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A meta-analysis on the susceptibility to the development of bladder cancer in the presence of DNMT3A, DNMT3B, and MTHFR gene polymorphisms

African Journal of Urology volume 28, Article number: 32 (2022) Cite this article

Abstract

Background

The etiology of bladder cancer is not yet well known. In this study, we want to evaluate the effect of polymorphisms of genes that have an epigenetic effect (MTHFR, DNMT3A/B) on the susceptibility to develop bladder cancer (BC).

Methods

A systematic review was performed for MTHFR, DNMT3A, and DNMT3B, followed by a meta-analysis conducted for rs1801131, rs1801133, rs2274976, rs1550117, and rs1569686 SNPs. A sensitivity and a subgroup analysis were then used.

Results

20 studies were included, where no statistically significant association between any of the analyzed SNPs and the occurrence of BC was detected. Subgroup analysis revealed a statistically significant association in North African population with rs1801133: TT vs. TC + CC (P = 0.013; OR 95% CI = 0.52 [0.311–0.872]); TT vs.TC (P = 0.003; OR 95% CI = 0.448 [0.261–0.769]) and in North American population with rs1801131: CC vs. CA (P = 0.039; OR 95% CI = 0.71 [0.523–0.984]). A sensitivity analysis revealed that there is a statistically significant association between rs1801131 and the occurrence of BC (OR = 0.79, 95%CI [0.65–0.97]), (OR = 0.80, 95%CI [0.65–0.98]) and (OR = 0.78, 95%CI [0.63–0.96]) which correspond to CC vs. CA + AA; CC vs. CA; and CC vs. AA genetic models.

Conclusion

This is the first study to assess the effect of DNMTs on bladder cancer risk. No statistically significant association was found between polymorphisms of MTHFR, DNMT3A/B genes and bladder cancer development, except for the North African and the North American populations with rs1801133 and  rs1801131, respectively, with a protective effect of rs1801131 based on a sensitivity analysis.

1 Background

According to GLOBOCAN data, bladder cancer (BC) holds the 10th place among the most frequently diagnosed cancers in the world, with approximately 573,000 new cases and 213,000 deaths in 2020. Men are more likely to develop bladder cancer than women, with incidences and mortality rates of 9.5 and 3.3 per 100,000 for men worldwide, about four times higher than for women [1].

The etiology of bladder cancer is not yet well known. However, there are risk factors associated with the disease, including environmental factors such as smoking and occupational exposure to some types of chemicals [2]. Furthermore, among people exposed to the same environment, only a small fraction develop bladder cancer, which implies the role of genetic as well as epigenetic factors in bladder carcinogenesis [3].

The term epigenetics refers to inherited changes in gene expression that are not induced by changes in the DNA sequence itself. The DNA methylation mechanism is one of the earliest and most extensively studied types of epigenetic regulation [4]. Its association with developmental processes, aging, and carcinogenesis through mechanisms such as hypermethylation of tumor suppressor genes and hypomethylation of oncogenes is well known [5].

There are several genes classified as having an epigenetic effect, including the Methylene Tetra Hydrofolate Reductase (MTHFR) gene. It belongs to the one-carbon metabolic chain (OCM) and has an essential role in the release of free methyl groups (CH3) during the irreversible reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate [6].

In the process of DNA methylation, the methyl group (CH3) is added to the 5-carbon of cytosine, leading to the formation of 5-methylcytosine (5-mC) through enzymes of the DNA methyltransferase (DNMTs) family. Two main methylation pathways are found: the establishment pathway and the maintenance pathway. They take place during embryonic development via DNMT3A and DNMT3B, and during DNA replication via DNMT1 [7]. By means of these enzymes, DNA methylation patterns are established and inherited throughout the subsequent cell generations. It is therefore a mechanism of cellular memory and it is linked to critical information on how gene expression is programmed [8].

Ren et al. suggest that the MTHFR gene rs1801133 is associated with global DNA hypomethylation, which might consequently contribute to quantitative alterations at the genetic level and thus to susceptibility to cancer development [9]. In addition, possible associations between SNPs of DNMTs and the predisposition to different types of cancers have been reported: DNMT3A with gastric cancer [10], DNMT3B with breast cancer [11], and lung cancer [12].

In light of these findings, we set out to evaluate the impact of five SNPs in the MTHFR and DNMT genes on bladder cancer susceptibility, both qualitatively and quantitatively.

2 Methods

2.1 Research strategy

A literature search was performed on PubMed and Google Scholar databases in order to identify relevant studies published until November 6th, 2021. We used the same search strategy for the three genes, changing only the gene names each time. As an example, in the search strategy for the MTHFR gene on PubMed:((Bladder) AND (cancer)) AND (MTHFR), ((Urothelial carcinoma) OR (Bladder cancer)) AND (MTHFR), (Urothelial carcinoma) AND (MTHFR), (Transitional cell carcinoma) AND (MTHFR gene), (Urothelial carcinoma) AND (MTHFR polymorphism), (MTHFR polymorphism) AND (Transitional cell carcinoma), (Bladder cancer) AND (MTHFR polymorphism), (Transitional cell carcinoma) AND (MTHFR polymorphism). See Appendix A for DNMT3A’s research strategy. The working language was limited to English.

