The association between methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and colorectal cancer (CRC) susceptibility has been researched in numerous studies. However, the results of these studies were controversial. Therefore, the objective of this meta-analysis was to offer a more convincible conclusion about such association with more included studies. Eligible studies published till May 1, 2017 were searched from PubMed, Embase, Web of Science, and CNKI database about such association. Pooled odds ratios (ORs) together with 95% confidence intervals (CIs) were calculated to evaluate such association. And the Begg’s funnel plot and Egger’s test were applied to assess the publication bias. This meta-analysis contained 37049 cases and 52444 controls from 87 publications with 91 eligible case–control studies. Because of lack of data for a particular genotype in several studies, all the included studies were analysed barely in the dominant model. Originally, there was no association between MTHFR C677T polymorphism and CRC susceptibility (OR =0.99, 95% CI =0.94–1.05). After excluding 13 studies according to their heterogeneity and publication bias, rs1801133 polymorphism was found to reduce the risks of CRC significantly (OR =0.96, 95% CI =0.94–0.99). In the subgroup analysis of ethnicity, there was a significant association in Asians (OR =0.94, 95% CI =0.89–1.00). Furthermore, when stratified by the source of controls and genotyping methods, the positive results were observed in population-based control group (OR =0.97, 95% CI =0.93–1.00) and PCR-restriction fragment length polymorphism (PCR-RFLP) method (OR =0.95, 95% CI =0.91–0.99. The results of the meta-analysis suggested that MTHFR C677T polymorphism was associated with CRC susceptibility, especially in Asian population.

Introduction

Colorectal cancer (CRC) is a critical public health problem, which is the third most commonly diagnosed cancer and the third common cause of cancer deaths in both males and females. There were 134490 new CRC cases and 49190 mortalities by estimation in the United States in 2016 [1]. The colorectal carcinogenesis is a complex multistep progress (a benign adenomatous polyp – an advanced adenoma with high-grade dysplasia – an invasive cancer) with altered expression of oncogenes, tumor suppressor genes and DNA repair genes [2]. However, the etiology of CRC is still unclear. It is known to all that CRC is a multifactorial and multigenic disease, and is influenced by environment conditions, diet habits, genetic mutations, and Escherichia coli infection [3,4]. With increasing numbers of studies, more gene polymorphisms were found to contribute to CRC [5]. These single nucleotide polymorphisms (SNPs) can be used as makers for improving cancer diagnosis and determination of treatment plans [6].

As a key enzyme and an important regulator for the metabolism of folate/vitamin B9, methylenetetrahydrofolate reductase (MTHFR) catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate [7]. Simultaneously, the 5-methyltetrahydrofolate is the main circulatory form of folate in the body and provides a methyl group to convert the amino acid homocysteine into methionine, which is the precursor of S-adenosylmethionine (SAM). SAM is the major methyl donor in the cell and takes part in DNA methylation [8]. Therefore, MTHFR not only plays a role in making proteins and other important compounds, but also is an important factor in DNA methylation, synthesis, and repair [9]. The enzyme is encoded by the MTHFR gene located on the short arm of chromosome 1-1p36.3 [10]. Previously, several mutations of MTHFR gene have been found and MTHFR C677T (rs1801133) is the most common type amongst them. MTHFR C677T represents an alanine-to-valine substitution at nucleotide position 677 in exon 4 resulting in thermolability and concurrent decreased activity of the enzyme [11,12]. MTHFR gene mutations lead to MTHFR enzyme dificiency, low plasma folate levels, hyperhomocysteinemia [13,14] and certain diseases such as cardiovascular disease, pregnancy complications, neural defect, and several cancers including CRC [1521]. With a growing number of studies conducted to explore such association, we hypothesized that rs1801133 was likely to relate to colorectal carcinogenesis.

Many researchers have carried out a large number of studies to examine the potential association between MTHFR C677T polymorphism and CRC susceptibility. But, the results are still inconclusive so far. Thus, the aim of this meta-analysis including all available case–control studies was to investigate a more reliable association.

Materials and methods

We searched several databases including PubMed, Embase, Web of Science, and CNKI database for published studies about exploring the association between MTHFR C677T polymorphism and CRC susceptibility till May 1, 2017. The search strategy included listed key words: ‘methylenetetrahydrofolate reductase’, ‘MTHFR polymorphism’, ‘C677T’, ‘rs1801133’, and ‘risk or susceptibility’ and ‘colorectal or colon or rectal cancer’. Furthermore, we manually searched the reference lists of clinical trials and former meta-analyses for more relevant studies. When duplicate data appeared in different publications, this meta-analysis only adopted the most recent study or the study with the most complete information. The meta-analysis was on the basis of the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) [22]. The eligible studies needed to accord with the following inclusion criteria: (i) case–control studies; (ii) the language was not restricted to English; (iii) investigating the association between MTHFR C677T polymorphism and CRC susceptibility; (iv) offering enough raw data to calculate odds ratio (OR) with 95% confidence interval (CI). Additionally, exclusion criteria were as follows: (i) non-case–control studies; (ii) lack of sufficient data for calculating genotype frequency; (iii) case–control studies about examining the relationship between MTHFR C677T polymorphism and colorectal adenoma; (iv) duplicated publications.

Data extraction

In order to guarantee the accuracy of extracted information, two authors individually reviewed each publication and extracted useful data on the basis of the inclusion criteria listed above. When disagreements arose in the course of data extraction, discussion was carried out with other authors until the agreements were reached. The following information were extracted from each study to accomplish a standardized sheet: first author’s name, year of publication, ethnicity of population, source of controls (hospital based or population based), genotyping method, sample size of cases and controls, genotype frequency of rs1801133 in cases and controls, and the results of the Hardy–Weinberg equilibrium (HWE) test.

