1.33 (0.85, 2.06) 0.213 Random 0.000 92.9 
 Digestive system tumor 976/1177 1.19 (0.85, 1.67) 0.315 Fixed 0.420 
 Non-small cell lung cancer 1106/1069 1.97 (1.38, 2.81) 0.000 Fixed 0.514 
 Prostate cancer 127/165 1.85 (0.94, 3.65) 0.077 Fixed 
 PB 5940/6693 1.49 (1.05, 2.09) 0.024 Random 0.000 89.0 
 HB 976/1177 1.19 (0.85, 1.67) 0.315 Fixed 0.420 51.1 
 PCR-RFLP 3049/3198 1.66 (1.03, 2.69) 0.039 Random 0.000 79.7 
 TaqMan 2177/2236 1.25 (0.92, 1.69) 0.157 Random 0.078 56 
 Illumina 1690/2436 1.14 (0.96, 1.37) 0.145 Fixed 0.816 
 High quality (>9) 5079/5095 1.52 (0.99, 2.30) 0.052 Random 0.000 90.7 
 Low quality (≤9) 1837/2775 1.17 (0.96, 1.43) 0.116 Fixed 0.435 
Heterozygote Overall 13 6916/7870 1.18 (0.98, 1.43) 0.081 Random 0.000 68.4 
 Caucasian 4834/5624 1.14 (0.90, 1.45) 0.279 Random 0.000 79.1 
 Asian 2082/2246 1.28 (1.00, 1.63) 0.047 Fixed 0.103 45.3 
 Breast cancer 4707/5459 1.15 (0.88, 1.49) 0.303 Random 0.000 82.5 
 Digestive system tumor 976/1177 1.12 (0.80, 1.58) 0.508 Fixed 0.322 14.0 
 Non-small cell lung cancer 1106/1069 1.52 (0.70, 3.29) 0.285 Random 0.032 78.4 
 Prostate cancer 127/165 1.09 (0.64, 1.83) 0.758 Fixed 
 PB 5940/6693 1.20 (0.96, 1.49) 0.110 Random 0.000 76.6 
 HB 976/1177 1.12 (0.80, 1.58) 0.508 Fixed 0.322 14.0 
 PCR-RFLP 3049/3198 1.48 (1.09, 2.01) 0.013 Random 0.071 50.7 
 TaqMan 2177/2236 0.99 (0.84, 1.17) 0.904 Fixed 0.975 
 Illumina 1690/2436 1.03 (0.87, 1.21) 0.769 Fixed 0.463 
 High quality (>9) 5079/5095 1.25 (0.96, 1.63) 0.103 Random 0.000 80.6 
 Low quality (≤9) 1837/2775 1.05 (0.86, 1.27) 0.639 Fixed 0.580 
Allele Overall 13 6916/7870 1.17 (1.02, 1.33) 0.022 Random 0.000 85.2 
 Caucasian 4834/5624 1.17 (0.95, 1.44) 0.133 Random 0.000 91.7 
 Asian 2082/2246 1.20 (1.09, 1.32) 0.000 Fixed 0.137 40.2 
 Breast cancer 4707/5459 1.15 (0.92, 1.44) 0.217 Random 0.000 93.1 
 Digestive system tumor 976/1177 1.08 (0.94, 1.23) 0.283 Fixed 0.750 
 Non-small cell lung cancer 1106/1069 1.32 (1.09, 1.60) 0.004 Random 0.144 53.1 
 Prostate cancer 127/165 1.33 (0.95, 1.85) 0.096 Fixed 
 PB 5940/6693 1.20 (1.02, 1.42) 0.029 Random 0.000 89.4 
 HB 976/1177 1.08 (0.94, 1.23) 0.283 Fixed 0.750 
 PCR-RFLP 3049/3198 1.21 (0.95, 1.53) 0.121 Random 0.000 87.0 
 TaqMan 2177/2236 1.16 (0.96, 1.41) 0.130 Random 0.004 77.6 
 Illumina 1690/2436 1.07 (0.98, 1.17) 0.128 Fixed 0.919 
 High quality (>9) 5079/5095 1.21 (0.99, 1.48) 0.057 Random 0.000 91.1 
 Low quality (≤9) 1837/2775 1.08 (0.99, 1.18) 0.086 Fixed 0.728 
Model Overall and Stratification analyses Number of studies Number of cases/controls OR (95% CI) P-value Random/Fixed effect model P for heterogeneity I2 (%) 
Dominant Overall 13 6916/7870 1.22 (1.03, 1.44) 0.019 Random 0.000 80.0 
 Caucasian 4834/5624 1.25 (0.96, 1.63) 0.092 Random 0.000 87.3 
 Asian 2082/2246 1.19 (0.98, 1.45) 0.077 Random 0.030 59.7 
 Breast cancer 4707/5459 1.21 (0.92, 1.60) 0.179 Random 0.000 89.2 
 Digestive system tumor 976/1177 1.08 (0.91, 1.28) 0.364 Fixed 0.811 
 Non-small cell lung cancer 1106/1069 1.38 (0.89, 2.14) 0.150 Random 0.012 84.1 
 Prostate cancer 127/165 1.76 (0.96, 3.22) 0.067 Fixed 
 PB 5940/6693 1.28 (1.04, 1.59) 0.023 Random 0.000 85.2 
 HB 976/1177 1.08 (0.91, 1.28) 0.364 Fixed 0.811 
 PCR-RFLP 3049/3198 1.23 (0.90, 1.68) 0.198 Random 0.000 86.2 
 TaqMan 2177/2236 1.29 (0.95, 1.75) 0.102 Random 0.002 79.3 
 Illumina 1690/2436 1.11 (0.97, 1.28) 0.119 Fixed 0.796 
 High quality (>9) 5079/5095 1.28 (0.99, 1.64) 0.057 Random 0.000 87.5 
 Low quality (≤9) 1837/2775 1.11 (0.98, 1.25) 0.104 Fixed 0.653 
