Abstract
An increasing number of studies have highlighted the potential link between EXO1 polymorphisms and cancer risk, although no consensus has yet been obtained. Thus, we aimed to obtain a thorough and current assessment of EXO1 polymorphisms and cancer susceptibility by performing a meta-analysis. A comprehensive literature retrieval was performed on PubMed, EMbase, Web of Science and Wanfang databases. The odds ratio (OR) and 95% confidence interval (CI) were applied to assess the results. Finally, 39 case-control studies of the nine EXO1 polymorphisms that involved 21,651 cases and 21,348 controls met our inclusion criteria. The pooled analysis indicated that the rs1047840 polymorphism conferred a significantly increased susceptibility to cancer in an allelic model. Similarly, the rs3754093, rs1776177, rs9350, rs10802996, rs1635498, rs1776148 and rs851797 polymorphisms were also associated with an increased susceptibility to cancer in an allelic model, respectively, while no significant association was identified for rs1635517 polymorphism. For the rs1047840 polymorphism, in an ethnicity subgroup analysis, a significantly increased susceptibility to cancer for Asians was identified in all the genetic models, and for Caucasians in an allelic model. Our findings provide the evidence that the rs1047840, rs9350, rs10802996, rs1635498, rs1776148, rs1776177, rs3754093 and rs851797 polymorphisms may act as risk factors for cancer.
Currently, cancer is a primary cause of human death, which can be attributed to its high rates of morbidity and mortality in the United States and many other countries1. Large epidemiological and clinical investigations had indicated that a multitude of factors contribute to the initiation of tumourigenesis, such as environmental factors, hereditary factors and cancer-related lifestyle factors. Additionally, susceptibility genes, including EXO12, have been found to play a key role in the initiation of cancer.
The Exonuclease 1 (EXO1) gene, which belongs to the RAD2 nuclease family, encodes a member of the mismatch repair (MMR) system that plays a critical role in maintaining genomic stability3. EXO1 is located on chromosome 1q42–q43, includes one untranslated exon and 13 coding exons, and encodes an 846 amino acid protein. The products of the EXO1 gene function in DNA replication, repair, mutation avoidance and recombination, which are necessary processes for both male and female meiosis4.
Recently, the associations between EXO1 genetic polymorphisms and susceptibility to various type of cancers had been widely investigated. An EXO1 polymorphism at codon 589 (rs1047840) is a non-synonymous single nucleotide polymorphism (SNP) that has been associated with susceptibility to lung cancer (LC)5,6,7, glioma8, breast cancer (BC)9, and gastric cancer (GC)10. As such, it may be a novel useful marker for primary tumour prevention and anticancer interventions. However, other common low-penetrance susceptibility alleles may also exist, which lead to a moderate increase or reduction in cancer susceptibility. To date, only a few molecular epidemiological studies have investigated other EXO1 polymorphisms and cancer susceptibility in various populations, such as A-1419G (rs3754093), G670E (rs1776148), C498T (rs1635517), and L757P (rs9350). Additionally, no consensus had yet been obtained, which was partially a consequence of the heterogeneity within cancer subtypes, the diverse ethnicity of patient cohorts, and the small sample sizes. In the present meta-analysis, we had widely reviewed all eligible publications that were based on case-control data to derive a more precise and up-to-date estimation of associations between polymorphisms in EXO1 and cancer susceptibility.
Methods
Literature search and eligibility
We performed a comprehensive literature search using the PubMed, Web of Science, EMbase and Wangfang databases (last research update: September 29, 2015) in which we applied the following search terms: (EXO1 OR exonuclease 1) AND (polymorphism OR SNP OR variant OR mutation OR allele) AND (cancer OR tumour OR carcinoma OR neoplasm OR malignancy). We also manually retrieved reference lists from these enrolled publications, aiming to ensure that all eligible studies were included.
Inclusion and exclusion criteria
The detailed inclusion criteria were as follows: 1) the study was a case-control study; 2) the study evaluated the association between EXO1 polymorphisms and cancer susceptibility; 3) the study comprised useful allele and genotype frequencies to estimate the crude ORs at 95% CIs. However, all meta-analyses, reviews, animal studies and case-only studies, as well as those duplicated previous publications, were definitely excluded. Studies deviated from Hardy Weinberg Equilibrium (HWE), studies that were not concerned with cancer susceptibility and abstracts with incomplete genetic data were also removed from this analysis. When a case group was investigated in more than one publication, the publication with the largest number of participants was selected.
Data extraction
The following information was extracted from each study by two independent investigators (Meng Zhang and Duran Zhao): name of the first author, year of publication, ethnicity, genotyping methods, source of controls, cancer type, total number of cases and controls, and HWE. Any discrepancies between the two investigators’ selections were resolved by consensus. Regarding the sources of controls, all eligible case-control studies were defined as either population-based (PB) or hospital-based (HB).
Statistical analysis
Susceptibility to cancer related to EXO1 polymorphisms was calculated directly from data provided in the eligible studies. Crude OR corresponding to 95% CI was applied to evaluate the strength of association between EXO1 polymorphisms and cancer susceptibility. Variants in EXO1 included rs1635498, rs1047840, rs851797, rs3754093, rs1776177, rs1776148, rs1635517, rs10802996 and rs9350. Each variable was analysed in allelic comparison, heterozygote comparison, homozygote comparison, dominant and recessive models. For example, the pooled ORs of EXO1 rs9350 polymorphisms were calculated by allelic comparison (T vs. C), heterozygote comparison (TC vs. CC), homozygote comparison (TT vs. CC), dominant (TT + TC vs. CC) and recessive models (TT vs. TC + CC). We used a χ2-based Q-test to evaluate between-study heterogeneity within the studies11. Heterogeneity was considered to be significant when the P-value was less than 0.1. If there was no significant heterogeneity, a fixed effect model (Der-Simonian Laird) was used to evaluate the point estimates and 95% CI; otherwise, a random effects model (Der-Simonian Laird) was used12. The Z-test was used to determine the significance of overall ORs. In addition, Bonferroni corrections were also performed to adjust the results13. If significant heterogeneity existed among the enrolled case-control studies, meta-regression was used to elaborate the source of heterogeneity with the Stata version 12.0 software. Moreover, one-way sensitivity analyses were carried out to evaluate the stability of the pooled ORs, in which each individual study was removed from the meta-analysis to detect the effect of each individual data set on the pooled ORs. Publication bias was evaluated using Begg’s funnel plot and Egger’s test based on Stata 12.0 software14.
