1. Introduction
According to the International Agency for Research on Cancer, breast cancer is the leading neoplasm in incidence and mortality in women worldwide. In 2022, there were 2.3 million women diagnosed with this disease [1]. Current evidence indicates the existence of a wide range of well-known risk factors for the disease, such as alcohol intake, obesity, substitutive hormonal treatment usage, physical inactivity, no breastfeeding, breast density, family history of breast cancer, and genetics. Furthermore, mutations in BRCA1 and BRCA2 genes are responsible for 5–10% of breast cancer cases [2,3]. However, more than 50% of new breast cancer cases do not present any other risk factor than age [4].
Carcinogenesis is a complex cellular phenomenon related to DNA stability and expression, driven by the dysregulation of various cellular pathways due to the presence of genetic mutations and the erratic activity of DNA-repair enzymes. In addition, epigenetic mechanisms such as DNA methylation or histone modifications may induce cell phenotypic changes, either independently or through genetic mutations [5,6]. DNA methylation is regulated by one-carbon metabolism (1CM), which is a complex network of biochemical reactions that involve the folate metabolism, the methionine cycle, and the trans-sulphuration pathway. Folate metabolism occurs in the liver, while methionine cycle, trans-sulphuration, and substrate methylation take place in the target tissue. Apart from folate, which initiates the cycle, vitamins B2, B6, and B12 participate in 1CM as enzymatic cofactors. Additionally, alcohol is a common dietary component related to this pathway due to the inhibition of folate absorption and metabolism (Figure 1) [7,8]. The 1CM regulates the cellular function throughout the one-carbon moieties (methenyl, formyl, and methyl groups), which are required for molecular biosynthesis, regulation of nucleotide pools, epigenetic control of gene expression, and redox defense. Thus, 1CM is involved in several cellular mechanisms, such as growth and proliferation [9]. Also, the 1CM pathway presents a dual role in carcinogenesis. On the one hand, it maintains cellular stability in the previous stages of its initiation. On the other hand, once the carcinogenic process has been triggered, 1CM acquires a central role in the tumoral cell due to its involvement in cell proliferation [10]. Thus, nutrients, genes, and single nucleotide polymorphisms (SNPs) related to 1CM may influence breast carcinogenesis. Concerning breast cancer, several studies have provided evidence about the relationship between the 1CM participating enzymes or involved nutrients and the disease throughout epidemiological studies and in vivo and in vitro models. The dietary methyl group (folate, choline, betaine, and methionine) intake role in breast cancer has been assessed within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, enrolling 318,686 women (13,320 malignant breast cancer cases). Results showed a potential U-shaped relationship between dietary folate intake and overall breast cancer risk [11]. In addition, the relationship between folate and triple-negative breast cancer subtype (TNBC) has been assessed in vitro and in vivo. Folate restriction promotes changes in cells, decreasing their migratory capacity and invasiveness, which is associated with depleted metabolic plasticity. These effects were higher in murine models with mitochondrial disfunction, which leads to an increased dependence on 1CM for tumoral cells [12,13]. Moreover, it has been noted that loss of ALDH1L2, a 1CM-related enzyme, drives an increased migration in breast cancer cells and enhances metastasis in vivo [14]. Indeed, different metabolomic profiles have been observed between invasive ductal carcinoma and adjacent tissue, where 1CM-involved metabolites presented a pivotal role [15]. To provide evidence of their role in this disease, it is possible to make use of biological databases on genes and molecular, metabolic, and cellular pathways linking genes and their related SNPs with diseases. These biological databases are, therefore, valuable tools for conducting in silico functional analyses from different sources, providing molecular mechanisms and facilitating the interpretation and visualization of the results.
Thus, this study aimed to interrogate some biological databases on the potential associations between the genes related to 1CM and cancer, focusing on breast cancer, to explore the possible underlying mechanisms between them. Furthermore, we have revised the association of the water-soluble B group vitamins involved in 1CM (folate, B2, B6, and B12) and alcohol with breast cancer.
2. Results
2.1. Relationship between Dietary Components Related to One-Carbon Metabolism (Vitamins B2, B6, B9, B12, and Alcohol) and Breast Cancer
The B group vitamins (B1, B2, B3, B5, B7, B9, and B12) are involved in a wide range of essential processes in cellular activity maintenance. Several of these B vitamins are involved in 1CM, either by initiating the cycle, as in the case of folate, or by participating as enzyme cofactors. Additionally, alcohol involvement in this pathway lies in its inhibition of folate activity [7,8,9,10]. In the text, alcohol and ethanol will be treated as synonyms. While several studies support that those 1CM nutrients are meaningful for breast cancer prevention, some inconsistencies remain (Table S1).