Recessive Overall 13 6916/7870 1.27 (1.02, 1.58) 0.030 Random 0.000 79.2 
 Caucasian 4834/5624 1.21 (0.91, 1.62) 0.192 Random 0.000 88.0 
 Asian 2082/2246 1.40 (1.11, 1.77) 0.005 Fixed 0.179 35.2 
 Breast cancer 4707/5459 1.21 (0.88, 1.66) 0.248 Random 0.000 90.0 
 Digestive system tumor 976/1177 1.16 (0.84, 1.61) 0.377 Fixed 0.356 7.4 
 Non-small cell lung cancer 1106/1069 1.73 (1.05, 2.84) 0.031 Random 0.153 51.1 
 Prostate cancer 127/165 1.26 (0.77, 2.07) 0.354 Fixed 
 PB 5940/6693 1.30 (1.01, 1.68) 0.045 Random 0.000 85.1 
 HB 976/1177 1.16 (0.84, 1.61) 0.377 Fixed 0.356 7.4 
 PCR-RFLP 3049/3198 1.55 (1.10, 2.19) 0.013 Random 0.017 63.8 
 TaqMan 2177/2236 1.06 (0.90, 1.24) 0.497 Fixed 0.498 
 Illumina 1690/2436 1.07 (0.91, 1.25) 0.400 Fixed 0.589 
 High quality (>9) 5079/5095 1.35 (0.99, 1.85) 0.055 Random 0.000 87.6 
 Low quality (≤9) 1837/2775 1.10 (0.92, 1.32) 0.298 Fixed 0.571 
Homozygote Overall 13 6916/7870 1.41 (1.06, 1.88) 0.017 Random 0.000 84.5 
 Caucasian 4834/5624 1.38 (0.92, 2.07) 0.124 Random 0.000 91.5 
 Asian 2082/2246 1.52 (1.19, 1.93) 0.001 Fixed 0.203 31.1 
 Breast cancer 4707/5459 1.33 (0.85, 2.06) 0.213 Random 0.000 92.9 
 Digestive system tumor 976/1177 1.19 (0.85, 1.67) 0.315 Fixed 0.420 
 Non-small cell lung cancer 1106/1069 1.97 (1.38, 2.81) 0.000 Fixed 0.514 
 Prostate cancer 127/165 1.85 (0.94, 3.65) 0.077 Fixed 
 PB 5940/6693 1.49 (1.05, 2.09) 0.024 Random 0.000 89.0 
 HB 976/1177 1.19 (0.85, 1.67) 0.315 Fixed 0.420 51.1 
 PCR-RFLP 3049/3198 1.66 (1.03, 2.69) 0.039 Random 0.000 79.7 
 TaqMan 2177/2236 1.25 (0.92, 1.69) 0.157 Random 0.078 56 
 Illumina 1690/2436 1.14 (0.96, 1.37) 0.145 Fixed 0.816 
 High quality (>9) 5079/5095 1.52 (0.99, 2.30) 0.052 Random 0.000 90.7 
 Low quality (≤9) 1837/2775 1.17 (0.96, 1.43) 0.116 Fixed 0.435 
Heterozygote Overall 13 6916/7870 1.18 (0.98, 1.43) 0.081 Random 0.000 68.4 
 Caucasian 4834/5624 1.14 (0.90, 1.45) 0.279 Random 0.000 79.1 
 Asian 2082/2246 1.28 (1.00, 1.63) 0.047 Fixed 0.103 45.3 
 Breast cancer 4707/5459 1.15 (0.88, 1.49) 0.303 Random 0.000 82.5 
 Digestive system tumor 976/1177 1.12 (0.80, 1.58) 0.508 Fixed 0.322 14.0 
 Non-small cell lung cancer 1106/1069 1.52 (0.70, 3.29) 0.285 Random 0.032 78.4 
 Prostate cancer 127/165 1.09 (0.64, 1.83) 0.758 Fixed 
 PB 5940/6693 1.20 (0.96, 1.49) 0.110 Random 0.000 76.6 
 HB 976/1177 1.12 (0.80, 1.58) 0.508 Fixed 0.322 14.0 
 PCR-RFLP 3049/3198 1.48 (1.09, 2.01) 0.013 Random 0.071 50.7 
 TaqMan 2177/2236 0.99 (0.84, 1.17) 0.904 Fixed 0.975 
 Illumina 1690/2436 1.03 (0.87, 1.21) 0.769 Fixed 0.463 
 High quality (>9) 5079/5095 1.25 (0.96, 1.63) 0.103 Random 0.000 80.6 
 Low quality (≤9) 1837/2775 1.05 (0.86, 1.27) 0.639 Fixed 0.580 
Allele Overall 13 6916/7870 1.17 (1.02, 1.33) 0.022 Random 0.000 85.2 
 Caucasian 4834/5624 1.17 (0.95, 1.44) 0.133 Random 0.000 91.7 
 Asian 2082/2246 1.20 (1.09, 1.32) 0.000 Fixed 0.137 40.2 
 Breast cancer 4707/5459 1.15 (0.92, 1.44) 0.217 Random 0.000 93.1 
 Digestive system tumor 976/1177 1.08 (0.94, 1.23) 0.283 Fixed 0.750 
 Non-small cell lung cancer 1106/1069 1.32 (1.09, 1.60) 0.004 Random 0.144 53.1 
 Prostate cancer 127/165 1.33 (0.95, 1.85) 0.096 Fixed 
 PB 5940/6693 1.20 (1.02, 1.42) 0.029 Random 0.000 89.4 
 HB 976/1177 1.08 (0.94, 1.23) 0.283 Fixed 0.750 
 PCR-RFLP 3049/3198 1.21 (0.95, 1.53) 0.121 Random 0.000 87.0 
 TaqMan 2177/2236 1.16 (0.96, 1.41) 0.130 Random 0.004 77.6 
 Illumina 1690/2436 1.07 (0.98, 1.17) 0.128 Fixed 0.919 
 High quality (>9) 5079/5095 1.21 (0.99, 1.48) 0.057 Random 0.000 91.1 
 Low quality (≤9) 1837/2775 1.08 (0.99, 1.18) 0.086 Fixed 0.728 