Linkage disequilibrium (LD) analysis across populations
We extracted data from the 1000 genomes Project Phase III (http://hapmap.ncbi.nlm.nih.gov/cgi-perl/gbrowse/hapmap3r2_B36/) encompassing the polymorphisms in EXO1 evaluated in currently study15. Briefly, populations enrolled in the project including ASW (African ancestry in Southwest USA), CEU (Utah residents with Northern and Western European ancestry from the CEPH collection), CHB (Han Chinese in Beijing, China), CHD (Chinese in Metropolitan Denver, Colorado), GIH (Gujarati Indians in Houston, Texas), JPT (Japanese in Tokyo, Japan), LWK (Luhya in Webuye, Kenya), MEX (Mexican ancestry in Los Angeles, California), MKK (Maasai in Kinyawa, Kenya), TSI (Toscans in Italy) and YRI (Yoruba in Ibadan, Nigeria). Then, Haploview software was used to perform analysis and LD was evaluated by r2 statistics16 in each of the above mentioned populations.
Results
Study characteristics
As shown in Fig. 1, a total of 362 citations were retrieved from database searches. After reading the title or abstract, 41 studies concerning the associations of the nine EXO1 polymorphisms and cancer susceptibility were selected for further consideration. Then, 27 articles were removed because they were duplicated studies, lacked sufficient genotype information, or used ineligible samples. Finally, we collected 14 publications that encompassed 50 case-control studies, including 28,462 cases and 28,253 controls of the nine polymorphisms in EXO1 (rs1635498, rs1047840, rs851797, rs3754093, rs1776177, rs1776148, rs1635517, rs10802996 and rs9350) (Table 1)4,5,6,8,9,10,17,18,19,20,21,22,23,24. The quality of these enrolled case-control studies was evaluated using the Newcastle–Ottawa Scale (NOS) (Supplementary Table 1). Genotypic distribution of most of the studies was in agreement with HWE (P > 0.05) in controls population except for 11 case-control studies5,6,9,10,17,19,23, which were eventually excluded from the pooled analyses. In the end, a total of 11 publications, encompassing 39 case-control studies of the nine polymorphisms that involved 21,651 cases and 21,348 controls were finally enrolled.
Table 1. Characteristics of the enrolled studies on EXO1 polymorphisms and cancer.
SNP | First Author | Year | Ethnicity | Genotyping Method | Source of control | Cancer Type | Case | Control | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AA | AB | BB | AA | AB | BB | Y/N (HWE) | |||||||
rs9350 | Haghighi et al. | 2010 | Asian | PCR-RFLP | HB | CRC | 60 | 28 | 2 | 51 | 37 | 79 | N |
rs9350 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 62 | 78 | 39 | 56 | 84 | 39 | Y |
rs9350 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 433 | 563 | 276 | 402 | 596 | 274 | Y |
rs9350 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 235 | 297 | 148 | 214 | 313 | 153 | Y |
rs9350 | Ibarrola-Villava et al. | 2011 | Caucasian | TaqMan | HB | Melanoma | 485 | 186 | 13 | 297 | 99 | 10 | Y |
rs9350 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 124 | 156 | 78 | 112 | 167 | 79 | Y |
rs851797 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 38 | 87 | 54 | 36 | 90 | 53 | Y |
rs851797 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 266 | 625 | 381 | 258 | 630 | 384 | Y |
rs851797 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 139 | 328 | 213 | 133 | 344 | 203 | Y |
rs851797 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 76 | 173 | 109 | 72 | 179 | 107 | Y |
rs3754093 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 68 | 83 | 28 | 75 | 82 | 22 | Y |
rs3754093 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 504 | 599 | 169 | 535 | 577 | 160 | Y |
rs3754093 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 261 | 311 | 108 | 283 | 315 | 82 | Y |
rs3754093 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 135 | 167 | 56 | 149 | 164 | 45 | Y |
rs1776177 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 80 | 82 | 17 | 82 | 84 | 13 | Y |
rs1776177 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 581 | 587 | 104 | 589 | 592 | 91 | N |
rs1776177 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 309 | 306 | 65 | 319 | 308 | 53 | Y |
rs1776177 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 159 | 164 | 35 | 164 | 168 | 26 | Y |
rs1776148 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 9 | 39 | 131 | 8 | 36 | 135 | N |
rs1776148 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 64 | 267 | 941 | 59 | 255 | 958 | N |
rs1776148 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 35 | 148 | 497 | 31 | 138 | 511 | N |
rs1776148 | Chang et al. | 2008 | Caucasian | SNP Chip | PB | Glioblastoma | 11 | 57 | 44 | 18 | 47 | 46 | Y |
rs1776148 | Ibarrola-Villava et al. | 2011 | Caucasian | PCR-RFLP | HB | Melanoma | 67 | 293 | 239 | 48 | 171 | 160 | Y |
rs1776148 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 18 | 78 | 262 | 16 | 73 | 269 | N |
rs1635517 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 11 | 65 | 103 | 8 | 59 | 112 | Y |
rs1635517 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 52 | 449 | 771 | 54 | 421 | 797 | Y |