2.1.1. Vitamin B2 (Riboflavin)
Riboflavin, vitamin B2, or lactoflavin is part of the water-soluble vitamin group. It has two coenzyme derivatives responsible for its biological activity: flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) [16]. Vitamin B2 is found in foods of both animal (organ meats, eggs, and fish) and green vegetable origins in its co-enzymatic form, bound to apoenzymes. However, the free form of riboflavin is found in milk, which is the main food source [17].
Riboflavin plays a key role in the metabolism of carbohydrates, lipids, and proteins. FMN and FAD are closely related to flavoenzymes, which are involved in oxidation–reduction reactions and enable electron transport through the transformation of the isoalloxazine ring [17]. The functional versatility of FMN and FAD means that they are involved in a wide variety of cellular processes, such as ATP generation through the mitochondrial electron transport chain or cellular antioxidant defense. They are also involved in 1CM as coenzymes of the enzyme methylenetetrahydrofolate reductase (MTHFR), a key enzyme in the initiation of methionine metabolism. Oxidative stress and DNA methylation are processes closely linked to cell proliferation and, thus, to cancer [16,18].
Riboflavin participates in 1CM as an enzyme cofactor of the enzyme MTHFR. Therefore, dietary intake and serum levels of riboflavin could influence the development of breast cancer. Concerning the relationship between plasma levels of this vitamin and breast cancer risk, the meta-analysis conducted by Zeng et al. (2020) (27 prospective case–control and cohort studies) concluded that there is no relationship between blood levels of riboflavin and breast cancer risk [19]. On the other hand, dietary intake of riboflavin has been linked to the development of breast cancer in the study conducted by Hatami et al. (2020) (n = 151 cases/154 controls), showing that higher intake of B2, as measured by validated food frequency questionnaires, is associated with a lower risk of the disease [20]. However, the Canadian Study of Diet, Lifestyle, and Health cohort study (n = 922 cases/3088 controls) showed no significant association between riboflavin intake and different types of cancer, including breast cancer [21]. The association between high vitamin B2 intake and a lower risk of developing breast cancer is supported by two meta-analyses, both of which agree on this issue. The one conducted by Yu et al. (2017) (n = 12,268 cases/194,530 controls) showed a weak association, while the one conducted by Zeng et al. (2020) (n = 49,707 cases/1,274,060 controls) showed a stronger association [19,22].
2.1.2. Vitamin B6 (Pyridoxine, Pyridoxal, and Pyridoxamine)
The term vitamin B6 groups together three water-soluble pyridine derivatives: pyridoxine, pyridoxamine, and pyridoxal, which are metabolically interconvertible. Vitamin B6 is abundant in foods in all forms, especially liver, legumes, nuts, and bananas. Pyridoxine and pyridoxamine are found mainly in vegetables, while pyridoxal predominates in animal products [17]. The active form of vitamin B6 is pyridoxal phosphate (PLP), which intervenes as a coenzyme in multiple key reactions of amino acid metabolism. The action of PLP has three possible effects on amino acids, acting primarily as transaminases [18]. Vitamin B6 deficiency has been linked to an increased risk of cancer due to PLP’s protective role against DNA damage, among other mechanisms [23]. B6 is also involved in 1CM as an enzymatic factor in serine hydroxy-methyltransferase 1 and cystathionine β-synthase, both of which are involved in serine metabolism [7].
