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. 2017 Dec 4;8(66):110326–110336. doi: 10.18632/oncotarget.22887

Methylenetetrahydrofolate reductase tagging polymorphisms are associated with risk of non-small cell lung cancer in eastern Chinese Han population

Hao Ding 1,#, Yafeng Wang 2,#, Yuanmei Chen 3,#, Chao Liu 4, Hao Qiu 5, Mingqiang Kang 6, Weifeng Tang 6
PMCID: PMC5746385  PMID: 29299150

Abstract

Previous reports implicated 5,10-ethylenetetrahydrofolate reductase (MTHFR) polymorphisms acted as a potential risk factor for several cancers. In order to explore the effect of MTHFR SNPs on non-small cell lung cancer (NSCLC), we selected MTHFR tagging single nucleotide polymorphisms (SNPs) and carried out a case-control study to determine the potential relationship of MTHFR SNPs with NSCLC risk. Our study consisted of 521 NSCLC patients and 1,030 non-cancer controls. MTHFR SNPs were genotyped by SNPscanTM genotyping assay. Using four genetic models (additive, Homozygote, dominant, recessive), the genotype frequencies were compared using the chi-squared (χ2) test. Crude/adjusted odds ratios (ORs) with their 95% confidence intervals (CIs) were used to assess the difference for the genotype distribution. We found that MTHFR rs1801133 G>A polymorphism decreased the risk of overall NSCLC. In a subgroup analysis, MTHFR rs1801133 G>A polymorphism also decreased NSCLC risk in female, < 60 years and never smoking subgroups. However, we identified that MTHFR rs4845882 G>A polymorphism was associated with the development of NSCLC in female subgroup. In addition, MTHFR rs9651118 T>C polymorphism increased the risk of NSCLC in < 60 years, never smoking and BMI < 24 kg/m2 subgroups. In conclusion, the current study highlights MTHFR rs1801133 G>A variants decreases the risk of NSCLC. Nevertheless, MTHFR rs4845882 G>A and rs9651118 T > C polymorphisms may be associated with NSCLC susceptibility. Well-designed large-scale studies are needed to confirm these findings and explore the interactions of gene-gene and gene-environment involved in MTHFR SNPs and NSCLC.

Keywords: MTHFR, polymorphism, non-small cell lung cancer, risk

INTRODUCTION

Lung cancer (LC) caused by multiple risk factors is one of the common malignancies worldwide. With very complex biological characteristics and high degree of invasiveness, it is difficult to diagnose at an early stage and lack of very effective treatment at an advanced stage. Thus, LC is a common public health problem with a poor prognosis. LC involves two major subtypes, such as small cell LC and non-small cell LC (NSCLC). In addition, NSCLC cases account for most of the total LC cases. The increasing incidence of NSCLC is closely related to tobacco consumption, air pollution, cooking fumes, asbestos and other environmental factors [1]. However, these known risk factors might not contribute to overall susceptibility to NSCLC. Recently, individual's genetic factors have also been determined to cause NSCLC.

Folic acid, or 5-methyltetrahydrofolate, is a cofactor in the metabolism of homocysteine to methionine [2]. 5,10-ethylenetetrahydrofolate reductase (MTHFR) catalyzed reduction of 5,10-methylenetetrahydrofolate (methylene-THF), a donor of methyl for dUMP to dTMP transform, to methyl-THF, the primary methyl donor in methionine synthesis. Methionine is transformed to S-adenosyl-l-methionine (SAM) which is the principal methyl donor in over 100 biochemical responses, including cytosine methylation in DNA. By the catalysis of DNA (cytosine-5)-methyltransferase, methyl group of SAM was transferred to C5 of cytosine within CpG island in the genomic DNA in higher eukaryotes [3–5]. MTHFR is a dimeric flavoprotein in human and each monomer is bound to flavinadenosine-dinucleotide noncovalently [6]. Each monomer contains a N-terminal catalytic domain that binds the allosteric SAM inhibitory regulating enzyme activity in response to the methionine levels in the cell [7].The findings of the relationship between methylation patterns and folate status in individuals with cancer and healthy normal individuals provide stronger evidences for a mechanism by which folate may modify DNA methylation and alter the risk of cancer.

Methylenetetrahydrofolate reductase (MTHFR), whose gene maps to the short arm of Chromosome 1 and encodes a 77-kDa protein with 656 amino acids. Many single nucleotide polymorphisms (SNPs) have been identified (http://www.ncbi.nlm.nih.gov/SNP), such as rs1801131, rs1801133, rs1537514, rs9651118, rs1537516, rs3753584, rs4845882, rs4846048, rs2066462 and rs3737967 polymorphisms, etc. A number of case-control studies focused on the association between MTHFR polymorphisms and the risk of LC [8–15], however, the results were inconsistent. For example, a meta-analysis suggested that MTHFR rs1801133 G>A was not associated the risk of LC in Chinese population [16]. Nevertheless, Yang et al. reported that MTHFR rs1801133 G>A polymorphism increased the risk of lung cancer in Asians, but not in Caucasians [17]. These ambiguous findings may be due to the limited sample size or difference in populations. In order to extensively explore the relationship of MTHFR SNPs with LC susceptibility, we selected MTHFR tagging SNPs (rs3753584 T>C, rs4845882 G>A, rs1801133 G>A, rs4846048 A>G and rs9651118 T>C) and carried out a case-control study to determine the potential effect of MTHFR SNPs on NSCLC risk.

RESULTS

Baseline characteristics

In this study, a total of 521 sporadic NSCLC patients and 1,030 normal controls were enrolled. Age and sex were full matched (P = 0.843 and P = 0.453, respectively; Table 1). Of the NSCLC patients, 287 were male and 234 were female, with a mean age of 59.76 ± 10.71 years. The non-cancer controls were comprised of 588 males and 442 females with a mean age of 60.34 ± 9.11 years. Of the tobacco consumption and drinking and body mass index (BMI), differences were found between NSCLC patients and non-cancer controls (P < 0.001, Table 1). The genotype distribution of MTHFR was calculated after genotyping the 1,551 included participants. For MTHFR rs1801133 G>A, rs4845882 G>A, rs4846048 A>G, rs3753584 T>C and rs9651118 T>C polymorphisms, success rates of genotyping were 99.87%, 99.94%, 99.94%, 99.94% and 99.94%, respectively (Table 2). The genotype distribution of MTHFR SNPs reached Hardy–Weinberg equilibrium (HWE) in controls, except for MTHFR rs4846048 A>G polymorphism (P = 0.036) (Table 2).