Statistical analysis

The relationship between MTHFR C677T polymorphism and CRC susceptibility was analyzed by using five models including the dominant model (CT + TT compared with CC), the recessive model (TT compared with CT + CC), the homozygous model (TT compared with CC), the heterozygous model (CT compared with CC), and the allele model (T compared with C). The goodness-of-fit χ2 test was conducted to evaluate the HWE in control groups and P<0.05 was regarded as significant disequilibrium [23]. Stratified analysis were performed by ethnicity, source of controls, and genotyping method. Besides, the pooled OR together with 95% CI were measured to bring out the strength of such association. The fixed effects model (Mantel–Haenszel method) and the random effects model (Dersimonian–Laird method) were selected to use based on heterogeneity in the meta-analysis. If there was no or little heterogeneity, the fixed effects model was used; otherwise, the random effects model was used. Due to only particular genotypes extracted in several studies, the dominant model analysis were carried out for all the included studies [84]. Galbraith graph was performed to explore the impossible cause of heterogeneity [24]. A sensitivity analysis was conducted to assess the stability of the results. Begg’s funnel plot was performed for potential publication bias and Egger’s linear regression test was executed to assess funnel plot asymmetry statistically. If P<0.05, publication bias existed [25]. All statistical data analyses were carried out by using Stata software (version 12.0, StataCorp LP, College Station, TX, U.S.A.).

Results

Characteristics of the studies

According to PRISMA-P, this meta-analysis contained 37049 cases and 52444 controls that were combined from 87 publications with 91 eligible case–control studies to examine the relationship between rs1801133 polymorphism and CRC risks [26112]. The literature retrieval and selection process are shown in the flowchart in Figure 1. Detailed information of each study were listed in Table 1. The distribution of genotypes in controls was consistent with HWE except 15 studies [3335,37,39,47,63,71,76,80,87,88,106,110,111]. In these studies, four ethnicities of population were included: Asian, Caucasian, African, and mixed ethnic group. Nine genotyping methods were applied: PCR-restriction fragment length polymorphism (PCR-RFLP), real-time PCR (RT-PCR), PCR-single strand conformation polymorphism (PCR-SSCP), methylation-specific PCR (MS-PCR), mutagenically separated PCR (MSP), MALDI-TOF-MS, Taqman, MassARRAY, and Sequenom. Depending on different sources of control, population-based and hospital-based control groups were distinguished in all the included studies.