Vitamin B6 is involved as an enzyme cofactor for the enzymes serine hydroxymethyltransferase and cystathionine-β-synthase, which are involved in amino acid metabolism, a process essential for the maintenance of cell proliferation [10]. Thus, higher B6 intake is associated with a lower risk of overall breast cancer, as well as a lower risk of the ER+, PR+, or HER2 subtypes [20]. The effect of dietary intake and supplementation, as well as the combination of both, on breast cancer risk has also been evaluated. The study conducted within the prospective NutriNet-Santé cohort (N = 27,853 individuals, n = 462 incident cases of breast cancer), using 24 h recall and supplementation-specific questionnaires, indicated that both high intake and supplementation and the sum of both decrease breast cancer risk [24]. In line with these results is the meta-analysis by Zeng et al. (2020), which showed that high B6 intake reduces the risk of overall breast cancer and that of ER+/PR+ breast cancer subtypes [19]. Furthermore, vitamin B6 intake is inversely related to breast density [25]. Regarding the relationship between plasma vitamin B6 levels and breast cancer risk, the dose–response meta-analysis conducted by Wu et al. (2013) indicated that elevated levels of vitamin B6 and methionine reduce breast cancer risk, mainly in postmenopausal women [26]. However, subsequent studies have not shown a statistically significant association between plasma vitamin B6 levels and breast cancer risk [27,28]. Similarly, it has been observed that vitamin B6 may play a role in other aspects of breast cancer beyond risk. Indeed, high-dose vitamin B6 has been shown to potentiate the antitumor effect of 5-fluorouracil and folinic acid in women with advanced breast cancer [29].
2.1.3. Vitamin B9 (Folate and Folic Acid)
Vitamin B9 encompasses folic acid and all its reduced derivatives. All forms share a common structure, which is that of pteroylglutamic acid or folic acid itself. There are three distinct parts: a pteridine ring, a p-aminobenzoic acid residue, and a glutamate residue [30]. The number of glutamate residues can vary, being found as monoglutamates, pentaglutamates, and hexaglutamates [18,31]. The pteridine ring can be partially reduced at positions 7 and 8 (dihydrofolate or DHF) or completely reduced at positions 5, 6, 7, and 8 (tetrahydrofolate or THF). THF can accept methyl units, which are attached to positions 5, 10, or both [31]. Food sources of folate are mainly of plant origin, such as chard, spinach, turnip greens, and chickpeas [18,31]. In foods, it is usually found in reduced form and bound to polyglutamates. The structure presented by folic acid is not common in nature, although it is the most stable and, therefore, the one used for its commercialization as a supplement [30]. Folate is responsible for initiating 1CM, from which all its biological functions derive [7]. Vitamin B9 is involved in serine metabolism, the synthesis of S-adenosylmethionine (a key molecule in transmethylation reactions), and the synthesis of purines, especially thymine [30]. Together, these mechanisms support the key role of folates in cell proliferation and, therefore, their relationship with cancer. In fact, folate is known to have a dual role in this pathology, as it can both prevent the onset of cancer and maintain tumor survival once the disease has appeared [18,31,32].
Vitamin B9 or folate initiates 1CM, regulating gene expression and maintaining DNA stability. Folate, along with methionine, homocysteine, choline, and betaine, is one of the methyl donor nutrients [7]. Due to the role played by folate in the regulation of cell proliferation, amino acid metabolism, and the synthesis of nitrogenous bases, it has been considered that a high folate intake may have a protective role against cancer [30]. On the one hand, data from food fortification in the United States show that serum folate levels, both before and after fortification, are not associated with breast cancer risk [33]. However, several studies have found that women with higher folate intakes are less likely to develop breast and ovarian cancer [21], as well as hormone receptor-positive and HER2-positive subtypes [20]. This is reinforced by the results of the meta-analysis conducted by Zeng et al. (2020) (27 case–control and cohort studies), which found that dietary folate intake is inversely associated with the development of breast cancer. Furthermore, an increase of 100 μg/day of folate through diet has been found to reduce the risk of ER-/PR- breast cancer by 7% [19]. Furthermore, dietary folate intake has been linked to other key aspects of breast cancer, such that high intake is inversely related to breast density [25]. Concerning blood folate levels, high plasma levels of B9 have been associated with an increased risk of breast cancer in women with BRCA1 and BRCA2 mutations [27]. However, results of the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study (n = 2492 cases/2521 controls) showed that there is no clear association between folate levels and breast cancer risk [33].
2.1.4. Vitamin B12 (Cobalamin)
Cobalamin, or vitamin B12, has a complex structure in which the four pyrrole rings are arranged similarly to porphyrins, with a cobalt atom as the central nucleus. It also can bind to various ligands, giving rise to the various forms of vitamin B12. Its active forms are adenosyl-cobalamin and methyl-cobalamin [30]. This vitamin is synthesized exclusively by microorganisms; plants do not need it and, therefore, do not contain it. The source for animals is the ingestion of microorganisms or production by the intestinal microbiota [32]. Thus, the foods with the highest cobalamin content are liver, kidney, and brain, although egg yolk, sardines, salmon, clams, and oysters also have a high vitamin B12 content [30,34]. There are two metabolic reactions in which vitamin B12 is involved: the conversion of homocysteine to methionine and the conversion of L-methylmalonyl-CoA to succinyl-CoA. These biochemical functions highlight the need for cobalamin in the maintenance of the central and peripheral nervous system through the methylation of myelin, neurotransmitters, and phospholipids. It is also involved in nucleotide synthesis through its participation in 1CM [18,30]. In addition, B12 is involved in the regulation of certain immune cells, including natural killer and CD8+ T lymphocytes [18].
