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. 2016 Jun 6;6:27466. doi: 10.1038/srep27466

The SDF-1 rs1801157 Polymorphism is Associated with Cancer Risk: An Update Pooled Analysis and FPRP Test of 17,876 Participants

Xiang Tong 1,*, Yao Ma 1,*, Huajiang Deng 2,*, Xixi Wang 3, Sitong Liu 1, Zhipeng Yan 1, Shifeng Peng 1, Hong Fan 1,a
PMCID: PMC4893747  PMID: 27265091

Abstract

The stromal cell derived factor-1 (SDF-1) rs1801157 gene polymorphism has been implicated in susceptibility to cancer, but the results were inconclusive. The current study was to precisely investigate the association between SDF-1 rs1801157 polymorphism and cancer risk using meta-analysis and the false positive report probability (FPRP) test. All 17,876 participants were included in the study. The meta-analysis results indicated a significant association between the SDF-1 rs1801157 polymorphism and cancer risk. By subgroup analyses, the results detected that the SDF-1 rs1801157 polymorphism was associated with cancer susceptibility among Asians and Caucasians. Additionally, we also found significant associations between the SDF-1 rs1801157 polymorphism and susceptibility to different types of cancer. However, to avoid a “false positive report”, we further investigated the significant associations observed in the present meta-analysis using the FPRP test. Interestingly, the results of the FPRP test indicated that only 4 gene models were truly associated with cancer risk, especially in Asians. Moreover, we confirmed that the SDF-1 rs1801157 gene polymorphism was only associated with lung and urologic cancer risk. In summary, this study suggested that the SDF-1 rs1801157 polymorphism may serve as a risk factor for cancer development among Asians, especially an increased risk of urologic and lung cancers.

Cancer remains a major cause of mortality worldwide. It is estimated that the cancer death rate will be increased by 5-fold in developing countries, and the global cancer mortality is expected to increase by 104% in 20201. Cancer is a multi-factorial disease that results from complex interactions between numerous genetic and environmental factors2,3. In recent years, an increasing number of studies has focused on the association between chemokine gene variants and malignancy susceptibility. Among them, the stromal cell derived factor-1 (SDF-1) gene, also known as CXC chemokine ligand 12 (CXCL12), has been widely studied4,5,6. The SDF-1 gene is located on chromosome 10q11.1 and spans 10 kb7. The SDF-1 gene mainly encodes a CXC angiogenic chemokine8. One important SDF-1 gene polymorphism named rs1801157 (G801A) involves a guanine to adenine (G→C) substitution at base pair 801 of the 3′-untranslated region of SDF-1 gene9.

Previous studies have indicated that SDF-1 participates in several biological activities, including neuronal and cardiogenesis development, haematopoiesis and lymphocyte trafficking10. Furthermore, growing evidence suggests that the SDF-1 rs1801157 polymorphism plays an important role in the pathogenesis of cancer. Razmkhah et al. reported that the SDF-1 rs1801157 polymorphism increased the risk of breast and lung cancer11,12. Kucukgergin and co-workers showed that the SDF-1 rs1801157 polymorphism was associated with bladder cancer susceptibility13. Hirata et al. reported that the SDF-1 rs1801157 polymorphism is potentially associated with an increased risk of prostate cancer among Japanese individuals14. However, Petersen et al. did not identify an association between the SDF-1 rs1801157 polymorphism and prostate cancer susceptibility15, and similar negative results were also detected by Tee and colleagues16.

The results of those genetic association studies were inconclusive. Moreover, a single study may be insufficient to detect a small effect of the SDF-1 rs1801157 gene polymorphism on cancer susceptibility, especially when the sample size is relatively small. Considering the crucial role of the SDF-1 rs1801157 gene polymorphism in the pathogenesis of cancer, we performed a meta-analysis to accurately investigate the association of the SDF-1 rs1801157 gene polymorphism with cancer risk. Furthermore, to avoid a “false positive report”, we further assessed the significant associations observed in the present meta-analysis using the false positive report probability (FPRP) test. To our knowledge, this study is most recent and accurate meta-analysis exploring the SDF-1 rs1801157 gene polymorphism in cancer susceptibility.