Flowchart of literature search and selection process

Figure 1
Flowchart of literature search and selection process
Figure 1
Flowchart of literature search and selection process
Table 1
Characteristics of individual studies included in the meta-analysis
MTHFR rs1801133Case (n)Control (n)
YearSurname (References)EthnicitySOCGenotypingCaseControlCCCTTTCCCTTTHWE
2016 Haerian [26Asian HB Taqman 1123 1298 607 421 95 667 523 108 
2015 Kim [27Asian PB PCR-RFLP 477 514 159 248 70 172 265 77 
2014 Rai [28Asian PB PCR-RFLP 155 294 137 17 261 31 
2014 Ozen [29Caucasian PB RT-PCR 86 212 36 32 18 207 5 0 Y 
2013 Ashmore [30Caucasian PB RT-PCR 625 603 241 309 75 263 259 81 Y 
2013 Delgado- Plasencia [31Caucasian HB PCR-RFLP 50 103 32 16 2 44 50 9 Y 
2013 Yousef [32Asian PB PCR-RFLP 128 116 79 45 59 45 12 
2012 Lee [33Caucasian PB Taqman 531 1004 250 229 52 464 391 149 
2012 Promthet [34Asian HB PCR-RFLP 112 242 93 18 185 49 
2012 Kim [35Asian HB Taqman 787 656 265 393 129 205 289 162 
2012 Yin [36Asian HB RT-PCR 370 370 124 167 79 139 178 53 
2011 Sameer [37Asian PB PCR-RFLP 86 160 59 18 121 27 12 
2011 Vossen [38Caucasian PB Taqman 1762 1811 737 823 202 795 807 209 
2011 Kang [39Asian PB PCR-RFLP 255 448 87 134 34 145 238 65 
2011 Zhu [40Asian PB PCR-RFLP 86 100 29 42 15 49 41 10 Y 
2011 Pardini [41Caucasian HB PCR-RFLP 666 1376 317 307 42 613 627 136 
2011 Kim [42Asian HB MSP 67 53 30 30 15 21 17 
2011 Prasad [43Asian PB PCR-RFLP 110 241 97 12 1 228 12 1 Y 
2011 Li [44Asian PB PCR-RFLP 137 145 68 54 15 55 64 26 
2011 Jokic [45Caucasian PB Taqman 300 300 139 130 31 142 130 28 
2011 Guimaracs(a) [46Caucasian HB PCR-RFLP 101 188 42 44 15 92 79 17 
2011 Guimaracs(b) [46African HB PCR-RFLP 12 188 92 79 17 
2010 Komlosi [47Caucasian PB PCR-RFLP 951 939 398 427 126 442 380 117 N 
2010 Karpinski [48Caucasian HB MSP 186 140 74 97 15 71 55 14 Y 
2010 Cui [49Asian PB PCR-RFLP 1829 1700 622 923 284 540 863 297 
2010 Eussen [50Caucasian PB MALDI-TOF-MS 1329 2366 567 608 154 1019 1076 271 
2010 Chandy [51Asian HB PCR-RFLP 100 86 74 25 66 19 
2010 Naghibalhossaini [52Asian PB MS-PCR 151 231 64 80 7 150 68 13 Y 
2010 Promthet [53Asian HB PCR-RFLP 130 130 104 26 94 31 
2010 Yang [54Asian PB Sequenom 141 165 58 61 22 62 75 28 
2010 Fernández - Peralta [55Caucasian HB PCR-RFLP 143 103 89 52 2 44 50 9 Y 
2010 Zhu [56Asian PB PCR-RFLP 216 111 88 102 26 50 53 
2009 Vogel [57Caucasian PB RT-PCR 689 1793 318 320 51 876 750 167 
2009 Iacopetta [58Mixed PB PCR-SSCP 850 958 382 386 82 428 429 101 
2009 Arreola [59Caucasian PB PCR-RFLP 369 170 124 126 119 59 79 32 
2009 Reeves [60Caucasian HB Taqman 206 211 105 83 18 101 91 19 
2009 Awady [61African HB PCR-RFLP 35 68 6 23 6 44 20 4 Y 
2009 Derwinger [62Caucasian PB Taqman 544 299 273 216 55 167 107 25 
2008 Haghighi [63Asian HB PCR/pyrosequencing 234 257 117 68 49 94 80 83 N 
2008 Sharp [64Caucasian PB PCR-RFLP 251 394 117 111 23 170 177 47 
2008 Kury [65Caucasian PB Taqman 1023 1121 435 452 136 457 515 149 
2008 Mokarram [66Asian HB MSP 151 81 64 80 40 31 10 
2008 Cao [67Asian PB PCR-RFLP 315 370 109 154 52 121 183 66 
2008 Theodoratou [68Caucasian PB MassARRAY 999 1010 447 441 111 439 455 116 
2008 Ekolf [69Caucasian PB Taqman 220 414 123 85 12 212 160 42 
2008 Zhang [70Asian HB PCR-RFLP 300 299 97 136 67 91 139 69 
2008 Guerreiro [71Caucasian HB Taqman 196 200 94 76 26 84 107 
2007 Osian [72Caucasian HB PCR-RFLP 69 67 38 25 47 17 
2007 Zeybek [73Asian HB PCR-RFLP 52 144 18 27 64 65 15 
2007 Lima(a) [74Caucasian HB PCR-RFLP 90 300 36 40 14 143 127 30 
2007 Lima(b) [74African HB PCR-RFLP 10 300 143 127 30 
2007 Chang [75Asian HB RT-PCR 195 195 85 86 24 92 87 16 
2007 Murtaugh [76Mixed PB PCR-RFLP 742 970 357 301 84 466 392 112 
2007 Jin [77Asian PB Taqman 449 672 182 211 56 211 325 136 Y 
2007 Curtin [78Mixed PB PCR-RFLP 916 1972 432 402 82 887 858 227 
2007 Hubner [79Caucasian PB Taqman 1685 2691 743 759 183 1173 1192 326 
2006 Koushik [80Caucasian PB Taqman 349 794 166 145 38 355 327 112 
2006 Battistelli [81Caucasian HB PCR-RFLP 93 100 32 40 21 30 51 19 