Abbreviations: HB, hospital-based control; PB, publication-based control. Bold values are statistically significant (P<0.05).

As shown in Figure 3A, although the total number of cases did not exceed the O’Brien–Fleming boundary, the cumulative Z-curve exceeded the test sequence monitoring boundary, which verified that rs7041 was significantly associated with cancer susceptibility.

TSAs of the association between rs4588, rs7041, rs3782130 and rs4646537 polymorphisms (dominant model) and cancer risk

Figure 3
TSAs of the association between rs4588, rs7041, rs3782130 and rs4646537 polymorphisms (dominant model) and cancer risk

(A) rs7041; (B) rs4588; (C) rs3782130; (D) rs4646537.

Figure 3
TSAs of the association between rs4588, rs7041, rs3782130 and rs4646537 polymorphisms (dominant model) and cancer risk

(A) rs7041; (B) rs4588; (C) rs3782130; (D) rs4646537.

Meta-analysis and TSA of rs4588

Seven publications including ten studies with 4759 cases and 5262 controls examined rs4588 polymorphism. As shown in Table 3, we found that rs4588 polymorphism significantly increased cancer risk in all five models: dominant (OR = 1.10, 95% CI = 1.02–1.19, P=0.016), recessive (OR = 1.27, 95% CI = 1.11–1.46, P=0.001), homozygote (OR = 1.31, 95% CI = 1.13–1.51, P=0.000, Figure 4A), heterozygote (OR = 1.23, 95% CI = 1.06–1.42, P=0.005), and allele (OR = 1.11, 95% CI = 1.05–1.18, P=0.001) models. Stratification analyses indicated that rs4588 polymorphism significantly increased cancer risk in Caucasians (dominant, OR = 1.10, 95% CI = 1.01–1.21, P=0.040; recessive, OR = 1.17, 95% CI = 1.00–1.39, P=0.049; homozygote, OR = 1.22, 95% CI = 1.02–1.45, P=0.026; allele, OR = 1.10, 95% CI = 1.02–1.18, P=0.015) and Asians (recessive, OR = 1.51, 95% CI = 1.18–1.94, P=0.001; homozygote, OR = 1.56, 95% CI = 1.06–2.29, P=0.024; heterozygote, OR = 1.51, 95% CI = 1.16–1.96, P=0.002, Figure 4B). When studies were stratified by cancer type, significant associations were found in breast cancer (dominant, OR = 1.10, 95% CI = 1.00–1.21, P=0.046; homozygote, OR = 1.20, 95% CI = 1.00–1.43, P=0.047; allele, OR = 1.09, 95% CI = 1.01–1.17, P=0.030) and digestive system tumor (recessive, OR = 1.58, 95% CI = 1.02–2.46, P=0.042; heterozygote, OR = 1.54, 95% CI = 1.15–2.08, P=0.004), but not in prostate cancer and non-small cell lung cancer. Moreover, when studies were stratified by quality score, an increased cancer risk was observed in high quality subgroup in all five genetic models. When studies were stratified by control source and genotyping method, significant associations were found in publication-based controls, hospital-based controls and PCR-RFLP method, but not in TaqMan method. The FPRP values for significant findings at different prior probability levels are shown in Supplementary Table S4. With the assumption of prior probability of 0.1, these statistically significant associations were noteworthy for overall cancer risk (in all five models), Caucasians (homozygote and allele models), Asians (recessive and heterozygote models), digestive system tumor (heterozygote model), breast cancer (allele model) publication-based controls (homozygote and allele models), PCR-RFLP (recessive, homozygote, heterozygote and allele models) and high quality (in all five models) subgroups.