Table 1. Distribution of selected demographic variables and risk factors in NSCLC cases and controls.

Variable Overall Cases (n = 521) Overall Controls (n = 1,030) Pa
n (%) n (%)
Age (years) 59.76 ± 10.71 60.34 ± 9.11 0.268
Age (years) 0.843
 < 60 238 (45.68) 476 (46.21)
 ≥ 60 283 (54.32) 554 (53.79)
Sex 0.453
 Male 287 (55.09) 588 (57.09)
 Female 234 (44.91) 442 (42.91)
Smoking status < 0.001
 Never 317 (60.84) 828 (80.39)
 Ever 204 (39.16) 202 (19.61)
Alcohol use < 0.001
 Never 444 (85.22) 949 (92.14)
 Ever 77 (14.78) 81 (7.86)
BMI (kg/m2) 23.00 (± 3.03) 23.84 (± 3.06) < 0.001
BMI (kg/m2)
 < 24 337 (64.68) 547 (53.11) < 0.001
 ≥ 24 184 (35.32) 483 (46.89)
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a Two-sided χ2 test and Student t test

Table 2. Primary information for MTHFR polymorphisms (rs1801133 G>A, rs9651118 T>C rs4845882 G>A, rs4846048 A>G and rs3753584 T>C).

Genotyped SNPs rs1801133 G>A rs3753584 T>C rs4845882 G>A rs4846048 A>G rs9651118 T>C
Chromosome 1 1 1 1 1
Function Missense NearGene-5 Intron Intron Intron
Chr Pos (Genome Build 36.3) 11778965 11787173 11765754 11768839 11784801
MAFa for Chinese in database 0.439 0.093 0.198 0.105 0.382
MAF in our controls (n = 1,030) 0.345 0.118 0.214 0.095 0.383
P value for HWEb test in our controls 0.947 0.712 0.454 0.036 0.081
Genotyping method SNPscan SNPscan SNPscan SNPscan SNPscan
% Genotyping value 99.87% 99.94% 99.94% 99.94% 99.94%
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aMAF: minor allele frequency;

bHWE: Hardy–Weinberg equilibrium

Association of MTHFR rs1801133 G>A, rs4845882 G>A, rs4846048 A>G, rs3753584 T>C and rs9651118 T>C polymorphisms with the development of NSCLC

Table 3 summarizes the genotypes of MTHFR SNPs. MTHFR rs1801133 G>A polymorphism decreased the risk of NSCLC in two genetic models [AA vs. GG: crude odds ratio (OR) = 0.66, 95% confidence interval (CI): 0.45–0.96, P = 0.031; and AA vs. GA/GG: crude OR = 0.69, 95% CI: 0.48–0.99, P = 0.042; Table 3]. Adjustment for age, sex, BMI, smoking and drinking, the decreased risk of NSCLC was also found (AA vs. GG: adjusted OR = 0.66, 95% CI: 0.47–0.97, P = 0.035; Table 3). However, the above findings were not significant after the Bonferroni correction for multiple comparisons. For MTHFR rs3753584 T>C, rs4845882 G>A, rs4846048 A>G and rs9651118 T>C polymorphisms, we found null association between these SNPs and the risk of NSCLC (Table 3).

Table 3. Logistic regression analyses of associations between MTHFR rs1801133 G>A, rs3753584 T>C, rs4845882 G>A, rs4846048 A>G and rs9651118 T>C polymorphisms and the risk of NSCLC.

Genotype Cases(n = 521) Controls(n = 1,030) Crude OR(95%CI) P Adjusted OR a(95%CI) P
n % n %
MTHFR rs1801133 G>A
GG 241 46.35 441 42.86 1.00 1.00
GA 235 45.19 466 45.29 0.92 (0.74–1.15) 0.467 0.92 (0.73–1.16) 0.461
AA 44 8.46 122 11.86 0.66 (0.45–0.96) 0.031 0.66 (0.44–0.97) 0.035
GA + AA 279 53.65 588 57.14 0.87 (0.70–1.07) 0.192 0.87 (0.70–1.08) 0.207
GG+ GA 476 91.54 907 88.14 1.00 1.00
AA 44 8.46 122 11.86 0.69 (0.48–0.99) 0.042 0.69 (0.47–1.00) 0.050
A allele 323 31.06 710 34.50
MTHFR rs3753584 T>C
TT 403 77.35 800 77.75 1.00 1.00
CT 111 21.31 216 20.99 1.02 (0.79–1.32) 0.872 1.03 (0.79–1.35) 0.829
CC 7 1.34 13 1.26 1.07 (0.42–2.71) 0.885 1.04 (0.39–2.76) 0.937
CT+CC 118 22.65 229 22.25 1.02 (0.80–1.32) 0.860 1.03 (0.79–1.34) 0.826
TT+CT 514 98.66 1,016 98.74 1.00 1.00
CC 7 1.34 13 1.26 1.07 (0.42–2.69) 0.894 1.03 (0.39–2.74) 0.948
C allele 125 12.00 242 11.76
MTHFR rs4845882 G>A
GG 309 59.31 632 61.42 1.00 1.00
GA 191 36.66 354 34.40 1.11 (0.89–1.38) 0.378 1.12 (0.89–1.42) 0.326
AA 21 4.03 43 4.18 1.00 (0.58–1.72 0.999 1.14 (0.65–2.01) 0.642
GA+AA 212 40.69 397 38.58 1.09 (0.88–1.35) 0.422 1.12 (0.90–1.10) 0.308
GG+GA 500 95.97 986 95.82 1.00 1.00
AA 21 4.03 43 4.18 0.96 (0.57–1.64) 0.891 1.09 (0.63–1.91) 0.753
A allele 233 22.36 440 21.38
MTHFR rs4846048 A>G
AA 428 82.15 849 82.51 1.00 1.00
AG 90 17.27 165 16.03 1.08 (0.82–1.44) 0.578 1.13 (0.84–1.51) 0.423
GG 3 0.58 15 1.46 0.40 (0.11–1.38) 0.146 0.48 (0.13–1.73) 0.264
AG+GG 93 17.85 180 17.49 1.03 (0.78–1.35) 0.861 1.08 (0.81–1.44) 0.609
AA+AG 518 99.42 1,014 98.54 1.00 1.00
GG 3 0.58 15 1.46 0.39 (0.11–1.36) 0.140 0.47 (0.13–1.70) 0.250
G allele 96 9.21 195 9.48
MTHFR rs9651118 T>C
TT 187 35.89 378 36.73 1.00 1.00
TC 245 47.02 513 49.85 0.97 (0.77–1.22) 0.783 0.94 (0.74–1.20) 0.636
CC 89 17.08 138 13.41 1.31 (0.95–1.80) 0.100 1.30 (0.93–1.81) 0.124
TC+CC 334 64.11 651 63.27 1.04 (0.83–1.29) 0.745 1.02 (0.81–1.28) 0.895
TT+TC 432 82.92 891 86.59 1.00 1.00
CC 89 17.08 138 13.41 1.33 (1.00–1.78) 0.054 1.34 (0.99–1.82) 0.057
C allele 423 40.60 789 38.34
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a Adjusted for age, sex, smoking, BMI and drinking status; Bold values are statistically significant (P < 0.05).