2006 Van Guelpen [82Caucasian PB Taqman 220 415 123 85 12 212 161 42 
2006 Wang [83Asian PB PCR-RFLP 302 291 257 43 255 36 
2006 Chen [84Asian PB PCR-RFLP 138 340 52 86 133 207 
2005 Matsuo [85Asian HB PCR-RFLP 256 771 106 114 36 289 348 134 
2005 Landi [86Caucasian HB RT-PCR 350 309 128 158 64 109 139 61 
2005 Marchand [87Mixed PB PCR-RFLP 817 2021 394 336 87 987 779 255 
2005 Jiang [88Asian PB PCR-RFLP 125 339 51 59 15 134 143 62 
2005 Otani [89Asian HB MassARRAY 106 222 32 49 25 51 114 57 
2005 Miao [90Asian PB PCR-RFLP 198 420 53 87 58 133 201 86 
2004 Kim [91Asian HB PCR-RFLP 243 225 86 122 35 83 109 33 
2004 Ulvik [92Caucasian PB Taqman 2159 2190 1103 899 157 1092 886 212 
2004 Yin [93Asian PB PCR-RFLP 685 778 270 330 85 278 367 133 
2004 Curtin [94Mixed HB PCR-RFLP 1608 1972 734 724 150 887 858 227 
2003 Pufulete [95Caucasian HB PCR-RFLP 28 76 16 41 29 
2003 Plaschke [96Caucasian PB PCR-RFLP 287 346 133 120 34 149 159 38 
2003 Toffoli [97Caucasian PB PCR-RFLP 276 279 93 145 38 83 140 56 
2003 Heijmans [98Caucasian PB PCR-RFLP 18 793 399 329 65 
2003 Huang [99Asian HB PCR-RFLP 82 82 36 40 40 33 
2003 Barna [100Caucasian PB PCR-RFLP 101 196 46 48 84 97 15 
2002 Keku(a) [101Caucasian PB Taqman/PCR-PFLP 308 539 144 140 24 265 223 51 
2002 Keku(b) [101African PB Taqman/PCR-PFLP 244 329 198 43 264 59 
2002 Marchand(a) [102Caucasian PB PCR-RFLP 149 171 66 64 19 66 81 24 
2002 Marchand(b) [102Asian PB PCR-RFLP 399 485 170 180 49 191 214 80 
2002 Shannon [103Caucasian PB PCR-SSCP/RFLP 501 1207 249 197 55 533 560 114 
2002 Matsuo [104Asian HB PCR-RFLP 142 241 39 81 22 81 124 36 
2002 Sachse [105Caucasian PB PCR-RFLP 490 592 238 199 53 271 272 49 
2002 Chen [106Caucasian PB PCR-RFLP 202 326 92 92 18 145 132 49 
2001 Ryan Caucasian PB PCR-RFLP 136 848 49 73 14 439 326 83 Y 
2000 Slattery [108Caucasian PB PCR-RFLP 232 164 106 107 19 73 71 20 
1999 Slattery [109Mixed PB PCR-RFLP 1467 1821 673 655 139 827 787 207 
1999 Park [110Asian PB PCR-RFLP 200 460 65 107 28 140 246 74 
1997 Ma [111Caucasian PB PCR-RFLP 202 326 92 92 18 145 132 49 
1996 Chen [112Caucasian PB PCR-RFLP 144 627 67 64 13 280 263 84 
MTHFR rs1801133Case (n)Control (n)
YearSurname (References)EthnicitySOCGenotypingCaseControlCCCTTTCCCTTTHWE
2016 Haerian [26Asian HB Taqman 1123 1298 607 421 95 667 523 108 
2015 Kim [27Asian PB PCR-RFLP 477 514 159 248 70 172 265 77 
2014 Rai [28Asian PB PCR-RFLP 155 294 137 17 261 31 
2014 Ozen [29Caucasian PB RT-PCR 86 212 36 32 18 207 5 0 Y 
2013 Ashmore [30Caucasian PB RT-PCR 625 603 241 309 75 263 259 81 Y 
2013 Delgado- Plasencia [31Caucasian HB PCR-RFLP 50 103 32 16 2 44 50 9 Y 
2013 Yousef [32Asian PB PCR-RFLP 128 116 79 45 59 45 12 
2012 Lee [33Caucasian PB Taqman 531 1004 250 229 52 464 391 149 
2012 Promthet [34Asian HB PCR-RFLP 112 242 93 18 185 49 
2012 Kim [35Asian HB Taqman 787 656 265 393 129 205 289 162 
2012 Yin [36Asian HB RT-PCR 370 370 124 167 79 139 178 53 
2011 Sameer [37Asian PB PCR-RFLP 86 160 59 18 121 27 12 
2011 Vossen [38Caucasian PB Taqman 1762 1811 737 823 202 795 807 209 
2011 Kang [39Asian PB PCR-RFLP 255 448 87 134 34 145 238 65 
2011 Zhu [40Asian PB PCR-RFLP 86 100 29 42 15 49 41 10 Y 
2011 Pardini [41Caucasian HB PCR-RFLP 666 1376 317 307 42 613 627 136 
2011 Kim [42Asian HB MSP 67 53 30 30 15 21 17 
2011 Prasad [43Asian PB PCR-RFLP 110 241 97 12 1 228 12 1 Y 
2011 Li [44Asian PB PCR-RFLP 137 145 68 54 15 55 64 26 
2011 Jokic [45Caucasian PB Taqman 300 300 139 130 31 142 130 28 
2011 Guimaracs(a) [46Caucasian HB PCR-RFLP 101 188 42 44 15 92 79 17 
2011 Guimaracs(b) [46African HB PCR-RFLP 12 188 92 79 17 
2010 Komlosi [47Caucasian PB PCR-RFLP 951 939 398 427 126 442 380 117 N 
2010 Karpinski [48Caucasian HB MSP 186 140 74 97 15 71 55 14 Y 
2010 Cui [49Asian PB PCR-RFLP 1829 1700 622 923 284 540 863 297 
2010 Eussen [50Caucasian PB MALDI-TOF-MS 1329 2366 567 608 154 1019 1076 271 
2010 Chandy [51Asian HB PCR-RFLP 100 86 74 25 66 19 
2010 Naghibalhossaini [52Asian PB MS-PCR 151 231 64 80 7 150 68 13 Y 
