In the present study, we aimed at determining the potential role of rs12917 polymorphism of the O-6-methylguanine-DNA methyltransferase (MGMT) gene in the occurrence of cancer. Based on the available data from the online database, we performed an updated meta-analysis. We retrieved 537 articles from our database research and finally selected a total of 54 case–control studies (21010 cases and 34018 controls) for a series of pooling analyses. We observed an enhanced risk in cancer cases compared with controls, using the genetic models T/T compared with C/C (P-value of association test <0.001; odds ratio (OR) = 1.29) and T/T compared with C/C+C/T (P<0.001; OR = 1.32). We detected similar positive results in the subgroups ‘Caucasian’, and ‘glioma’ (all P<0.05; OR > 1). However, we detected negative results in our analyses of most of the other subgroups (P>0.05). Begg’s and Egger’s tests indicated that the results were free of potential publication bias, and sensitivity analysis suggested the stability of the pooling results. In summary, the T/T genotype of MGMT rs12917 is likely to be linked to an enhanced susceptibility to cancer overall, especially glioma, in the Caucasian population.

Keywords:
MGMT, polymorphism, Cancer, meta-analysis
Subjects:
Bioenergetics, Cancer, Genomics, RNA

In humans, the O-6-methylguanine-DNA methyltransferase (MGMT) protein, encoded by the MGMT gene located on chromosome 10 (10q26) [1], is involved in the DNA repair process [2,3]. By means of methyl transfer, MGMT removes alkylating agents from the DNA direct reversal repair pathway and thus repairs the DNA [2,3]. Two potential functional polymorphisms have been identified in the MGMT gene, namely rs12917 (Leu84Phe) and rs2308321 (Ile143Val) [4,5]. In addition, the promoter methylation status of the gene is reportedly correlated with several clinical diseases, such as glioblastoma [6,7], gastric cancer [8], and oral carcinoma [9].

Both genetic and environmental factors contribute to the occurrence and progression of clinical cancers [10,11]. A number of studies have been conducted on the potential genetic effect of MGMT rs12917 polymorphism on its susceptibility to cancer, but the results were inconclusive. Before 2013, only three relative meta-analyses investigated the potential role of this polymorphism in the overall risk for cancer [12–14]. Based on the currently available data, we performed an updated meta-analysis to reassess the genetic relationship between MGMT rs12917 polymorphism and cancer risk. We enrolled a total of 54 case–control studies for the study.

Database searching strategy

To identify potential publications, we searched four online electronic databases (PubMed, Embase, Cochrane Library, and WANFANG) up through August 2018. We used the terms ‘MeSH (Medical Subject Headings)’ and ‘Entry Terms’ to search PubMed and Cochrane Library, and ‘Emtree’ and ‘Synonyms’ for Embase. The search string we used for PubMed was as follows: (((((((((((((((O(6)-Methylguanine-DNA Methyltransferase [MeSH Terms]) OR Methylated-DNA-Protein-Cysteine S-Methyltransferase) OR Methylated DNA Protein Cysteine S Methyltransferase) OR S-Methyltransferase, Methylated-DNA-Protein- Cysteine) OR O(6)-Methylguanine Methyltransferase) OR O(6)-Alkylguanine-DNA Alkyltransferase) OR O(6)-MeG-DNA Methyltransferase) OR O(6)-Methylguanine DNA Transmethylase) OR Guanine-O(6)-Alkyltransferase) OR O(6)-AGT) OR DNA Repair Methyltransferase II) OR DNA Repair Methyltransferase I) OR MGMT)) AND ((((((((Polymorphism, Genetic [MeSH Terms]) OR Polymorphisms, Genetic) OR Genetic Polymorphisms) OR Genetic Polymorphism) OR Polymorphism (Genetics)) OR Polymorphisms (Genetics)) OR Polymorphism) OR Polymorphisms)) AND ((((((((((((((((((Neoplasms [MeSH Terms]) OR Neoplasia) OR Neoplasias) OR Neoplasm) OR Tumors) OR Tumor) OR Cancer) OR Cancers) OR Malignant Neoplasms) OR Malignant Neoplasm) OR Neoplasm, Malignant) OR Neoplasms, Malignant) OR Malignancy) OR Malignancies) OR Benign Neoplasms) OR Neoplasms, Benign) OR Benign Neoplasm) OR Neoplasm, Benign).

Article screening strategy

We designed our inclusion and exclusion criteria according to Patient, Intervention, Comparison and Outcome and Study design (PICOS) principles. We ruled out duplicates and screened improper articles. Exclusion criteria were as follows: (P), non-cancer patients; (I), other variants, gene expression or methylation; (C), lack of study controls or P-value of Hardy–Weinberg equilibrium (HWE) <0.05; (O), lack of full genotype frequency data; (S), review, meta, poster, or meeting abstract. Eligible articles had to be designed as case–control studies, targetting the genetic relationship between MGMT rs12917 and cancer risk and containing the full genotype (C/C, C/T, T/T) frequencies in both cancer cases and negative controls.