2.2 Selection process

All included studies in the present analysis fulfilled the following inclusion criteria: studies that examined the association between SNPs of DNMTs and MTHFR with susceptibility to the development of bladder cancer in humans; case control studies; and availability of genotypic frequencies. The exclusion criteria were: papers or studies with overlapping data, duplicate studies, lack of full text, not a case/control study, not related to bladder Cancer, Review/ Meta-analysis. Data and SNPs are not available.

2.3 Data extraction

Two authors independently examined the records. While one author extracted the data (first author, year of publication, study population, genotyping methods, genes, SNPs, number of cases and controls, and Hardy–Weinberg equilibrium (HWE), the second author was consulted in cases where the data was uncertain. After a number of discussions, a common agreement has finally been reached regarding each uncertain data point. Three authors were contacted to obtain either additional information or to request the full text of the article. No feedback has been received yet.

2.4 Quality control

In the quantitative analysis, each study included was scored according to the quality assessment criteria used by Li et al., with overall quality scores ranging from 0 to 15. Thus, studies with a score of ≥ 9 are considered to be of high quality; otherwise, they are classified as being of low quality [13].

2.5 Statistical analysis

In this meta-analysis, Hardy–Weinberg equilibrium (HWE) was recalculated in controls. p values and FDR-adjusted p values greater than 0.05 were considered to be in HWE. The OR (95% CI) and p-values calculation of the quantitative analysis of MTHFR and DNMTs gene polymorphisms was performed with the statistical methods Inverse Variance and DerSimonian-Laird, which correspond, respectively, to the fixed-effect and random-effect analysis models. A p-value less than 0.05 was considered significant. Calculations were performed using MetaGenyo [14] https://metagenyo.genyo.es/.

3 Results

3.1 Literature search

A total of 394 records were identified through the database search. After removing duplicates, 143 records were analyzed based on their titles and abstracts. There were 98 publications excluded because: they did not evaluate the polymorphism of interest (29), were not related to bladder cancer (42), were systematic reviews and/or meta-analyses (24), or had no relation to our genes of interest (3).

After that, 45 full-text articles were still evaluable according to the eligibility criteria, 11 of which were excluded because of: duplication (4), absence of the required data (4), missing of the full-text (2), and indetermination of the polymorphisms (1). After cotenant verification, 14 more articles were excluded because: studies were not related to bladder cancer (2), they were reviews/meta-analyses (3), they were not case/control studies (7), and finally, one study was found to be overlapping.

Finally, we end up with 20 eligible studies, from which 4723 BC cases and 5784 healthy controls were included in the current meta-analysis [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]. Figure 1 depicts the selection process based on the «PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only» [35].

Fig. 1
figure 1

Flow chart of the studies selection process

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3.2 Study characteristics

Table 1 describes the main characteristics of the included studies. Four studies were conducted in Taiwan, three in the USA, two in China, two in Tunisia, and one study for each of the following populations: Turkey, Argentina, Germany, Sweden, India, Spain, Algeria, Pakistan, and Iran. The quality of the 20 studies included in the meta-analysis was calculated with scores found to range from two to 11. We achieved a score of ≥ 9 (high quality) in 20% of the included papers. All studies included in the present meta-analysis are in Hardy–Weinberg equilibrium with adjusted p values greater than 0.05.

Table 1 The main characteristics of the 20 studies included in the meta-analysis
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3.3 Meta-analysis

MTHFR rs1801133, rs1801131, and rs2274976 were investigated, as were DNMT3A rs1550117 and DNMT3B rs1569686. The following genetic models were used: allele contrast, recessive, dominant, as well as homozygous and heterozygous models. Table 2 shows the heterogeneity tests (P-value and I2) and the association tests between the polymorphisms of the MTHFR and DNMTs genes with the occurrence of bladder cancer. No significant associations were found among the adopted genetic models. Figures 2 and 3 Represents the result of the 5 SNPs under the allelic contrast model.

Table 2 Meta-analysis of the association between polymorphisms of genes MTHFR, DNMTs and bladder cancer susceptibility
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Fig. 2
figure 2

Forest-plot for MTHFR polymorphisms under the allele contrast genetic model

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Fig. 3
figure 3

Forest-plot for DNMTs polymorphisms under the allele contrast genetic model

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No significant heterogeneity was found in the majority of the studies’ data included in this analysis (P > 0.1), for that DNMT3A/B revealed no heterogeneity (P > 0.1 and I2 = 0%) among their tested SNPs, in contrast to the MTHFR, which revealed significant heterogeneity for rs1801133 and rs1801131 (P < 0.1) in some genetic models (Allele contrast, Recessive, Dominant, and Homozygous). There was no impact of these two SNPs on the risk of developing bladder cancer. The analysis also revealed that there was no significant heterogeneity in the rs2274976 studies (P > 0.1; I2 < 50%) and there was no association with BC.