Association of MTHFR rs1801133 G>A, rs4845882 G>A, rs4846048 A>G, rs3753584 T>C and rs9651118 T>C polymorphisms with the development of NSCLC in Different Stratification Groups

After adjustment by logistic regression analysis, we found MTHFR rs1801133 G>A variants were associated with the decreased risk of NSCLC in some subgroups (female group: AA vs. GG: adjusted OR = 0.53, 95% CI 0.30–0.94, P = 0.031 and AA vs. GA/GG: adjusted OR = 0.58, 95% CI 0.33–1.00, P = 0.048; < 60 years subgroup: AA vs. GG: adjusted OR = 0.53, 95% CI 0.28–1.00, P = 0.048; never smoking group: AA vs. GG: adjusted OR = 0.58, 95% CI 0.36–0.93, P = 0.024 and AA vs. GA/GG: adjusted OR = 0.62, 95% CI 0.39–0.99, P = 0.044; Table 4).

Table 4. Stratified analyses between MTHFR rs1801133 G>A polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption.

Variable MTHFR rs1801133 G>A (case/control)a Adjusted ORb (95% CI); P
GG GA AA GG GA AA GA /AA AA vs. (GA/GG)
Sex
Male 125/254 136/275 25/58 1.00 0.99 (0.72–1.35);P: 0.930 0.83 (0.48–1.45);P: 0.518 0.97 (0.71–1.32);P: 0.840 0.84 (0.50–1.43);P: 0.527
Female 116/187 99/191 19/64 1.00 0.85 (0.61–1.20);P: 0.357 0.53 (0.30–0.94);P: 0.031 0.78 (0.56–1.07);P: 0.126 0.58 (0.33–1.00);P: 0.048
Age
< 60 125/213 97/213 16/49 1.00 0.79 (0.56–1.12);P: 0.187 0.53 (0.28–1.00);P: 0.048 0.74 (0.53–1.03);P: 0.072 0.59 (0.32–1.09);P: 0.090
≥ 60 116/228 138/253 28/73 1.00 1.05 (0.77–1.44);P: 0.759 0.78 (0.47–1.30);P: 0.344 1.01 (0.74–1.36);P: 0.976 0.77 (0.47–1.24);P: 0.276
Smoking status
Never 153/352 137/372 26/103 1.00 0.86 (0.65–1.14);P: 0.298 0.58 (0.36–0.93);P: 0.024 0.81 (0.62–1.05);P: 0.112 0.62 (0.39–0.99);P: 0.044
Ever 88/89 98/94 18/19 1.00 1.06 (0.70–1.60);P: 0.786 0.92 (0.45–1.90);P: 0.829 1.04 (0.70–1.54);P: 0.861 0.90 (0.45–1.78);P: 0.754
Alcohol consumption
Never 210/411 197/426 36/111 1.00 0.93 (0.72–1.19);P: 0.543 0.68 (0.44–1.03);P: 0.071 0.88 (0.70–1.12);P: 0.293 0.71 (0.47–1.06);P: 0.092
Ever 31/30 38/40 8/11 1.00 0.88 (0.44–1.75);P: 0.711 0.60 (0.21–1.75);P: 0.353 0.82 (0.42–1.58);P: 0.550 0.65 (0.24–1.75);P: 0.393
BMI (kg/m2)
< 24 159/242 151/247 27/57 1.00 0.94 (0.70–1.27);P: 0.699 0.68 (0.41–1.15);P: 0.149 0.89 (0.67–1.19);P: 0.427 0.70 (0.43–1.16);P: 0.164
≥ 24 82/199 84/219 17/65 1.00 0.88 (0.61–1.27);P: 0.490 0.64 (0.35–1.17);P: 0.147 0.84 (0.59–1.19);P: 0.323 0.69 (0.38–1.23);P: 0.203
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a The genotyping was successful in 521 (99.81%) NSCLC cases, and 1030 (99.90%) controls for MTHFR rs1801133 G>A;

b Adjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model;

The correlation between MTHFR rs3753584 T>C polymorphism and NSCLC risk in the stratified analyses are summarized Table 5. We found that MTHFR rs3753584 T>C vatiants were not associated with the susceptibility of NSCLC in any subgroup (Table 5).

Table 5. Stratified analyses between MTHFR rs3753584 T>C polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption.

Variable MTHFR rs3753584 T>C (case/control)a Adjusted ORb (95% CI); P
TT TC CC TT TC CC TC / CC CC vs. (TC/TT)
Sex
Male 231/450 52/127 4/10 1.00 0.80 (0.54–1.17);P: 0.242 0.68 (0.20–2.32);P: 0.533 0.78 (0.54–1.14);P: 0.201 0.71 (0.21–2.42);P: 0.578
Female 172/350 59/89 3/3 1.00 1.35 (0.92–1.97);P: 0.128 2.51 (0.479–12.75);P: 0.268 1.38 (0.95–2.01);P: 0.093 2.34 (0.46–11.88);P: 0.304
Age
< 60 181/362 54/110 3/3 1.00 0.95 (0.65–1.40);P: 0.806 1.30 (0.25–6.67);P: 0.756 0.96 (0.66–1.41);P: 0.848 1.31 (0.26–6.73);P: 0.745
≥ 60 222/438 57/106 4/10 1.00 1.12 (0.77–1.63);P: 0.560 0.89 (0.26–3.04);P: 0.855 1.10 (0.76–1.58);P: 0.610 0.87 (0.26–2.97);P: 0.827
Smoking status
Never 240/650 73/168 4/9 1.00 1.16 (0.84–1.60);P: 0.359 1.48 (0.44–5.00);P: 0.528 1.18 (0.86–1.61);P: 0.314 1.43 (0.43–4.83);P: 0.562
Ever 163/150 38/48 3/4 1.00 0.77 (0.47–1.25);P: 0.281 0.66 (0.14–3.02);P: 0.593 0.76 (0.47–1.21);P: 0.247 0.70 (0.15–3.20);P: 0.645
Alcohol consumption
Never 341/735 96/202 7/11 1.00 0.99 (0.74–1.32);P: 0.937 1.43 (0.52–3.92);P: 0.489 1.01 (0.76–1.33);P: 0.951 1.43 (0.52–3.92);P: 0.486
Ever 62/65 15/14 0/2 1.00 1.31 (0.56–3.07);P: 0.537 - 1.08 (0.48–2.45);P: 0.852 -
BMI (kg/m2)
< 24 265/432 69/105 3/9 1.00 1.04 (0.73–1.48);P: 0.830 0.47 (0.12–1.84);P: 0.278 0.99 (0.70–1.40);P: 0.960 0.47 (0.12–1.82);P: 0.272
≥ 24 138/368 42/111 4/4 1.00 0.99 (0.65–1.50);P: 0.946 3.34 (0.80–14.04);P: 0.100 1.06 (0.70–1.59);P: 0.788 3.35 (0.80–14.04);P: 0.098
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a The genotyping was successful in 521 (100.00%) NSCLC cases, and 1030 (99.90%) controls for MTHFR rs3753584 T>C;

b Adjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model;

The relationship of MTHFR rs4845882 G>A polymorphism with NSCLC susceptibility in the stratified analysis is listed in Table 6. We identified that MTHFR rs4845882 G>A polymorphism was associated with the development of NSCLC in female subgroup (GA vs. GG: adjusted OR = 1.47, 95% CI 1.05–2.05, P = 0.025).

Table 6. Stratified analyses between MTHFR rs4845882 G>A polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption.

Variable MTHFR rs4845882 G>A (case/control)a Adjusted ORb (95% CI); P
GG GA AA GG GA AA GA /AA AA vs. (GA/GG)
Sex
Male 177/349 94/212 16/26 1.00 0.87 (0.63–1.21);P: 0.411 1.42 (0.71–2.85);P: 0.322 0.93 (0.68–1.26);P: 0.627 1.49 (0.75–2.96);P: 0.254
Female 132/283 97/142 5/17 1.00 1.47 (1.05–2.05);P: 0.025 0.68 (0.24–1.90);P: 0.460 1.39 (1.00–1.93);P: 0.051 0.59 (0.21–1.63);P: 0.304
Age
<60 136/286 92/169 10/20 1.00 1.17 (0.83–1.65);P: 0.361 1.15 (0.50–2.61);P: 0.746 1.17 (0.84–1.63);P: 0.359 1.08 (0.48–2.42);P: 0.862
≥60 173/346 99/185 11/23 1.00 1.07 (0.78–1.47);P: 0.685 1.12 (0.52–2.43);P: 0.777 1.07 (0.79–1.46);P: 0.653 1.09 (0.51–2.35);P: 0.820
Smoking status
Never 186/510 119/282 12/35 1.00 1.16 (0.88–1.53);P: 0.301 1.09 (0.54–2.18);P: 0.815 1.15 (0.88–1.51);P: 0.313 1.03 (0.52–2.04);P: 0.940
Ever 123/122 72/72 9/8 1.00 1.02 (0.68–1.55);P: 0.914 1.21 (0.45–3.27);P: 0.705 1.04 (0.70–1.56);P: 0.842 1.20 (0.45–3.20);P: 0.714
Alcohol consumption
Never 260/581 168/327 16/40 1.00 1.14 (0.89–1.45);P: 0.312 0.99 (0.53–1.85);P: 0.982 1.12 (0.88–1.42);P: 0.354 0.95 (0.51–1.75);P: 0.858
Ever 49/51 23/27 5/3 1.00 0.95 (0.47–1.92);P: 0.893 2.23 (0.47–10.67);P: 0.315 1.06 (0.54–2.08);P: 0.858 2.27 (0.48–10.64);P: 0.298
BMI (kg/m2)
< 24 207/338 120/190 10/18 1.00 1.06 (0.78–1.42);P: 0.724 1.03 (0.45–2.35);P: 0.952 1.05 (0.79–1.41);P: 0.737 1.00 (0.44–2.28);P: 0.992
≥ 24 102/294 71/164 11/25 1.00 1.24 (0.85–1.79);P: 0.262 1.26 (0.58–2.72);P: 0.557 1.24 (0.87–1.77);P: 0.236 1.16 (0.55–2.48);P: 0.697
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a The genotyping was successful in 521 (100.00%) NSCLC cases, and 1030 (99.90%) controls for MTHFR rs4845882 G>A;

b Adjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model;

Table 7 demonstrated that MTHFR rs4846048 A>G polymorphism was not associated with the development of NSCLC in any subgroup.

Table 7. Stratified analyses between MTHFR rs4846048 A>G polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption.

Variable MTHFR rs4846048 A>G (case/control)a Adjusted ORb (95% CI); P
AA AG GG AA AG GG AG/GG GG vs. (AG/AA)
Sex
Male 233/488 51/88 3/11 1.00 1.29(0.86-1.94);P: 0.221 0.68(0.18-2.64);P: 0.576 1.22(0.83-1.81);P: 0.314 0.65(0.17-2.52);P: 0.535
Female 195/361 39/77 0/4 1.00 0.97(0.63-1.49);P: 0.884 - 0.92(0.60-1.41);P: 0.701 -
Age
< 60 191/393 47/74 0/8 1.00 1.45(0.95-2.22);P: 0.088 - 1.32(0.87-2.00);P: 0.197 -
≥ 60 237/456 43/91 3/7 1.00 0.88(0.59-1.33);P: 0.555 0.94(0.23-3.86);P: 0.936 0.89(0.60-1.32);P: 0.558 0.96(0.24-3.93);P: 0.959
Smoking status
Never 262/676 53/140 2/11 1.00 0.98(0.69-1.40);P: 0.925 0.60(0.13-2.79);P: 0.512 0.96(0.68-1.36);P: 0.810 0.60(0.13-2.79);P: 0.513
Ever 166/173 37/25 1/4 1.00 1.54(0.89-2.68);P: 0.126 0.31(0.03-2.87);P: 0.302 1.39(0.81-2.36);P: 0.230 0.29(0.03-2.67);P: 0.274
Alcohol consumption
Never 366/779 76/156 2/13 1.00 1.06(0.77-1.45);P: 0.723 0.41(0.09-1.89);P: 0.254 1.01(0.75-1.38);P: 0.931 0.41(0.09-1.87);P: 0.248
Ever 62/70 14/9 1/2 1.00 1.95(0.77-4.96);P: 0.162 0.57(0.05-7.23);P: 0.664 1.70(0.71-4.09);P: 0.238 0.52(0.04-6.52);P: 0.610
BMI(kg/m2)
< 24 281/455 55/86 1/5 1.00 1.10(0.75-1.62);P: 0.629 0.55(0.06-4.92);P: 0.591 1.08(0.74-1.57);P: 0.708 0.54(0.06-4.83);P: 0.579
≥ 24 147/394 35/79 2/10 1.00 1.20(0.76-1.89);P: 0.433 0.41(0.08-2.02);P: 0.274 1.10(0.71-1.71);P: 0.678 0.40(0.08-1.96);P: 0.258
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aThe genotyping was successful in 521 (100.00%) NSCLC cases, and 1030 (99.90%) controls for MTHFR rs4846048 A>G;

bAdjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.