rs1635517 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 39 | 241 | 400 | 28 | 218 | 434 | Y |
rs1635517 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 22 | 130 | 206 | 15 | 118 | 225 | Y |
rs1635498 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 132 | 43 | 4 | 137 | 39 | 3 | Y |
rs1635498 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 968 | 289 | 15 | 972 | 283 | 17 | Y |
rs1635498 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 508 | 161 | 11 | 522 | 148 | 10 | Y |
rs1635498 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 265 | 85 | 8 | 274 | 79 | 5 | Y |
rs10802996 | Luo et al. | 2012 | Asian | PCR-RFLP | HB | CC | 77 | 39 | 10 | 172 | 89 | 17 | Y |
rs10802996 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 100 | 59 | 20 | 102 | 61 | 16 | Y |
rs10802996 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 735 | 408 | 129 | 728 | 426 | 118 | N |
rs10802996 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 380 | 235 | 65 | 383 | 235 | 62 | N |
rs10802996 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 199 | 119 | 40 | 203 | 122 | 33 | N |
rs1047840 | Kabzińśki et al. | 2014 | Caucasian | TaqMan | PB | CRC | 22 | 95 | 33 | 49 | 62 | 39 | N |
rs1047840 | Nogueira et al. | 2014 | Mix | TaqMan | HB | HNSCC | 179 | 209 | 62 | 175 | 211 | 64 | Y |
rs1047840 | Tang et al. | 2014 | Mix | SNP Chip | HB | PC | 827 | 910 | 296 | 815 | 993 | 304 | Y |
rs1047840 | Luo et al. | 2012 | Asian | PCR-RFLP | HB | CC | 73 | 48 | 5 | 196 | 77 | 5 | Y |
rs1047840 | Tsai et al. | 2009 | Asian | PCR-RFLP | HB | OC | 391 | 244 | 45 | 482 | 183 | 15 | Y |
rs1047840 | Wang et al. | 2009 | Asian | PCR-RFLP | HB | BC | 794 | 421 | 57 | 898 | 341 | 33 | Y |
rs1047840 | Bau et al. | 2009 | Asian | PCR-RFLP | HB | GC | 103 | 64 | 12 | 125 | 49 | 5 | Y |
rs1047840 | Jin et al. | 2008 | Asian | SNP Chip | PB | LC | 304 | 172 | 24 | 355 | 138 | 24 | N |
rs1047840 | Zienolddiny et al. | 2005 | Caucasian | Beckman | HB | LC | 115 | 106 | 35 | 116 | 145 | 30 | Y |
rs1047840 | Hsu et al. | 2009 | Asian | PCR-RFLP | HB | LC | 214 | 125 | 19 | 251 | 97 | 10 | Y |
rs1047840 | Bayram et al. | 2011 | Caucasian | PCR-RFLP | HB | HCC | 95 | 94 | 35 | 99 | 108 | 17 | Y |
rs1047840 | Chang et al. | 2008 | Caucasian | SNP Chip | PB | Glioblastoma | 55 | 42 | 15 | 29 | 59 | 22 | Y |
rs1047840 | Ibarrola-Villava et al. | 2011 | Caucasian | PCR-RFLP | HB | Melanoma | 234 | 282 | 83 | 136 | 175 | 68 | Y |
CRC: Colorectal cancer; GC: gastric cancer; BC: Breast cancer; OC: Oral cancer; LC: Lung cancer; CC: Cervical cancer; HNSCC: Head and eck squamous cell carcinoma; PC: Pancreatic cancer; HCC: Hepatic cellular cancer; H-B: Hospital based; P-B: Population based; HWE: Hardy Weinberg Equilibrium.
For the rs1047840 polymorphism, 11 case-control studies with 6,289 cases and 6,333 controls met our inclusion criteria. Among these, four studies were of Caucasian individuals, five were of Asians and others were of mixed groups. For the rs9350 polymorphism, five case-control studies, which included 3,173 cases and 2,895 controls were enrolled. Among these, only one study was performed in a Caucasian cohort, while the others considered as Asian individuals. For rs1776177 polymorphism, three case-controls of Asians that encompassed 1,217 cases and 1,217 controls were enrolled. For the rs1776148 polymorphism, we ultimately enrolled two case-control studies of Caucasians that included 711 cases and 490 controls. For the rs10802996 polymorphism, two case-control studies of Asians that included 305 cases and 457 controls met our eligibility criteria. Moreover, for the rs851797, rs3754093, rs1635517 and rs1635498 polymorphisms, there were four case-control studies with 2,489 cases and 2,489 controls that were included in this present meta-analysis, and the ethnicity of these studies were of Asian populations.
Quantitative synthesis
The results of rs1047840 and rs9350 polymorphisms and cancer risk were shown in Table 2, and the results of other polymorphisms were shown in Supplementary Table 2. Overall, the pooled analysis indicated that the A allele (variant allele) of the rs1047840 polymorphism conferred a significantly increased overall susceptibility to cancer in an allelic model (A vs. G: OR = 4.082, 95% CI = 3.009–5.538, P = 0.000, Fig. 2). Similarly, rs3754093 and rs1776177 polymorphisms were related to an increased susceptibility to cancer in an allelic model (rs3754093: G vs. A: OR = 2.976, 95% CI = 2.711–3.268, P = 0.000, Fig. 3; rs1776177: G vs. A: OR = 3.234, 95% CI = 2.815–3.716, P = 0.000). Additionally, rs9350, rs10802996, rs1635498, rs1776148 and rs851797 polymorphisms were also conferred an increased overall susceptibility to cancer in an allelic model (rs9350: T vs. C: OR = 2.930, 95% CI = 2.124–4.042, P = 0.000; rs10802996: G vs. C: OR = 5.013, 95% CI: 3.717–6.762, P = 0.000; rs1635498: G vs. A: OR = 7.965, 95% CI: 6.924–9.163, P = 0.000; rs1776148: G vs. A: OR = 1.448, 95% CI: 1.209–1.734, P = 0.000; rs851797: T vs. C: OR = 1.841, 95% CI: 1.686–2.009, P = 0.000). However, no significant association was identified for rs1635517 polymorphism and overall cancer susceptibility.