3.4 Sensitivity and subgroup analysis

The genetic models of rs1801133 showed heterogeneity (Table 2): T vs.C (I2 = 48%, P-Het = 0.013); TT vs. TC + CC (I2 = 32%, P-Het = 0.093); TT + TC vs. CC (I2 = 42%, P-Het = 0.035), TT vs. CC (I2 = 42%, P-Het = 0.032), as well as for rs1801131: C vs. A (I2 = 74%, p-Het = 0); CC vs. CA + AA (I2 = 37%, P-Het = 0.092); CC + CA vs. AA (I2 = 74%, p-Het = 0); CC vs. AA (I2 = 65%, P-Het = 0.0008); CA vs. AA (I2 = 69%, P-Het = 0.0002). Subsequently, a sensitivity analysis was performed to determine whether any studies had a primary effect on the pooled ORs. For rs1801133, there was no source of heterogeneity caused by the studies included in the meta-analysis. For rs1801131, the heterogeneity was primarily caused by the study of Safarinejad et al. In fact, when the latter was removed, we moved to an association (OR = 0.79, 95% CI [0.65–0.97]), (OR = 0.80, 95% CI [0.65–0.98]) and (OR = 0.78, 95% CI [0.63–0.96]) which correspond to CC vs. CA + AA; CC vs. CA; and CC vs. AA, respectively Fig. 4 [24].

Fig. 4
figure 4

Leave-1-out forest plot for MTHFR rs1801131 (CC vs. CA + AA)

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In the subgroup analysis, two factors were selected to explain the heterogeneity: geographic regions and genotyping methods (Table 3):

Table 3 Subgroup analysis of MTHFR polymorphisms rs1801133 and rs1801131
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For geographical regions: the North African and North American populations were, respectively, associated with the occurrence of BC via rs1801133 and rs1801131: TT vs. TC + CC (P-value: 0.013; OR 95% CI = 0.52 [0.311–0.872]), TT vs. TC (P-value: 0.003; OR 95% CI = 0.448 [0.261–0.769]). Rs1801131: CC vs. CA (P-value: 0.039; OR 95% CI = 0.71 [0.523–0.984]). However, for the remaining genetic models, no statistically significant association was considered because of the limited combined studies (< 2).

For genotyping methods, only PCR–RFLP under rs1801133 was found to be associated with BC using the TC vs. CC model (P-value: 0.040; OR 95% CI = 1.12 [1.005–1.269]). For the other genotyping methods (TaqMan and PCR-Sequencing), no significant association was observed, neither for rs1801133 nor rs1801131.

3.5 Publication bias

Publication bias assessment for each genetic model was performed using a funnel plot , Additional file 1: example of allele contrast and Egger's test (Table 2). For the SNPs MTHFR rs2274976, DNMT3A rs1550117, and DNMT3B rs1569686, numerical results of Egger's test were not available; this could be explained by the limited number of studies. However, according to funnels Plots of the three SNPs (Additional file 1) and the Egger's test, of the other two SNPs (rs 1801133 and rs 1801131), no publication bias was detected among the studies included in the current meta-analysis.

4 Discussion

Throughout a systematic review, we found a total of 5 SNPs belonging to 3 different genes that were intensively analyzed for their association to BC occurrence: MTHFR (rs1801133, rs1801131 and rs2274976), DNMT3A (rs1550117) and DNMT3B (rs1569686). Hence, we used them in the present meta-analysis. No statistically significant associations were found except for rs1801133, which showed a significant association in the North African population [TT vs. TC + CC, TT vs. TC], and for rs1801131 in the North American population [CC vs. CA]. On the other hand, sensitivity analysis revealed an association between rs1801131 and bladder cancer occurrence Fig. 4.

As for the PCR–RFLP genotyping method, it has been found to be the most widely used technique that has revealed an association between bladder cancer and rs1801133 [TC vs. CC]. However, this technique has some limitations, particularly in detecting heterozygotes, which is not the case for TaqMan real-time PCR and sequencing.

4.1 MTHFR

4.1.1 rs1801133

No significant association was found in the overall analysis of the association of rs1801133 polymorphism with susceptibility to BC development. This result is in agreement with Xu's research [36], and in discordance with other studies where it was found to be a risk factor for lung cancer [37], and esophageal cancer [38].

Subgroup analysis revealed a statistically significant association with a protective effect of SNP rs1801133 against bladder cancer in the North African population. This result correlates with the study of You et al. [39] and with a meta-analysis of Kennedy et al. for colorectal cancer [40]. Inconsistency in results could be explained in part by differences in allele distribution frequencies of the C677T SNP across ethnic groups.

Association studies between the MTHFR rs1801133 polymorphism and susceptibility to the development of BC have revealed conflicting results. A protective effect has been found in various studies, which may result from the spontaneous deamination of cytosine and 5-methylcytosine residues to uracil and thymine, respectively. If not repaired, spontaneous deamination results in G:C > A:T transitions. These kinds of alterations represent about 37% of the mutations affecting the TP53 tumor suppressor gene in BC [41]. Plus, several studies have shown that nearly half of MIBC (Muscle Invasive Bladder Cancer) samples had a mutation in TP53, with function inactivation in 76% of samples [42].

In the 1-carbon cycle, there is a balance in the use of 5,10-CH2-THF between methionine and purine synthesis. This balance depends on the presence of the thermolabile variant 677 T.

The enzyme activity of heterozygous and homozygous mutant individuals is at 67% and 25%, respectively, as compared to wild type CC [43].

Consequently, in the presence of an adequate amount of folate, the T allele preferentially diverts the content of the 1-carbon cycle to DNA synthesis at the expense of the methionine cycle.