Results

Study characteristics

As indicated in the flow diagram (Fig. 1), 247 articles were identified after an initial search. After reading the titles and abstracts, we excluded 198 articles. The remaining 49 articles were further screened by a full-text review. Six articles were excluded because they investigated other SDF-1 gene polymorphisms (e.g., rs2839693 and rs1065297) rather than the rs1801157 polymorphism. Four articles were excluded because they were not case-control studies. One article was not included because it was a meta-analysis, and one article was excluded because the available data could not be extracted to further assess the effect size. Therefore, all 37 articles were identified4,5,6,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44. However, according to the results of the HWE test, 4 articles18,38,39,44 were excluded because they did not achieve HWE in the control group. Finally, a total of 34 eligible case-control studies from 33 articles4,5,6,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,40,41,42,43 were included in the study. Among them, 18 articles were performed on Caucasians4,6,15,17,19,20,21,22,23,24,27,28,29,31,33,34,40,43, 13 articles on Asians5,11,12,13,14,16,25,26,30,32,35,36,37, and two studies on individuals of mixed ethnicity41,42. In addition, based on the quality score, eleven studies were considered low quality. The characteristics of included studies are summarized in Tables 1 and 2.

Figure 1.

Figure 1

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

First author Year Country Ethnicity Type Sample size Genotyping method HWE Score
Case/Control
Bodelon C 2013 USA Caucasian BC 840/801 IGGMP Yes 6
Bracci PM 2010 USA + UK Caucasian NHL 255/511 IGGMP Yes 8
Cacina C 2012 Turkey Asian EC 113/139 PCR-RFLP Yes 6
Cai C 2013 China Asian RC 322/402 PCR-RFLP Yes 7
de Lourdes Perim A 2013 Brazil Caucasian ALL 54/58 PCR-RFLP Yes 6
de Oliveira CE 2007 Brazil Caucasian CML 25/60 PCR-RFLP Yes 5
de Oliveira KB 2009 Brazil Caucasian BC 103/97 PCR-RFLP Yes 5
de Oliveira KB 2010 Brazil Caucasian NHL + HL 106/90 PCR-RFLP Yes 5
Dimberg J 2007 Sweden Caucasian CRC 151/141 PCR-RFLP Yes 5
Gawron AJ 2011 Poland Caucasian GC 292/414 TaqMan Yes 7
Hidalgo-Pascual M 2007 Spain Caucasian CRC 349/516 Real-time PCR Yes 6
Hirata H 2007 Japan Asian PC 167/167 PCR-RFLP Yes 5
Isman FK 2012 Turkey Asian PC 152/149 PCR-RFLP Yes 7
Khademi B 2008 Iran Asian HC + NC 156/262 PCR-RFLP Yes 6
Kontogianni P 2013 Greece Caucasian BC 261/480 PCR-RFLP Yes 6
Kruszyna L 2010 Poland Caucasian BC 193/199 PCR-RFLP Yes 6
Kruszyna L 2010 Poland Caucasian LAC 118/250 PCR-RFLP Yes 6
Kucukgergin C 2012 Turkey Asian BLC 142/197 PCR-RFLP Yes 7
Lee YL 2011 China Asian LC 247/328 PCR-RFLP Yes 7
Liarmakopoulos E 2013 Greece Caucasian GC 88/480 PCR-RFLP Yes 6
Lin GT 2009 China Asian BC 220/334 PCR-RFLP Yes 4
Maley SN 2009 USA Caucasian CC 899/820 TaqMan Yes 7
Mazur G 2013 Poland Caucasian MM 54/75 PCR-RFLP Yes 4
Pemberton NC 2006 UK Caucasian CLL 323/108 PCR-RFLP Yes 5
Petersen DC 2008 Australia Caucasian PC 815/727 TaqMan Yes 6
Razmkhah M 2013 Iran Asian GC + CC 233/262 PCR-RFLP Yes 4
Razmkhah M 2005 Iran Asian BC 278/181 PCR-RFLP Yes 6
Razmkhah M 2005 Iran Asian LC 72/262 PCR-RFLP Yes 4
Shi MD 2013 China Asian CRC 258/300 DHPLC Yes 5
Singh V 2014 India Asian BLC 200/200 PCR-RFLP Yes 7
Tee YT 2012 China Asian CC 137/337 PCR-RFLP Yes 7
Vairaktaris E 2008 Europe Caucasian OC 159/101 PCR-RFLP Yes 6
Vázquez-Lavista LG 2009 Mexico Mixed BLC 47/126 PCR-RFLP Yes 7
Wong HL 2010 USA Mixed NHL 233/240 TaqMan Yes 7
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BC = breast cancer; EC = endometrial cancer; RC = renal cancer; ALL = acute lymphatic leukaemia; CML = chronic myeloid leukaemia; NHL non-Hodgkin’s lymphoma; HL = Hodgkin’s lymphoma; CRC = colorectal cancer; GC = gastric cancer; PC = prostate cancer; HC = head cancer; NC = neck cancer; LAC = laryngeal cancer; BLC = bladder cancer; LC = lung cancer; CC = cervical cancer; MM = multiple myeloma; CLL = chronic lymphocytic leukaemia; OC = ovarian cancer; IGGMP = Illumina Golden Gate multiplex platform; PCR-RFLP = polymerase chain reaction-restricted fragment length polymorphism; DHPLC  = denaturing high performance liquid chromatography.