Meta-analysis for the association between rs4588 polymorphism and cancer risk

Figure 4
Meta-analysis for the association between rs4588 polymorphism and cancer risk

(A) Overall comparison (homozygote model); (B) stratification analysis by ethnicity (heterozygote model).

Figure 4
Meta-analysis for the association between rs4588 polymorphism and cancer risk

(A) Overall comparison (homozygote model); (B) stratification analysis by ethnicity (heterozygote model).

Table 3
Meta-analysis of associations between the rs4588 polymorphism and cancer risk
Model Overall and Stratification analyses Number of studies Number of cases/controls OR (95% CI) P-value Random/Fixed effect model P for heterogeneity I2 (%) 
Dominant Overall 10 4759/5262 1.10 (1.02, 1.19) 0.016 Fixed 0.614 
 Caucasian 3350/3649 1.10 (1.01, 1.21) 0.040 Fixed 0.770 
 Asian 1409/1613 1.10 (0.95, 1.27) 0.214 Fixed 0.248 26.0 
 Breast cancer 3185/3525 1.10 (1.00, 1.21) 0.046 Fixed 0.791 
 Digestive system tumor 964/1187 1.12 (0.94, 1.32) 0.210 Fixed 0.154 42.9 
 Prostate cancer 165/124 1.36 (0.85, 2.17) 0.203 Fixed 
 Non-small cell lung cancer 445/426 1.05 (0.80, 1.37) 0.727 Fixed 
 PB 3795/4075 1.10 (1.00, 1.20) 0.040 Fixed 0.857 
 HB 964/1187 1.12 (0.94, 1.32) 0.210 Fixed 0.154 42.9 
 PCR-RFLP 2944/3221 1.07 (0.97, 1.18) 0.202 Fixed 0.342 11.4 
 TaqMan 1815/2041 1.16 (0.98, 1.32) 0.083 Fixed 0.899 
 High quality (>9) 3410/3501 1.16 (1.02, 1.32) 0.047 Fixed 0.314 15.5 
 Low quality (≤9) 1349/1761 1.08 (0.98, 1.19) 0.109 Fixed 0.859 
Recessive Overall 10 4759/5262 1.27 (1.11, 1.46) 0.001 Fixed 0.204 26.1 
 Caucasian 3350/3649 1.17 (1.00, 1.39) 0.049 Fixed 0.652 
 Asian 1409/1613 1.51 (1.18, 1.94) 0.001 Fixed 0.128 44.1 
 Breast cancer 3185/3525 1.16 (0.98, 1.37) 0.092 Fixed 0.588 
 Digestive system tumor 964/1187 1.58 (1.02, 2.46) 0.042 Random 0.070 57.5 
 Prostate cancer 165/124 1.57 (0.71, 3.49) 0.266 Fixed 
 Non-small cell lung cancer 445/426 1.40 (0.83, 2.35) 0.210 Fixed 
 PB 3795/4075 1.19 (1.02, 1.40) 0.029 Fixed 0.724 
 HB 964/1187 1.58 (1.02, 2.46) 0.042 Random 0.070 57.5 
 PCR-RFLP 2944/3221 1.35 (1.13, 1.61) 0.001 Fixed 0.121 42.6 
 TaqMan 1815/2041 1.16 (0.93, 1.44) 0.189 Fixed 0.488 
 High quality (>9) 3410/3501 1.55 (1.23, 1.96) 0.000 Fixed 0.216 29.2 
 Low quality (≤9) 1349/1761 1.14 (0.96, 1.36) 0.121 Fixed 0.758 
Homozygote Overall 10 4759/5262 1.31 (1.13, 1.51) 0.000 Fixed 0.173 29.6 
 Caucasian 3350/3649 1.22 (1.02, 1.45) 0.026 Fixed 0.683 
 Asian 1409/1613 1.56 (1.06, 2.29) 0.024 Random 0.072 53.4 
 Breast cancer 3185/3525 1.20 (1.00, 1.43) 0.047 Fixed 0.671 
 Digestive system tumor 964/1187 1.62 (0.98, 2.68) 0.061 Random 0.037 64.5 
 Prostate cancer 165/124 1.73 (0.76, 3.94) 0.191 Fixed 
 Non-small cell lung cancer 445/426 1.39 (0.82, 2.38) 0.224 Fixed 
 PB 3795/4075 1.23 (1.05, 1.45) 0.013 Fixed 0.775 
 HB 964/1187 1.62 (0.98, 2.68) 0.061 Random 0.037 64.5 
 PCR-RFLP 2944/3221 1.45 (1.08, 1.94) 0.014 Random 0.072 50.5 
 TaqMan 1815/2041 1.23 (0.98, 1.54) 0.077 Fixed 0.518 