Due to its role in 1CM, vitamin B12 is involved in DNA methylation and proper expression, as well as nucleotide synthesis. These phenomena are critical in cancer initiation and development [18]. In fact, several reviews of the scientific literature have associated higher cobalamin levels with an elevated risk of various types of cancer, including breast, liver, and lung cancer [18,35]. Cobalamin has diverse biochemical and physiological functions due to its activity as a cofactor for the enzymes methionine synthase and methionine synthase reductase. These enzymes link folate and methionine metabolism in 1CM [8]. The scientific literature has provided contradictory results on the association between B12 intake and breast cancer. On the one hand, some studies do not support a connection between this vitamin and breast cancer disease [28,35]. However, the study by Hatami et al. (2020) demonstrated that higher vitamin B12 intake decreases the risk of breast cancer overall and of ER+, PR+, and HER2 subtypes [20]. Furthermore, like dietary intake of B6 and folate, B12 intake has been inversely related to breast density [25]. Moreover, there is more consensus on plasma cobalamin levels and disease. The dose–response meta-analysis by Wu et al. (2013) indicated that there is no relationship with breast cancer [26]. In line with these results, other studies support this lack of association [21,27].
2.1.5. Alcohol
Alcohol is the most widely consumed substance in the world. Thus, its moderate consumption is taken into account in the Mediterranean diet. However, alcohol consumption has been linked to an increased risk of several chronic pathologies, including cancer. Indeed, it is the only dietary component established as a risk factor for breast cancer. Consumption of 10 g of alcohol per day has been found to increase the risk of breast cancer by 10.5% and 11.1% in postmenopausal women. In addition, occasional heavy alcohol consumption is associated with an increased risk of breast cancer compared to low–moderate and prolonged consumption. However, it should be noted that the only safe amount of alcohol consumption is 0 g/day [34,35,36,37,38,39]. The role of alcohol in cancer development is primarily attributable to two mechanisms: its ability to inhibit the action of folate, preventing the initiation of 1CM; and the cytotoxic effect of its metabolites. Alcohol metabolism involves several enzymes performing at the hepatic level that catalyze the molecule via two pathways: non-oxidative and oxidative. The latter involves the CYP2E1 enzyme, which belongs to the cytochrome P450 superfamily [37]. The oxidative pathway in alcohol metabolism is thought to lead to cell damage by reactive oxygen species and acetaldehyde. Although the production of these metabolites mainly occurs in the liver, it also takes place in breast tissue. Additionally, an accumulation and persistent concentration of acetaldehyde in breast cells compared to blood in animal models has been observed [40]. On the other hand, alcohol’s role in carcinogenesis is also related to the inhibition of folate absorption and metabolism. Concerning the first step, ethanol is responsible for repressing folate transporter gene expression through the methylation in CpG sites. Thus, alcohol consumers present a deficient folate absorption. Moreover, ethanol has been shown to produce an inhibitory effect on folate metabolism-related enzymes such as MTHFR and MTR (affecting S-adenosylmethionine pool and methylation activity in cells) and decreases TYMS mRNA levels. Otherwise, it increases ALDH1L1 and ALDH1L2 expression to alleviate ethanol-induced oxidative stress [41]. Nevertheless, despite its clear influence on breast cancer, the molecular mechanisms are not entirely deciphered. It has been proposed that it may be due to the mechanism previously described, increased levels of estrogen and its receptors derived from alcohol intake, and aromatase increased activity [36,42].