Table 2. Distributions of the SDF-1 rs1801157 allele and genotypes in cases and controls.

First author Year Case Control
AA AG GG A G AA AG GG A G
Bodelon C 2013 29 288 523 346 1334 35 251 515 321 1281
Bracci PM 2010 6 85 164 97 413 14 161 336 189 833
Cacina C 2012 12 52 49 76 150 6 64 69 76 202
Cai C 2013 61 111 150 233 411 29 136 237 194 610
de Lourdes Perim A 2013 3 18 33 24 84 1 11 46 13 103
de Oliveira CE 2007 4 11 10 19 31 3 18 39 24 96
de Oliveira KB 2009 3 41 59 47 159 4 32 61 40 154
de Oliveira KB 2010 5 43 58 53 159 5 26 59 36 144
Dimberg J 2007 5 62 84 72 230 4 56 81 64 218
Gawron AJ 2011 99 193 NA NA 156 258 NA NA
Hidalgo-Pascual M 2007 9 128 212 146 552 25 172 319 222 810
Hirata H 2007 17 78 72 112 222 13 63 91 89 245
Isman FK 2012 17 66 69 100 204 22 57 70 101 197
Khademi B 2008 8 84 64 100 212 20 97 145 137 387
Kontogianni P 2013 29 118 114 176 346 35 198 247 268 692
Kruszyna L 2010 9 61 123 79 307 5 58 136 68 330
Kruszyna L 2010 3 46 69 52 184 2 67 181 71 429
Kucukgergin C 2012 26 58 58 110 174 23 80 94 126 268
Lee YL 2011 36 112 99 184 310 21 136 171 178 478
Liarmakopoulos E 2013 6 43 39 55 121 46 229 205 321 639
Lin GT 2009 16 98 106 130 310 23 136 175 182 486
Maley SN 2009 30 276 593 336 1462 23 274 523 320 1320
Mazur G 2013 1 13 39 15 91 1 21 53 23 127
Pemberton NC 2006 114 209 NA NA 43 65 NA NA
Petersen DC 2008 NA NA NA 326 1304 NA NA NA 276 1178
Razmkhah M 2013 18 87 128 123 343 8 39 62 55 163
Razmkhah M 2005 34 139 105 207 349 13 67 101 93 269
Razmkhah M 2005 9 38 25 56 88 20 97 145 137 387
Shi MD 2013 4 113 141 121 395 0 52 248 52 548
Singh V 2014 9 132 59 150 250 16 83 101 115 285
Tee YT 2012 16 58 63 90 184 33 140 164 206 468
Vairaktaris E 2008 4 51 104 59 259 5 41 55 51 151
Vázquez-Lavista LG 2009 3 15 29 21 73 4 39 83 47 205
Wong HL 2010 7 78 148 92 374 13 64 163 90 390
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NA = not available.