Data extraction and quality assessment

After extracting usable data, we listed the basic information in tables. We assessed methodological quality via the Newcastle–Ottawa Scale (NOS) [15]. High-quality articles with NOS score > 5 were regarded as eligible and included in our statistical analysis.

Statistical analysis

We used STATA software version 12.0-SE (StataCorp, College Station, TX) to perform our analyses. We first assessed the inter-study heterogeneity using Cochran’s Q statistic and the I2 test. A P-value of Cochran’s Q statistic < 0.1 or I2 value > 50% was considered to show a high level of heterogeneity. We thus used the DerSimonian–Laird association test with a random-effects model. Otherwise, we used the Mantel–Haenszel association test with a fixed-effects model. The P-value of association test, summary odds ratio (OR), along with the corresponding 95% confidence interval (CI) could be obtained for the allele (T compared with C), homozygous (T/T compared with C/C), recessive (T/T compared with C/C+C/T), heterozygous (C/T compared with C/C), dominant (C/T+T/T compared with C/C), and carrier (T compared with C) models.

We performed subgroup analyses by race, cancer type, and control source. Additionally, we assessed possible publication bias by means of Begg’s and Egger’s tests and evaluated the robustness of the results through sensitivity analysis.

Eligible case–control studies

Figure 1 depicts the flowchart for the identification of eligible case–control studies. We initially obtained a total of 537 articles by searching four databases, including PubMed (245 articles), Cochrane Library (1 article), Embase (241 articles), and WANFANG (50 articles). We then excluded 233 duplicates plus another 258 articles based strictly on our screening strategy. Finally, we identified 46 full-text articles for inclusion [4,5,16–59]. After data extraction and quality evaluation, we enrolled a total of 54 case–control studies free of poor quality (all NOS score > 5) in our pooling analyses. The basic information and genotype frequency distribution are presented in Supplementary Table S1 and Table 1, respectively.