Table 2. Results of meta-analysis for polymorphisms in and cancer susceptibility.
Polymorphisms | Comparison | Subgroup | N | PH | PZ | Random | Fixed |
---|---|---|---|---|---|---|---|
rs1047840 | B VS. A | Overall | 11 | 0.000 | 0.000 | 4.082 (3.009–5.538) | 3.571 (3.350–3.807) |
B VS. A | Asian | 5 | 0.963 | 0.000 | 6.861 (6.127–7.682) | 6.864 (6.130–7.687) | |
B VS. A | Caucasian | 4 | 0.028 | 0.000 | 2.709 (2.075–3.538) | 2.654 (2.301–3.060) | |
B VS. A | Mix | 2 | 0.838 | 0.000 | 2.692 (2.452–2.955) | 2.692 (2.452–2.955) | |
B VS. A | PCR-RFLP | 7 | 0.000 | 0.000 | 5.327 (3.631–7.815) | 5.057 (4.600–5.560) | |
B VS. A | SNP-Chip | 2 | 0.175 | 0.000 | 2.488 (1.881–3.292) | 2.662 (2.407–2.944) | |
B VS. A | Other methods | 2 | 0.434 | 0.000 | 2.778 (2.330–3.313) | 2.778 (2.330–3.312) | |
B VS. A | HB | 10 | 0.000 | 0.000 | 4.357 (3.172–5.986) | 3.615 (3.389–3.857) | |
B VS. A | LC | 2 | 0.000 | 0.000 | 4.510 (2.094–9.713) | 4.382 (3.546–5.416) | |
B VS. A | Other tumors | 9 | 0.000 | 0.000 | 3.993 (2.824–5.647) | 3.499 (3.272–3.742) | |
BA VS. AA | Overall | 11 | 0.000 | 0.396 | 1.091 (0.892–1.336) | 1.100 (1.019–1.187) | |
BA VS. AA | Asian | 5 | 0.819 | 0.000 | 1.503 (1.335–1.691) | 1.502 (1.335–1.691) | |
BA VS. AA | Caucasian | 4 | 0.048 | 0.071 | 0.748 (0.545–1.026) | 0.799 (0.665–0.961) | |
BA VS. AA | Mix | 2 | 0.661 | 0.143 | 0.914 (0.811–1.031) | 0.914 (0.811–1.031) | |
BA VS. AA | PCR-RFLP | 7 | 0.020 | 0.002 | 1.339 (1.117–1.605) | 1.358 (1.223–1.508) | |
BA VS. AA | SNP-Chip | 2 | 0.005 | 0.261 | 0.612 (0.261–1.44) | 0.866 (0.761–0.985) | |
BA VS. AA | Other methods | 2 | 0.243 | 0.229 | 0.865 (0.665–1.125) | 0.873 (0.699–1.090) | |
BA VS. AA | HB | 10 | 0.000 | 0.112 | 1.165 (0.965–1.406) | 1.121 (1.038–1.210) | |
BA VS. AA | LC | 2 | 0.004 | 0.870 | 1.061 (0.525–2.143) | 1.098 (0.866–1.393) | |
BA VS. AA | Other tumors | 9 | 0.000 | 0.418 | 1.097 (0.877–1.372) | 1.100 (1.015–1.192) | |
BA + BB VS. AA | Overall | 11 | 0.000 | 0.256 | 1.132 (0.914–1.401) | 1.129 (1.051–1.214) | |
BA + BB VS. AA | Asian | 5 | 0.611 | 0.000 | 1.585 (1.414–1.776) | 1.584 (1.414–1.775) | |
BA + BB VS. AA | Caucasian | 4 | 0.020 | 0.131 | 0.774 (0.555–1.080) | 0.824 (0.693–0.980) | |
BA + BB VS. AA | Mix | 2 | 0.739 | 0.173 | 0.925 (0.826–1.035) | 0.925 (0.826–1.035) | |
BA + BB VS. AA | PCR-RFLP | 7 | 0.002 | 0.001 | 1.405 (1.144–1.726) | 1.413 (1.278–1.562) | |
BA + BB VS. AA | SNP-Chip | 2 | 0.002 | 0.270 | 0.609 (0.252–1.471) | 0.877 (0.777–0.990) | |
BA + BB VS. AA | Other methods | 2 | 0.441 | 0.340 | 0.903 (0.732–1.114) | 0.903 (0.732–1.114) | |
BA + BB VS. AA | HB | 10 | 0.000 | 0.052 | 1.219 (0.998–1.489) | 1.152 (1.071–1.239) | |
BA + BB VS. AA | LC | 2 | 0.005 | 0.699 | 1.137 (0.593–2.179) | 1.169 (0.931–1.467) | |
BA + BB VS. AA | Other tumors | 9 | 0.000 | 0.322 | 1.129 (0.888–1.437) | 1.125 (1.042–1.214) | |
BB VS. AA | Overall | 11 | 0.000 | 0.068 | 1.379 (0.977–1.948) | 1.139 (1.003–1.293) | |
BB VS. AA | Asian | 5 | 0.556 | 0.000 | 2.456 (1.818–3.317) | 2.480 (1.839–3.344) | |
BB VS. AA | Caucasian | 4 | 0.002 | 0.778 | 0.914 (0.488–1.711) | 0.903 (0.695–1.173) | |
BB VS. AA | Mix | 2 | 0.954 | 0.615 | 0.957 (0.808–1.135) | 0.957 (0.808–1.135) | |
BB VS. AA | PCR-RFLP | 7 | 0.000 | 0.011 | 2.004 (1.176–3.414) | 1.625 (1.309–2.018) | |
BB VS. AA | SNP-Chip | 2 | 0.019 | 0.350 | 0.636 (0.246–1.643) | 0.910 (0.759–1.092) | |