At the same time, the rs1801133 variant can also lead to reduced availability of methyl groups for CpG methylation, which can lead to hypomethylated DNA [44], thus improving DNA stability, which suggests a new mechanism by which MTHFR variants may reduce cancer risk [45].

4.1.2 rs1801131

Generally, no statistically significant association was observed between the rs1801131 polymorphism and the risk of developing BC. These results are supported by the work of Shi et al. and Xu et al. [46], [47]. On the other hand, it is interesting to highlight that the subgroups’ analysis related to geographic regions revealed a protective effect of the rs1801131 allele in the North American population. Similarly, a statistically significant association was found between rs1801131 and BC when the sensitivity analysis is applied to the Safarinejad et al. study, with OR = 0.79 [0.65–0.97] Fig. 4. These results may be due to the difference in the allelic and genotypic frequency distribution of A1298C polymorphism between different populations, such as the Italian population (CC = 8.2%, C = 29.6%) [48], the Korean population (CC = 1.80%, C = 16.3%) [49], and the Russian population (CC = 11%, C = 32%) [50].

The protective role of A1298C can be explained by the fact that the enzymatic activity of MTHFR under the homozygous stat 1298C variant is at 61% only [51]. At least, this does not lead to a thermolabile protein [52], as in the case of the 677 T variant. Thus, the 1298CC variant may improve the chances of genomic stability under a changed temperature via the DNA repair system due to a preferential orientation of the 1C cycle towards purine synthesis. Further studies are needed to confirm these findings.

4.1.3 rs2274976

The present quantitative analysis included only two studies, where no statistically significant association between rs2274976 and BC was found. This result is in agreement with You et al. bladder cancer meta-analysis [39], and in disagreement with Haghighi et al.’s works about colorectal cancer [53]. Further studies are needed to either confirm or reject these findings. Because the 1793G > A polymorphism of the MTHFR gene in exon 11 is a new site reported, nothing yet has been published about its implication in MTHFR activity [54].

4.2 DNMT3A rs1550117/DNMT3B rs1569686

According to our literature search, this meta-analysis is the first pilot study which discusses a possible association between dnmt3a/b SNPs (DNMT3A rs1550117/DNMT3B rs1569686) and BC occurrence.

There was no association between these two polymorphisms with the risk of bladder carcinogenesis. This result is in contrast to what has been reported on the DNMT3A rs1550117 variant, which showed a significantly increased risk of developing non-small cell lung cancer (NSCLC) in the Han Chinese population [55]. While Wang et al. found that the rs1550117 AG/AA genotype was significantly associated with an increased mortality rate compared with individuals carrying the GG genotype, suggesting that DNMT3a rs1550117 may be a relevant prognostic element for gastric cancer [56].

There was no statistically significant association between the rs1569686 variant of DNMT3b and the risk of bladder cancer, which is in agreement with what was found for gastric cancer. However, it was found that individuals with the TG/GG genotype were significantly associated with a poor prognosis of gastric cancer compared with those carrying the TT genotype [57].

4.3 Advantages and limits

To our knowledge, this is the first meta-analysis to examine the susceptibility of bladder cancer development based on DNMT3A/B gene polymorphisms. Our study's main strengths are its systematic focus on BC with the three main genes, DNMT3A/B and MTHFR, and the use of MetaGenyo, which provides a comprehensive, guided workflow, which facilitated the exclusion of methodological errors in the choice of statistical tests, leading to a lower error rate. Some limitations of our mata-analysis should be noted. First, the number of studies available for rs2274976, rs1550117, and rs1569686 is relatively small. Second, the articles found according to our selection were limited to the English language.

5 Conclusion

A total of 20 studies targeting five SNPs of three epigenetic effect genes, DNMTs and MTHFR, were identified. No statistically significant association was found between rs1801133, rs1801131, rs2274976, rs1550117, and rs1569686 with the occurrence of BC.

However, subgroup analysis allowed us to find a significant association in the North African and North American populations for SNPs rs1801133 and rs1801131, respectively, along with a protective effect of rs1801131 under sensitivity analysis. Further studies in different populations are needed to prove whether the SNPs selected in the present analysis have an ethnic or geographic-based impact on BC occurrence.

This meta-analysis is the first study to address the effect of DNMTs on BC occurrence. However, caution should be taken in interpreting these results because of the small number of studies included. Therefore, further studies are necessary in other populations in order to verify the possible involvement of these SNPs in bladder cancer carcinogenesis.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

BC:

Bladder cancer

DNA:

Deoxyribonucleic acid

DNMTs :

DNA methyltransferase

SNP:

Single nucleotide polymorphism

Rs:

Reference SNP cluster ID

HWE:

Hardy–Weinberg equilibrium

P-Het:

P-value of heterogeneity

OR:

Odd ratio

PCR–RFLP:

Polymerase chain reaction-restriction fragment length polymorphism

PB:

Population based

HB:

Hospital based

HWE:

Hardy–Weinberg equilibrium

NA:

Not available

DNMT3A :

DNA (cytosine-5)-methyltransferase 3 alpha

DNMT3B :

DNA (cytosine-5-)-methyltransferase 3 beta

MTHFR :

5,10-Methylenetetrahydrofolate reductase

REM:

Random effect model

FEM:

Fixed effect model

MIBC:

Muscle Invasive Bladder Cancer

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  Google Scholar 

  2. Dobruch J, Oszczudłowski M (2021) Bladder cancer: current challenges and future directions. Medicina 57(8):749. https://doi.org/10.3390/medicina57080749

    Article  PubMed  PubMed Central  Google Scholar 

  3. Li Y, Sun L, Guo X, Mo N, Zhang J, Li C (2021) Frontiers in bladder cancer genomic research. Front Oncol 11:1791. https://doi.org/10.3389/fonc.2021.670729

    Article  CAS  Google Scholar 

  4. Han H, Wolff EM, Liang G (2012) Epigenetic alterations in bladder cancer and their potential clinical implications. Adv Urol 2012:e546917. https://doi.org/10.1155/2012/546917

    Article  Google Scholar 

  5. Lee K-H, Song CG (2017) Epigenetic regulation in bladder cancer: development of new prognostic targets and therapeutic implications. Transl Cancer Res. https://doi.org/10.21037/14111

    Article  Google Scholar 

  6. Yadav S, Longkumer I, Joshi S, Saraswathy KN (2021) Methylenetetrahydrofolate reductase gene polymorphism, global DNA methylation and blood pressure: a population based study from North India. BMC Med Genomics 14(1):59. https://doi.org/10.1186/s12920-021-00895-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chen Z, Zhang Y (2020) Role of mammalian DNA methyltransferases in development. Annu Rev Biochem 89(1):135–158. https://doi.org/10.1146/annurev-biochem-103019-102815

    Article  CAS  PubMed  Google Scholar 

  8. Bommarito PA, Fry RC (2019) Chapter 2–1: The role of DNA methylation in gene regulation. In: McCullough SD, Dolinoy DC (eds) Toxicoepigenetics. Academic Press, pp 127–151. Doi: https://doi.org/10.1016/B978-0-12-812433-8.00005-8

  9. Ren J-C, Wu Y-X, Wu Z, Zhang G-H, Wang H, Liu H, Xia Z-L (2018) MTHFR gene polymorphism is associated with DNA hypomethylation and genetic damage among benzene-exposed workers in Southeast China. J Occup Environ Med 60(4):e188–e192. https://doi.org/10.1097/JOM.0000000000001288

    Article  CAS  PubMed  Google Scholar 

  10. Neves M, Ribeiro J, Medeiros R, Sousa H (2016) Genetic polymorphism in DNMTs and gastric cancer: a systematic review and meta-analysis. Porto Biomed J 1(5):164–172. https://doi.org/10.1016/j.pbj.2016.10.005

    Article  PubMed  PubMed Central  Google Scholar 

  11. Montgomery KG, Liu MC, Eccles DM, Campbell IG (2004) The DNMT3B C→T promoter polymorphism and risk of breast cancer in a British population: a case-control study. Breast Cancer Res 6(4):R390–R394. https://doi.org/10.1186/bcr807

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lee S-Y, Lee J-H, Park J-H, Kim E-J, Lee S-J, Jeon H-S, Park J-Y (2004) DNMT3b 39179G>T polymorphism and risk of primary lung cancer in Koreans. Korean J Med 66(6):601–608

    Google Scholar 

  13. Li H, Li W, Liu S, Zong S, Wang W, Ren J, Shi Q (2016) DNMT1, DNMT3A and DNMT3B polymorphisms associated with gastric cancer risk: a systematic review and meta-analysis. EBioMedicine 13:125–131. https://doi.org/10.1016/j.ebiom.2016.10.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Martorell-Marugan J, Toro-Dominguez D, Alarcon-Riquelme ME, Carmona-Saez P (2017) MetaGenyo: a web tool for meta-analysis of genetic association studies. BMC Bioinform 18(1):563. https://doi.org/10.1186/s12859-017-1990-4

    Article  CAS  Google Scholar 

  15. Karagas MR, Park S, Nelson HH, Andrew AS, Mott L, Schned A, Kelsey KT (2005) Methylenetetrahydrofolate reductase (MTHFR) variants and bladder cancer: a population-based case-control study. Int J Hyg Environ Health 208(5):321–327. https://doi.org/10.1016/j.ijheh.2005.04.005

    Article  CAS  PubMed  Google Scholar 

  16. Chung C-J (2014) The effect of cigarette smoking and DNA methyltransferase genes polymorphisms on urothelial carcinoma risk in Taiwan. Department of Health Risk Management Proceedings. Retrieved from http://ir.cmu.edu.tw/ir/handle/310903500/52491

  17. Chung C-J (2014) Association of DNA methyltransferases 3A and 3B polymorphisms, and plasma folate levels with the risk of urothelial carcinoma. PLoS ONE 9(8):e104968. https://doi.org/10.1371/journal.pone.0104968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Izmirli M, Inandiklioglu N, Abat D, Alptekin D, Demirhan O, Tansug Z, Bayazit Y (2011) MTHFR gene polymorphisms in bladder cancer in the Turkish population. Asian Pacific J Cancer Prevent: APJCP 12(7):1833–1835