Meta-analysis results

In total, 17,876 participants (8,062 cases and 9,814 controls) from 34 case-control studies were included in the current meta-analysis assessing the relationship between the SDF-1 rs1801157 polymorphism and cancer risk. In the overall meta-analysis, the results suggested a significant association between the SDF-1 rs1801157 polymorphism and cancer susceptibility in the recessive model (AA vs. AG + GG, OR: 1.28, 95% CI: 1.11–1.47, P = 0.001), a co-dominant model (AA vs. GG, OR: 1.43, 95% CI: 1.24–1.65, P < 0.001) as assessed by a fixed-effect model (I2 = 47%, 49.7%, respectively) and other models (AA + AG vs. GG, OR: 1.33, 95% CI: 1.17–1.51, P < 0.001; AG vs. GG, OR: 1.35, 95% CI: 1.19–1.54, P < 0.001; A vs. G, OR: 1.26, 95% CI: 1.14–1.40, P < 0.001) (Figs 2 and 3) as assessed by a random-effect model (I2 = 70.8%, 66.4%, 69.5%, respectively) (Table 3).

Figure 2.

Figure 2

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Figure 3.

Figure 3

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Table 3. Summary the results of the total and subgroup analyses in different genetic models.

Variables AA + AG vs. GG
 AA vs. AG + GG
 AA vs. GG
 AG vs. GG
 A vs. G
OR 95% CI P OR 95% CI P OR 95% CI P OR 95% CI P OR 95% CI P
Total 1.33 1.17–1.51 <0.001 1.28 1.11–1.47 0.001 1.43 1.24–1.65 <0.001 1.35 1.19–1.54 <0.001 1.26 1.14–1.40 <0.001
 Ethnicities
 Caucasian 1.07 0.98–1.16 0.15 1.03 0.82–1.29 0.81 1.07 0.85–1.35 0.55 1.10 1.00–1.21 0.05 1.07 1.00–1.15 0.06
 Asian 1.63 1.34–1.98 <0.001 1.42 1.05–1.93 0.02 1.71 1.28–2.30 <0.001 1.58 1.28–1.95 <0.001 1.45 1.25–1.68 <0.001
 Mixed 1.21 0.87–1.69 0.26 0.92 0.25–3.34 0.90 0.82 0.37–1.81 0.62 1.28 0.90–1.82 0.16 1.11 0.83–1.47 0.48
Types
 Breast cancer 1.26 1.11–1.44 <0.001 1.21 0.93–1.59 0.16 1.36 1.03–1.79 0.03 1.26 1.10–1.44 <0.001 1.20 1.08–1.33 <0.001
 Gynaecological cancer 0.98 0.83–1.16 0.81 1.36 0.93–1.99 0.12 1.36 0.92–2.01 0.13 0.94 0.79–1.12 0.52 1.03 0.89–1.18 0.71
 Urologic cancer 1.56 1.32–1.85 <0.001 1.34 0.75–2.38 0.32 1.62 0.97–2.69 0.06 1.46 1.10–1.93 <0.001 1.31 1.09–1.57 <0.001
 Digestive system cancer 1.25 0.79–1.98 0.34 0.83 0.55–1.27 0.40 0.88 0.57–1.35 0.55 1.38 0.82–2.33 0.23 1.26 0.80–1.97 0.32
 Hematologic malignancy 1.17 0.97–1.41 0.09 0.95 0.57–1.59 0.98 1.06 0.63–1.78 0.83 1.29 1.04–1.59 0.02 1.18 0.99–1.40 0.07
 Lung cancer 1.80 1.36–2.39 <0.001 2.24 1.41–3.57 <0.001 2.86 1.75–4.69 <0.001 1.62 1.20–2.18 <0.001 1.65 1.34–2.04 <0.001
 Head and neck cancer 1.3 0.68–2.48 0.43 0.76 0.40–1.46 0.41 0.92 0.47–1.80 0.81 1.35 0.70–1.61 0.37 1.16 0.71–1.91 0.55
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OR = odds ratio; CI = confidence interval.