2010 Promthet [53Asian HB PCR-RFLP 130 130 104 26 94 31 
2010 Yang [54Asian PB Sequenom 141 165 58 61 22 62 75 28 
2010 Fernández - Peralta [55Caucasian HB PCR-RFLP 143 103 89 52 2 44 50 9 Y 
2010 Zhu [56Asian PB PCR-RFLP 216 111 88 102 26 50 53 
2009 Vogel [57Caucasian PB RT-PCR 689 1793 318 320 51 876 750 167 
2009 Iacopetta [58Mixed PB PCR-SSCP 850 958 382 386 82 428 429 101 
2009 Arreola [59Caucasian PB PCR-RFLP 369 170 124 126 119 59 79 32 
2009 Reeves [60Caucasian HB Taqman 206 211 105 83 18 101 91 19 
2009 Awady [61African HB PCR-RFLP 35 68 6 23 6 44 20 4 Y 
2009 Derwinger [62Caucasian PB Taqman 544 299 273 216 55 167 107 25 
2008 Haghighi [63Asian HB PCR/pyrosequencing 234 257 117 68 49 94 80 83 N 
2008 Sharp [64Caucasian PB PCR-RFLP 251 394 117 111 23 170 177 47 
2008 Kury [65Caucasian PB Taqman 1023 1121 435 452 136 457 515 149 
2008 Mokarram [66Asian HB MSP 151 81 64 80 40 31 10 
2008 Cao [67Asian PB PCR-RFLP 315 370 109 154 52 121 183 66 
2008 Theodoratou [68Caucasian PB MassARRAY 999 1010 447 441 111 439 455 116 
2008 Ekolf [69Caucasian PB Taqman 220 414 123 85 12 212 160 42 
2008 Zhang [70Asian HB PCR-RFLP 300 299 97 136 67 91 139 69 
2008 Guerreiro [71Caucasian HB Taqman 196 200 94 76 26 84 107 
2007 Osian [72Caucasian HB PCR-RFLP 69 67 38 25 47 17 
2007 Zeybek [73Asian HB PCR-RFLP 52 144 18 27 64 65 15 
2007 Lima(a) [74Caucasian HB PCR-RFLP 90 300 36 40 14 143 127 30 
2007 Lima(b) [74African HB PCR-RFLP 10 300 143 127 30 
2007 Chang [75Asian HB RT-PCR 195 195 85 86 24 92 87 16 
2007 Murtaugh [76Mixed PB PCR-RFLP 742 970 357 301 84 466 392 112 
2007 Jin [77Asian PB Taqman 449 672 182 211 56 211 325 136 Y 
2007 Curtin [78Mixed PB PCR-RFLP 916 1972 432 402 82 887 858 227 
2007 Hubner [79Caucasian PB Taqman 1685 2691 743 759 183 1173 1192 326 
2006 Koushik [80Caucasian PB Taqman 349 794 166 145 38 355 327 112 
2006 Battistelli [81Caucasian HB PCR-RFLP 93 100 32 40 21 30 51 19 
2006 Van Guelpen [82Caucasian PB Taqman 220 415 123 85 12 212 161 42 
2006 Wang [83Asian PB PCR-RFLP 302 291 257 43 255 36 
2006 Chen [84Asian PB PCR-RFLP 138 340 52 86 133 207 
2005 Matsuo [85Asian HB PCR-RFLP 256 771 106 114 36 289 348 134 
2005 Landi [86Caucasian HB RT-PCR 350 309 128 158 64 109 139 61 
2005 Marchand [87Mixed PB PCR-RFLP 817 2021 394 336 87 987 779 255 
2005 Jiang [88Asian PB PCR-RFLP 125 339 51 59 15 134 143 62 
2005 Otani [89Asian HB MassARRAY 106 222 32 49 25 51 114 57 
2005 Miao [90Asian PB PCR-RFLP 198 420 53 87 58 133 201 86 
2004 Kim [91Asian HB PCR-RFLP 243 225 86 122 35 83 109 33 
2004 Ulvik [92Caucasian PB Taqman 2159 2190 1103 899 157 1092 886 212 
2004 Yin [93Asian PB PCR-RFLP 685 778 270 330 85 278 367 133 
2004 Curtin [94Mixed HB PCR-RFLP 1608 1972 734 724 150 887 858 227 
2003 Pufulete [95Caucasian HB PCR-RFLP 28 76 16 41 29 
2003 Plaschke [96Caucasian PB PCR-RFLP 287 346 133 120 34 149 159 38 
2003 Toffoli [97Caucasian PB PCR-RFLP 276 279 93 145 38 83 140 56 
2003 Heijmans [98Caucasian PB PCR-RFLP 18 793 399 329 65 
2003 Huang [99Asian HB PCR-RFLP 82 82 36 40 40 33 
2003 Barna [100Caucasian PB PCR-RFLP 101 196 46 48 84 97 15 
2002 Keku(a) [101Caucasian PB Taqman/PCR-PFLP 308 539 144 140 24 265 223 51 
2002 Keku(b) [101African PB Taqman/PCR-PFLP 244 329 198 43 264 59 
2002 Marchand(a) [102Caucasian PB PCR-RFLP 149 171 66 64 19 66 81 24 
2002 Marchand(b) [102Asian PB PCR-RFLP 399 485 170 180 49 191 214 80 
2002 Shannon [103Caucasian PB PCR-SSCP/RFLP 501 1207 249 197 55 533 560 114 
2002 Matsuo [104Asian HB PCR-RFLP 142 241 39 81 22 81 124 36 
2002 Sachse [105Caucasian PB PCR-RFLP 490 592 238 199 53 271 272 49 
2002 Chen [106Caucasian PB PCR-RFLP 202 326 92 92 18 145 132 49 
2001 Ryan Caucasian PB PCR-RFLP 136 848 49 73 14 439 326 83 Y 
2000 Slattery [108Caucasian PB PCR-RFLP 232 164 106 107 19 73 71 20 
1999 Slattery [109Mixed PB PCR-RFLP 1467 1821 673 655 139 827 787 207 
1999 Park [110Asian PB PCR-RFLP 200 460 65 107 28 140 246 74 
1997 Ma [111Caucasian PB PCR-RFLP 202 326 92 92 18 145 132 49 
1996 Chen [112Caucasian PB PCR-RFLP 144 627 67 64 13 280 263 84 