We found that MTHFR rs9651118 T>C polymorphism increased the risk of NSCLC in several stratified analyses (<60 years group: CC vs. TT: adjusted OR = 1.64, 95% CI 1.00–2.69, P = 0.049 and CC vs. TC/TT: adjusted OR = 1.75, 95% CI 1.12–2.74, P = 0.014; never smoking subgroup: CC vs. TC/TT: adjusted OR = 1.50, 95% CI 1.05–2.14, P = 0.025; BMI < 24 kg/m2 group: CC vs.TT: adjusted OR = 1.56, 95% CI 1.01–2.39, P = 0.044 and CC vs. TC/TT: adjusted OR = 1.56, 95% CI 1.06–2.29, P = 0.023; Table 8).

Table 8. Stratified analyses between MTHFR rs9651118 T>C polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption.

Variable MTHFR rs9651118 T>C (case/control)a Adjusted ORb (95% CI); P
TT TC CC TT TC CC TC / CC CC vs. (TC/TT)
Sex
Male 110/209 133/300 44/78 1.00 0.85 (0.61–1.18);P: 0.330 1.11 (0.69–1.76);P: 0.676 0.90 (0.66–1.23);P: 0.503 1.21 (0.79–1.86);P: 0.381
Female 77/169 112/213 45/60 1.00 1.05 (0.73–1.51);P: 0.794 1.48 (0.92–2.39);P: 0.109 1.15 (0.81–1.61);P: 0.435 1.44 (0.94–2.22);P: 0.098
Age
<60 82/163 110/255 46/57 1.00 0.90 (0.62–1.29);P: 0.549 1.64 (1.00–2.69);P: 0.049 1.03 (0.73–1.45);P: 0.868 1.75 (1.12–2.74);P: 0.014
≥60 105/215 135/258 43/81 1.00 1.00 (0.72–1.39);P: 1.00 1.05 (0.66–1.65);P: 0.848 1.01 (0.74–1.38);P: 0.946 1.05 (0.69–1.59);P: 0.834
Smoking status
Never 111/305 146/412 60/110 1.00 0.93 (0.69–1.25);P: 0.646 1.45 (0.98–2.14);P: 0.066 1.04 (0.79–1.37);P: 0.791 1.50 (1.05–2.14);P: 0.025
Ever 76/73 99/101 29/28 1.00 0.95 (0.62–1.46);P: 0.819 0.97 (0.52–1.80);P: 0.918 0.96 (0.64–1.44);P: 0.824 1.00 (0.56–1.76);P: 0.989
Never 159/340 205/479 80/129 1.00 0.87 (0.67–1.13);P: 0.296 1.25 (0.88–1.78);P: 0.212 0.95 (0.74–1.21);P: 0.677 1.35 (0.99–1.86);P: 0.062
Ever 28/38 40/34 9/9 1.00 1.66 (0.83–3.30);P: 0.153 1.45 (0.49–4.24);P: 0.501 1.61 (0.83–3.11);P: 0.155 1.11 (0.40–3.03);P: 0.845
BMI (kg/m2)
< 24 108/187 165/287 64/72 1.00 0.99 (0.72–1.36);P: 0.967 1.56 (1.01–2.39);P: 0.044 1.10 (0.81–1.49);P: 0.532 1.56 (1.06–2.29);P: 0.023
≥ 24 79/191 80/226 25/66 1.00 0.85 (0.58–1.25);P: 0.406 0.95 (0.55–1.64);P: 0.850 0.87 (0.61–1.25);P: 0.456 1.03 (0.62–1.72);P: 0.908
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aThe genotyping was successful in 521 (100.00%) NSCLC cases, and 1030 (99.90%) controls for MTHFR rs9651118 T>C;

bAdjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model;

SNP haplotypes

Using SHEsis software (http://analysis.bio-x.cn) [18], we further constructed seven MTHFR haplotypes (Table 9). Haplotype comparison analysis indicated that MTHFR haplotypes were not associated with the risk of NSCLC.

Table 9. MTHFR haplotype frequencies (%) in cases and controls and risk of NSCLC.

Case (n = 1042) Control (n = 2060) Crude OR (95% CI) P
n % n %
G T G A C 414 39.81 774 37.63 1.00
A T G A T 317 30.48 687 33.40 0.86 (0.72–1.03) 0.105
G C A A T 121 11.63 220 10.70 1.03 (0.80–1.32) 0.828
G T A G T 92 8.85 185 8.99 0.93 (0.70–1.23) 0.606
G T G A T 68 6.54 131 6.37 0.97 (0.71–1.33) 0.853
G T A A T 13 1.25 21 1.02 1.16 (0.57–2.34) 0.683
Others 15 1.44 39 1.90 0.72 (0.39–1.32) 0.285
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With the order of rs1801133 G>A, rs3753584 T>C, rs4845882 G>A, rs4846048 A>G and rs9651118 T>C in gene position.

DISCUSSION

In the present study, the relationships of MTHFR tagging polymorphisms with the development of NSCLC risk were explored. The results highlighted that MTHFR rs1801133 G>A might decrease the risk of overall NSCLC. In addition, we found MTHFR rs1801133 G>A variants were associated with the decreased risk of NSCLC in female, < 60 years and never smoking subgroups. However, we found that MTHFR rs4845882 G>A polymorphism was associated with the development of NSCLC in female subgroup. The association between MTHFR rs9651118 T>C polymorphism and the increased the risk of NSCLC was also evident in < 60 years, never smoking and BMI < 24 kg/m2 subgroups.