 High quality (>9) 3410/3501 1.59 (1.25, 2.04) 0.000 Fixed 0.130 41.3 
 Low quality (≤9) 1349/1761 1.18 (0.99, 1.40) 0.069 Fixed 0.892 
Heterozygote Overall 10 4759/5262 1.23 (1.06, 1.42) 0.005 Fixed 0.314 14.0 
 Caucasian 3350/3649 1.12 (0.94, 1.34) 0.203 Fixed 0.662 
 Asian 1409/1613 1.51 (1.16, 1.96) 0.002 Fixed 0.305 17.2 
 Breast cancer 3185/3525 1.11 (0.93, 1.33) 0.251 Fixed 0.534 
 Digestive system tumor 964/1187 1.54 (1.15, 2.08) 0.004 Fixed 0.191 36.8 
 Prostate cancer 165/124 1.37 (0.58, 3.20) 0.474 Fixed 
 Non-small cell lung cancer 445/426 1.40 (0.81, 2.41) 0.227 Fixed 
 PB 3795/4075 1.15 (0.97, 1.35) 0.113 Fixed 0.704 
 HB 964/1187 1.24 (0.99, 1.65) 0.127 Fixed 0.191 36.8 
 PCR-RFLP 2944/3221 1.34 (1.12, 1.62) 0.002 Fixed 0.279 20.5 
 TaqMan 1815/2041 1.07 (0.85, 1.35) 0.546 Fixed 0.552 
 High quality (>9) 3410/3501 1.51 (1.18, 1.93) 0.001 Fixed 0.438 
 Low quality (≤9) 1349/1761 1.11 (0.92, 1.32) 0.272 Fixed 0.593 
Allele Overall 10 4759/5262 1.11 (1.05, 1.18) 0.001 Fixed 0.284 17.3 
 Caucasian 3350/3649 1.10 (1.02, 1.18) 0.015 Fixed 0.685 
 Asian 1409/1613 1.15 (0.99, 1.35) 0.077 Random 0.086 50.9 
 Breast cancer 3185/3525 1.09 (1.01, 1.17) 0.030 Fixed 0.764 
 Digestive system tumor 964/1187 1.18 (0.95, 1.45) 0.131 Random 0.049 61.8 
 Prostate cancer 165/124 1.33 (0.92, 1.93) 0.127 Fixed 
 Non-small cell lung cancer 445/426 1.09 (0.88, 1.35) 0.425 Fixed 
 PB 3795/4075 1.10 (1.02, 1.17) 0.010 Fixed 0.809 
 HB 964/1187 1.18 (0.95, 1.45) 0.131 Random 0.049 61.8 
 PCR-RFLP 2944/3221 1.10 (1.02, 1.19) 0.014 Fixed 0.104 45.3 
 TaqMan 1815/2041 1.13 (0.97, 1.25) 0.087 Random 0.085 50.6 
 High quality (>9) 3410/3501 1.19 (1.07, 1.33) 0.001 Fixed 0.137 40.0 
 Low quality (≤9) 1349/1761 1.08 (0.99, 1.16) 0.054 Fixed 0.988 
Model Overall and Stratification analyses Number of studies Number of cases/controls OR (95% CI) P-value Random/Fixed effect model P for heterogeneity I2 (%) 
Dominant Overall 10 4759/5262 1.10 (1.02, 1.19) 0.016 Fixed 0.614 
 Caucasian 3350/3649 1.10 (1.01, 1.21) 0.040 Fixed 0.770 
 Asian 1409/1613 1.10 (0.95, 1.27) 0.214 Fixed 0.248 26.0 
 Breast cancer 3185/3525 1.10 (1.00, 1.21) 0.046 Fixed 0.791 
 Digestive system tumor 964/1187 1.12 (0.94, 1.32) 0.210 Fixed 0.154 42.9 
 Prostate cancer 165/124 1.36 (0.85, 2.17) 0.203 Fixed 
 Non-small cell lung cancer 445/426 1.05 (0.80, 1.37) 0.727 Fixed 
 PB 3795/4075 1.10 (1.00, 1.20) 0.040 Fixed 0.857 
 HB 964/1187 1.12 (0.94, 1.32) 0.210 Fixed 0.154 42.9 
 PCR-RFLP 2944/3221 1.07 (0.97, 1.18) 0.202 Fixed 0.342 11.4 
 TaqMan 1815/2041 1.16 (0.98, 1.32) 0.083 Fixed 0.899 
 High quality (>9) 3410/3501 1.16 (1.02, 1.32) 0.047 Fixed 0.314 15.5 
 Low quality (≤9) 1349/1761 1.08 (0.98, 1.19) 0.109 Fixed 0.859 
Recessive Overall 10 4759/5262 1.27 (1.11, 1.46) 0.001 Fixed 0.204 26.1 
 Caucasian 3350/3649 1.17 (1.00, 1.39) 0.049 Fixed 0.652 
 Asian 1409/1613 1.51 (1.18, 1.94) 0.001 Fixed 0.128 44.1 
 Breast cancer 3185/3525 1.16 (0.98, 1.37) 0.092 Fixed 0.588 