Alcohol’s influence on breast cancer and cell metabolism has been assessed in several studies. According to data reported by the study developed in the EPIC cohort, which included 360,000 women, alcohol consumption increases the risk of ER+ breast cancer [43]. Furthermore, the impact on DNA expression has been explored by the study conducted by Perrier et al. (2019) within the EPIC project. They concluded that alcohol intake is associated with methylation patterns at two CpG sites (cg03199996 and cg07382687) located in genomic regions associated with tumor suppressor activity (GSDMD and HOXA5 genes) [44]. Additionally, the immune system intimately related gene, Cd14, presents expression changes in the presence of ethanol [45]. Concerning breast cancer, alcohol-induced genes BRAF and ITPR1 presented a higher expression in ER+ breast cancer patients with a higher alcohol intake. Otherwise, alcohol-repressed genes BAFT and ITPKA had a higher expression in patients with the lowest alcohol intake [46]. According to alcohol metabolism-involved genes, three were up-regulated (ITGA5, CBS, and SOD2), and six were down-regulated (XDH, XRCC1, MTHFR, CYP1B1, XPC, and GSTP1) among women who died for breast cancer [47]. Nevertheless, alcohol has been associated with a higher TNBC cell proliferation, migration, and invasion through alcohol-induced reactive oxygen species and p38 and JNK phosphorylation, essential elements of the NK-κB signaling pathway [48].
2.2. Description of Genes Related to One-Carbon Metabolism
The 1CM features the confluence of three pathways: folate metabolism, methionine cycle, and trans-sulphuration. According to the current scientific literature, a total of 46 genes are related to 1CM. These genes are shown in Table 1. The information has been collected from the public repositories GeneCards® 5.20 version (
The 48 genes involved in 1CM are distributed across 20 chromosomes, although chromosome 5 contains the highest number of 1CM-related genes (DHFR, MTRR, MAT2B, BHMT, and DMGDH), followed by chromosomes 2, 11, and 21, with 4 genes in each one of them (MTHD2, MAT2A, ATIC, and DNMT3A in chromosome 2; FOLR1, FOLH1, CBLIF, and TCN1 in chromosome 11; and SLC19A1, CBS, FTCD, and GART in chromosome 21) and chromosomes 1 and 19, which contain 3 genes each (MTHFR, MTR, and CTH in chromosome 1, and DNMT1, PRMT1, and CD320 in chromosome 19).
2.3. Description of Single Nucleotide Polymorphisms Related to One-Carbon Metabolism
The search of genes related to 1CM in the GWAS Catalog provided a total of 706 SNPs associated with an outcome related to cancer at a p-value of 5 × 10−8. In addition, literature consultation of 1CM-related SNPs associated with breast cancer retrieved a total of 23 SNPs (Table 1). The study of minor allele frequencies (MAF) in the Caucasian population of the selected SNPs left 331 SNPs. Finally, 48 SNPs potentially related to both 1CM and cancer remained after restricting to linkage disequilibrium (LD) (R2 and/or D’ value < 0.7) between those SNPs located in the same chromosome (Table 2).
Most of the candidate genes remained with at least one potential SNP related to cancer. However, SNPs located in CBS, FOLR1, AMT, GART, FPGS, MTFMT, SLC25A32, MAT2B, ATIC, and SHMT2 genes did not remain after the above-mentioned selection criteria. On the other hand, the DNMT3A (rs11890065, rs7581217, and rs752208), CUBN (rs1801222, rs61841503, and rs796667), and SARDH (rs2519125, rs2073817, and rs476835) were the ones with the highest number of selected SNPs. According to the type of variant, most of the SNPs selected were intronic variants (23 SNPs) followed by missense type variants (12 SNPs). Complete information about the risk allele, the p-value, and the outcome the SNPs are associated with is shown in Table S2.
2.4. Genes Functional and Enrichment Analyses
FUMA, REVIGO, ShinyGO, and R software package ClusterProfiler 4.12.0 in R Studio were the sources used for functional and enrichment analyses. These tools provided information about the 1CM-related gene expression patterns in different tissues and their function according to the molecular and cellular pathways in which they are involved. Additionally, information was obtained about the genes’ role in the underlying mechanisms of several diseases.
The FUMA tool supplied data on the expression pattern of the 1CM-related genes in different tissues or cells. Genetic expression profiles are illustrated in a heat-map shown in Figure 2. We observed a higher expression pattern along all tissues and cells for AHCY, MAT2A, MAT2B, ATIC, and PRMT1 genes, while the genes BHMT, CBS, FOLH1, FOLR1, TCN1, DNMT3B, and MAT1A had a smaller tissular expression compared with the interrogated genes. Interestingly, 1CM-related genes with the higher expression pattern in the assessed tissues or cells participate in the methionine cycle (AHCY, MAT2A, and MAT2B), the biosynthesis of purines (ATIC), and the methylation of biological substrates different from DNA (PRMT1). Nevertheless, those genes with a reduced expression pattern are involved in the absorption of folate (FOLH1 and FOLR1), the methionine cycle (BHMT and MAT1A), trans-sulphurization pathway (CBS), DNA methylation (DNMT3B), and cobalamin metabolism (TCN1).