BB VS. AA | Other methods | 2 | 0.535 | 0.894 | 1.022 (0.737–1.418) | 1.022 (0.737–1.418) | |
BB VS. AA | HB | 10 | 0.000 | 0.014 | 1.529 (1.089–2.149) | 1.175 (1.033–1.337) | |
BB VS. AA | LC | 2 | 0.193 | 0.097 | 1.519 (0.823–2.804) | 1.462 (0.934–2.288) | |
BB VS. AA | Other tumors | 9 | 0.000 | 0.140 | 1.350 (0.907–2.011) | 1.114 (0.976–1.272) | |
BB VS. BA + AA | Overall | 11 | 0.000 | 0.030 | 1.369 (1.031–1.819) | 1.151 (1.022–1.298) | |
BB VS. BA + AA | Asian | 5 | 0.642 | 0.000 | 2.155 (1.600–2.904) | 2.177 (1.618–2.929) | |
BB VS. BA + AA | Caucasian | 4 | 0.005 | 0.771 | 1.085 (0.626–1.881) | 1.007 (0.789–1.284) | |
BB VS. BA + AA | Mix | 2 | 0.812 | 0.954 | 1.005 (0.858–1.176) | 1.005 (0.858–1.176) | |
BB VS. BA + AA | PCR-RFLP | 7 | 0.000 | 0.015 | 1.832 (1.123–2.987) | 1.489 (1.209–1.835) | |
BB VS. BA + AA | SNP-Chip | 2 | 0.189 | 0.867 | 0.900 (0.594–1.361) | 0.986 (0.833–1.166) | |
BB VS. BA + AA | Other methods | 2 | 0.275 | 0.578 | 1.100 (0.784–1.542) | 1.090 (0.804–1.478) | |
BB VS. BA + AA | HB | 10 | 0.000 | 0.011 | 1.460 (1.089–1.959) | 1.173 (1.039–1.324) | |
BB VS. BA + AA | LC | 2 | 0.467 | 0.050 | 1.533 (0.995–2.362) | 1.539 (1.000–2.367) | |
BB VS. BA + AA | Other tumors | 9 | 0.000 | 0.081 | 1.336 (0.965–1.852) | 1.124 (0.992–1.273) | |
rs9350 | B VS. A | Overall | 5 | 0.000 | 0.000 | 2.930 (2.124–4.042) | 2.656 (2.435–2.896) |
B VS. A | Asian | 4 | 0.999 | 0.000 | 2.412 (2.201–2.643) | 2.412 (2.201–2.643) | |
B VS. A | PCR-RFLP | 4 | 0.999 | 0.000 | 2.412 (2.201–2.643) | 2.412 (2.201–2.643) | |
BA VS. AA | Overall | 5 | 0.513 | 0.105 | 0.908 (0.809–1.020) | 0.909 (0.809–1.020) | |
BA VS. AA | Asian | 4 | 0.996 | 0.027 | 0.866 (0.762–0.983) | 0.866 (0.762–0.983) | |
BA VS. AA | PCR-RFLP | 4 | 0.996 | 0.027 | 0.866 (0.762–0.983) | 0.866 (0.762–0.983) | |
BA + BB VS. AA | Overall | 5 | 0.637 | 0.105 | 0.914 (0.820–1.019) | 0.914 (0.820–1.019) | |
BA + BB VS. AA | Asian | 4 | 0.993 | 0.035 | 0.880 (0.782–0.991) | 0.880 (0.782–0.991) | |
BA + BB VS. AA | PCR-RFLP | 4 | 0.993 | 0.035 | 0.880 (0.782–0.991) | 0.880 (0.782–0.991) | |
BB VS. AA | Overall | 5 | 0.995 | 0.207 | 0.907 (0.780–1.055) | 0.907 (0.780–1.055) | |
BB VS. AA | Asian | 4 | 0.989 | 0.236 | 0.911 (0.782–1.062) | 0.911 (0.782–1.062) | |
BB VS. AA | PCR-RFLP | 4 | 0.989 | 0.236 | 0.911 (0.782–1.062) | 0.911 (0.782–1.062) | |
BB VS. BA + AA | Overall | 5 | 0.978 | 0.816 | 0.984 (0.862–1.124) | 0.984 (0.862–1.124) | |
BB VS. BA + AA | Asian | 4 | 0.991 | 0.891 | 0.991 (0.866–1.133) | 0.991 (0.866–1.133) | |
BB VS. BA + AA | PCR-RFLP | 4 | 0.991 | 0.891 | 0.991 (0.866–1.133) | 0.991 (0.866–1.133) |
PH: P value of Q test for heterogeneity test; PZ: means statistically significant (P < 0.05); P (Adjust): Multiple testing P value according to Bonferroni Correction; LC: Lung cancer; H-B: Hospital based; P-B: Population based; HWE: Hardy Weinberg Equilibrium; P value less than 0.05/(9polymorphisms*5models) was considered as statistically significant, which was marked with bold font in the table). Note: Heterogeneity was considered to be significant when the P-value was less than 0.1. If there was no significant heterogeneity, a fixed effect model (Der-Simonian Laird) was used to evaluate the point estimates and 95% CI; otherwise, a random effects model (Der-Simonian Laird) was used. And the Pz was calculated based on the actual model adopted.