In the subgroup analyses of ethnicity, the meta-analysis results indicated a strong association between the SDF-1 rs1801157 polymorphism and cancer susceptibility among Asians (AA + AG vs. GG, OR: 1.63, 95% CI: 1.34–1.98, P < 0.001) and a weak association in Caucasians (AG vs. GG, OR: 1.10, 95% CI: 1.00–1.21, P = 0.05), but no association in mixed populations using different genetic models (Table 3). Additionally, we also conducted subgroup analyses of cancer types. As shown in Table 3, significant associations between the SDF-1 rs1801157 polymorphism and breast cancer, urologic cancer, hematologic malignancy and lung cancer susceptibility were found in different genetic models. However, no association was observed between the SDF-1 rs1801157 polymorphism and risk of head and neck cancer, gynaecological cancer and digestive system cancer (Table 3).

Publication bias and sensitivity analysis

The funnel plot is a symmetrical inverted funnel (Fig. 4). No publication biases were noted using Begg’s (P = 0.329) and Egger’s tests (P = 0.082). Additionally, to further investigate the possible source of heterogeneity, we executed a sensitivity analysis by sequentially excluding studies from the meta-analysis to investigate the influence of each study on the pooled results. The sensitivity analysis results reported that the pooled ORs were not materially altered, suggesting the stability of our meta-analysis (Fig. 5).

Figure 4.

Figure 4

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Figure 5.

Figure 5

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FPRP test results

Moreover, we further investigated the significant associations (P < 0.05) observed in the present meta-analysis using the FPRP test. As listed in Table 4, the FPRP test results revealed that all 4 gene models (AA + AG vs. GG, AA vs. GG, AG vs. GG, A vs. G) of the SDF-1 rs1801157 gene polymorphism were truly associated with cancer risk (FPRP = 0.011, 0.001, 0.008, 0.017, respectively) at an a priori probability level of 0.001 with an OR of 1.5, especially in Asians. In addition, according to the results of the FPRP test, we confirmed that the SDF-1 rs1801157 gene polymorphism was only associated with lung cancer risk in the allele model (A vs. G, FPRP = 0.019), whereas the polymorphism was associated with susceptibility to urologic cancer in the dominant gene model (AA + AG vs. GG, FPRP = 0.001) at an a priori probability level of 0.001 and an OR of 1.5.

Table 4. The results of the FPRP tests in each gene models.