These 13 studies in bold were removed afterward because of its heterogeneity and publication bias. Abbreviations: HB: hospital-based control; PB, population-based control; SOC, source of control.

Results of quantitative synthesis

Initially, there was no association between MTHFR C677T polymorphism and CRC susceptibility in the dominant model (OR =0.99, 95% CI =0.94–1.05). 0.94–1.05). Nevertheless, for the sake of looking for possible reasons that might lead to such result, we performed heterogeneity analysis and tested publication bias. According to these results, 13 studies were excluded [2931,40,43,47,48,52,55,61,63,77,107], the P-value was estimated to be 0.824, and the fixed effect model was applied. Ultimately, the results demonstrated that the rs1801133 polymorphism was significantly correlated with the risk of CRC (Figure 2) (dominant model: OR =0.96, 95% CI =0.94–0.99; recessive model: OR =0.90, 95% CI =0.83–0.96; homozygous model: OR =0.88, 95% CI =0.82–0.95; allele model: OR =0.95, 95% CI =0.93–0.98). All detailed results in the present meta-analysis are shown in Table 2.

Forest plots of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model after omitting these 13 studies with heterogeneity and publication bias

Figure 2
Forest plots of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model after omitting these 13 studies with heterogeneity and publication bias
Figure 2
Forest plots of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model after omitting these 13 studies with heterogeneity and publication bias
Table 2
Meta-analysis results for the included studies of the association between MTHFR rs1801133 polymorphism and risk of CRC
VariablesNumber of studiesDominant modelRecessive modelHomozygous modelHeterozygous modelAllele model
OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)
rs1801133C>T  (CT + TT) compared with CC TT compared with (CT + CC) TT compared with CC CT compared with CC T compared with C 
All 78 0.96 (0.94–0.99) 0.824 0.0 0.90 (0.83–0.96) <0.001 49.9 0.88 (0.82–0.95) <0.001 42.5 0.99 (0.96–1.02) 0.950 0.0 0.95 (0.93–0.98) 0.006 31.2 
Ethnicity                 
  Asian 33 0.94 (0.89–1.00) 0.418 3.0 0.88 (0.77–1.00) 0.001 51.2 0.86 (0.75–1.00) 0.001 49.2 0.96 (0.91–1.02) 0.933 0.0 0.94 (0.88–1.00) 0.002 47.9 
  Caucasian 36 0.97 (0.93–1.01) 0.711 0.0 0.93 (0.83–1.04) <0.001 57.8 0.91 (0.82–1.01) 0.001 47.7 0.99 (0.95–1.03) 0.505 0.0 0.96 (0.93–1.00) 0.079 26.2 
  African 0.98 (0.67–1.42) 0.866 0.0 0.69 (0.24–2.03) 0.873 0.0 0.72 (0.24–2.15) 0.837 0.0 1.02 (0.69–1.51) 0.852 0.0 0.93 (0.67–1.30) 0.816 0.0 
  Mixed 0.98 (0.92–1.04) 0.959 0.0 0.83 (0.75–0.92) 0.829 0.0 0.84 (0.75–0.93) 0.830 0.0 1.02 (0.95–1.09) 0.967 0.0 0.95 (0.90–0.99) 0.908 0.0 
Source of control 
  HB 28 0.96 (0.90–1.03) 0.357 7.2 0.97 (0.81–1.16) <0.001 59.6 0.96 (0.80–1.15) <0.001 54.4 0.98 (0.92–1.04) 0.550 0.0 0.97 (0.90–1.05) 0.007 44.4 
  PB 50 0.97 (0.93–1.00) 0.911 0.0 0.88 (0.81–0.95) 0.001 43.3 0.87 (0.80–0.93) 0.012 34.1 0.99 (0.96–1.03) 0.970 0.0 0.95 (0.92–0.98) 0.087 22.4 
Geotyping 
  Taqman 14 0.96 (0.92–1.01) 0.568 0.0 0.86 (0.73–1.00) <0.001 65.0 0.85 (0.74–0.99) 0.004 57.3 0.99 (0.94–1.05) 0.460 0.0 0.94 (0.89–0.99) 0.085 36.4 
  PCR-RFLP 50 0.95 (0.91–0.99) 0.886 0.0 0.90 (0.81–0.99) 0.001 43.6 0.88 (0.79–0.97) 0.005 37.5 0.98 (0.94–1.03) 0.992 0.0 0.95 (0.91–0.99) 0.027 30.0 
  RT-PCR 1.10 (0.97–1.26) 0.746 0.0 1.12 (0.76–1.64) 0.017 70.4 1.15 (0.79–1.66) 0.042 63.4 1.11 (0.96–1.27) 0.771 0.0 1.08 (0.95–1.22) 0.207 34.2 
VariablesNumber of studiesDominant modelRecessive modelHomozygous modelHeterozygous modelAllele model
OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)OR (95% CI)P-valuesI-squared (%)
rs1801133C>T  (CT + TT) compared with CC TT compared with (CT + CC) TT compared with CC CT compared with CC T compared with C 
All 78 0.96 (0.94–0.99) 0.824 0.0 0.90 (0.83–0.96) <0.001 49.9 0.88 (0.82–0.95) <0.001 42.5 0.99 (0.96–1.02) 0.950 0.0 0.95 (0.93–0.98) 0.006 31.2 
Ethnicity                 
  Asian 33 0.94 (0.89–1.00) 0.418 3.0 0.88 (0.77–1.00) 0.001 51.2 0.86 (0.75–1.00) 0.001 49.2 0.96 (0.91–1.02) 0.933 0.0 0.94 (0.88–1.00) 0.002 47.9 
  Caucasian 36 0.97 (0.93–1.01) 0.711 0.0 0.93 (0.83–1.04) <0.001 57.8 0.91 (0.82–1.01) 0.001 47.7 0.99 (0.95–1.03) 0.505 0.0 0.96 (0.93–1.00) 0.079 26.2 
  African 0.98 (0.67–1.42) 0.866 0.0 0.69 (0.24–2.03) 0.873 0.0 0.72 (0.24–2.15) 0.837 0.0 1.02 (0.69–1.51) 0.852 0.0 0.93 (0.67–1.30) 0.816 0.0 
  Mixed 0.98 (0.92–1.04) 0.959 0.0 0.83 (0.75–0.92) 0.829 0.0 0.84 (0.75–0.93) 0.830 0.0 1.02 (0.95–1.09) 0.967 0.0 0.95 (0.90–0.99) 0.908 0.0 
Source of control 
  HB 28 0.96 (0.90–1.03) 0.357 7.2 0.97 (0.81–1.16) <0.001 59.6 0.96 (0.80–1.15) <0.001 54.4 0.98 (0.92–1.04) 0.550 0.0 0.97 (0.90–1.05) 0.007 44.4 
  PB 50 0.97 (0.93–1.00) 0.911 0.0 0.88 (0.81–0.95) 0.001 43.3 0.87 (0.80–0.93) 0.012 34.1 0.99 (0.96–1.03) 0.970 0.0 0.95 (0.92–0.98) 0.087 22.4 
Geotyping 
  Taqman 14 0.96 (0.92–1.01) 0.568 0.0 0.86 (0.73–1.00) <0.001 65.0 0.85 (0.74–0.99) 0.004 57.3 0.99 (0.94–1.05) 0.460 0.0 0.94 (0.89–0.99) 0.085 36.4 
  PCR-RFLP 50 0.95 (0.91–0.99) 0.886 0.0 0.90 (0.81–0.99) 0.001 43.6 0.88 (0.79–0.97) 0.005 37.5 0.98 (0.94–1.03) 0.992 0.0 0.95 (0.91–0.99) 0.027 30.0 
  RT-PCR 1.10 (0.97–1.26) 0.746 0.0 1.12 (0.76–1.64) 0.017 70.4 1.15 (0.79–1.66) 0.042 63.4 1.11 (0.96–1.27) 0.771 0.0 1.08 (0.95–1.22) 0.207 34.2 

These 13 studies by Ozen et al., Ashmore et al., Delgado-Plasencia et al., Zhu et al., Prasad et al., Komlosi et al., Karpinski et al., Naghibalhossaini et al., Fernández-Peralta et al., Awady et al., Haghighi et al., Jin et al., Ryan et al. were removed [29, 30, 31, 40, 43, 47, 48, 52, 55, 61, 63, 77, 107].

