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. 2015 Jul 10;5:12069. doi: 10.1038/srep12069

Association of three 8q24 polymorphisms with prostate cancer susceptibility: evidence from a meta-analysis with 50,854 subjects

Qiaoxin li 1,a,*, Xia Liu 1,*, Rui-Xi Hua 2, Feng Wang 3, Hengqing An 3, Wei Zhang 1, Jin-Hong Zhu 4,b
PMCID: PMC4498192  PMID: 26159557

Abstract

The 8q24 polymorphisms have been implicated in various cancers. Three 8q24 polymorphisms (rs1447295 C>A, rs16901979 C>A, and rs6983267 T>G) have been extensively investigated for their association with prostate cancer (PCa) susceptibility, yet conclusions are contradictory. We conducted a comprehensive meta-analysis to reevaluate the associations between those polymorphisms and PCa susceptibility, according to the latest meta-analysis guidelines (PRISMA). Eligible publications were searched from MEDLINE, EMBASE and CBM. False positive report possibility analysis was performed. We totally collected 20184 cases and 20439 controls from 20 studies for the rs1447295 C>A, 1850 cases and 2090 controls from 7 studies for the rs16901979 C>A, and 12233 cases and 7582 controls from 17 studies for the rs6983267 T>G. Overall, each of studied 8q24 polymorphisms was significantly associated with PCa risk individually. Significant associations were also observed in stratified analysis by ethnicity, source of control, and quality score. Interestingly, the effect of rs1447295 on PCa risk was observed among Caucasians and Asians, but not Africa-Americans. The effect of rs16901979 was more prominent among Africa-Americans than Asians. Likewise, rs6983267 conferred a higher Pca risk among Caucasians than Asians. Collectively, these 8q24 variant(s) may modulate PCa risk in an ethnic-specific manner.

Prostate cancer (PCa) is one of the most common non-cutaneous malignancies among men in the US, with an estimated 240,890 new cases and 33,720 deaths in 20101. Although little is known about the etiology of the disease, accumulating evidence has demonstrated that genetic variations may play a crucial role in the carcinogenesis of PCa. For example, genome-wide association studies (GWASs) have identified more than 40 human PCa-predisposing variants, among which single nucleotide polymorphisms (SNPs) located in the 8q24 region were considered as promising biomarkers for PCa2,3,4,5,6. However, not all of those significant findings can be validated by the subsequent candidate-based studies.

Chromosomal region 8q24 has emerged recently as bona fide risk locus for multiple cancers7,8,9. Fine mapping and additional genome scans have identified three 8q24 regions (region 1: 128.54–128.62 Mb; region 2: 128.12–128.28 Mb; region 3: 128.47–128.54 Mb) that contain variants independently associated with PCa risk2,3,10,11. Since the 8q24 region was originally shown to confer a PCa risk in a genome wide linkage scan of 871 Icelandic men in 20068, numerous association studies have been performed to extensively explore the roles of 8q24 single nucleotide polymorphisms (SNPs) in the etiology of PCa. To date, there are about 64 variants in 8q24 investigated for the association with PCa risk, and only 20 of those variants were confirmed to be PCa risk-associated SNPs. Of those PCa risk SNP, rs1447295 C>A in region 1, rs16901979 C>A in region 2, and rs6983267 T>G in region 3, have shown strong association with PCa, with respective adjusted P value of 4 × 10−29 , 1 × 10−19, and 1 × 10−11 12. Similarly, significant associations with 8q24 polymorphisms were also identified for a wide spectrum of cancers, including cancers of the breast13,14, prostate2,4, bladder15, colon16, lung17, ovaries18, pancreas19, and brain20 among different ethnicities (Asian, Caucasian, and African of Americans). Taken together, these findings have made SNPs on 8q24 of particular interest because of their potential roles in screening strategies for high-risk individuals and discovering new therapeutic targets.

The mechanisms by which 8q24 influences the course of PCa are not yet fully understood. The 8q24 region has been described as a “gene desert” since the 600-kbp gene-poor region appears to have little or no transcriptional activity. Nevertheless, several lines of evidence has suggested that 8q24 may play an active role in PCa carcinogenesis. First, 8q24 is a highly conserved genomic region. Second, POU5F1P1, an important gene on 8q24, encodes a weak transcriptional activator that may contribute to carcinogenesis21. Third, recent studies have shown that the activity of the nearby oncogene MYC is associated with 8q244,22,23,24.

Although PCa carcinogenesis has been found to be associated with hereditary background, increasing molecular epidemiology studies have presented conflicting results on the association between 8q24 SNPs and PCa risk, which may be partially attributable to various sample sizes, different genetic backgrounds, and heterogeneous inclusion criteria among studies. With this in mind, we carried out the current meta-analysis to provide a quality assessment of the association of the most frequently analyzed 8q24 SNPs (i.e., rs1447295 C>A in region1, rs16901979 C>A in region2, and rs6983267 T>G in region3) with PCa risk.

Results

Eligible studies

Based on the inclusion criteria, 22 eligible articles consisting of 44 studies were included in this meta-analysis1,12,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40. The sample sizes of those studies ranged from 103 to 24454. Of those articles, 9 and 13 were categorized as high and low quality, respectively, using methods described in the Methods section. Literature search and study selection yielded 20 studies for rs1447295 C>A analysis, which were performed among Caucasians (10 studies), Asians (7 studies), and Africa-Americans (3 studies). A total of 7 eligible studies were retrieved for rs16901979 C>A analysis with 3, 3, and 1 studies conducted in these three ethnic groups, respectively. Moreover, of 17 studies eligible for rs6983267 T>G analysis, there were 9, 6, and 2 carried out in these three ethnic groups, respectively. Other details were shown in Table 1. Genotype frequency distributions of studied SNPs in all of the control populations were agreed with HWE.