Subgroup analyses
Results of subgroup analyses were presented in Table 2. In an ethnicity subgroup analysis, a significantly increased susceptibility to cancer in Asian populations was identified in all the genetic models for rs1047840 polymorphism, while an increased susceptibility to cancer was identified for Caucasian populations only in an allelic model (A vs. G: OR = 2.709, 95% CI = 2.075–3.538, P = 0.000). When the subgroup analysis was conducted by source of control, an increased susceptibility to cancer was identified for P-B groups in an allelic model (A vs. G: OR = 4.357, 95% CI = 3.172–5.986, P = 0.000). In addition, we uncovered an increased susceptibility to lung cancer (LC) in an allelic model (A vs. G: OR = 4.510, 95% CI = 2.094–9.713, P = 0.000). In the stratification analysis by genotyping methods, an increased susceptibility to cancer was revealed for PCR-RFLP (A vs. G: OR = 5.327, 95% CI = 3.631–7.815, P = 0.000; AA + AG vs. GG: OR = 1.405, 95% CI = 1.144–1.726, P = 0.001) and SNP-Chip (A vs. G: OR = 2.488, 95% CI = 1.881–3.292, P = 0.000) groups, respectively.
For rs9350 polymorphism, when the stratification analysis was conducted based on genotyping method, we also identified a significant increased susceptibility to cancer in PCR-RFLP group in an allelic model (T vs. C: OR = 2.412, 95% CI = 2.201–2.643, P = 0.000) for Asian population.
Sensitivity analyses and publication bias
Sensitivity analysis was performed by excluding each single case-control study in turn, and no individual study showed a significant influence on the pooled ORs. Sensitivity analysis of the rs1047840 polymorphism in an allelic comparison is presented in Fig. 4 (Supplementary Table 3). Additionally, Begg’s funnel plot was generated and Egger’s test was performed to assess potential publication bias. The funnel plot of all polymorphisms was symmetrical. The funnel plot for the rs3754093 and rs1047840 polymorphisms in the allelic comparison was presented in Figs 5 and 6, respectively (rs3754093: G vs. A, Egger’s funnel plot, P > |t| = 0.349; rs1047840: G vs. A, Egger’s funnel plot, P > |t| = 0.337, Supplementary Table 4).
LD analysis across populations
In order to better understand these results, LD analysis was performed to test for the existence of bins in the region comprising these eight polymorphisms (Meta-P value < 0.05/45). However, only six of them can be matched from the database, including rs1635498, rs1047840, rs9350, rs1635517, rs1776148 and rs1776177 polymorphisms. LD plots for TSI population presented high LD value (r2 = 0.70) between rs1635517 and rs1776177 polymorphisms, for CHB, JPT, CHD and GIH populations presented moderate LD values (r2 ≥ 0.50), while for YRI, ASW and LWK populations presented a lower LD value (r2 < 0.35) (Fig. 6 and Supplementary Table 5). LD plots for CHB, JPT, CHD, GIH, LWK and MEX populations presented a lower LD value (r2 < 0.35) between rs1776148 and rs9350 polymorphisms.
Discussion
Multiple factors are involved in cancer formation and progression. Abundant evidence suggests that genetics play an important role in determining cancer susceptibility, and understanding associations between genetic polymorphisms and malignancies may provide personalized analysis and reveal the predictive value of certain carcinomas. An increasing number of research studies of tumourigenesis have highlighted EXO1 as a promising target.
The DNA repair system is known to be essential for maintaining genetic stability and offering protection from cancer initiation. EXO1 is an exclusive exonuclease gene that participates in the human MMR system. Genetic disorders in the MMR system result in the absence of DNA MMR function, resulting in an increased frequency of spontaneous mutations, which may give rise to the steady accumulation of oncogenes and tumour suppressors, which eventually contribute to tumourigenesis20.
For rs1047840, one polymorphism in EXO1 is located on exon 12, and its variation leads to a change of the 589th amino acid of the Exo1 protein from lysine to glutamic acid, which might affect EXO1 expression. For the rs1047840 polymorphism, it is located in an exonic splicing enhancer (ESE) region6. Previous studies suggest that the A allele (variant allele) of the rs1047840 polymorphism may influence EXO1 activity, which would mildly affect its normal function5; in addition, as people who harbour the A allele (s) become older, transformations caused by carcinogens may accumulate through an increased number of unremoved DNA adducts. Thus, for an individual who has a risk-imparting genetic variant, such as the A allele (variant allele) of the rs1047840 polymorphism, that variant will likely synergistically increase their cancer risk.
Owing to the importance of EXO1 polymorphisms, an increasing number of studies have explored the associations of EXO1 polymorphisms and cancer risk. Luo et al.20 identified that the A allele of the EXO1 rs1047840 polymorphism was significantly related to an increased cervical cancer (CC) risk compared with the G allele (OR = 1.67, 95% CI: 1.13–2.45, P < 0.05). Similarly, many other molecular epidemiological studies are consistent with this association of the EXO1 rs1047840 with BC, LC, oral cancer (OC) and GC9,10,23,25. However, Zienolddiny et al.7 have shown no significant association of the rs1047840 polymorphism and non-small-cell gastric cancer (NSCGC) risk. Additionally, Song et al.26 indicated that the rs3902093 polymorphism was related to reduced expression of EXO1 (Pdiscovery = 6.6 × 10−4, Preplication = 0.039, Pjoint = 2.5 × 10−4; ORjoint = 0.80, 95% CI: 0.71–0.90), while carriers of the A allele had lower expression (P = 0.002).