In the subgroup analysis of ethnicity, MTHFR C677T polymorphism was found to reduce CRC susceptibility in Asians significantly (dominant model: OR =0.94, 95% CI =0.89–1.00 (Figure 3A); recessive model: OR =0.88, 95% CI =0.77–1.00; homozygous model: OR =0.86, 95% CI =0.75–1.00; allele model: OR =0.92, 95% CI =0.88–1.00). Simultaneously, significantly reduced risks were also found in mixed group (recessive model: OR =0.83, 95% CI =0.75–0.92; homozygous model: OR =0.84, 95% CI =0.75–0.93; allele model: OR =0.95, 95% CI =0.90–0.99). Amongst Caucasians, yet significantly reduced risks were only observed in the allele model (OR =0.96, 95% CI =0.93–1.00). Nevertheless, no significant associations were detected in Africans for all genetic models. When stratified by the source of controls, the positive results were observed in population-based control group (dominant model: OR =0.97, 95% CI =0.93–1.00 (Figure 3B); recessive model: OR =0.88, 95% CI =0.81–0.95; homozygous model: OR =0.87, 95% CI =0.80–0.93; allele model: OR =0.95, 95% CI =0.92–0.98). The similar significant associations were absent from hospital-based group for all the genetic models. The stratified analysis by genotyping methods showed that PCR-RFLP method (dominant model: OR =0.95, 95% CI =0.91–0.99 (Figure 3C); recessive model: OR =0.90, 95% CI =0.81–0.99; homozygous model: OR =0.88, 95% CI =0.79–0.97; allele model: OR =0.95, 95% CI =0.91–0.99) and Taqman method (recessive model: OR =0.86, 95% CI =0.73–1.00; homozygous model: OR =0.85, 95% CI =0.74–0.99; allele model: OR =0.94, 95% CI =0.89–0.99) were significantly correlated with risks of decreased CRC. However, RT-PCR method was not relevant to significant associations for all genetic models. In conclusion, the present meta-analysis suggested that MTHFR C677T polymorphism was connected with CRC susceptibility.

Forest plots of subgroup analysis of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model

Figure 3
Forest plots of subgroup analysis of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model

(A) Stratified by ethnicity; (B) stratified by source of controls; (C) stratified by genotyping method.

Figure 3
Forest plots of subgroup analysis of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model

(A) Stratified by ethnicity; (B) stratified by source of controls; (C) stratified by genotyping method.

Test of heterogeneity

Heterogeneity analysis was performed in this meta-analysis, and heterogeneity was significantly observed between all the included studies in the dominant model (I2 =62.0%, P<0.001; Figure 4A). In addition, the Galbraith radial plot illustrated heterogeneity obviously. Meanwhile, it specifically pointed out 13 studies that might have led to the obvious heterogeneity and insignificant results of the meta-analysis [2729,38,41,45,46,50,53,59,61,75,105]. After excluding 13 studies, the heterogeneity decreased significantly (I2 =0.0%, P=0.789; Figure 4B) in the present meta-analysis.

Galbraith plot of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model

Figure 4
Galbraith plot of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model

(A) Before removing these 13 studies. (B) After the exclusion of these studies.

Figure 4
Galbraith plot of the association between MTHFR C677T polymorphism and CRC susceptibility in dominant model

(A) Before removing these 13 studies. (B) After the exclusion of these studies.

Publication bias

The Begg’s funnel plot and Egger’s test were performed to assess the publication bias. Initially, the Begg’s funnel plot was asymmetrical obviously with all the included studies and it suggested a potential publication bias (Begg’s test: P=0.103; Egger’s test: P=0.058; Figure 5A). After the removal of 13 studies mentioned above [2729,38,41,45,46,50,53,59,61,75,105], the plots seemed to have a symmetrical distribution in the funnel plot and then Egger’s test was used to provide statistical evidence (Begg’s test: P=0.369; Egger’s test: P=0.136; Figure 5B). No significant publication bias was observed in the present studies.

Begg’s funnel plot of publication bias test

Figure 5
Begg’s funnel plot of publication bias test

(A) Before omitting these 13 studies. (B) After the exclusion of these studies.

Figure 5
Begg’s funnel plot of publication bias test

(A) Before omitting these 13 studies. (B) After the exclusion of these studies.

Sensitivity analysis

In order to distinguish the impact of each study on the pooled ORs, we conducted one-way sensitivity analysis. Each time one study was omitted, meta-analysis was repeated and the statistical significance of the results was not changed. Therefore, the results confirmed that the present meta-analysis was relatively stable and reliable.

Discussion

MTHFR is a key enzyme in the folate metabolism and may play a role in the CRC carcinogenesis. It is an essential enzyme in the catalytic reaction that converts 5,10-methylenetetrahydrofolate into 5-methyltetrahydrofolate. On one hand, 5,10-methylenetetrahydrofolate takes part in the thymidylate synthesis. On the other hand, 5-methyltetrahydrofolate promotes methionine synthesis and SAM-mediated methylations. In brief, MTHFR has an influence on DNA synthesis, methylation, and repair [113]. The MTHFR polymorphisms result in the decreased enzyme activity and then low levels of plasma folate and high homocysteine come to light. Folate is one of water-soluble B vitamins that takes part in various biochemical reactions with its activity to provide or accept one-carbon units [13]. Folate deficiency is likely to contribute to the development of CRC, and several mechanisms may explain how it leads to CRC, including DNA strand breaks, abnormal DNA methylation, and impaired DNA repair [114].