Variants of MTHFR, which is an important regulator of intracellular folate metabolism, were found that they were associated with the increased level of circulating homocysteine and many diseases involving NSCLC. A number of case-control studies focused on the association of NSCLC with MTHFR SNPs and had controversial findings. Recently, a meta-analysis which included twenty-six studies demonstrated that MTHFR contribute to the risk of NSCLC in Asians and overall populations, but not Caucasians [17]. Another meta-analysis which enrolled 10 studies with 2487 cases and 3228 controls suggested that rs1801133 G>A polymorphism in MTHFR gene may not be a risk factor of NSCLC in Chinese populations; however, the association between this SNP and NSCLC risk might alter in different region of China [16]. Clearly, these ambiguous findings indicated that the function of MTHFR rs1801133 G>A polymorphism might be varied in different race or even in the different region of the same ethnicity, which suggested large-scale case-control studies in different regions and ethnicities were needed to further explore the potential relationship. It was found that activation and variant frequencies of MTHFR might alter among different region and different latitude with the various ultraviolet-exposure levels [19, 20]. Furthermore, the sample sizes were relatively small in most of included studies. A functional study indicated that MTHFR rs1801133 G>A polymorphism was a protective factor of prostate cancer (PC) susceptibility by elevating homocysteine level, promoting cell apoptosis, and inhibiting proliferation of PC cells [21]. In the present study, we found that MTHFR rs1801133 A allele might be a protective factor for NSCLC, which was similar to the findings of previous study conducted in Eastern Chinese Han populations. However, these potential associations were not significant after the Bonferroni correction for multiple comparisons. Thus, our findings should be explained with very cautions.

Rs4845882 G>A, a SNP locate in intron region of MTHFR gene, was strongly complete linkage disequilibrium (LD) with MTHFR rs1801131 A>C polymorphism [(r2 = 0.935); http://gvs.gs.washington.edu/GVS147/]. Wang et al. reported there was no significant correlation between MTHFR rs4845882 G>A polymorphism and gastric cardia carcinoma (GCA) risk [22]. However, another study saw a decreased esophageal squamous cell carcinoma (ESCC) risk in Chinese Han individuals with MTHFR rs4845882 AA genotype [23]. In the present study, we found that MTHFR 4845882 G>A might be a risk factor for NSCLC in female subgroup. These inconsistent findings may be due to the limited sample size or other confounding factors. In the future, large-scale study with comprehensive functional exploring should be conducted. And the confounding gene or environmental factors also could not be ignored.

In this study, we found MTHFR rs9651118 T>C polymorphism was associated with the NSCLC risk in some subgroups. And recent case-control studies indicated that this SNP might play different roles among different type of cancer. For example, some studies suggested that MTHFR rs9651118 T>C polymorphism was associated with the decreased susceptibility of LC and PC [14, 24]. While Tang et al. and Wang et al. observed a null association of MTHFR rs9651118 T>C polymorphism with the risk of ESCC and GCA [22, 23]. Therefore, whether the T-to-C transition in the intron 2 region does alter the functions of MTHFR gene needs to be further explored.

However, several limitations in our study must be acknowledged. Firstly, other functional SNP loci in the region of the MTHFR gene may be related to NSCLC susceptibility. Unfortunately, because of genotyping cost, we were unable to perform a fine-mapping study focusing on the association between MTHFR SNPs and NSCLC risk. Secondly, the sample size of NSCLC patients was moderate and detailed information of some NSCLC patients was not available. The relationship of MTHFR SNPs with tumor stages or cancer subtypes was not carried out. This could limit the validity of the findings because these potentially factors might not be well understood. Thirdly, selected biases might result in spurious findings because the NSCLC patients and the controls were enrolled from the local hospitals. Finally, other potential gene-environment factors were not considered. Further studies focusing on the interactions of multiple environment and gene factors on NSCLC risk are needed to confirm our findings.

In conclusion, the current study highlights MTHFR rs1801133 G>A variants are associated with the decreased risk of NSCLC. However, MTHFR rs4845882 G>A and MTHFR rs9651118 T>C polymorphisms may increase the risk of NSCLC. Well-designed large-scale studies are needed to confirm these findings and explore the interactions of gene-gene and gene-environment involved in MTHFR SNPs and NSCLC.

MATERIALS AND METHODS

Ethics Statement

This case-control study was conducted in Fujian and Jiangsu Province in Eastern of China. The ethical board approval from Ethics Committee of Fujian Medical University (Fuzhou, China) and Jiangsu University (Zhenjiang, China) was obtained, and all of the participants signed written informed consent.

Subjects

All sporadic NSCLC patients were enrolled from the Affiliated Union Hospital of Fujian Medical University and the Affiliated People's Hospital of Jiangsu University. Our study consisted of 521 NSCLC patients (mean age 59.76 ± 10.71 years) from January 2014 to December 2016. The diagnosis was confirmed based on pathological findings. For comparison, 1,030 non-cancer controls (mean age 60.34 ± 9.11 years) were recruited from normal volunteers who conducted health check in the Physical Examination Center of these hospitals. The controls had no history of autoimmune disorders or personal malignancy, and were frequency well-matched to patients by age and sex. The included risk factors (tobacco consumption and drinking) and demographic details of the NSCLC patients and controls were obtained by using a structured questionnaire. The data are listed in Table 1.

Preparation of genomic DNA

Lymphocytes were separated from EDTA-anticoagulated whole blood. Genomic DNA was carefully extracted using the Promega DNA kit (Promega, Madison, USA).

SNP selection

SNPs were selected using Haploview 4.2 software and the HapMap database. Five haplotype-tagging SNPs of MTHFR gene (rs3753584 T>C, rs4845882 G>A, rs1801133 G>A, rs4846048 A>G and rs9651118 T>C) were selected, with MAF > 5%, call rate ≥ 95 %, HWE P ≥ 0.05 and pair-wise r2 < 0.8 for each SNP pair. In total, the five tagging SNPs were selected by spaning the entire MTHFR gene region (upstream and downstream extending 5 Kb, respectively). The primary information of the selected SNPs is presented in Table 2.

Genotyping

All SNPs were genotyped using the SNPscanTM genotyping assay (Genesky Biotechologies Inc., Shanghai, China), which is a double ligation and multiplex fluorescence PCR [25]. The accuracy of genotyping results were verified by reanalyzing the genotypes in 4% random samples.