Gene models OR (95% CI) Power Prior Probability  = 0.001
AA + AG vs. GG OR = 1.50 FPRP
Total 1.33 (1.17–1.51) 0.968 0.011
Asians 1.63 (1.34–1.98) 0.201 0.004
Breast cancer 1.26 (1.11–1.44) 0.995 0.41
Urologic cancer 1.56 (1.32–1.85) 0.326 0.001
Lung cancer 1.80 (1.36–2.39) 0.104 0.318
AA vs. AG + GG
 Total 1.28 (1.11–1.47) 0.988 0.323
 Asian 1.42 (1.05–1.93) 0.637 0.975
 Lung cancer 2.24 (1.41–3.57) 0.046 0.938
AA vs. GG
 Total 1.43 (1.24–1.65) 0.744 0.001
 Asians 1.71 (1.28–2.30) 0.193 0.668
 Breast cancer 1.36 (1.03–1.79) 0.758 0.974
 Lung cancer 2.86 (1.75–4.69) 0.005 0.856
AG vs. GG
 Total 1.35 (1.19–1.54) 0.942 0.008
 Caucasians 1.10 (1.00–1.21) 1 0.980
 Asians 1.58 (1.28–1.95) 0.314 0.061
 Breast cancer 1.26 (1.10–1.44) 0.995 0.410
 Urologic cancer 1.46 (1.10–1.93) 0.575 0.932
 Hematologic malignancy 1.29 (1.04–1.59) 0.921 0.949
 Lung cancer 1.62 (1.20–2.18) 0.306 0.826
A vs. G
 Total 1.26 (1.14–1.40) 0.999 0.017
 Asians 1.45 (1.25–1.68) 0.674 0.001
 Breast cancer 1.20 (1.08–1.33) 1 0.339
 Urologic cancer 1.31 (1.09–1.57) 0.929 0.788
 Lung cancer 1.65 (1.34–2.04) 0.189 0.019
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OR = odds ratio; FPRP = false positive report probability.

Discussion

Cancer has high morbidity and mortality worldwide. Previous studies have suggested that cancer risk factors include an unhealthy life style, environmental pollution, radiation, infection, and immunity dysfunction45,46,47. Additionally, studies have increasingly focused on genetic factors48,49,50. In recent years, numerous studies reported an association between the SDF-1 rs1801157 polymorphism and cancer susceptibility, but the results were inconsistent or inconclusive. In addition, a single study may lack sufficient power to detect the potentially small effect of the SDF-1 rs1801157 polymorphism on cancer, especially when the sample size is relatively small. Meta-analysis is a useful method for investigating cancer associations with genetic factors because this method uses a quantitative approach by combining the results of different studies on the same topic, potentially providing more reliable conclusions. Thus, we conducted a pooled analysis to investigate the association of the SDF-1 rs1801157 polymorphism and risk of cancer.

In fact, two recently published meta-analyses51,52 investigated the association between the SDF-1 rs1801157 gene polymorphism and cancer risk. One study51 included 29 papers involving 4932 cases and 7917 controls, and another article52 only included 4,435 cancer cases and 6,898 controls from 25 studies. Additionally, the two meta-analyses did not assess the quality of the included studies. Furthermore, the previous two studies did not use the FPRP test to explore truly significant associations, and the results of those studies were potentially unable to reflect the true association between the SDF-1 rs1801157 gene polymorphism and cancer risk. Therefore, we performed a meta-analysis and FPRP test to assess the association between the SDF-1 rs1801157 gene polymorphism and cancer risk.

In total, 8,062 cases and 9,814 controls were included in the current meta-analysis. Based on our overall meta-analysis and FPRP tests, we found that the SDF-1 rs1801157 polymorphism obviously increased the risk of cancer. Additionally, we identified significant heterogeneity between studies in the present meta-analysis. Although heterogeneity can be considered using the random-effect model, heterogeneity increases the probability of type-I errors. The following factors may have contributed to the significant heterogeneity: 1) different demographic and genetic characteristics of Caucasian, Asian and mixed populations; 2) different types of cancer may be caused by different mechanisms; 3) different stages of cancer in each study among the studied cancer patients; 4) different genotyping methods of each study; and 5) different qualities among included studies.