As these findings are not conclusive, we conducted the present comprehensive meta-analysis to shed light on the role of EXO1 polymorphisms in tumourigenesis. Unlike the previous work conducted by Duan et al.27, they only investigated one polymorphism in EXO1 and cancer risk and concluded that rs1047840 polymorphism A allele may be applied as a novel biomarker for tumour susceptibility. Recently, Chen et al.28 also conducted a meta-analysis encompassing three polymorphisms in EXO1 and cancer risk, and they concluded that rs9350 polymorphism was a protective factor against cancer, while the rs1047840 polymorphism may be a risk factor. Besides, the rs1776148 polymorphism may have no influence on cancer risk. In our present work, which considered 21,651 cancer patients and 21,348 cancer free controls, we concluded that the A allele of rs1047840 polymorphism conferred a significantly increased susceptibility to overall cancer in an allelic model, a result consistent with previous meta-analysis27,28. Similarly, the rs3754093, rs1776177, rs9350, rs10802996, rs1635498, rs1776148 and rs851797 polymorphisms were also identified related to an increased overall susceptibility to cancer in an allelic model, respectively, while no significant association was revealed for rs1635517 polymorphism. For rs9350 and rs1776148, our conclusions were not consistent with Chen et al.’s27 study, potentially because we excluded the studies that were deviated from HWE and our results were further adjusted by Bonferroni corrections13. In addition, when the stratification analyses were conducted by ethnicity and source of control, we identified a significantly increased susceptibility to Asian populations in all the genetic models of rs1047840 polymorphism, to Caucasians in an allelic model, and population-based (P-B) group in an allelic model. In addition, we uncovered an increased susceptibility to LC also in an allelic model. Moreover, we performed LD analyses to find the LD between the six polymorphisms, which showed that the rs1635517 and rs1776177 polymorphisms were in a high LD value (r2 = 0.70) for TSI population, and for CHB, JPT, CHD and GIH populations presented moderate LD values (r2 ≥ 0.50), while for YRI, ASW and LWK populations presented a lower LD value (r2 < 0.35). However, our meta-analysis results seemed not to be in accordance with the LD analyses that rs1776177 polymorphism is a risk factor for cancer rather than rs1635517. Limited number of studies and sample size may account for this discrepancy, therefore, future studies are warranted to verify this finding.
There are some strengths and limitations in the present study. The most important strength is that we have conducted a comprehensive retrieval for all eligible studies and polymorphisms, and the sample size was markedly expanded, which helped to reveal some findings not suggested in the previous work. Secondly, studies deviated from HWE were excluded, ensuring the accuracy of the finally data. Simultaneously, several limitation should be discussed. Firstly, the phenotype of our study is a heterogeneous aggregation of a variety of cancer types, and only for rs1047840 polymorphism, a subgroup analysis based on cancer type was conducted, while for others, attributing to the limited number of studies for specific cancers, such as BC, CC and etc., we are unable to validate the potential effects on these cancers homogeneous or not. In addition, as ten case-control studies were excluded from the pooled analysis, there retained only two case-control studies for rs10802996 and rs1776148 polymorphisms, respectively. On the basis of small samples, possibly leading to an underestimate of the true association. All these confusions should be further confirmed in a series of much larger studies. Secondly, the effects of EXO1 polymorphisms on cancer susceptibility might be affected by several factors, such as age, sex, smoking status and matching criteria. A lack of samples and data may cause inconsistency in results and lead to possible publication bias, which often affect the precision of overall results. Thirdly, tumours are polygenic diseases, and analyses of haplotype and gene-to-gene associations are also important because gene-to-gene and gene-to-environment interactions may modulate cancer susceptibility.
In conclusion, our meta-analysis suggests that the rs1047840, rs9350, rs10802996, rs1635498, rs1776148, rs1776177, rs3754093 and rs851797 polymorphisms in EXO1 may be risk factors for cancer. In the future, large-scale well-designed case-control studies are warranted to verify our findings.
Additional Information
How to cite this article: Zhang, M. et al. Associations between Nine Polymorphisms in EXO1 and Cancer Susceptibility: A Systematic Review and Meta-Analysis of 39 Case-control Studies. Sci. Rep. 6, 29270; doi: 10.1038/srep29270 (2016).
Supplementary Material
Acknowledgments
This work was supported by the Clinical Key Subjects Program of the Ministry of Public Health (Urology) and National Natural Science Foundation of China (81370856, 81401518). And we are grateful to Dr. Michael J. Hackett at Seoul National University for participating in the critical revision of this meta-analysis.
Footnotes
Author Contributions M.Z. and D.Z. performed the literature search, data extraction, and statistical analysis and wrote the manuscript. C.Y., L.Z. and C.L. supervised the literature search, data extraction, analysis, M.Z. and C.L. reviewed the manuscript.