Table 1. Characteristics of studies included in the current meta-analysis.

Surname Year Country Ethnicity Source Genotype method Case
 control
 MAF HWE Score
WW VW VV ALL WW VW VV ALL
For rs1447295 C>A (region1)
 Chan 2013 Singapore Asian HB Illumina 1M chip 180 92 17 289 94 44 5 143 0.19 0.957 7
 Schumacher 2007 USA Caucasians PB TaqMan 8462 2736 268 11466 10344 2472 172 12988 0.11 0.079 14
 Zheng 2007 USA Caucasians HB iPLEX 1169 346 31 1546 485 82 4 571 0.08 0.794 11
 Joung 2012 Korea Asian HB iPLEX 114 67 12 193 127 38 3 168 0.13 0.936 7
 Francisco 2013 Chile Caucasians HB TaqMan 56 23 4 83 16 4 1 21 0.14 0.309 4
 Terada 2008 Japan Asian HB PCR-RFLP 310 172 25 507 335 165 11 511 0.18 0.071 9
 Okobia 2011 USA Africa-American HB TaqMan 156 162 36 354 173 207 58 438 0.37 0.291 11
 Brankovie 2013 Sebia Caucasians HB PCR-RFLP 86 61 3 150 11 82 7 100 0.48 0.000 7
 Suurinirmi 2007 USA Caucasians PB TaqMan 435 136 11 582 427 107 4 538 0.11 0.390 15
 Zeegers 2011 USA Caucasians PB TaqMan 224 53 4 281 196 64 7 267 0.15 0.523 10
 Salinas 2008 USA Caucasians PB TaqMan 937 288 27 1252 994 225 14 1233 0.10 0.753 15
 Benford 2010 USA Africa-American HB TaqMan 86 77 26 189 237 221 65 523 0.34 0.000 8
 Liu 2012 China Asian PB PCR-RFLP 150 102 8 260 197 86 4 287 0.16 0.111 11
 Liu 2009 Japan Asian HB TaqMan 217 149 25 391 218 89 16 323 0.19 0.088 8
 Severi 2007 Australian Caucasians PB TaqMan 595 212 14 821 586 135 11 732 0.11 0.319 13
 Wokolorczyk 2010 Poland Caucasians HB PCR-RFLP 515 156 19 690 484 115 3 602 0.10 0.165 8
 Chen 2009 Taiwan Asian PB TaqMan 215 119 6 340 253 75 9 337 0.14 0.237 10
 Cheng 2008 USA Caucasians HB TaqMan 318 97 2 417 344 69 4 417 0.09 0.795 11
 Cheng 2008 USA Africa-American HB TaqMan 39 44 6 89 43 35 11 89 0.32 0.919 11
 Zheng 2010 China Asian PB iPLEX 173 96 15 284 110 35 6 151 0.16 0.147 12
For rs16901979 C>A (region2)
 Chan 2013 Singapore Asian HB Illumina 1M chip 139 119 31 289 64 68 12 144 0.32 0.302 7
 Joung 2012 Korea Asian HB iPLEX 99 81 14 194 100 57 12 169 0.24 0.333 7
 Okobia 2011 USA Africa-American HB TaqMan 81 158 99 338 131 193 102 426 0.47 0.064 11
 Chen 2010 Taiwan Asian HB TaqMan 148 148 35 331 173 138 24 335 0.28 0.62 11
 Benford 2010 USA Africa-American HB TaqMan 45 97 50 192 188 237 87 512 0.40 0.406 8
 Cheng 2008 USA Caucasians HB TaqMan 375 41 1 417 393 22 1 416 0.03 0.252 11
 Cheng 2008 USA Africa-American HB TaqMan 23 43 23 89 27 50 11 88 0.41 0.1 11
For rs6983267 T>G (region3)
 Chan 2013 Singapore Asian HB Illumina 1M chip 89 136 63 288 47 74 23 144 0.42 0.493 7
 Zheng 2007 USA Caucasians HB iPLEX 495 771 285 1551 142 299 132 573 0.49 0.293 11
 Joung 2012 Korea Asian HB iPLEX 56 92 46 194 51 86 31 168 0.44 0.618 7
 Francisco 2013 Chile Caucasians HB TaqMan 19 33 30 82 5 13 3 21 0.45 0.253 4
 Terada 2008 Japan Asian HB PCR-RFLP 211 219 77 507 206 225 80 511 0.38 0.94 9
 Okobia 2011 USA Africa-American HB TaqMan 307 34 2 343 373 52 1 426 0.06 0.562 11
 Brankovie 2013 Sebia Caucasians HB PCR-RFLP 53 80 17 150 69 129 52 250 0.47 0.561 7
 Salinas 2008 USA Caucasians PB PCR-RFLP 364 652 242 1258 313 617 308 1238 0.50 0.91 15
 Liu 2012 China Asian PB PCR-RFLP 70 137 53 260 94 137 51 282 0.42 0.93 11
 Liu 2009 Japan Asian HB TaqMan 151 181 59 391 147 151 25 323 0.31 0.104 8
 Papanikolopoulou 2011 Greece Caucasians HB TaqMan 16 46 24 86 39 47 13 99 0.37 0.844 6
 Cheng 2008 USA Caucasians HB TaqMan 76 215 126 417 106 206 105 417 0.50 0.807 11
 Cheng 2008 USA Africa-American HB TaqMan 74 14 1 89 74 11 4 89 0.11 0.001 11
 Zheng 2010 China Asian PB iPLEX 86 134 62 282 51 72 29 152 0.43 0.69 12
 Penney 2009 USA Caucasians PB iPLEX 400 644 261 1305 372 707 323 1402 0.48 0.714 15
 Penney 2009 USA Caucasians PB iPLEX 1184 1776 812 3772 69 134 46 249 0.45 0.177 15
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HB, Hospital based; PB, Population based; PCR-RFLP, Polymorphism chain reaction-restriction fragment length polymorphism; MAF, Minor allele frequency; HWE, Hardy-Weinberg equilibrium. WW, wild homozygous; WV, heterozygous; VV, variant homozygous.