Hence, to identify the cause of heterogeneity, we conducted subgroup analyses based on ethnicity and cancer type. The results suggested that the SDF-1 rs1801157 gene polymorphism strongly increased the cancer risk among Asians, whereas the heterozygote genotype (AG) of the SDF-1 rs1801157 gene polymorphism only weakly increased cancer susceptibility in Caucasians. Unfortunately, the SDF-1 rs1801157 gene polymorphism did not increase or decrease the cancer risk among mixed populations. Furthermore, the results of sub-group analyses after stratification based on cancer type indicated that the SDF-1 rs1801157 polymorphism increased the risk of breast cancer, urologic cancer, lung cancer, head and neck cancer, and haematological malignancies. However, the SDF-1 rs1801157 polymorphism did not influence gynaecological cancer and digestive system cancer susceptibility. Despite the fact that the heterogeneity was still detected when we performed stratified analyses based on ethnicity and cancer type, the subgroup analyses may provide more precise results compared with the overall analysis.

Several previous studies indicated that the published “statistically significant” results for genetic variants were false-positive findings, even in large and well-designed studies53,54. Fortunately, we could use the FPRP test to estimate true significant associations in the current meta-analysis. The FPRP is calculated from the statistical power of the test, the observed p-value, and a given a priori probability for the association.

Therefore, based on the positive results of the current meta-analysis, we further investigated whether a significant association between the SDF-1 rs1801157 gene polymorphism and cancer risk is “noteworthy”. Interestingly, the FPRP test results revealed that the SDF-1 rs1801157 gene polymorphism actually increased cancer susceptibility. Additionally, the FPRP test also demonstrated that the SDF-1 rs1801157 gene polymorphism could increase cancer risk among Asians, but it did not increase cancer risk in Caucasians. Moreover, the results of the FPRP test confirmed that the SDF-1 rs1801157 gene polymorphism increased the risk of lung cancer and urologic cancer. Surprisingly, the significant associations with breast cancer, head and neck cancer, and haematological malignancies in the present meta-analysis were false positive at the a priori probability level of 0.001 and an OR of 1.5. Some of the discoveries may be false-positive findings due to the limited sample size in each stratum, so it is important to perform FPRP analysis to avoid these findings, especially when the sample size is not of sufficient size.

The mechanism by which the SDF-1 rs1801157 polymorphism affects cancer risk is unclear. Previous studies indicated that SDF-1 is a chemokine that plays a pivotal role in different stages of cancer development and metastasis55,56. Combined with previous results57 and our present meta-analysis results, we put forward a simple hypothesis that the SDF-1 rs1801157 polymorphism, which has a G > A transition in the 3′-UTR, may have an important regulatory function of up-regulating SDF-1 production and the high serum concentrations of SDF-1 might contribute to cancer susceptibility. Finally, people who carried the variant A allele are more likely to develop cancer, especially urologic cancer and lung cancer.

Several limitations in the present meta-analysis should be noted. First, the published studies include small sample sizes, and only published articles were included in a few databases. Thus, these limitations might lead to additional biases or overall overestimated associations. Moreover, a language bias potentially also occurred because the including articles were only published in English and Chinese. Second, given the lack sufficient data for each included study, we failed to perform further subgroup analysis to investigate the risk factors of cancer, such as gender, gene-environment/gene-gene interactions, life style and age. Third, a small number of studies were included in the subgroup analysis to investigate the association between the SDF-1 rs1801157 polymorphism and head and neck cancer risk, so we must be cautious when referring to the pooled results. A study with larger samples is needed to confirm the results in the future. Despite these limitations, we minimized the likelihood of bias throughout the entire process by creating a detailed protocol and carefully performing study identification, statistical analysis and data selection. Thus, the reliability of the results is guaranteed.

In conclusion, the current study suggested that the SDF-1 rs1801157 polymorphism may contribute to the risk of cancer in Asians. In particular, the polymorphism could increase urologic and lung cancer susceptibility. More well-designed studies with larger sample sizes focusing on ethnicities or cancer types should be conducted to confirm the results in the future.