Flowchart for the identification of eligible case–control studies

Figure 1
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Flowchart for the identification of eligible case–control studies
Figure 1
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Flowchart for the identification of eligible case–control studies
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Table 1
Genotype and allele frequency of MGMT rs12917 in the enrolled case–control studies
AuthorsYearGenotype (case)Allele (case)Cancer type (case)Genotype (control)Allele (control)HWE (control)
C/CC/TT/TCTC/CC/TT/TCTχ2P
Agalliu et al. [16] 2010 949 269 32 2167 333 Prostate cancer1 916 298 23 2130 344 0.05 0.83 
106 35 6 247 47 Prostate cancer2 60 20 1 140 22 0.22 0.64 
Akbari et al. [17] 2009 142 53 1 337 55 Esophageal cancer 185 63 2 433 67 1.84 0.17 
Betti et al. [18] 2011 95 36 2 226 40 MPM3 179 64 8 422 80 0.59 0.44 
50 17 1 117 19 MPM4 32 12 0 76 12 1.10 0.29 
Bye et al. [19] 2011 225 111 10 561 131 Esophageal cancer1 300 155 14 755 183 1.28 0.26 
120 65 11 305 87 Esophageal cancer5 294 116 13 704 142 1.28 0.26 
Chae et al. [20] 2006 344 84 4 772 92 Lung cancer 341 81 10 763 101 3.65 0.06 
Chuang et al. [21] 2011 1105 307 43 2517 393 Head and neck cancer 2256 823 81 5335 985 0.33 0.57 
Doecke et al. [22] 2008 416 136 14 968 164 Esophageal cancer 1029 281 27 2339 335 2.25 0.13 
Felini et al. [23] 2007 289 84 6 662 96 Glioma 369 84 6 822 96 0.24 0.63 
Feng et al. [24] 2008 96 58 47 250 152 Esophageal cancer 87 85 29 259 143 1.20 0.27 
Gu et al. [25] 2009 152 60 2 364 64 Melanoma 168 43 1 379 45 1.01 0.31 
Hall et al. [26] 2007 548 193 38 1289 269 UADT 730 281 23 1741 327 0.44 0.51 
Han et al. [27] 20061 344 82 8 770 98 Endometrial cancer 822 242 21 1886 284 0.42 0.52 
Han et al. [28] 20062 964 279 33 2207 345 Breast cancer 1,306 382 26 2994 434 0.10 0.75 
Hu et al. [29] 2013 389 130 24 908 178 Glioma 405 84 6 894 96 0.48 0.49 
Hu et al. [4] 2007 418 77 5 913 87 Lung cancer 421 93 3 935 99 0.78 0.38 
Huang et al. [30] 2017 76 12 2 164 16 Glioma 75 14 1 164 16 0.14 0.71 
Huang et al. [31] 2007 372 156 11 900 178 Cervical cancer 592 198 10 1382 218 2.12 0.15 
Huang et al. [32] 2010 151 25 0 327 25 Oral cancer 89 21 0 199 21 1.22 0.27 
Huang et al. [33] 20051 190 82 8 462 98 Gastric cancer 279 99 9 657 117 0.00 0.95 
Huang et al. [34] 20052 386 117 11 889 139 Head and neck cancer 529 204 21 1262 246 0.06 0.80 
Inoue et al. [35] 2003 55 18 0 128 18 Primary brain cancer 160 55 9 375 73 2.24 0.13 
Kiczmer [36] 2018 49 11 9 109 29 Head and neck cancer 168 66 5 402 76 0.25 0.61 
Kietthubthew et al. [37] 2006 84 21 1 189 23 Oral cancer 130 33 1 293 35 0.50 0.48 
Li et al. [38] 2005 132 34 1 298 36 Bladder cancer 173 28 3 374 34 2.11 0.15 
Liu et al. [39] 20021 53 7 0 113 7 Lung cancer 89 11 0 189 11 0.34 0.56 
Liu et al. [40] 20022 21 3 0 45 3 Gynecologic tumor 89 11 0 189 11 0.34 0.56 
26 8 0 60 8 Digestive system cancer 89 11 0 189 11 0.34 0.56 
Liu et al. [41] 2006 82 16 2 180 20 Esophageal cancer 57 8 0 122 8 0.28 0.60 
Liu et al. [42] 2009 299 62 8 660 78 Glioma 267 89 7 623 103 0.02 0.89 
Loh et al. [43] 2011 146 37 5 329 47 Cancer 894 212 14 2000 240 0.13 0.72 
Lu et al. [44] 2006 142 45 4 329 53 Gastric cancer 186 59 6 431 71 0.26 0.61 
McKean-Cowdin et al. [45] 2009 774 204 20 1752 244 Glioblastoma 1,480 453 35 3413 523 0.00 0.96 
O’Mara et al. [46] 2011 889 261 23 2039 307 Endometrial cancer6 810 270 19 1890 308 0.42 0.52 
278 108 11 664 130 Endometrial cancer7 296 103 7 695 117 0.33 0.57 
Palli et al. [47] 2010 210 77 4 497 85 Gastric cancer 395 131 11 921 153 0.00 0.97 
Rajaraman et al. [48] 2010 265 77 9 607 95 Glioma 348 117 12 813 141 0.33 0.57 
102 23 4 227 31 Meningioma 348 117 12 813 141 0.33 0.57 
52 12 2 116 16 Acoustic neuroma 348 117 12 813 141 0.33 0.57 
Ritchey et al. [49] 2005 123 36 2 282 40 Prostate cancer 213 32 1 458 34 0.03 0.86 
Shah et al. [50] 2012 64 26 2 154 30 Esophageal cancer 57 20 0 134 20 1.72 0.19 
Shen et al. [51] 2005 778 265 21 1821 307 Breast cancer 824 263 20 1911 303 0.03 0.85 
Shen et al. [52] 2007 432 112 11 976 134 NHL 373 110 12 856 134 1.27 0.26 
Shi et al. [53] 2011 253 47 3 553 53 AML 459 91 4 1009 99 0.05 0.83 
Stern et al. [54] 2007 251 40 1 542 42 Colorectal cancer 959 194 13 2112 220 0.81 0.37 
Tranah et al. [55] 2006 147 33 6 327 45 Colorectal cancer8 1,634 471 32 3739 535 0.09 0.77 
204 47 6 455 59 Colorectal cancer9 330 93 6 753 105 0.04 0.85 
Wang et al. [5] 2006 832 259 30 1923 319 Lung cancer 872 272 19 2016 310 0.18 0.67 
Yang et al. [56] 2009 33 14 1 80 16 NHL 289 58 5 636 68 1.10 0.29 