Quantitative synthesis

A total 20 eligible studies were pooled together to evaluate the association between 8q24 rs1447295 C>A and PCa risk, with 20184 cases and 20439 controls. Pooled risk estimates indicated the significant associations of rs1447295 C>A with increased PCa risk under homozygous (OR= 1.39; 95% CI= 1.01–1.91, P = 0.042), heterozygous (OR= 1.26, 95% CI= 1.09–1.45, P = 0.002), dominant models (OR= 2.46, 95% CI= 1.89–3.20, P = 0.000), and alleles comparison (OR= 1.23, 95% CI= 1.09–1.40, P = 0.001); intriguingly, the direction of the association was reversed under recessive genetic models (OR= 0.51, 95% CI= 0.39–0.67, P = 0.001) as shown in Table 2. Moreover, there were 7 eligible studies available for 8q24 rs16901979 C>A analysis, consisting of 1850 cases and 2090 controls. We found that carrier of AA, AC, or combined carriers of AA/AC risk genotypes had an OR of 1.71 (homozygous: 95% CI= 1.36–2.16, P = 0.000), 1.31 (heterozygous 95% CI= 1.12–1.53, P = 0.001), or 1.39 (dominant: 95% CI= 1.20–1.61, P = 0.000) for developing prostate cancer, respectively, compared with those with CC genotype. And OR of alleles comparison (C vs. A) for the association was 1.31 (95% CI= 1.18–1.46, P = 0.000). For 8q24 rs6983267 T>G analysis, a total of 17 studies comprising 12233 cases and 7582 controls were incorporated into the meta-analysis. Pooled analysis observed significant associations between increased PCa risk and the variant of interest under homozygous (OR= 1.44; 95% CI= 1.31–1.58, P = 0.000), heterozygous (OR= 1.19, 95% CI= 1.10–1.29, P = 0.000), recessive (OR= 1.26, 95% CI= 1.18–1.36, P = 0.000), and alleles comparison (OR= 1.19, 95% CI= 1.14–1.25, P = 0.000).

Table 2. Meta-analysis of the association between 8q24 rs1447295, rs16901979 and rs6983267 polymorphisms and prostate cancer risk.

Variables No. of individuals No. of studies Homozygous Heterozygous
 Recessive
 Dominant
 Allele
OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet
rs1447295 C>A (region1)
All 20184/20439 20 1.39 (1.01–1.91) 0.000 1.26 (1.09–1.45) 0.000 0.51 (0.39–0.67) 0.001 2.46 (1.89–3.20) 0.000 1.23 (1.09–1.40) 0.000
publication bias (Begg’s test)   P = 0.296 P = 0.382 P = 0.713 P = 0.277 P = 0.272
Ethnicity
 Caucasion 17288/17469 10 1.35 (0.78–2.35) 0.000 1.15 (0.92–1.44) 0.000 0.48 (0.31–0.74) 0.014 4.15 (3.26–5.28) 0.000 1.18(0.97–1.43) 0.000
 Asian 2264/1920 7 1.87 (1.33–2.62) 0.469 1.49 (1.30–1.70) 0.112 0.38 (0.27–0.53) 0.438 1.97 (1.47–2.62) 0.000 1.44 (1.29–1.60) 0.425
 Africa-American 632/1050 3 0.82 (0.59–1.14) 0.349 0.95 (0.77–1.18) 0.411 0.91 (0.66–1.26) 0.992 0.74 (0.43–1.29) 0.001 0.92 (0.79–1.07) 0.487
Source of control
 HB 4898/3906 12 1.28 (0.75–2.17) 0.000 1.12 (0.84–1.49) 0.000 0.52 (0.33–0.82) 0.001 2.07 (1.16–3.67) 0.000 1.16 (0.92–1.47) 0.000
 PB 15286/16533 8 1.80 (1.52–2.13) 0.278 1.38 (1.22–1.57) 0.000 0.44 (0.37–0.52) 0.329 3.22 (2.78–3.74) 0.000 1.34 (1.21–1.50) 0.049
Score
 Low 2492/2391 8 1.49 (0.74–2.99) 0.000 1.17 (0.92–1.51) 0.000 0.45 (0.25–0.81) 0.003 1.79 (1.01–3.16) 0.000 1.14 (0.82–1.58) 0.000
 High 17692/18048 12 1.32 (0.92–1.91) 0.002 1.19 (0.82–1.71) 0.003 0.54 (0.39–0.74) 0.016 3.05 (2.31–4.02) 0.000 1.29 (1.14–1.46) 0.000
rs16901979 C>A (region2)
All 1850/2090 7 1.71 (1.36–2.16) 0.595 1.31 (1.12–1.53) 0.136 1.22 (0.80–1.87) 0.002 1.39 (1.20–1.61) 0.144 1.31 (1.18–1.46) 0.232
publication bias (Begg’s test)   P = 0.609 P = 0.948 P = 0.995 P = 0.854 P = 0.689
Ethnicity
 Asian 714/648 3 1.41 (0.95–2.09) 0.661 1.15 (0.92–1.43) 0.134 1.65 (1.13–2.41) 0.293 1.19 (0.97–1.47) 0.179 1.17 (1.00–1.38) 0.350
 Africa-American 619/1026 3 1.91 (1.44–2.55) 0.342 1.41 (1.10–1.80) 0.385 0.96 (0.54–1.71) 0.007 1.56 (1.24–1.96) 0.413 1.40 (1.21–1.62) 0.415
Source of control
 HB 1850/2090 7 1.71 (1.36–2.16) 0.595 1.31 (1.12–1.53) 0.136 1.22 (0.80–1.87) 0.002 1.39 (1.20–1.61) 0.144 1.31 (1.18–1.46) 0.232
Score
 Low 675/825 3 1.75 (1.22–2.51) 0.160 1.26 (0.8–1.97) 0.032 1.49 (1.08–2.05) 0.475 1.32 (0.84–2.08) 0.020 1.29 (1.10–1.52) 0.04
 High 1175/1265 4 1.68 (1.25–2.27) 0.826 1.34 (1.09–1.65) 0.438 1.46 (1.13–1.89) 0.548 1.42 (1.17–1.73) 0.668 1.33 (1.16–1.53) 0.585
rs6983267 T>G (region3)
All 12233/7582 17 1.44 (1.31–1.58) 0.065 1.19 (1.10–1.29) 0.065 1.26 (1.18–1.36) 0.473 1.54 (0.93–2.57) 0.000 1.19 (1.14–1.25) 0.187
publication bias (Begg’s test)   P = 0.308 P = 0.959 P = 0.072 P = 0.749 P = 0.303
Ethnicity
 Caucasion 9879/5487 9 1.47 (1.33–1.64) 0.065 1.25 (1.07–1.46) 0.027 1.27 (1.17–1.38) 0.427 1.77 (0.81–3.87) 0.000 1.21 (1.15–1.27) 0.102
 Asian 1922/1580 6 1.33 (1.08–1.62) 0.166 1.07 (0.92–1.24) 0.746 1.27 (1.06–1.52) 0.201 1.35 (1.04–1.75) 0.005 1.14 (1.03–1.25) 0.283
Source of control
 HB 4098/3021 11 1.68 (1.30–2.16) 0.038 1.17 (1.03–1.33) 0.141 1.38 (1.22–1.55) 0.223 1.43 (1.00–2.04) 0.000 1.24 (1.15–1.33) 0.092
 PB 8135/4561 6 1.37 (1.22–1.54) 0.532 1.20 (1.09–1.33) 0.065 1.21 (1.10–1.32) 1.000 1.80 (0.63–5.11) 0.000 1.17 (1.10–1.24) 0.686
Score
 Low 1698/1516 7 1.79 (1.19–2.67) 0.009 1.11 (0.94–1.30) 0.120 1.43 (1.19–1.73) 0.069 1.34 (0.98–1.84) 0.002 1.28 (1.07–1.54) 0.015
 High 10535/6066 10 1.42 (1.31–1.58) 0.547 1.22 (1.11–1.33) 0.121 1.24 (1.14–1.34) 0.986 1.66 (0.79–3.51) 0.000 1.19 (1.13–1.25) 0.839
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HB, Hospital based; PB, Population based. OR, Odds ratio; CI, confidence interval.