    Google Scholar 

  19. Ouerhani S, Rouissi K, Marrakchi R, Ben Slama MR, Sfaxi M, Chebil M, ElGaaied AB (2009) Combined effect of NAT2, MTR and MTHFR genotypes and tobacco on bladder cancer susceptibility in Tunisian population. Cancer Detect Prev 32(5–6):395–402. https://doi.org/10.1016/j.canep.2009.04.005

    Article  CAS  PubMed  Google Scholar 

  20. Cai DW, Liu XF, Bu RG, Chen XN, Ning L, Cheng Y, Wu B (2009) Genetic polymorphisms of MTHFR and aberrant promoter hypermethylation of the RASSF1A gene in bladder cancer risk in a Chinese population. J Int Med Res 37(6):1882–1889. https://doi.org/10.1177/147323000903700625

    Article  CAS  PubMed  Google Scholar 

  21. Ouerhani S, Oliveira E, Marrakchi R, Ben Slama MR, Sfaxi M, Ayed M, Prata MJ (2007) Methylenetetrahydrofolate reductase and methionine synthase polymorphisms and risk of bladder cancer in a Tunisian population. Cancer Genet Cytogenet 176(1):48–53. https://doi.org/10.1016/j.cancergencyto.2007.03.007

    Article  CAS  PubMed  Google Scholar 

  22. Beebe-Dimmer JL, Iyer PT, Nriagu JO, Keele GR, Mehta S, Meliker JR, Cooney KA (2012) Genetic variation in glutathione S-transferase omega-1, arsenic methyltransferase and methylene-tetrahydrofolate reductase, arsenic exposure and bladder cancer: a case-control study. Environ Health: Global Access Sci Source 11:43. https://doi.org/10.1186/1476-069X-11-43

    Article  CAS  Google Scholar 

  23. Lin J, Spitz MR, Wang Y, Schabath MB, Gorlov IP, Hernandez LM, Wu X (2004) Polymorphisms of folate metabolic genes and susceptibility to bladder cancer: a case-control study. Carcinogenesis 25(9):1639–1647. https://doi.org/10.1093/carcin/bgh175

    Article  CAS  PubMed  Google Scholar 

  24. Safarinejad MR, Shafiei N, Safarinejad S (2011) Genetic susceptibility of methylenetetrahydrofolate reductase (MTHFR) gene C677T, A1298C, and G1793A polymorphisms with risk for bladder transitional cell carcinoma in men. Med Oncol 28(Suppl 1):S398-412. https://doi.org/10.1007/s12032-010-9723-9

    Article  CAS  PubMed  Google Scholar 

  25. Moore LE, Wiencke JK, Bates MN, Zheng S, Rey OA, Smith AH (2004) Investigation of genetic polymorphisms and smoking in a bladder cancer case-control study in Argentina. Cancer Lett 211(2):199–207. https://doi.org/10.1016/j.canlet.2004.04.011

    Article  CAS  PubMed  Google Scholar 

  26. Kimura F, Florl AR, Steinhoff C, Golka K, Willers R, Seifert HH, Schulz WA (2001) Polymorphic methyl group metabolism genes in patients with transitional cell carcinoma of the urinary bladder. Mutat Res 458(1–2):49–54. https://doi.org/10.1016/s1383-5726(01)00010-3

    Article  CAS  PubMed  Google Scholar 

  27. Chung C-J, Pu Y-S, Su C-T, Chen H-W, Huang Y-K, Shiue H-S, Hsueh Y-M (2010) Polymorphisms in one-carbon metabolism pathway genes, urinary arsenic profile, and urothelial carcinoma. Cancer Causes Control: CCC 21(10):1605–1613. https://doi.org/10.1007/s10552-010-9589-3

    Article  PubMed  Google Scholar 

  28. Sanyal S, Festa F, Sakano S, Zhang Z, Steineck G, Norming U, Hemminki K (2004) Polymorphisms in DNA repair and metabolic genes in bladder cancer. Carcinogenesis 25(5):729–734. https://doi.org/10.1093/carcin/bgh058

    Article  CAS  PubMed  Google Scholar 

  29. Gautam KA, Tripathi P, Sankhwar PL, Sankhwar SN (2019) MTHFR functional polymorphisms and haplotypes are a risk factor for urinary bladder cancer: a case-control study and meta-analysis. Polymorphism 2:122–133

    Google Scholar 

  30. Wang M, Zhu H, Fu G, Wang M, Zhang Z, Lu Q, Zhang Z (2009) Polymorphisms of methylenetetrahydrofolate reductase and methionine synthase genes and bladder cancer risk: a case-control study with meta-analysis. Clin Exp Med 9(1):9–19. https://doi.org/10.1007/s10238-008-0013-1

    Article  CAS  PubMed  Google Scholar 

  31. Tsai, (Ping-Huan, Chien), (Ssu-Ning, 張兆祥(Chang, C.-H., Liu), 劉秋松(Chiu-Shong, & Chung)*, 鍾季容(Chi-Jung. (2014). Relationships among theplasma folate, DNMT3A-448A>G polymorphism and urothelial carcinoma. In: 7th General Assembly and International Conference of Asian, Pacifi-c.