Zhang et al. [57] 2008 352 53 1 757 55 Biliary track cancer 631 144 7 1406 158 0.15 0.70 
Zhang et al. [58] 2010 563 151 7 1277 165 Head and neck cancer 933 284 17 2150 318 0.78 0.38 
Zienolddiny et al. [59] 2006 189 102 13 480 128 Lung cancer 247 106 10 600 126 0.12 0.73 
AuthorsYearGenotype (case)Allele (case)Cancer type (case)Genotype (control)Allele (control)HWE (control)
C/CC/TT/TCTC/CC/TT/TCTχ2P
Agalliu et al. [16] 2010 949 269 32 2167 333 Prostate cancer1 916 298 23 2130 344 0.05 0.83 
106 35 6 247 47 Prostate cancer2 60 20 1 140 22 0.22 0.64 
Akbari et al. [17] 2009 142 53 1 337 55 Esophageal cancer 185 63 2 433 67 1.84 0.17 
Betti et al. [18] 2011 95 36 2 226 40 MPM3 179 64 8 422 80 0.59 0.44 
50 17 1 117 19 MPM4 32 12 0 76 12 1.10 0.29 
Bye et al. [19] 2011 225 111 10 561 131 Esophageal cancer1 300 155 14 755 183 1.28 0.26 
120 65 11 305 87 Esophageal cancer5 294 116 13 704 142 1.28 0.26 
Chae et al. [20] 2006 344 84 4 772 92 Lung cancer 341 81 10 763 101 3.65 0.06 
Chuang et al. [21] 2011 1105 307 43 2517 393 Head and neck cancer 2256 823 81 5335 985 0.33 0.57 
Doecke et al. [22] 2008 416 136 14 968 164 Esophageal cancer 1029 281 27 2339 335 2.25 0.13 
Felini et al. [23] 2007 289 84 6 662 96 Glioma 369 84 6 822 96 0.24 0.63 
Feng et al. [24] 2008 96 58 47 250 152 Esophageal cancer 87 85 29 259 143 1.20 0.27 
Gu et al. [25] 2009 152 60 2 364 64 Melanoma 168 43 1 379 45 1.01 0.31 
Hall et al. [26] 2007 548 193 38 1289 269 UADT 730 281 23 1741 327 0.44 0.51 
Han et al. [27] 20061 344 82 8 770 98 Endometrial cancer 822 242 21 1886 284 0.42 0.52 
Han et al. [28] 20062 964 279 33 2207 345 Breast cancer 1,306 382 26 2994 434 0.10 0.75 
Hu et al. [29] 2013 389 130 24 908 178 Glioma 405 84 6 894 96 0.48 0.49 
Hu et al. [4] 2007 418 77 5 913 87 Lung cancer 421 93 3 935 99 0.78 0.38 
Huang et al. [30] 2017 76 12 2 164 16 Glioma 75 14 1 164 16 0.14 0.71 
Huang et al. [31] 2007 372 156 11 900 178 Cervical cancer 592 198 10 1382 218 2.12 0.15 
Huang et al. [32] 2010 151 25 0 327 25 Oral cancer 89 21 0 199 21 1.22 0.27 
Huang et al. [33] 20051 190 82 8 462 98 Gastric cancer 279 99 9 657 117 0.00 0.95 
Huang et al. [34] 20052 386 117 11 889 139 Head and neck cancer 529 204 21 1262 246 0.06 0.80 
Inoue et al. [35] 2003 55 18 0 128 18 Primary brain cancer 160 55 9 375 73 2.24 0.13 
Kiczmer [36] 2018 49 11 9 109 29 Head and neck cancer 168 66 5 402 76 0.25 0.61 
Kietthubthew et al. [37] 2006 84 21 1 189 23 Oral cancer 130 33 1 293 35 0.50 0.48 
Li et al. [38] 2005 132 34 1 298 36 Bladder cancer 173 28 3 374 34 2.11 0.15 
Liu et al. [39] 20021 53 7 0 113 7 Lung cancer 89 11 0 189 11 0.34 0.56 
Liu et al. [40] 20022 21 3 0 45 3 Gynecologic tumor 89 11 0 189 11 0.34 0.56 
26 8 0 60 8 Digestive system cancer 89 11 0 189 11 0.34 0.56 
Liu et al. [41] 2006 82 16 2 180 20 Esophageal cancer 57 8 0 122 8 0.28 0.60 
Liu et al. [42] 2009 299 62 8 660 78 Glioma 267 89 7 623 103 0.02 0.89 
Loh et al. [43] 2011 146 37 5 329 47 Cancer 894 212 14 2000 240 0.13 0.72 
Lu et al. [44] 2006 142 45 4 329 53 Gastric cancer 186 59 6 431 71 0.26 0.61 
McKean-Cowdin et al. [45] 2009 774 204 20 1752 244 Glioblastoma 1,480 453 35 3413 523 0.00 0.96 
O’Mara et al. [46] 2011 889 261 23 2039 307 Endometrial cancer6 810 270 19 1890 308 0.42 0.52 
278 108 11 664 130 Endometrial cancer7 296 103 7 695 117 0.33 0.57 
Palli et al. [47] 2010 210 77 4 497 85 Gastric cancer 395 131 11 921 153 0.00 0.97 
Rajaraman et al. [48] 2010 265 77 9 607 95 Glioma 348 117 12 813 141 0.33 0.57 
102 23 4 227 31 Meningioma 348 117 12 813 141 0.33 0.57 
52 12 2 116 16 Acoustic neuroma 348 117 12 813 141 0.33 0.57 
Ritchey et al. [49] 2005 123 36 2 282 40 Prostate cancer 213 32 1 458 34 0.03 0.86 
Shah et al. [50] 2012 64 26 2 154 30 Esophageal cancer 57 20 0 134 20 1.72 0.19 
Shen et al. [51] 2005 778 265 21 1821 307 Breast cancer 824 263 20 1911 303 0.03 0.85 
Shen et al. [52] 2007 432 112 11 976 134 NHL 373 110 12 856 134 1.27 0.26 
Shi et al. [53] 2011 253 47 3 553 53 AML 459 91 4 1009 99 0.05 0.83 
Stern et al. [54] 2007 251 40 1 542 42 Colorectal cancer 959 194 13 2112 220 0.81 0.37 
Tranah et al. [55] 2006 147 33 6 327 45 Colorectal cancer8 1,634 471 32 3739 535 0.09 0.77 
204 47 6 455 59 Colorectal cancer9 330 93 6 753 105 0.04 0.85 
Wang et al. [5] 2006 832 259 30 1923 319 Lung cancer 872 272 19 2016 310 0.18 0.67 
Yang et al. [56] 2009 33 14 1 80 16 NHL 289 58 5 636 68 1.10 0.29 
Zhang et al. [57] 2008 352 53 1 757 55 Biliary track cancer 631 144 7 1406 158 0.15 0.70 
Zhang et al. [58] 2010 563 151 7 1277 165 Head and neck cancer 933 284 17 2150 318 0.78 0.38 
Zienolddiny et al. [59] 2006 189 102 13 480 128 Lung cancer 247 106 10 600 126 0.12 0.73 