Stratification analysis

In the stratification analyses, subgroups with less than three studies were excluded from further analysis because of small number of subgroups would likely lead to a false association. In the current meta-analysis, positive associations were found among most of the remaining subgroups for each of the three 8q24 SNPs. Briefly, the associations of rs1447295 C>A polymorphism and PCa risk were observed among Asians populations under the all assumed genetic comparisons, among Caucasians under recessive and dominant models, but not among Africa-Americans. Similarly, stratified analysis by source of control found that rs1447295 C>A showed significant association with PCa risk for PB subgroup under all genetic models, while the association was only detected under recessive, dominant model, and allele comparison for HB subgroup. Moreover, the associations were observed for both low and high quality subgroups.

As shown in Table 2, due to the limited number of study, stratification analysis for rs1601979 C>A polymorphism was not conducted among Caucasians and PB studies. The significant risk effects of rs1601979 C>A polymorphism on PCa risk were consistently observed under the most of assumed genetic comparisons for Africa-Americans, hospital-based studies, and studies with high quality score. The effects were also existed among Asians and studies with low quality score, with fewer genetic models detecting the significance. We then compared the strength of the associations within the subgroups, the association with larger OR and smaller P value was considered as more prominent than others. Interestingly, the association between PCa and rs16901979 was more prominent among Africa-Americans than Asians under most of genetic models (e.g. Africa-Americans with homozygous OR= 1.91, 95% CI= 1.44–2.55, P = 0.000; heterozygous OR= 1.41 (1.10–1.80), P = 0.000; dominant OR= 1.56, 95% CI= 1.24–1.96, P = 0.000; and allele comparison OR= 1.40, 95% CI= 1.21–1.62, P = 0.000. In contrast, Asians with recessive OR= 1.65, 95% CI= 1.13–2.41), P = 0.000; and allele comparison OR= 1.17, 95% CI= 1.00–1.38, P = 0.053) . Moreover, stratified analyses observed significantly increased PCa risk associated with rs6983267 T>G polymorphism among all subgroups defined by ethnicity, source of control, and quality score. Likewise, rs6983267 T>G seemed to confer a higher Pca risk in Caucasians than Asians (homozygous OR= 1.47, 95% CI= 1.33–1.64, P = 0.000 vs. OR= 1.33, 95% CI= 1.08–1.62, P = 0.006; Allele comparison OR= 1.21, 95% CI= 1.15–1.27, P = 0.000 vs. Allele comparison OR= 1.14, 95% CI= 1.03–1.25, P = 0.01).