  32. Moore LE, Malats N, Rothman N, Real FX, Kogevinas M, Karami S, García-Closas M (2007) Polymorphisms in one-carbon metabolism and trans-sulfuration pathway genes and susceptibility to bladder cancer. Int J Cancer 120(11):2452–2458. https://doi.org/10.1002/ijc.22565

    Article  CAS  PubMed  Google Scholar 

  33. Kherouatou-Chaoui N, Chellat-Rezgoune D, Ribouh A, Hireche A, Tellouche S, Semmame O, Satta D (2015) Combined effect of MTHFR genotypes, tobacco and occupational exposure on bladder cancer susceptibility in Algerian population. Retrieved from https://www.researchgate.net/publication/291523878_Combined_effect_of_MTHFR_genotypes_tobacco_and_occupational_exposure_on_bladder_cancer_susceptibility_in_algerian_population

  34. Ali SHB (2012) Single nucleotide polymorphism-based association studies of bladder cancer patients (Thesis). COMSATS Institute of Information Technology, Islamabad, Pakistan. Retrieved from http://173.208.131.244:9060/xmlui/handle/123456789/7648

  35. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71

    Article  PubMed  PubMed Central  Google Scholar 

  36. Xu W, Zhang H, Wang F, Wang H (2013) Quantitative assessment of the association between MHTFR C677T (rs1801133, Ala222Val) polymorphism and susceptibility to bladder cancer. Diagn Pathol 8:95. https://doi.org/10.1186/1746-1596-8-95

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Liu F, Qin G, Tang T, Huang Q, Li Z, Huang H, Lu X (2019) Methylenetetrahydrofolate Reductase (MTHFR) Gene rs1801133 C>T Polymorphisms and Lung Cancer Susceptibility: an updated meta-analysis. Pteridines 30(1):65–73. https://doi.org/10.1515/pteridines-2019-0008

    Article  CAS  Google Scholar 

  38. Kumar P, Rai V (2018) MTHFR C677T polymorphism and risk of esophageal cancer: an updated meta-analysis. Egypt J Med Hum Genet 19(4):273–284. https://doi.org/10.1016/j.ejmhg.2018.04.003

    Article  Google Scholar 

  39. You W, Li Z, Jing C, Qian-Wei X, Yu-Ping Z, Weng-Guang L, Hua-Lei L (2013) MTHFR C677T and A1298C polymorphisms were associated with bladder cancer risk and disease progression: a meta-analysis. DNA Cell Biol 32(5):260–267. https://doi.org/10.1089/dna.2012.1931

    Article  CAS  PubMed  Google Scholar 

  40. Kennedy DA, Stern SJ, Matok I, Moretti ME, Sarkar M, Adams-Webber T, Koren G (2012) Folate intake, MTHFR polymorphisms, and the risk of colorectal cancer: a systematic review and meta-analysis. J Cancer Epidemiol 2012:e952508. https://doi.org/10.1155/2012/952508

    Article  CAS  Google Scholar 

  41. Greenblatt MS, Bennett WP, Hollstein M, Harris CC (1994) Mutations in the p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis. Can Res 54(18):4855–4878

    CAS  Google Scholar 

  42. Wu G, Wang F, Li K, Li S, Zhao C, Fan C, Wang J (2019) Significance of TP53 mutation in bladder cancer disease progression and drug selection. PeerJ 7:e8261. https://doi.org/10.7717/peerj.8261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wan L, Li Y, Zhang Z, Sun Z, He Y, Li R (2018) Methylenetetrahydrofolate reductase and psychiatric diseases. Transl Psychiatry 8:242. https://doi.org/10.1038/s41398-018-0276-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Clare CE, Brassington AH, Kwong WY, Sinclair KD (2019) One-Carbon metabolism: linking nutritional biochemistry to epigenetic programming of long-term development. Annu Rev Anim Biosci 7:263–287. https://doi.org/10.1146/annurev-animal-020518-115206

    Article  CAS  PubMed  Google Scholar 

  45. Ulrich CM, Curtin K, Samowitz W, Bigler J, Potter JD, Caan B, Slattery ML (2005) MTHFR variants reduce the risk of G:C->A: T transition mutations within the p53 tumor suppressor gene in colon tumors. J Nutr 135(10):2462–2467. https://doi.org/10.1093/jn/135.10.2462

    Article  CAS  PubMed  Google Scholar 

  46. Shi R, Zhao Z, Zhou H, Zhou J, Tan W (2014) Lack of association between MTHFR Ala222Val and Glu429Ala polymorphisms and bladder cancer risk: a meta-analysis of case-control studies. Biomed Rep 2(3):396–403. https://doi.org/10.3892/br.2014.258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Xu S, Zuo L (2020) Association between methylenetetrahydrofolate reductase gene rs1801131 A/C polymorphism and urinary tumors’ susceptibility. Hereditas 157:16. https://doi.org/10.1186/s41065-020-00129-x

    Article  PubMed  PubMed Central  Google Scholar 

  48. Gemmati D, Ongaro A, Scapoli GL, Della Porta M, Tognazzo S, Serino ML, De Mattei M (2004) Common gene polymorphisms in the metabolic folate and methylation pathway and the risk of acute lymphoblastic leukemia and non-Hodgkin’s lymphoma in adults. Cancer Epidemiol Biomark Prevent 13(5):787–794