Abbreviations: AML, acute myeloid leukemia; MPM, malignant mesothelioma; NHL, non-Hodgkin’s lymphoma; UADT, upper aerodigestive tract.

1Data from Caucasian population. 2Data from African population. 3With population-based control. 4With hospital-based control. 5Data from mixed population. 6Data from Australia. 7Data from Poland. 8With controls from Nurses’ Health Study (NHS). 9With controls from Physicians’ Health Study (PHS) cohorts

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Meta-analysis data

First, we studied the association between the MGMT rs12917 polymorphism and cancer risk via an overall meta-analysis. As shown in Table 2, we included a total of 54 case–control studies with 21010 cases and 34018 controls under the genetic models of allele T compared with C, C/T compared with C/C, C/T+T/T compared with C/C, and carrier T compared with C; meanwhile, we included 50 studies with 20716 cases and 33608 controls under the models of T/T compared with C/C and T/T compared with C/C+C/T. For the homozygous, recessive and carrier genetic models, we performed a Mantel–Haenszel association test with a fixed-effects model, and we observed no high degree of heterogeneity (Table 2; all P-values of heterogeneity > 0.1; I2 < 50%). For other models (all P-values of heterogeneity <0.001), we performed a DerSimonian–Laird association test with a random-effects model. Pooling data (Table 2) indicated an increased risk of cancer in cases compared with controls for the T/T compared with C/C (P-value of association test <0.001; OR = 1.29) and T/T compared with C/C+C/T (P<0.001; OR = 1.32) genetic models. Nevertheless, we failed to detect any statistical difference between cancer cases and negative controls under other genetic models (Table 2; all P>0.05). Forest plot data are shown in Figure 2 and Supplementary Figures S1–S5; they revealed that the T/T genotype of the MGMT rs12917 polymorphism was likely to be associated with an increased susceptibility to cancer.

Forest plot of meta-analysis (T/T compared with C/C model)

Figure 2
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Forest plot of meta-analysis (T/T compared with C/C model)
Figure 2
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Forest plot of meta-analysis (T/T compared with C/C model)
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Table 2
Meta-analysis of the association between MGMT rs12917 and cancer susceptibility
ModelsSample sizeHeterogeneityAssociation
StudyCaseControlI2PFixed/randomPOR (95% CI)
Allele T compared with C 54 21010 34018 50.1% <0.001 Random 0.354 - 
T/T compared with C/C 50 20716 33608 4.5% 0.384 Fixed <0.001 1.29 (1.14–1.46) 
T/T compared with C/C+C/T 50 20716 33608 3.2% 0.410 Fixed <0.001 1.32 (1.17–1.49) 
C/T compared with C/C 54 21010 34018 46.1% <0.001 Random 0.442 - 
C/T+T/T compared with C/C 54 21010 34018 47.7% <0.001 Random 0.976 - 
Carrier T compared with C 54 21010 34018 20.0% 0.104 Fixed 0.642 - 
ModelsSample sizeHeterogeneityAssociation
StudyCaseControlI2PFixed/randomPOR (95% CI)
Allele T compared with C 54 21010 34018 50.1% <0.001 Random 0.354 - 
T/T compared with C/C 50 20716 33608 4.5% 0.384 Fixed <0.001 1.29 (1.14–1.46) 
T/T compared with C/C+C/T 50 20716 33608 3.2% 0.410 Fixed <0.001 1.32 (1.17–1.49) 
C/T compared with C/C 54 21010 34018 46.1% <0.001 Random 0.442 - 
C/T+T/T compared with C/C 54 21010 34018 47.7% <0.001 Random 0.976 - 
Carrier T compared with C 54 21010 34018 20.0% 0.104 Fixed 0.642 - 

-, OR (95% CI) data were not provided, when P-value of association >0.05.