In the current meta-analysis, a high level of heterogeneity was detected in the pooled analysis for rs1447295 C>A. However, the heterogeneity decreased in the stratified analysis by ethnicity, indicating that ethnicity might be a source of heterogeneity. Moreover, the between-study heterogeneity was detected among several subgroups for each of studied SNPs. However, leave-one-out sensitivity analyses suggested that no single study substantially altered the pooled ORs of any SNP of interest (data not shown), suggesting the stability of this meta-analysis. Finally, the FPRP values for all significant findings at different prior probability levels were summarized in Table 3. With the assumption of prior probability of 0.01, the association with rs1447295 C>A was noteworthy for Asians, population-based studies and high quality studies (FPRP range: 0.010–0.144, 0.010–0.022, 0.010–0.014, respectively) under the assumed comparisons. Moreover, the association between PCa and rs16901979 C>A among Africa-American (FPRP: 0.026) under dominant and allele comparison was considered noteworthy. In term of rs6983267 T>G, the association was noteworthy for Caucasians, population-based studies, high quality studies, low quality studies. In contrast, based on FPRP values, the rest of significant associations between 8q24 variants and PCa risk might not be deserving of attention, suggesting some possible bias or chance finding in this meta-analysis. Therefore, our findings need to be further validated in large and well-designed studies, involving different ethnicities (Fig. 1).

Table 3. False-positive report probability values for associations between the risk of cancer and the frequency of genotypes of 8q24 variants.

8q24 SNP genotype Crude OR (95%CI) Pa statistical powerb Prior probability
0.250 0.100 0.010 0.001 0.000
8q24-rs1447295 C>A
 All patients
  Homozygous 1.39 (1.01–1.91) <0.0001 0.993 0.000 0.001 0.010 0.091 0.502
  Heterozygous 1.26 (1.09–1.45) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Recessive 0.51 (0.39–0.67) <0.0001 0.896 0.000 0.001 0.011 0.100 0.527
  Dominant 2.46 (1.89–3.20) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Allele comparision 1.23 (1.09–1.40) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Ethnicity-Caucasian
  Recessive 0.48 (0.31–0.74) <0.0001 0.472 0.001 0.002 0.021 0.175 0.680
  Dominant 4.15 (3.26–5.28) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Ethnicity-Asian
  Homozygous 1.87 (1.33–2.62) <0.0001 0.059 0.005 0.015 0.144 0.630 0.944
  Heterozygous 1.49 (1.30–1.70) <0.0001 0.986 0.000 0.001 0.010 0.092 0.504
  Recessive 0.38 (0.27–0.53) <0.0001 0.028 0.011 0.031 0.262 0.781 0.973
  Dominant 1.97 (1.47–2.62) <0.0001 0.968 0.000 0.001 0.010 0.094 0.508
  Allele comparision 1.44 (1.29–1.60) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Source of control-HB
  Recessive 0.52 (0.33–0.82) 0.0436 0.948 0.121 0.293 0.820 0.979 0.998
  Dominant 2.07 (1.16–3.67) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Source of control-PB
  Homozygous 1.80 (1.52–2.13) <0.0001 0.772 0.000 0.001 0.013 0.115 0.564
  Heterozygous 1.38 (1.22–1.57) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Recessive 0.44 (0.37–0.52) <0.0001 0.440 0.001 0.002 0.022 0.185 0.694
  Dominant 3.22 (2.78–3.74) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Allele comparision 1.34 (1.21–1.50) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Score-Low
  Recessive 0.45 (0.25–0.81) <0.0001 0.114 0.003 0.008 0.080 0.467 0.898
  Dominant 1.79 (1.01–3.16) <0.0001 0.995 0.000 0.001 0.010 0.091 0.501
 Score-High
  Recessive 0.54 (0.39–0.74) <0.0001 0.696 0.000 0.001 0.014 0.126 0.590
  Dominant 3.05 (2.31–4.02) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Allele comparision 1.29 (1.14–1.46) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
8q24-rs16901979 C>A
 All patients
  Homozygous 1.71 (1.36–2.16) 0.0663 0.995 0.167 0.375 0.868 0.985 0.999
  Heterozygous 1.31 (1.12–1.53) 0.3859 1.000 0.537 0.776 0.974 0.997 1.000
  Dominant 1.39 (1.20–1.61) 0.1506 0.999 0.312 0.576 0.937 0.993 0.999
  Allele comparision 1.31 (1.18–1.46) 0.0524 1.000 0.136 0.320 0.838 0.981 0.998
 Ethnicity-Asian
  Recessive 1.65 (1.13–2.41) 0.3859 0.908 0.560 0.793 0.977 0.998 1.000
  Allele comparision 1.17 (1.00–1.38) 0.0394 0.999 0.106 0.262 0.796 0.975 0.997
 Ethnicity-Africa-American
  Homozygous 1.91 (1.44–2.55) <0.0001 0.902 0.116 0.282 0.812 0.978 0.998
  Heterozygous 1.41 (1.10–1.80) 0.0028 0.837 0.010 0.029 0.249 0.770 0.971
  Dominant 1.56 (1.24–1.96) <0.0001 0.375 0.001 0.002 0.026 0.210 0.727
  Allele comparision 1.40 (1.21–1.62) <0.0001 0.371 0.001 0.002 0.026 0.212 0.730
 Source of control-HB
  All patients
  Homozygous 1.71 (1.36–2.16) 0.0663 0.995 0.167 0.375 0.868 0.985 0.999
  Heterozygous 1.31 (1.12–1.53) 0.3859 1.000 0.537 0.776 0.974 0.997 1.000
  Dominant 1.39 (1.20–1.61) 0.1506 1.000 0.311 0.575 0.937 0.993 0.999
  Allele comparision 1.31 (1.18–1.46) 0.0524 1.000 0.136 0.320 0.838 0.981 0.998
 Score-Low
  Homozygous 1.75 (1.22–2.51) 0.6976 0.993 0.678 0.863 0.986 0.999 1.000
  Recessive 1.49 (1.08–2.05) 0.7287 0.994 0.687 0.868 0.986 0.999 1.000
  Allele comparision 1.29 (1.10–1.52) 0.6989 1.000 0.677 0.863 0.986 0.999 1.000
 Score-High
  Homozygous 1.68 (1.25–2.27) 0.0297 0.883 0.092 0.232 0.769 0.971 0.997
  Heterozygous 1.34 (1.09–1.65) 0.2149 1.000 0.392 0.659 0.955 0.995 1.000
  Recessive 1.46 (1.13–1.89) 0.0551 0.926 0.151 0.349 0.855 0.983 0.998
  Dominant 1.42 (1.17–1.73) 0.0546 0.999 0.141 0.330 0.844 0.982 0.998
  Allele comparision 1.33 (1.16–1.53) 0.0131 1.000 0.038 0.105 0.565 0.929 0.992
8q24-rs6983267 T>G
 All patients
  Homozygous 1.44 (1.31–1.58) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Heterozygous 1.19 (1.10–1.29) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Recessive 1.26 (1.18–1.36) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Allele comparision 1.19 (1.14–1.25) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Ethnicity-Caucasian
  Homozygous 1.47 (1.33–1.64) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Heterozygous 1.25 (1.07–1.46) 0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Recessive 1.27 (1.17–1.38) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Allele comparision 1.21 (1.15–1.27) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Ethnicity-Aasian
  Homozygous 1.33 (1.08–1.62) 0.0026 0.939 0.008 0.024 0.215 0.734 0.965
  Recessive 1.27 (1.06–1.52) 0.0048 0.959 0.015 0.043 0.331 0.833 0.980
  Dominant 1.35 (1.04–1.75) 0.0477 1.000 0.125 0.300 0.825 0.979 0.998
  Allele comparision 1.14 (1.03–1.25) 0.0038 1.000 0.011 0.033 0.273 0.792 0.974
 Source of control-HB
  Homozygous 1.68 (1.30–2.16) 0.001 1.000 0.003 0.009 0.090 0.500 0.909
  Heterozygous 1.17 (1.03–1.33) 0.002 1.000 0.006 0.018 0.165 0.666 0.952
  Recessive 1.38 (1.22–1.55) 0.0755 1.000 0.185 0.405 0.882 0.987 0.999
  Dominant 1.43 (1.00–2.04) 0.0004 1.000 0.001 0.004 0.038 0.286 0.800
  Allele comparision 1.24 (1.15–1.33) 0.0009 1.000 0.003 0.008 0.082 0.473 0.900
 Source of control-PB
  Homozygous 1.37 (1.22–1.54) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Heterozygous 1.20 (1.09–1.33) 0.0021 1.000 0.006 0.019 0.172 0.677 0.955
  Recessive 1.21 (1.10–1.32) <0.0001 1.000 0.006 0.019 0.172 0.677 0.955
  Allele comparision 1.17 (1.10–1.24) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
 Score-Low
  Homozygous 1.79 (1.19–2.67) 0.0008 0.871 0.003 0.008 0.083 0.478 0.902
  Recessive 1.43 (1.19–1.73) 0.0007 0.863 0.002 0.007 0.074 0.448 0.890
  Allele comparision 1.28 (1.07–1.54) 0.0016 1.000 0.005 0.014 0.137 0.615 0.941
 Score-High
  Homozygous 1.42 (1.31–1.58) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
  Heterozygous 1.22 (1.11–1.33) 0.0003 1.000 0.001 0.003 0.029 0.231 0.710
  Recessive 1.24 (1.14–1.34) 0.0034 1.000 0.010 0.030 0.252 0.773 0.971
  Allele comparision 1.19 (1.13–1.25) <0.0001 1.000 0.000 0.001 0.010 0.091 0.500
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OR, Odds ratio; CI, Confidence interval. The results in false-positive report probability analysis were in bold, if the prior probability <0.2.