References
- Siegel R., Ma J., Zou Z. & Jemal A. Cancer statistics, 2014. CA: a cancer journal for clinicians 64, 9–29, 10.3322/caac.21208 (2014). [DOI] [PubMed] [Google Scholar]
- Lichtenstein P. et al. Environmental and heritable factors in the causation of cancer–analyses of cohorts of twins from Sweden, Denmark, and Finland. The New England journal of medicine 343, 78–85, 10.1056/nejm200007133430201 (2000). [DOI] [PubMed] [Google Scholar]
- Jiricny J. The multifaceted mismatch-repair system. Nature reviews. Molecular cell biology 7, 335–346, 10.1038/nrm1907 (2006). [DOI] [PubMed] [Google Scholar]
- Bayram S., Akkiz H., Bekar A., Akgollu E. & Yildirim S. The significance of Exonuclease 1 K589E polymorphism on hepatocellular carcinoma susceptibility in the Turkish population: a case-control study. Molecular biology reports 39, 5943–5951, 10.1007/s11033-011-1406-x (2012). [DOI] [PubMed] [Google Scholar]
- Hsu N. Y. et al. Lung cancer susceptibility and genetic polymorphisms of Exo1 gene in Taiwan. Anticancer Res. 29, 725–730 (2009). [PubMed] [Google Scholar]
- Jin G. et al. Potentially functional polymorphisms of EXO1 and risk of lung cancer in a Chinese population: A case-control analysis. Lung cancer 60, 340–346, 10.1016/j.lungcan.2007.11.003 (2008). [DOI] [PubMed] [Google Scholar]
- Zienolddiny S. et al. Polymorphisms of DNA repair genes and risk of non-small cell lung cancer. Carcinogenesis 27, 560–567, 10.1093/carcin/bgi232 (2006). [DOI] [PubMed] [Google Scholar]
- Chang J. S. et al. Pathway analysis of single-nucleotide polymorphisms potentially associated with glioblastoma multiforme susceptibility using random forests. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 17, 1368–1373, 10.1158/1055-9965.epi-07-2830 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang H. C. et al. Association of genetic polymorphisms of EXO1 gene with risk of breast cancer in Taiwan. Anticancer Res. 29, 3897–3901 (2009). [PubMed] [Google Scholar]
- Bau D. T. et al. Single-nucleotide polymorphism of the Exo1 gene: association with gastric cancer susceptibility and interaction with smoking in Taiwan. The Chinese journal of physiology 52, 411–418 (2009). [DOI] [PubMed] [Google Scholar]
- Cochran W. G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954). [Google Scholar]
- Mantel N. & Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute 22, 719–748 (1959). [PubMed] [Google Scholar]
- Bonferroni C. E. Teoria statistica delle classi e calcolo delle probabilit’a. Comm Firenze, 216–218 (1933). [Google Scholar]
- Seagroatt V. & Stratton I. Bias in meta-analysis detected by a simple, graphical test. Test had 10% false positive rate. BMJ (Clinical research ed.) 316, 470; author reply 470–471 (1998). [PMC free article] [PubMed] [Google Scholar]
- International HapMap C. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58, 10.1038/nature09298 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Barrett J. C., Fry B., Maller J. & Daly M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265, 10.1093/bioinformatics/bth457 (2005). [DOI] [PubMed] [Google Scholar]
- Haghighi M. M. et al. Impact of EXO1 polymorphism in susceptibility to colorectal cancer. Genetic testing and molecular biomarkers 14, 649–652, 10.1089/gtmb.2010.0034 (2010). [DOI] [PubMed] [Google Scholar]
- Ibarrola-Villava M. et al. Genetic polymorphisms in DNA repair and oxidative stress pathways associated with malignant melanoma susceptibility. European journal of cancer (Oxford, England: 1990) 47, 2618–2625, 10.1016/j.ejca.2011.05.011 (2011). [DOI] [PubMed] [Google Scholar]
- Kabzinski J. et al. Association of polymorphism of Lys589Glu Exo1 gene with the risk of colorectal cancer in the Polish population. Polski przeglad chirurgiczny 86, 370–373, 10.2478/pjs-2014-0066 (2014). [DOI] [PubMed] [Google Scholar]
- Luo X., Hong X. S., Xiong X. D., Zeng L. Q. & Lim C. E. A single nucleotide polymorphism in EXO1 gene is associated with cervical cancer susceptibility in Chinese patients. International journal of gynecological cancer: official journal of the International Gynecological Cancer Society 22, 220–225, 10.1097/IGC.0b013e318234fd8a (2012). [DOI] [PubMed] [Google Scholar]
- Nogueira G. A. et al. Association between genetic polymorphisms in DNA mismatch repair-related genes with risk and prognosis of head and neck squamous cell carcinoma. International journal of cancer. Journal international du cancer 137, 810–818, 10.1002/ijc.29435 (2015). [DOI] [PubMed] [Google Scholar]
- Tang H. et al. Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data. Carcinogenesis 35, 1039–1045, 10.1093/carcin/bgu010 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tsai M. H. et al. Interaction of Exo1 genotypes and smoking habit in oral cancer in Taiwan. Oral oncology 45, e90–94, 10.1016/j.oraloncology.2009.03.011 (2009). [DOI] [PubMed] [Google Scholar]
- Zienolddiny S. et al. Polymorphisms of DNA repair genes and risk of non-small cell lung cancer. Carcinogenesis 27, 560–567, 10.1093/carcin/bgi232 (2006). [DOI] [PubMed] [Google Scholar]
- Tang J. et al. Genetic risk of lung cancer associated with a single nucleotide polymorphism from EXO1: a meta analysis. International journal of clinical and experimental medicine 8, 11132–11138 (2015). [PMC free article] [PubMed] [Google Scholar]
- Song F. et al. Exonuclease 1 (EXO1) gene variation and melanoma risk. DNA repair 11, 304–309, 10.1016/j.dnarep.2011.12.005 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Duan F. et al. The significance of Exo1 K589E polymorphism on cancer susceptibility: evidence based on a meta-analysis. Plos one 9, e96764, 10.1371/journal.pone.0096764 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen Z.-Y., Zheng S.-R., Zhong J.-H., Zhuang X.-D. & Zhou J.-Y. Association between three exonuclease 1 polymorphisms and cancer risks: a meta-analysis. OncoTargets and Therapy 9, 899–910 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.