 Digestive system tumor 964/1187 1.58 (1.02, 2.46) 0.042 Random 0.070 57.5 
 Prostate cancer 165/124 1.57 (0.71, 3.49) 0.266 Fixed 
 Non-small cell lung cancer 445/426 1.40 (0.83, 2.35) 0.210 Fixed 
 PB 3795/4075 1.19 (1.02, 1.40) 0.029 Fixed 0.724 
 HB 964/1187 1.58 (1.02, 2.46) 0.042 Random 0.070 57.5 
 PCR-RFLP 2944/3221 1.35 (1.13, 1.61) 0.001 Fixed 0.121 42.6 
 TaqMan 1815/2041 1.16 (0.93, 1.44) 0.189 Fixed 0.488 
 High quality (>9) 3410/3501 1.55 (1.23, 1.96) 0.000 Fixed 0.216 29.2 
 Low quality (≤9) 1349/1761 1.14 (0.96, 1.36) 0.121 Fixed 0.758 
Homozygote Overall 10 4759/5262 1.31 (1.13, 1.51) 0.000 Fixed 0.173 29.6 
 Caucasian 3350/3649 1.22 (1.02, 1.45) 0.026 Fixed 0.683 
 Asian 1409/1613 1.56 (1.06, 2.29) 0.024 Random 0.072 53.4 
 Breast cancer 3185/3525 1.20 (1.00, 1.43) 0.047 Fixed 0.671 
 Digestive system tumor 964/1187 1.62 (0.98, 2.68) 0.061 Random 0.037 64.5 
 Prostate cancer 165/124 1.73 (0.76, 3.94) 0.191 Fixed 
 Non-small cell lung cancer 445/426 1.39 (0.82, 2.38) 0.224 Fixed 
 PB 3795/4075 1.23 (1.05, 1.45) 0.013 Fixed 0.775 
 HB 964/1187 1.62 (0.98, 2.68) 0.061 Random 0.037 64.5 
 PCR-RFLP 2944/3221 1.45 (1.08, 1.94) 0.014 Random 0.072 50.5 
 TaqMan 1815/2041 1.23 (0.98, 1.54) 0.077 Fixed 0.518 
 High quality (>9) 3410/3501 1.59 (1.25, 2.04) 0.000 Fixed 0.130 41.3 
 Low quality (≤9) 1349/1761 1.18 (0.99, 1.40) 0.069 Fixed 0.892 
Heterozygote Overall 10 4759/5262 1.23 (1.06, 1.42) 0.005 Fixed 0.314 14.0 
 Caucasian 3350/3649 1.12 (0.94, 1.34) 0.203 Fixed 0.662 
 Asian 1409/1613 1.51 (1.16, 1.96) 0.002 Fixed 0.305 17.2 
 Breast cancer 3185/3525 1.11 (0.93, 1.33) 0.251 Fixed 0.534 
 Digestive system tumor 964/1187 1.54 (1.15, 2.08) 0.004 Fixed 0.191 36.8 
 Prostate cancer 165/124 1.37 (0.58, 3.20) 0.474 Fixed 
 Non-small cell lung cancer 445/426 1.40 (0.81, 2.41) 0.227 Fixed 
 PB 3795/4075 1.15 (0.97, 1.35) 0.113 Fixed 0.704 
 HB 964/1187 1.24 (0.99, 1.65) 0.127 Fixed 0.191 36.8 
 PCR-RFLP 2944/3221 1.34 (1.12, 1.62) 0.002 Fixed 0.279 20.5 
 TaqMan 1815/2041 1.07 (0.85, 1.35) 0.546 Fixed 0.552 
 High quality (>9) 3410/3501 1.51 (1.18, 1.93) 0.001 Fixed 0.438 
 Low quality (≤9) 1349/1761 1.11 (0.92, 1.32) 0.272 Fixed 0.593 
Allele Overall 10 4759/5262 1.11 (1.05, 1.18) 0.001 Fixed 0.284 17.3 
 Caucasian 3350/3649 1.10 (1.02, 1.18) 0.015 Fixed 0.685 
 Asian 1409/1613 1.15 (0.99, 1.35) 0.077 Random 0.086 50.9 
 Breast cancer 3185/3525 1.09 (1.01, 1.17) 0.030 Fixed 0.764 
 Digestive system tumor 964/1187 1.18 (0.95, 1.45) 0.131 Random 0.049 61.8 
 Prostate cancer 165/124 1.33 (0.92, 1.93) 0.127 Fixed 
 Non-small cell lung cancer 445/426 1.09 (0.88, 1.35) 0.425 Fixed 
 PB 3795/4075 1.10 (1.02, 1.17) 0.010 Fixed 0.809 
 HB 964/1187 1.18 (0.95, 1.45) 0.131 Random 0.049 61.8 
 PCR-RFLP 2944/3221 1.10 (1.02, 1.19) 0.014 Fixed 0.104 45.3 
 TaqMan 1815/2041 1.13 (0.97, 1.25) 0.087 Random 0.085 50.6 
 High quality (>9) 3410/3501 1.19 (1.07, 1.33) 0.001 Fixed 0.137 40.0 
 Low quality (≤9) 1349/1761 1.08 (0.99, 1.16) 0.054 Fixed 0.988 