aChi-square test was used to calculate the genotype frequency distributions.

bStatistical power was calculated using the number of observations in the subgroup and the OR and P values in this table.

Figure 1. Forest plots of effect estimates for 8q24-rs1447295 polymorphism and prostate cancer risk (AA+AC vs. CC).

Figure 1

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The size of the squares indicates the relative weight of each study. Weights were derived from random-effects analysis. Bars, 95% confidence interval (CI).

Publication Bias Analysis

Publication bias was examined by Begg’s funnel plots and Egger’s tests across all genetic models for each of the three polymorphisms in the 8q24 region. The shape of funnel plot (Fig. 2) was symmetrical, suggesting that there was no evidence of publication bias in the meta-analysis.

Figure 2. Funnel plot analysis to detect publication bias for each of the 8q24 polymorphism by homozygous model.

Figure 2

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(A) For rs1447295 polymorphism, (B) For rs16901979 polymorphism, and (C) For rs6983267 polymorphism. Each point represented an individual study for the indicated association.

Discussion

To the best of our knowledge, this is the largest meta-analysis to investigate the association between the selected 8q24 SNPs and PCa risk based on the candidate association studies. Up to now, more than 64 8q24SNPs have been explored for their role in PCa carcinogenesis, among which about 20 SNPs are regarded as PCa-related SNPs. However, those results were often controversial. In this meta-analysis, we evaluated the association of PCa risk with three most frequently investigated SNPs that scatter through the three regions of 8q24, respectively. We found significant associations between each of these 8q24 SNPs and PCa risk under most of the assumed comparisons, either in overall or in stratified analyses by ethnicity, source of control, and quality score. Intriguingly, stratified analyses by ethnicity revealed significant association between rs1447295 C>A and PCa risk among Caucasians and Asians, but not Africa-Americans. The effect of polymorphism rs16901979 C>A on PCa risk was more apparent in Africa-Americans populations when compared with Asian populations. Similarly, rs6983267 conferred a higher Pca risk among Caucasians than Asians. Although it is difficult to explain the ethnic-specific findings, the disparity in different ethnic populations is not completely surprising. Incidence rates of PCa vary internationally by over 25-fold. Developed countries in Oceania, Europe, and North American have the highest rates of PCa, while incidence in Asia is relatively low41. On the contrary, the highest prostate cancer mortality rates are recorded in African descent in the Caribbean region, which is partly explained by genetic predisposition42,43. Taken together, these epidemiological findings have suggested that both environmental and genetic factors may have profound impacts on PCa. Therefore, we speculated that differences in environments, genetic backgrounds, and gene-environment interaction may help to interpret the different results in the different ethnic groups. Future studies may provide some insights into the ethnic-specific findings, focusing on a difference in LD between these variants and the causal variants in each ethnic group, epistasis with other variants found in these ethnicities, or gene-environment interaction.