Statistical analysis

Age of NSCLC patients and controls was described as the mean ± deviation (SD). And a Student's t-test was used to examine the difference for age. The deviation from HWE was assessed using an online goodness-of-fit chi-squared test (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) in controls [26–32]. Using different genetic models (additive, homozygote, dominant, recessive), the genotype frequencies of the subjects were compared using the chi-squared (χ2) test. Multivariate logistic regression analysis was harnessed to assess the risk of mutant genotype with respect to wild type and considered established confounders such as age, sex, smoking, BMI and drinking status. Crude/adjusted ORs with their 95% CIs were used to assess the difference for the genotype distribution. All data were analyzed using SAS software (Version 9.4; SAS Institute Inc., Cary, NC, USA). SHESIS program (Bio-X Inc., Shanghai, China, http://analysis.bio-x.cn/myAnalysis.php)] [18] was used to construct haplotypes of MTHFR gene. The association of MTHFR haplotypes with NSCLC risk was estimated as crude ORs with the corresponding 95% CIs. In this study, the threshold for significance was P < 0.05 (two tailed). We used Bonferroni correction to perform multiple comparisons [33].

Acknowledgments

We appreciate all subjects who participated in this study. We wish to thank Dr. Yan Liu (Genesky Biotechnologies Inc., Shanghai, China) for technical support.

Footnotes

CONFLICTS OF INTEREST

The authors have no potential financial conflicts of interest.

GRANT SUPPORT

This study was supported by Natural Science Foundation of Universities and Colleges of Jiangsu Province (Grant No. 16KJB310002), Young and Middle-aged Talent Training Project of Health Development Planning Commission in Fujian Province (2016-ZQN-25 and 2014-ZQN-JC-11), Medical Innovation Project of Fujian Province (2014-CX-15 and 2014-CX-18), Nursery Garden Project of Fujian Medical University (2015MP020) and Science and Technology Project of Fujian Province (2060203).