Abbreviations: HB, hospital-based control; PB, publication-based control. Bold values are statistically significant (P<0.05).

To analyze the reliability of our results, we performed a TSA. As shown in Figure 3B, the cumulative number of cases did not meet the O’Brien–Fleming boundary and test sequence monitoring boundary. Current TSA results suggested that more sample size was still needed for more robust results.

Meta-analysis and TSA of rs4646537 and rs3782130

Two publications including three studies with 1403 cases and 1325 controls examined rs4646537 polymorphism; five publications including six studies with 2721 cases and 2761 controls examined rs3782130 polymorphism. As shown in Supplementary Table S5, we found these two polymorphisms were not associated with cancer risk.

As for rs4646537 and rs3782130, the cumulative number of cases did not exceed the O’Brien–Fleming boundary and test sequence monitoring boundary (Figure 3C,D). Therefore, more sample sizes were still needed for more robust results.

Publication bias and sensitivity analysis

As showed in Supplementary Figure S1 and Table 4, Begg’s and Egger’s tests indicated that there was no evidence of significant publication bias in our current meta-analysis. Sensitivity analysis found that none of the single study significantly changed the final conclusion (Supplementary Figure S2).

Table 4
Begg’s and Egger’s tests for publication bias
Model rs4588 rs7041 rs4646537 rs3782130 
 PBegg PEgger PBegg PEgger PBegg PEgger PBegg PEgger 
Dominant 0.669 0.573 0.502 0.221 0.602 0.838 0.707 0.727 
Recessive 0.132 0.119 0.200 0.498 0.546 0.588 0.310 0.945 
Homozygote 0.231 0.124 0.161 0.362 0.573 0.597 0.452 0.833 
Heterozygote 0.107 0.132 0.127 0.722 1.000 0.562 0.348 0.736 
Allele 0.208 0.130 0.200 0.166 0.609 0.721 0.851 0.947 
Model rs4588 rs7041 rs4646537 rs3782130 
 PBegg PEgger PBegg PEgger PBegg PEgger PBegg PEgger 
Dominant 0.669 0.573 0.502 0.221 0.602 0.838 0.707 0.727 
Recessive 0.132 0.119 0.200 0.498 0.546 0.588 0.310 0.945 
Homozygote 0.231 0.124 0.161 0.362 0.573 0.597 0.452 0.833 
Heterozygote 0.107 0.132 0.127 0.722 1.000 0.562 0.348 0.736 
Allele 0.208 0.130 0.200 0.166 0.609 0.721 0.851 0.947 

Discussion

It has long been clear that genetics has the ability to intervene in the cancer risk in the coming decades. Since polymorphism is the most important cause of human genetic material and information variation, the specific relationship between polymorphisms and cancer susceptibility has attracted widespread attention. With the rapid development of medical science and technology, the field of tumor genetic susceptibility has gradually attracted great interest, and the research on tumor genetic polymorphism is also increasing. Genetic polymorphisms involving the vitamin D pathway has become an important class of genes in the extensive study of polymorphisms in risk factors associated with malignant tumors.

CYP27B1 and GC are two important enzymes involved in vitamin D binding and transport. Nowadays, a growing body of evidence suggests that differential expression of CYP27B1 and GC may play an important role in carcinogenesis development. Reduced CYP27B1 gene expression level has been found in various tumors, including prostate cancer [31–32], non-small cell lung cancer [23]. Whitlatch et al. [32] investigated CYP27B1 expression in normal prostate, prostatic hyperplasia and prostate cancer, and they found that normal prostate exhibited the highest expression of CYP27B1, while its expression was decreased in the following order: prostatic hyperplasia and prostate cancer. These findings suggest that the malignant progression of prostate tissue certainly reduces CYP27B1 expression. Furthermore, Kong et al. [23] found that non-small cell lung cancer patients with high CYP27B1 expression had better overall survival than those with low CYP27B1, which indicated that low CYP27B1 expression was also correlated with a poorer prognosis. In addition, there are two common single nucleotide polymorphisms (rs7041 and rs4588) in GC gene. In the previous reports, genetic variants in the GC gene, including rs7041 and rs4588, have been investigated in breast cancer [18–19,22,25], non-small cell lung cancer [21], prostate cancer [26] and digestive system tumor [27]. However, to date, there is no systematic evaluation on how CYP27B1 and GC polymorphisms are involved in development of cancers.