Moreover, although mechanisms by which 8q24 modulates the risk of PCa is poorly understood, mounting evidence from genetic association studies and fine-mapping informatics supports that 8q24 genetic variant may impart susceptibility to PCa in an ethnic-specific manner. 8q24 region is commonly subdivided into three regions on the basis of the local genetic characteristics of 8q24 and the fine-mapping study11. Region 1 (126.54–128.62 Mb) was initially identified by the original linkage and admixture studies in Icelandic families populations; and initial association studies indicated that this region might contribute to a higher incidence of PCa in Africa-American men than men of European ancestry7,8. Region 2 (128.14–128.28 Mb) harbors a 14-SNP haplotype that efficiently tags a relatively uncommon (2–4%) susceptibility variant in individuals of European descent, which happens to be very common (42%) in Africa-Amricans2,10. And region 3 (128.47–128.54 Mb) is defined as a recombination hot-spot among European Americans. There is little linkage disequilibrium among SNPs across the three regions. Although regions 1 and 3 are physically close to each other, they are separated by a recombination hotspot among individuals of European ancestry2. Hence, SNPs across all three neighboring regions seem to independently influence 8q24 signal transduction, and the combined effects of SNPs across regions closely follow a multiplicative model2,10. Human chromosome 8q24 contains three regions spanning 6000 kb, according to the dbSNP database (http://www.ncbi.nlm.nih.gov/SNP), in which more than 207 variants were reported. Twenty previously reported 8q24SNPs statistically significantly associated with PCa risk show various linkage disequilibrium (LD) with a range from low to high degree (Fig. 3). These SNPs can be divided into three blocks. For example, block1 included rs698267, rs10505476, rs10505474, and rs7837328, showing LD with r2 values ranging from 0.28 to 0.95. Block2 consisted of rs1447295, rs4242382, rs7017300, rs11986220, rs10090154, and 7837688, with LD (r2) varying from 0.23 to 1.0. Additionally, rs16901979 was found in high LD of 1.0 with some polymorphisms including rs6983561, rs16901966, and rs1551512. None of the variants resides within known genes, of which there are few across the regions of 8q24. It was predicted that those risk-associated variants could affect the regulation or transcription of a causal pseudogene or gene outside the region, including proto-oncogene MYC (located approximately 260 kb telomeric to region 1), FAM84B, TCF7L2, and POU5F121,44, but the biological mechanisms underlying these findings remain unclear. Another speculation that may help to explain the association is that some functional polymorphisms linked to those SNPs may account for the association. However, based on current understanding, none of these variants and their respectively linked-SNPs seems to be potentially functional. Therefore, the exact mechanisms underlying the observed association of the SNPs with PCa risk need more investigations.

Figure 3. Linkage disequilibrium in the 8q24 region for the previously reported polymorphisms.

Figure 3

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Linkage disequilibrium coefficients (r2) between SNPs in the 8q24 region between 128.12 and 128.54 Mb.

8q24 was considered as a gene-free region, flanked by the FAM84B and MYC genes on the centromeric and telomeric ends, respectively. Although data based on epidemiologic studies have indicated the possible association between 8q24 variants and PCa risk, little is known about the mechanisms. There are significant differences in allele frequency distribution of the three selected SNPs among different ethnic-populations, indicating these 8q24 variant(s) may modulate PCa in an ethnic-specific manner. One study suggested that variants in this region may be a part of the cis-regulatory enhancer element for the MYC gene, with a long-range physical interaction with this gene in PCa4. For example, a recent report indicated that rs6983267 displayed an altered capacity of binding of transcription factor 7-like 2 (TCF7L2), thereby leading to a different physical interaction with MYC44. However, others failed to find clear association between rs6983267 genotype and MYC expression. Therefore, more powerful studies should be performed to further investigate the function of the risk- associated SNPs.

Up to now, several meta-analyses have investigated the associations between 8q24 polymorphisms and PCa risk45,46,47. And consistent findings were observed among the previously reported meta-analyses and the present study, indicating that 8q24-rs1447295, -rs16901979, and –rs6983267 polymorphisms might confer genetic susceptibility to PCa. Additionally, this meta-analysis was the first to further explore the associations by stratification analyses (ethnicity, source of control, and quality score), as compared to others. However, although significant associations were found in the stratification analyses by ethnicity, these findings warrant further validation in large, well-designed, multiethnic case-control studies.

Finally, some limitations in the meta-analysis should be addressed. First, relatively small samples were included for the assessment, especially for rs16901979 polymorphism. Second, the controls were not uniformly defined, which might result in obscure findings. Third, there was a significant heterogeneity in different ethnic groups; the heterogeneity may be partially attributed to the difference in individuals’ genetic backgrounds or the environment in which subjects live, since these genetic and environmental factors may modify the risk effects of studied SNP on cancer susceptibility. Fourth, due to inadequate information, we failed to control for possible confounders such as age, smoking, obesity, alcohol consumption, and other lifestyle risk factors, and our results were based on unadjusted estimates. Finally, the ethnic-specific effects of the studied SNPs on PCa risk were not adequately discussed due to lack of relevant publications.