REFERENCES

  • 1.Hamra GB, Guha N, Cohen A, Laden F, Raaschou-Nielsen O, Samet JM, Vineis P, Forastiere F, Saldiva P, Yorifuji T, Loomis D. Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environ Health Perspect. 2014;122:906–11. doi: 10.1289/ehp/1408092. https://doi.org/10.1289/ehp.1408092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Kopp M, Morisset R, Rychlik M. Characterization and Interrelations of One-Carbon Metabolites in Tissues, Erythrocytes, and Plasma in Mice with Dietary Induced Folate Deficiency. Nutrients. 2017:9. doi: 10.3390/nu9050462. https://doi.org/10.3390/nu9050462 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bird AP. The relationship of DNA methylation to cancer. Cancer Surv. 1996;28:87–101. [PubMed] [Google Scholar]
  • 4.Zingg JM, Jones PA. Genetic and epigenetic aspects of DNA methylation on genome expression, evolution, mutation and carcinogenesis. Carcinogenesis. 1997;18:869–82. doi: 10.1093/carcin/18.5.869. [DOI] [PubMed] [Google Scholar]
  • 5.Baylin SB. Tying it all together: epigenetics, genetics, cell cycle, and cancer. Science. 1997;277:1948–9. doi: 10.1126/science.277.5334.1948. [DOI] [PubMed] [Google Scholar]
  • 6.Igari S, Ohtaki A, Yamanaka Y, Sato Y, Yohda M, Odaka M, Noguchi K, Yamada K. Properties and crystal structure of methylenetetrahydrofolate reductase from Thermus thermophilus HB8. PLoS One. 2011;6:e23716. doi: 10.1371/journal.pone.0023716. https://doi.org/10.1371/journal.pone.0023716 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Marini NJ, Gin J, Ziegle J, Keho KH, Ginzinger D, Gilbert DA, Rine J. The prevalence of folate-remedial MTHFR enzyme variants in humans. Proc Natl Acad Sci U S A. 2008;105:8055–60. doi: 10.1073/pnas.0802813105. https://doi.org/10.1073/pnas.0802813105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Rai V. Folate pathway gene MTHFR C677T polymorphism and risk of lung cancer in Asian populations. Asian Pac J Cancer Prev. 2014;15:9259–64. doi: 10.7314/apjcp.2014.15.21.9259. [DOI] [PubMed] [Google Scholar]
  • 9.Cheng Z, Wang W, Dai LL, Kang Y. MTHFR C667T polymorphism association with lung cancer risk in Henan province: a case-control study. Asian Pac J Cancer Prev. 2012;13:2491–4. doi: 10.7314/apjcp.2012.13.6.2491. [DOI] [PubMed] [Google Scholar]
  • 10.Cui LH, Yu Z, Zhang TT, Shin MH, Kim HN, Choi JS. Influence of polymorphisms in MTHFR 677 C-->T, TYMS 3R-->2R and MTR 2756 A-->G on NSCLC risk and response to platinum-based chemotherapy in advanced NSCLC. Pharmacogenomics. 2011;12:797–808. doi: 10.2217/pgs.11.27. https://doi.org/10.2217/pgs.11.27 [DOI] [PubMed] [Google Scholar]
  • 11.Al-Motassem Y, Shomaf M, Said I, Berger S, Ababneh N, Diab O, Obeidat N, Awidi A. Allele and Genotype Frequencies of the Polymorphic Methylenetetrahydrofolate Reductase and Lung Cancer in ther Jordanian Population: a Case Control Study. Asian Pac J Cancer Prev. 2015;16:3101–9. doi: 10.7314/apjcp.2015.16.8.3101. [DOI] [PubMed] [Google Scholar]
  • 12.Cavic M, Krivokuca A, Spasic J, Brotto K, Malisic E, Radosavljevic D, Radulovic S, Jankovic R. The influence of methylenetetrahydrofolate reductase and thymidylate synthetase gene polymorphisms on lung adenocarcinoma occurrence. J BUON. 2014;19:1024–8. [PubMed] [Google Scholar]
  • 13.Yilmaz M, Kacan T, Sari I, Kilickap S. Lack of association between the MTHFRC677T polymorphism and lung cancer in a Turkish population. Asian Pac J Cancer Prev. 2014;15:6333–7. doi: 10.7314/apjcp.2014.15.15.6333. [DOI] [PubMed] [Google Scholar]
  • 14.Swartz MD, Peterson CB, Lupo PJ, Wu X, Forman MR, Spitz MR, Hernandez LM, Vannucci M, Shete S. Investigating multiple candidate genes and nutrients in the folate metabolism pathway to detect genetic and nutritional risk factors for lung cancer. PLoS One. 2013;8:e53475. doi: 10.1371/journal.pone.0053475. https://doi.org/10.1371/journal.pone.0053475 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Liu ZB, Wang LP, Shu J, Jin C, Lou ZX. Methylenetetrahydrofolate reductase 677TT genotype might be associated with an increased lung cancer risk in Asians. Gene. 2013;515:214–9. doi: 10.1016/j.gene.2012.11.036. https://doi.org/10.1016/j.gene.2012.11.036 [DOI] [PubMed] [Google Scholar]
  • 16.Wang X, Yue K, Hao L. Meta-analysis of methylenetetrahydrofolate reductase polymorphism and lung cancer risk in Chinese. Int J Clin Exp Med. 2015;8:1521–5. [PMC free article] [PubMed] [Google Scholar]
  • 17.Yang Y, Yang LJ, Deng MZ, Luo YY, Wu S, Xiong L, Wang D, Liu Y, Liu H. MTHFR C677T and A1298C polymorphisms and risk of lung cancer: a comprehensive evaluation. Genet Mol Res. 2016:15. doi: 10.4238/gmr.15027615. https://doi.org/10.4238/gmr.15027615 [DOI] [PubMed] [Google Scholar]
  • 18.Shi YY, He L. SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 2005;15:97–8. doi: 10.1038/sj.cr.7290272. https://doi.org/10.1038/sj.cr.7290272 [DOI] [PubMed] [Google Scholar]
  • 19.Botto LD, Yang Q. 5,10-Methylenetetrahydrofolate reductase gene variants and congenital anomalies: a HuGE review. Am J Epidemiol. 2000;151:862–77. doi: 10.1093/oxfordjournals.aje.a010290. [DOI] [PubMed] [Google Scholar]
  • 20.Jablonski NG, Chaplin G. Colloquium paper: human skin pigmentation as an adaptation to UV radiation. Proc Natl Acad Sci U S A. 2010;10:8962–8. doi: 10.1073/pnas.0914628107. https://doi.org/10.1073/pnas.0914628107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Wu JL, Zhou SX, Zhao R, Zhang X, Chang K, Gu CY, Gan HL, Dai B, Zhu Y, Zhang HL, Shi GH, Qu YY, Zhao JY, et al. MTHFR c.677C>T Inhibits Cell Proliferation and Decreases Prostate Cancer Susceptibility in the Han Chinese Population in Shanghai. Sci Rep. 2016;6:36290. doi: 10.1038/srep36290. https://doi.org/10.1038/srep36290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wang Y, Chen S, Kang M, Tang W, Gu H, Yin J, Huang Z. Genetic variations in MTHFR and gastric cardia adenocarcinoma susceptibility in the Chinese Han population. Int J Clin Exp Med. 2015;8:18936–44. [PMC free article] [PubMed] [Google Scholar]
  • 23.Tang W, Zhang S, Qiu H, Wang L, Sun B, Yin J, Gu H. Genetic variations in MTHFR and esophageal squamous cell carcinoma susceptibility in Chinese Han population. Med Oncol. 2014;31:915. doi: 10.1007/s12032-014-0915-6. https://doi.org/10.1007/s12032-014-0915-6 [DOI] [PubMed] [Google Scholar]
  • 24.Lin VC, Lu TL, Yin HL, Yang SF, Lee YC, Liu CC, Huang CY, Yu CC, Chang TY, Huang SP, Bao BY. Prognostic Relevance of Methylenetetrahydrofolate Reductase Polymorphisms for Prostate Cancer. Int J Mol Sci. 2016:17. doi: 10.3390/ijms17121996. https://doi.org/10.3390/ijms17121996 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yin J, Wang X, Wei J, Wang L, Shi Y, Zheng L, Tang W, Ding G, Liu C, Liu R, Chen S, Xu Z, Gu H. Interleukin 12B rs3212227 T > G polymorphism was associated with an increased risk of gastric cardiac adenocarcinoma in a Chinese population. Dis Esophagus. 2015;28:291–8. doi: 10.1111/dote.12189. https://doi.org/10.1111/dote.12189 [DOI] [PubMed] [Google Scholar]
  • 26.Zhang S, Wang Y, Jiang H, Liu C, Gu H, Chen S, Kang M, Tang W. Association between the CD28 IVS3 +17T>C (rs3116496) polymorphism and cancer susceptibility: a meta-analysis involving 8,843 subjects. Int J Clin Exp Med. 2015;8:17353–61. [PMC free article] [PubMed] [Google Scholar]
  • 27.Zhang S, Wang Y, Jiang H, Liu C, Sun B, Chen S, Kang M, Tang W. Peroxisome proliferator-activated receptor gamma rs1801282 C>G polymorphism is associated with polycystic ovary syndrome susceptibility: a meta-analysis involving 7,069 subjects. Int J Clin Exp Med. 2015;8:17418–29. [PMC free article] [PubMed] [Google Scholar]
  • 28.Tang W, Wang Y, Jiang H, Liu P, Liu C, Gu H, Chen S, Kang M. Programmed death-1 (PD-1) rs2227981 C > T polymorphism is associated with cancer susceptibility: a meta-analysis. Int J Clin Exp Med. 2015;8:22278–85. [PMC free article] [PubMed] [Google Scholar]
  • 29.Qiu H, Cheng C, Wang Y, Kang M, Tang W, Chen S, Gu H, Liu C, Chen Y. Investigation of cyclin D1 rs9344 G>A polymorphism in colorectal cancer: a meta-analysis involving 13,642 subjects. Onco Targets Ther. 2016;9:6641–50. doi: 10.2147/OTT.S116258. https://doi.org/10.2147/OTT.S116258 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Tang W, Qiu H, Ding H, Sun B, Wang L, Yin J, Gu H. Association between the STK15 F31I polymorphism and cancer susceptibility: a meta-analysis involving 43,626 subjects. PLoS One. 2013;8:e82790. doi: 10.1371/journal.pone.0082790. https://doi.org/10.1371/journal.pone.0082790 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Tang W, Qiu H, Jiang H, Sun B, Wang L, Yin J, Gu H. Lack of association between cytotoxic T-lymphocyte antigen 4 (CTLA-4) -1722T/C (rs733618) polymorphism and cancer risk: from a case-control study to a meta-analysis. PLoS One. 2014;9:e94039. doi: 10.1371/journal.pone.0094039. https://doi.org/10.1371/journal.pone.0094039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tang W, Wang Y, Chen S, Lin J, Chen B, Yu S, Chen Y, Gu H, Kang M. Investigation of Cytotoxic T-lymphocyte antigen 4 Polymorphisms in Gastric Cardia Adenocarcinoma. Scand J Immunol. 2016;83:212–8. doi: 10.1111/sji.12409. https://doi.org/10.1111/sji.12409 [DOI] [PubMed] [Google Scholar]
  • 33.Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. 1995;310:170. doi: 10.1136/bmj.310.6973.170. [DOI] [PMC free article] [PubMed] [Google Scholar]

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