Several polymorphisms have been reported about the MTHFR gene coding relevant enzyme, and MTHFR C677T polymorphism is the most common one. Heretofore, various studies conducted to detect such association and obtained inconsistent results. Chen et al. [112], first reported that MTHFR variant homozygous (TT) genotype was closely linked to reduced incidence of CRC with low consumption of alcohol. In the next few years, similar results were replicated by several other studies [109111]. However, another study of a homogeneous northern European population obtained different conclusions that MTHFR CT heterozygote had a significantly increased risk of developing CRC and no increased cancer risk was observed in TT homozygotes [107]. In addition, a hospital-based case–control study conducted by Matsuo et al. [104] found no significant relativity between MTHFR C677T and the risks of CRC. Owing to the difference in study design and the sample size, the different ethnicity, and the diverse stratification, these controversial results were found in published studies. Hence, meta-analysis is essential to be carried out by combining all studies that meet the requirements to get more precise conclusions.

In recent years, there were several meta-analyses performed to elucidate the association of MTHFR C677T polymorphism and the susceptibility to CRC before [26,115118]. Compared with them, this meta-analysis included the most eligible reported studies with the largest sample size and had no restrictions in ethnicity. Since the quality of included documents were disequilibrium, our initial analysis achieved no significant results with all eligible studies. In order to obtain more reliable results, the final conclusion were obtained excluding 13 studies in accordance with the analysis of heterogeneity and publication bias. In this meta-analysis, the pooled conclusions revealed that rs1801133 polymorphism significantly reduced the risk of CRC in the dominant model. The findings agreed with the overwhelming majority results reported by the published studies.

When stratified by ethnicity, there was a significant association with reduced risks of CRC in Asians. The result was consistent with the two previous meta-analysis based on the Asians [116,117]. Zhong et al. [118], carried out a meta-analysis obtaining similar results in East Asians and further subgroup analyses by country identified such association in Korea and Japan. Nevertheless, the recent meta-analysis failed to identify that rs1801133 polymorphism was connected with CRC susceptibility in Iranian population [26]. By means of stratified analysis based on the source of controls and genotyping methods, the positive results were observed in population-based control group and PCR-RFLP method. In general, the source of controls included healthy individuals and patients without CRC. Since the risks of CRC varies amongst individuals over a few years, it might have an impact on the results of relevant studies and make them unreliable. Therefore, inclusion criteria should be improved and studies with large sample sizes should be accepted. In the subgroup of genotyping method, there were nine methods applied for genotyping such as PCR-RFLP, RT-PCR, PCR-SSCP, MS-PCR, MSP, MALDI-TOF-MS, Taqman, MassARRAY, and Sequenom in the including studies. Specific methods and steps were described in each article. Amongst these 87 studies, the majority method was PCR-RFLP. Different methods have their own merits, and when all included studies used the same method, the final results would be more reliable.

In the present meta-analysis, we had obtained weak associations significantly with a large sample size. However, the potential limitations of the meta-analysis should be acknowledged. First, this meta-analysis was based on unadjusted effect estimates and 95% CI, and the influence of multiple cofactors such as age, gender, diet habits including intake of alcohol and consumption of cigarette, the level of folate, and the other environmental factors should be taken into consideration. Second, because of incomplete data of some genotypes, only the dominant model was analyzed in all the included studies. Third, we did not perform stratification analysis by serum folate levels, locations of the tumor and so on, which might result in confounding bias. In addition, after excluding 13 studies according to the analysis of heterogeneity and publication bias, the heterogeneity decreased significantly and the publication bias seemed to disappear. However, the selection bias existed because all the studies were published. Furthermore, the gene–gene and gene–environment interactions were not mentioned in this meta-analysis. In addition, the potential roles of the gene polymorphism which were hidden or magnified by other interactions were omitted.

Conclusion

In summary, the present meta-analysis revealed that there was a significant association between MTHFR C677T polymorphism and susceptibility to CRC. Simultaneously, the TT genotype of MTHFR C677T polymorphism could reduce the risk of CRC. In addition, the associated risk of CRC was also reduced in Asians and those studies with population-based controls and used the PCR-RFLP method. Therefore, detection of the MTHFR C677T polymorphism might be used as markers for CRC prediction and treatment selection.

Competing interests

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

Funding

This work has been supported by the Natural Science Funding of Jiangsu Province [grant number BK20141492]; and the ‘333 Project’ of Jiangsu Province [grant number BRA2016517].

Author contribution

Y.G., H.Y., and Z.Q. were responsible for conception and design. Y.G., H.Y., and F.W. provided the administrative support. S.S., Z.Q., and L.L. were responsible for the collection and assembly of data. P.L., X.H., and X.C. were responsible for data analysis and interpretation. L.X., Z.Q., and F.W. were responsible for manuscript writing. All the authors approved the final manuscript.

Abbreviations

     
  • CI

    confidence interval

  •  
  • CRC

    colorectal cancer

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • MSP

    mutagenically separated PCR

  •  
  • MS-PCR

    methylation-specific PCR

  •  
  • MTHFR

    methylenetetrahydrofolate reductase

  •  
  • OR

    odds ratio

  •  
  • PCR-RFLP

    PCR-restriction fragment length polymorphism

  •  
  • PCR-SSCP

    PCR-single strand conformation polymorphism

  •  
  • PRISMA-P

    preferred reporting items for systematic review and meta-analysis protocol

  •  
  • RT-PCR

    real-time PCR

  •  
  • SAM

    S-adenosylmethionine

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Author notes

*

Lingyan Xu, Zhiqiang Qin and Feng Wang contributed equally to this work.

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).