    Article  CAS  Google Scholar 

  49. Kim JK, Kim S, Han JH, Kim HJ, Chong SY, Hong SP, Kim NK (2005) Polymorphisms of 5,10-methylenetetrahydrofolate reductase and risk of stomach cancer in a Korean population. Anticancer Res 25(3B):2249–2252

    CAS  PubMed  Google Scholar 

  50. Weiner AS, Beresina OV, Voronina EN, Voropaeva EN, Boyarskih UA, Pospelova TI, Filipenko ML (2011) Polymorphisms in folate-metabolizing genes and risk of non-Hodgkin’s lymphoma. Leuk Res 35(4):508–515. https://doi.org/10.1016/j.leukres.2010.10.004

    Article  CAS  PubMed  Google Scholar 

  51. Iverson, genetics (2016) MTHFR panel. Retrieved from http://www.iversongenetics.com/mthfr.html

  52. Friedman G, Goldschmidt N, Friedlander Y, Ben-Yehuda A, Selhub J, Babaey S, Bar-On H (1999) A common mutation A1298C in human methylenetetrahydrofolate reductase gene: association with plasma total homocysteine and folate concentrations. J Nutr 129(9):1656–1661. https://doi.org/10.1093/jn/129.9.1656

    Article  CAS  PubMed  Google Scholar 

  53. Haghighi MM, Mohebbi SR, Najjar Sadeghi R, Vahedi M, Ghiasi S, Zali MR (2008) Association between the 1793G> A MTHFR polymorphism and sporadic colorectal cancer in Iran. Asian Pacific J Cancer Prevent: APJCP 9(4):659–662

    Google Scholar 

  54. Moritz Neto AI, de Moura Júnior JR, Persuhn DC (2013) Frequency of MTHFR G1793A polymorphism in individuals with early coronary artery disease: cross-sectional study. Sao Paulo Med J 131:296–300. https://doi.org/10.1590/1516-3180.2013.1315500

    Article  Google Scholar 

  55. Wang J, Li C, Wan F, Li Z, Zhang J, Zhang J, Chen B (2017) The rs1550117 A>G variant in DNMT3A gene promoter significantly increases non-small cell lung cancer susceptibility in a Han Chinese population. Oncotarget 8(14):23470–23478. https://doi.org/10.18632/oncotarget.15625

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wang C, Jia Z, Ma H, Cao D, Wu X, Wen S, Jiang J (2015) DNA methyltransferase 3a rs1550117 genetic polymorphism predicts poor survival in gastric cancer patients. Int J Clin Exp Pathol 8(11):14864–14874

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang C, Jia Z, Cao D, You L, Jin M, Wu X, Jiang J (2015) Polymorphism of DNA methyltransferase 3b and association with development and prognosis in gastric cancer. PLoS ONE 10(8):e0134059. https://doi.org/10.1371/journal.pone.0134059

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Université Des Sciences Et de La Technologie d’Oran Mohamed-Boudiaf USTOMB El Mnaouar, BP 1505, Bir El Djir, 31000, Oran, Algeria

    Zohra Touala-Chaila, Rym-Khadidja Abderrahmane, Khedidja Benseddik & Djebaria Naima Meroufel

  2. Laboratoire de La Génétique Moléculaire Et Cellulaire (LGMC) USTOMB El Mnaouar, BP 1505, Bir El Djir, 31000, Oran, Algeria

    Zohra Touala-Chaila, Rym-Khadidja Abderrahmane & Djebaria Naima Meroufel

  3. École Supérieure en Sciences Biologiques d’Oran (ESSB- Oran, Algerie) Cité du Chercheur (Ex- IAP), Essenia -Oran. Boîte Postale 1042, Saim Mohamed, 31003, Oran, Algeria

    Khedidja Benseddik

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  1. Zohra Touala-Chaila
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Contributions

Z-TC contributed to conceptualization, methodology, and software. R-K A contributed to methodology and writing—original draft preparation. K B: contributed to resources and writing—review & editing. D-N M contributed to supervision. All authors read and approved the final manuscript.

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Correspondence to Zohra Touala-Chaila.

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Supplementary Information

Additional file 1

. Example of allele contrast and Egger's test (Table 2).

Appendix A

Appendix A

The search strategy conducted on PubMed included the following key elements:

  1. 1.

    ((Bladder) AND (cancer)) AND (DNMT3A)

  2. 2.

    ((Urothelial carcinoma) OR (Bladder cancer)) AND (DNMT3A)

  3. 3.

    (Urothelial carcinoma) AND (DNMT3A)

  4. 4.

    (Transitional cell carcinoma) AND (DNMT3A gene)

  5. 5.

    (Urothelial carcinoma) AND (DNMT3A polymorphism)

  6. 6.

    (Transitional cell carcinoma) AND (DNMT3A polymorphism)

  7. 7.

    (DNMT3A polymorphism) AND (Transitional cell carcinoma)

  8. 8.

    (Bladder cancer) AND (DNMT3A polymorphism)

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Touala-Chaila, Z., Abderrahmane, RK., Benseddik, K. et al. A meta-analysis on the susceptibility to the development of bladder cancer in the presence of DNMT3A, DNMT3B, and MTHFR gene polymorphisms. Afr J Urol 28, 32 (2022). https://doi.org/10.1186/s12301-022-00301-x

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