Our data found that rs4588 was significantly associated with an increased risk of cancer susceptibility, and current result was confirmed by FPRP and TSA analyses. Among these studies, there were four studies on breast cancer, four on digestive system tumor, one on prostate cancer and one on non-small cell lung cancer. Stratified analyses by cancer type revealed a significant association between rs4588 and breast cancer and digestive system tumor, but not in prostate cancer and non-small cell lung cancer. However, our outcomes were different from the results shown by Anderson et al. [18], McCullough et al. [19], Reimers et al. [22], and Deschasaux et al. [25], who demonstrated that rs4588 polymorphism was not associated with breast cancer. This discrepancy may be caused by the limited sample size. Anderson et al. [18] included only 3143 subjects (1535 cases and 1608 controls), McCullough et al. [19] included only 966 subjects (490 cases and 476 controls), Reimers et al. [22] included only 1931 subjects (940 cases and 991 controls), Deschasaux et al. [25] included only 670 subjects (220 cases and 450 controls), which may lack sufficient power to support or deny an association. Previous studies also focused on the relationship between the rs4588 and digestive system tumor. However, our outcomes were different from previous study [27], which indicated that rs4588 polymorphism was not associated with hepatocellular carcinoma, esophageal cancer and gastric cancer. Possible reasons for this difference could be explained as the limited sample size. There was only one study for hepatocellular carcinoma, esophageal cancer and gastric cancer, which was far from enough to obtain trustworthy results. Based on current TSA results, more studies by standardized unbiased methods are required to offer more detailed data.

As for rs7041, we found that this polymorphism significantly increased cancer risk. Stratification analyses of ethnicity suggested rs7041 increased cancer risk in Asians, but not in Caucasians. Possible reasons can be explained as the different genetic backgrounds of cancer across ethnicities. In this meta-analysis, the pooled rs7041 C allele frequency of the controls showed a large difference across ethnicities (Asians: 30.2%; Caucasians: 45.4%), which may possibly affect the relationships between rs7041 polymorphism and cancer risk among different racial subgroups. Moreover, when studies were stratified by cancer type, we also found that rs7041 polymorphism was significantly associated with an increased risk in the non-small cell lung cancer. However, most subgroups had insufficient numbers, which may attenuate the statistical power. Our results were partially consistent with the consequence of the study by Wang et al. [20], which reported that there was no significant association between rs7041 and breast cancer in Asians and Caucasians. However, study by Reimers et al [22]. suggested that rs7041 was associated with an increased risk of breast cancer in Caucasians. It is noteworthy that Yao et al. [33] indicated that increased polymorphism may be related to the higher prevalence of estrogen receptor (ER)-negative but not ER-positive breast cancer. At present, a large number of researches indicated that there were important differences in genetic susceptibility between ER-negative and ER-positive breast cancer [11]. Therefore, it is reasonable to hypothesize that rs7041 polymorphism may have a specific effect on the susceptibility to ER-negative breast cancer. Of note, due to limited data, lack of further evaluation between rs7041 and ER-negative and ER-positive breast cancer prevented our comprehensive understanding. Further large-cohort and well-designed studies are necessary to identify the possible association between them. With respect to the remaining two polymorphisms, we failed to find any associations between rs4646537 and rs3782130 and cancer risk. Given the limited sample size, our results should be interpreted with caution.

In general, current analysis has the following advantages: (1) Our research results were validated based on TSA to ensure the reliability of the results. (2) All included studies were consistent with the HWE balance law, which may improve the reliability of our study. (3): This system evaluation is the first analysis of reviewing the relationships between CYP27B1 (rs4646537, rs3782130) and GC (rs4588 and rs7041) polymorphisms with cancer susceptibility. (4) To avoid false positive findings, FPRP analyses were used for all significant findings observed in our study. However, current study still has the following shortcomings: (1) The subjects we included were limited to Caucasians and Asians, and the results of the present study still lack information from other ethnic groups, which may lead to publication bias. (2) The number of studies on rs4646537, rs3782130, rs4588 and rs7041 was relatively small in some subgroups, which may create significant or insignificant results by chance. (3) In some included studies, detailed information (e.g., radiation exposure, carcinogen, smoking and other risk factors) was not gathered, which further prevented the stratification analyses. Thus, a larger sample size, multi-racial, multi-center standardized research is needed to provide more detailed data in the future.

Conclusions

In conclusion, this systematical meta-analysis indicated that rs4588 and rs7041 polymorphisms play important roles in cancer pathogenesis, especially in non-small cell lung cancer, breast cancer and digestive system tumor, which were noteworthy findings as evaluated by FPRP. However, the other two polymorphisms (rs4646537 and rs3782130) are not associated with cancer risk. Further well-designed studies are necessary to validate our results.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contribution

M.Z., Z.z.L., Z.T., H.W. and Z.x. L. performed the research design. T.W. and H.H. assessed the studies quality and data collection. M.Z. and S.F. performed the sensitive analysis and publication bias test. M.Z., Z.T. and Z.x.L. wrote the paper. All authors confirmed the final edition.

Funding

This work was supported by the Key Project of Natural Sciences Foundation of Hubei Province [grant number 2015CFA078]; the Research Fund of Wuhan Public Health Bureau [grant numbers WX15A12, WX17Q10, WX18Y11]; and the Research Fund of Hubei Province Public Health Bureau [grant number WJ2015MB144].

Abbreviations

     
  • CI

    confidence interval

  •  
  • CNKI

    China national knowledge infrastructure

  •  
  • EMBASE

    excerpta medica database

  •  
  • ER

    estrogen receptor

  •  
  • FPRP

    false-positive report probability

  •  
  • GC

    group-specific component

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • MAF

    minor allele frequency

  •  
  • OR

    odds ratio

  •  
  • PCR-RFLP

    polymerase chain reaction-restrictionfragment length polymorphism

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • TSA

    trial sequential analysis

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