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Subgroup analysis data

Next, we carried out four subgroup analyses by race, cancer type, and control source. For the T/T compared with C/C model (Table 3), the association test data showed an increased cancer risk in the subgroups ‘Caucasian’ (P<0.001; OR = 1.35), ‘glioma’ (P=0.022; OR = 1.70), ‘population-based control (PB)’ (P<0.001; OR = 1.32) and ‘hospital-based control (HB)’ (P<0.030; OR = 1.39). Figure 3 and Supplementary Figures S6–S7 present the forest plot data.

Forest plot of subgroup analysis by race (T/T compared with C/C model)

Figure 3
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Forest plot of subgroup analysis by race (T/T compared with C/C model)
Figure 3
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Forest plot of subgroup analysis by race (T/T compared with C/C model)
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Table 3
Data of subgroup analysis under T/T compared with C/C model
FactorSubgroupSample sizeHeterogeneityAssociation
StudyCaseControlI2PPOR (95% CI)
Race Caucasian 27 13158 20678 0.0% 0.573 <0.001 1.35 (1.15, 1.58) 
 African 3 796 1104 0.0% 0.538 0.560 - 
 Asian 16 4031 6152 28.6% 0.136 0.088 - 
Cancer type Urinary system cancer 4 1725 1768 0.0% 0.526 0.174 - 
 Esophageal cancer 8 2131 3907 0.0% 0.781 0.069 - 
 Lung cancer 4 2357 2475 40.7% 0.167 0.155 - 
 Head and neck cancer 14 5863 10581 39.5% 0.064 0.138 - 
 Gastric cancer 3 762 1175 0.0% 0.692 0.891 - 
 Blood cancer 3 906 1401 0.0% 0.702 0.882 - 
 Colorectal cancer 3 735 3732 38.5% 0.197 0.416 - 
 Brain cancer 9 2998 5030 17.4% 0.288 0.106 - 
 Glioma 5 1735 1884 37.9% 0.168 0.022 1.70 (1.08, 2.68) 
Control source PB 39 16526 26488 6.3% 0.358 <0.001 1.32 (1.14, 1.52) 
 HB 8 2482 4148 3.2% 0.405 0.030 1.39 (1.03, 1.86) 
FactorSubgroupSample sizeHeterogeneityAssociation
StudyCaseControlI2PPOR (95% CI)
Race Caucasian 27 13158 20678 0.0% 0.573 <0.001 1.35 (1.15, 1.58) 
 African 3 796 1104 0.0% 0.538 0.560 - 
 Asian 16 4031 6152 28.6% 0.136 0.088 - 
Cancer type Urinary system cancer 4 1725 1768 0.0% 0.526 0.174 - 
 Esophageal cancer 8 2131 3907 0.0% 0.781 0.069 - 
 Lung cancer 4 2357 2475 40.7% 0.167 0.155 - 
 Head and neck cancer 14 5863 10581 39.5% 0.064 0.138 - 
 Gastric cancer 3 762 1175 0.0% 0.692 0.891 - 
 Blood cancer 3 906 1401 0.0% 0.702 0.882 - 
 Colorectal cancer 3 735 3732 38.5% 0.197 0.416 - 
 Brain cancer 9 2998 5030 17.4% 0.288 0.106 - 
 Glioma 5 1735 1884 37.9% 0.168 0.022 1.70 (1.08, 2.68) 
Control source PB 39 16526 26488 6.3% 0.358 <0.001 1.32 (1.14, 1.52) 
 HB 8 2482 4148 3.2% 0.405 0.030 1.39 (1.03, 1.86) 

-, OR (95% CI) data were not provided, when P-value of association > 0.05.

View Large

For the T/T compared with C/C+C/T model (Table 4), we also observed positive correlations in the subgroups ‘Caucasian’ (P<0.001; OR = 1.37), ‘Asian’ (P=0.036; OR = 1.37), ‘glioma’ (P=0.026; OR = 1.68), ‘PB’ (P<0.001; OR = 1.32), and ‘HB’ (P=0.004; OR = 1.52). Supplementary Figures S8–S10 present the forest plot data.