In summary, this meta-analysis provided evidence of the association between 8q24 polymorphisms (i.e. rs1447295 C>A, rs16901979 C>A, and rs6983267 T>G) and PCa risk, suggesting that these 8q24 variants may be a low penetrance contributor to PCa. Considering that PCa is one of genetic and geographic related malignancies, more relevant SNPs, variants, SNP-induced gene structural changes, epigenetic changes, and interactions of SNP-SNP, gene-gene, and gene-environment should be addressed in future large multicentric studies, which should lead to better, comprehensive understanding of the association between the 8q24 Polymorphisms and PC risk.

Methods

Search strategy and selection criteria

A comprehensive literature search was carried out for publications investigating the association between 8q24 polymorphisms and PCa risk. We searched PubMed, Embase and China Biology Medicine (CBM) databases up to June 2014 using the following keywords, including “8q24”, “variant or polymorphism”, “cancer, carcinoma, or malignancy”, and “prostate disease”. Further limited searching strategy included “English and Chinese language publications” and “human species”. Abstracts alone and unpublished articles were not considered. Moreover, the reference lists of the retrieved articles and reviews were sought manually to find relevant original articles. Eligible Studies for this meta-analysis were required to meet all of the following criteria: (i) studies on associations of at least one of 8q24 polymorphisms (rs1447295 C>A, rs6983267 T>G, rs16901979 C>A) with PCa risk; (ii) enrolled patients had sporadic, primary and histologically confirmed PCa, and controls had no history of cancer or neoplasm; (iii) case-control or nested case-control studies; (iv) availability of genotype data of both case and control group to calculate the odds ratio (OR) with its 95% confidence interval (CI) and P value; and (v) genotype distribution in controls is complied with the Hardy-Weinberg equilibrium (HWE, P>0.05). All of the included studies were candidate-based association studies. If participants in studies overlapped, only the study with the largest sample size was included in the meta-analysis. The flow chart of identification the eligible studies was shown in Fig. 4, and the protocol was checked according to the PRISMA checklist (Supporting Information).

Figure 4. Flow chart of identification of included studies.

Figure 4

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Assessment of study quality: Extended Quality Score

The quality of each included investigation was estimated using Extended Quality Score to limit the risk of introducing bias into meta-analyses or systematic reviews. Each article is defined as “high” or “poor” quality according to an extended-quality scale system by evaluating special characteristics of the participant groups and genotyping methods of the studies48. In this meta-analysis, two investigators appraised the quality of each investigation. If there was any disagreement, a third investigator (Jin-Hong Zhu) would mediate and a decision would be made by voting.

Data extraction

Two reviewers (Qiaoxin Li and Xia Liu) independently appraised and extracted data according to the inclusion criteria listed above. Disagreements were documented and resolved by full discussion with a third reviewer (Jin-Hong Zhu) until a consensus was reached. The following information were extracted from each eligible study: the first author’s surname, year of publication, country in which study was conducted, ethnicity of participants (categorized as Caucasians, Asians, and African descents), study design, sample size, source of control, P value of HWE test, genotyping methods, genotype counts of case and controls for three studied SNPs, and the clinical/pathological characteristics of cases. For studies involving subjects of different ethnic groups, data were extracted and split by ethnicity.

Statistical analysis

HWE in the control groups was assessed by the Pearson’s goodness-of-fit chi-square test, with a P value less than 0.05 indicating derivation from HWE, and population was regarded as suitable if more than one SNP agreed with HWE. The strength of association between 8q24 polymorphisms and PCa risk was estimated by calculating odds ratios (OR) and the corresponding 95% confidence intervals (CI). For each of studied SNP, we conducted comparison of the frequency of wild (W) and variant (V) alleles between PCa cases and controls (V vs. W). Moreover, pooled ORs were calculated under the following genetic models: homozygous (VV vs. WW), heterozygous (VW vs. WW), dominant (VV + VW vs. WW), and recessive (VV vs. VW + WW) models. Stratification analyses were performed by ethnicity, source of control, and quality score, but not by tumor stage, Gleason score, and PAS levels because of the small size of the eligible studies. For each genetic model, a χ2-based Q-test was performed to check the presence of between-study heterogeneity across all of the comparisons at a level of significance of 0.1049. In addition, the heterogeneity was also quantified with I2 statistics, which is independent of the number of studies included in the meta-analysis. I2 lies between 0% and 100% with higher values indicating a greater heterogeneity50. The fixed-effects model was chosen to calculate the combined OR in the absence of heterogeneity. Otherwise, a random-effects model was used to estimate the pooled OR. The significance of the pooled OR was determined using the Z test. Finally, false positive report probability (FPRP) was introduced as a multiple testing for the whole significant associations. The statistical power of the current study was calculated with an FPRP threshold of 0.2 was defined as true finding, i.e., the probability of a false positive result is <20%49. In sensitivity analysis, following the sequential exclusion of a single study at a time, the pooled estimates were recomputed to determine the stability of the results. Furthermore, potential publication bias was assessed by visual inspection of Begg’s funnel plots with an asymmetry plot suggested possible publication bias51. All statistical analyses were performed using STATA statistical software (version 11.0, StataCorp, College Station, TX) and SAS version 9.1 (SAS Institute, Cary, NC). Two-sided P values were adopted, and P <0.05 were considered statistically significant without further notification.

Additional Information

How to cite this article: li, Q. et al. Association of three 8q24 polymorphisms with prostate cancer susceptibility: evidence from a meta-analysis with 50,854 subjects. Sci. Rep. 5, 12069; doi: 10.1038/srep12069 (2015).

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 81460513).

Footnotes

Author Contributions All authors contributed significantly to this work. Q.L., X.L., R.H. and F.W. performed the research study and collected the data; Q.L., H.A. and W.Z. analyzed the data; J.Z. designed the research study; Q.L. and J.Z. wrote the paper. All authors reviewed the manuscript. In addition, all authors approved the final draft.

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