Methods

Study selection

A systematic literature search of the PubMed, Embase, and Wanfang databases and the China National Knowledge Internet (CNKI) was performed to identify studies involving the relationship between the SDF-1 rs1801157 gene polymorphism and cancer susceptibility, with the last updated search being conducted on May 14, 2015. The following key words were used: (stromal cell derived factor-1 OR SDF-1 OR CXC chemokine ligand 12 OR CXCL12) AND (cancer OR tumour OR neoplasm OR malignancy OR leukaemia OR myeloma OR sarcoma OR lymphoma) AND (polymorphism OR mutation OR variant). The language was restricted to English and Chinese.

Inclusion and exclusive criteria

The inclusion criteria were defined as follows: 1) the study should be a case-control study; 2) the association between the SDF-1 rs1801157 polymorphism and cancer risk should be evaluated; 3) the available genotype or allele frequencies for counting the odds ratio (OR) and 95% confidence interval (CI) should be provided in the study; 4) the study should evaluate humans; and 5) the genotype distributions of control cohorts should be in with Hardy-Weinberg equilibrium (HWE). The following exclusive criteria were adopted: 1) the study was not designed as a case-control study; 2) review, abstract or overlapping study; and 3) the study cannot provide available genotype or allele frequencies for determining the effect size.

Quality score evaluation

The qualities of included studies were assessed by the Newcastle-Ottawa Scale (case control study). The scale estimates quality based on three aspects: selection, comparability and exposure in the study. The total score ranged from 0 to 9, and a score greater than 6 was considered to represent high quality. In addition, we assessed the quality of the studies in a consensus meeting with all authors.

Data extraction

The independent authors (Xiang Tong and Huajiang Deng) collected detailed data from each study according to the inclusive criteria. If a disagreement was encountered, the third author (Xixi Wang) evaluated the articles. For each study, the first author, publication year, country, ethnicity, number of case and control groups, type of cancer, genotype and allele distribution, and genotyping method were extracted. The information is listed in Tables 1 and 2.

Statistical methods

The OR and 95% CI were used to estimate the effect strength of association between the SDF-1 rs1801157 polymorphism and cancer susceptibility. We calculated heterogeneity using the χ2-based Q-test and I-squared (I2) statistics tests. The pooled OR was assessed by the random-effect model when the heterogeneity was considered to be statistically significant (I2 > 50% and P  < 0.10); alternatively, the fixed-effect model was applied. We explored the association between the SDF-1 rs1801157 polymorphism and cancer risk in different gene models (AA + AG vs. GG, AA vs. AG + GG, AA vs. GG, AG vs. GG and A vs. G). To evaluate the effects of ethnicity and type of cancer, we also performed subgroup analyses based on ethnicity group and type of cancer.

In addition, to evaluate whether significant associations (P < 0.05) detected in the present study are “noteworthy”, we further calculated the FPRP value at a probability level of 0.001 and an OR of 1.5 58. As suggested by the previous study59, we set a FPRP cut-off value of 0.2, and only FPRP results < 0.2 were considered to be “noteworthy”. Publication bias was assessed by several methods. A visual inspection of asymmetry in funnel plots was performed. Furthermore, the Begger’s and Egger’s tests were used to assess the publication bias60. Additionally, HWE was assessed in each study using the Chi-square before the present meta-analysis was performed. All analyses were conducted using STATA 11.0 software.

Additional Information

How to cite this article: Tong, X. et al. The SDF-1 rs1801157 Polymorphism is Associated with Cancer Risk: An Update Pooled Analysis and FPRP Test of 17,876 Participants. Sci. Rep. 6, 27466; doi: 10.1038/srep27466 (2016).

Acknowledgments

We thank Elsevier Language Editing Services for its linguistic assistance during the preparation of this manuscript.

Footnotes

Author Contributions H.F. designed the study, coordinated the study and directed its implementation. X.T., Y.M. and H.D. searched the publications, extracted the data and wrote the manuscript. X.T., Y.M., X.W., S.L. and Z.Y. were responsible for data synthesis and figures creation. Y.M., H.D. and S.P. wrote the abstract and made the tables. All authors read and approved the final manuscript.

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