Table 4
Data of subgroup analysis under T/T compared with C/C+C/T model
FactorSubgroupSample sizeHeterogeneityAssociation
StudyCaseControlI2PPOR (95% CI)
Race Caucasian 27 13158 20678 0.0% 0.528 <0.001 1.37 (1.17, 1.60) 
 African 3 796 1104 0.0% 0.542 0.535 - 
 Asian 16 4031 6152 27.2% 0.150 0.036 1.37 (1.02, 1.83) 
Cancer type Urinary system cancer 4 1725 1768 0.0% 0.527 0.152 - 
 Esophageal cancer 8 2131 3907 0.0% 0.725 0.021 - 
 Lung cancer 4 2357 2475 40.0% 0.467 0.174 - 
 Head and neck cancer 14 5863 10581 37.5% 0.077 0.064 - 
 Gastric cancer 3 762 1175 0.0% 0.718 0.815 - 
 Blood cancer 3 906 1401 0.0% 0.769 0.901 - 
 Colorectal cancer 3 735 3732 39.6% 0.191 0.344 - 
 Brain cancer 9 2998 5030 3.0% 0.410 0.088 - 
 Glioma 5 1735 1884 23.7% 0.263 0.026 1.68 (1.07, 2.65) 
Control source PB 39 16526 26488 2.5% 0.426 <0.001 1.32 (1.15, 1.52) 
 HB 8 2482 4148 11.0% 0.344 0.004 1.52 (1.14, 2.03) 
FactorSubgroupSample sizeHeterogeneityAssociation
StudyCaseControlI2PPOR (95% CI)
Race Caucasian 27 13158 20678 0.0% 0.528 <0.001 1.37 (1.17, 1.60) 
 African 3 796 1104 0.0% 0.542 0.535 - 
 Asian 16 4031 6152 27.2% 0.150 0.036 1.37 (1.02, 1.83) 
Cancer type Urinary system cancer 4 1725 1768 0.0% 0.527 0.152 - 
 Esophageal cancer 8 2131 3907 0.0% 0.725 0.021 - 
 Lung cancer 4 2357 2475 40.0% 0.467 0.174 - 
 Head and neck cancer 14 5863 10581 37.5% 0.077 0.064 - 
 Gastric cancer 3 762 1175 0.0% 0.718 0.815 - 
 Blood cancer 3 906 1401 0.0% 0.769 0.901 - 
 Colorectal cancer 3 735 3732 39.6% 0.191 0.344 - 
 Brain cancer 9 2998 5030 3.0% 0.410 0.088 - 
 Glioma 5 1735 1884 23.7% 0.263 0.026 1.68 (1.07, 2.65) 
Control source PB 39 16526 26488 2.5% 0.426 <0.001 1.32 (1.15, 1.52) 
 HB 8 2482 4148 11.0% 0.344 0.004 1.52 (1.14, 2.03) 

-, OR (95% CI) data was not provided, when P-value of association > 0.05.

View Large

We did not detect positive results for the other genetic models (Supplementary Tables S2–S5; P<0.05) except for the subgroups ‘colorectal cancer’ (Supplementary Table S3; P=0.041; OR = 0.79), ‘HB’ (Supplementary Table S3; P=0.027; OR = 0.86) under the C/T compared with C/C model; and the subgroup ‘head and neck cancer’ (Supplementary Table S5; P=0.020; OR = 0.92) under the carrier T compared with C model. Thus, the T/T genotype of MGMT rs12917 may have been associated with an increased risk of cancer in cases, especially the glioma cases, in the Caucasian population.

Publication bias and sensitivity analysis

Begg’s and Egger’s tests indicated that results were free of possible publication bias (Supplementary Table S6; P>0.05 for Begg’s test, >0.05 for Egger’s test). A Begg’s funnel plot with pseudo–95% confidence limits under the T/T compared with C/C model is shown in Figure 4. In addition, we observed the same stable results in our subsequent sensitivity analysis; data from this analysis under the homozygous model (Figure 5) are presented as an example.

Begg’s funnel plot with pseudo-95% confidence limits (T/T compared with C/C model)

Figure 4
Figure 4
View largeDownload slide
Begg’s funnel plot with pseudo-95% confidence limits (T/T compared with C/C model)
Figure 4
Figure 4
View largeDownload slide
Begg’s funnel plot with pseudo-95% confidence limits (T/T compared with C/C model)
Close modal

Sensitivity analysis result (T/T compared with C/C model)

Figure 5
Figure 5
View largeDownload slide
Sensitivity analysis result (T/T compared with C/C model)
Figure 5
Figure 5
View largeDownload slide
Sensitivity analysis result (T/T compared with C/C model)
Close modal

We observed conflicting conclusions about the genetic role of MGMT rs12917 polymorphism in its susceptibility to different cancers. For instance, the polymorphism seems to be associated with the risk of esophageal cancer in the Chinese population [41], but not in the Kashmiri population [50]. This merits a quantitative synthesis via the meta-analytic approach. Although there were already three meta-analyses of the MGMT rs12917 polymorphism and its role in the overall risk for cancer [12–14], expanding the sample size and employing a distinct analysis strategy led to better results in our updated pooling analysis.

We did our best to gather candidate articles from four online databases. After screening them based on strict inclusion and exclusion criteria, we enrolled only the case–control studies that were of high quality and those that followed HWE. We ultimately included a total of 46 articles in our updated meta-analysis. After data extraction, we enrolled 54 case–control studies with 21010 cases and 34018 controls in the meta-analysis. We used the carrier, allele, homozygous, recessive, heterozygous, and dominant genetic models, and also confirmed the stability of the statistical results via sensitivity analysis.

In 2010, Zhong et al. [12] performed the first meta-analysis on this topic, reviewing 28 case–control studies from 26 articles [4,5,20,22,23,26–28,31,33–35,37,38,42,45,49,51,52,54,55,59–63]. Another 24 case–control studies [16–19,21,24,25,29,30,32,36,39–41,43,44,46–48,50,53,56–58] were included in our study. We excluded three studies not in-line with the HWE principle [61–63] and one that focussed only on colorectal adenomatous or hyperplastic polyps but not on colorectal cancer [60]. In 2013, Du et al. [14] enrolled 41 case–control studies with 16643 cancer cases and 26720 negative controls from 37 articles [5,16–20,22–24,26–28,31–34,37–41,43,44,46,47,49–59,64] in a meta-analysis. We excluded one of these studies [64] from our meta-analysis because it did not meet the requirement of full genotype frequency in both case and control groups. Finally, we enrolled another ten case–control studies [4,21,25,29,30,35,36,42,45,48]. In addition, when compared with another meta-analysis of Liu et al. (2013) [13], which consisted of 44 case–control studies from 37 articles [4,5,16,17,19,20,22,23,25–27,31–33,35,37,38,42,43,45–47,49,51,52,54–63,65,66], we excluded four studies that were not in HWE [61–63,66], one that did not analyze colorectal cancer [60], and one that included other genetic variants [65]. We also added another 15 new case–control studies [18,21,24,28–30,34,36,39–41,44,48,50,53] for the analysis.

Our updated pooling analysis data demonstrated that cases had an overall enhanced risk for cancer when compared with negative controls under the T/T compared with C/C and T/T compared with C/C+C/T genetic models, especially in the European-descended population, which is partly consistent with the data of previous analyses [12–14]. Moreover, we observed that the MGMT rs12917 polymorphism is likely to be associated with the susceptibility to glioma, which is partly in-line with the two studies on the association between DNA repair gene polymorphisms and glioma risk [67,68]. Nevertheless, owing to the limitation of sample size, the previous three meta-analyses of the overall risk for cancer did not conduct subgroup analyses of ‘glioma’ [12–14].

Some of the limitations to our meta-analysis are as follows:

  1. Although the sample sizes enrolled were quite large (21010 cases and 34018 controls), genotype data were very limited in many subgroup analyses. For instance, we used only three case–control studies in our analyses of the subgroups for gastric [33,44,47], blood [52,53,56], and colorectal [54,55] cancers. Even for the subgroup analysis of ‘glioma’, with positive correlations under the T/T compared with C/C and T/T compared with C/C+C/T models, only five case–control studies [23,29,30,42,48] were included.

  2. We did not investigate the genetic effects of the MGMT rs12917 polymorphism in combination with other variants, such as rs2308321 of MGMT, rs25487 of X-ray cross-complementing group 1 (XRCC1), and rs13181 of xeroderma pigmentosum complementation group D (XPD), in certain specific cancers.

  3. We extracted certain demographic information such as the mean age at diagnosis and the sex of subject, but not other confounding factors such as lifestyle and clinical features. Moreover, we did not perform the relevant stratified meta-analyses due to lack of sufficient usable data.

  4. We detected significant heterogeneity amongst studies under the allele T compared with C, C/T compared with C/C, C/T+T/T compared with C/C, and carrier T compared with C genetic models. Complicating factors such as race and cancer type may be sources of inter-study heterogeneity. For instance, we detected decreased levels of heterogeneity in the ‘Caucasian’ and ‘esophageal cancer’ subgroups. Although we observed a positive conclusion in the ‘glioma’ subgroup, we failed to detect reduced inter-study heterogeneity. Only five case–control studies [23,29,30,42,48] were enrolled.

  5. There may be other undetected or unpublished articles containing potential eligible case–controls in other geographical locations or languages; in other words, our study may suffer from selection bias.

  6. Last but most important, our meta-analysis found a positive conclusion between MGMT rs12917 and the risk of cancer in general for the T/T compared with C/C and T/T compared with C/C+C/T models. Considering the distinct etiopathogenesis or pathogenesis of different kinds of cancers, more studies of large-scale populations of different ethnicities are required for a more scientific elucidation of MGMT rs12917’s functional role in each particular cancer type.

To sum up, our updated pooling analysis offered additional evidence that MGMT rs12917 polymorphism is likely to be associated with an enhanced susceptibility to cancer overall, especially glioma, in the Caucasian population.

Z.S. and H.W. conceived and designed the study. Z.S. and M.K. were responsible for the data extraction and statistical analysis. Z.S. wrote the manuscript and H.W. revised the manuscript.

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

This work was supported by grants from the key program of Tianjin health Bureau ( Grant NO.14KG104).

HB

hospital-based control

HWE

Hardy–Weinberg equilibrium

MeSH

Medical Subject Heading

MGMT

O-6-methylguanine-DNA methyltransferase

NOS

Newcastle–Ottawa scale

OR

odds ratio

PB

population-based control

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© 2018 The Author(s).
2018
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data

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