Concerning the gene expression pattern in the tissues or cells included, cultured fibroblasts were the biological matrix with the biggest number of highly expressed genes (AHCY, ATIC, CD320, DHFR, DNMT1, FPGS, GART, GGR, MAT2A, MAT2B, MATHFD1, MTHDF1L, MTHFD2, PRMT1, SHMT2, SLC25A32, and TYMS) as well as testis (AHCY, ALDH1L, AMT, ATIC, CD320, DNMT1, FPGS, FTCO, GART, GGH, MAT2A, MAT2B, MTR, PRMT1, SHMT1, TCN2, and TYMS) both presenting 17 genes with higher expression than the rest of the 1CM-related genes.
Additionally, in vitro B lymphocytes transformed by the Epstein–Barr virus (EBV) showed a high expression patter of the genes AHCY, ATIC, CD320, DNMT1, FPGS, GART, GGH, MAT2A, MAT2B, MATHFD1, MATHFD1L, MATHFD2, PRMT1, SHMT2, SLC25A32, and TYMS; followed by the liver where AHCY, ALDH1L1, BHMT, CTH, FPGS, FTCD, GGH, GNMT, MAT1A, MAT2A, MTHFD1, MTHFS, SHMT1, SHMT2, and SARDH presented a higher expression. Otherwise, the tissue with the lowest expression profile of the 1CM-related genes was the whole blood, in which the genes ALDH1L1, BHMT, CBS, CHDH, CTH, CUBN, DMGDH, DNMT3B, FOLH1, FOLR1, FTCD, MAT1A, MTHFD2L, SARDH, and SLC46A1 had a lower expression than the others. In addition, heart-related tissues present a lower expression of the 1CM-involved genes. Precisely, the heart’s left ventricle has a lower expression pattern for the genes ALDH1L2, CBS, DMGDH, FTCD, GNMT, MAT1A, GGH, SARDH, MTHFD1L1, MTHFD1L2, SLC19A1, SLC46A1, TCN1, and TYMS. Furthermore, the genes set with the lower expression patter in the heart atrial appendage tissue are BHMT, CBS, CUBN, DMGDH, DNMT3B, FOLH1, FOLR1, FTCD, MAT1A, and TCN1.
Focusing on the breast mammary tissue, AHCY, AMT, ATIC, CD320, DNMT1, FPGS, GART, MAT2A, MAT2B, MTHFD1, MTHFD2, PRMT1, SHMT1, SHMT2, SLC25A32, and TCN2 were the 1CM-related genes most highly expressed. On the other hand, the genes BHMT, CBS, FTCD, MAT1A, and SARDH were the ones with the lowest expression pattern.
Additionally, the joint expression of the 1CM-related genes differs between tissues, as it is shown in Figure 3. Those tissues where the 1CM-involved genes are up-regulated with a significant enrichment p-value after Bonferroni correction are the liver, the kidney, the nerves, and the pancreas, followed by the ovary and breast, although the enrichment is not significant. Otherwise, the down-regulated differential expression of the genes (DEG) occurs significantly in the heart, followed by the pancreas, the skin, the esophagus, and the brain, albeit the p-value of these last tissues was not significant. Concerning both sides, the liver is the tissue with the highest enrichment p-value for DEG of those genes involved in 1CM compared to other tissues. Furthermore, the pancreas, heart, nerve, and ovary also present a significant enrichment p-value of the 1CM-related gene DEG compared to the rest of the interrogated tissues. Conversely, tissues engaged in the digestive, excretory, and reproductive apparatus were the ones with the lowest DEG value of the studied genes (small intestine, colon, uterus, cervix uteri, testis, and bladder).
Focusing on breast tissue, it presents a higher proportion of up-regulated genes. Indeed, the breast is the tissue with one of the lowest DEG p-values within the down-regulated analysis. Considering both sides, breast tissue presents a higher expression of the 1CM-related genes than most of the retrieved tissues.
More detailed information about the number of background and overlapped genes and the adjusted and non-adjusted p-value for each tissue can be consulted in Table S3.
The enrichment and functional analyses were performed on the Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathways, MsigDB, and GeneOntology (GO) via the bioinformatic tools ShinyGO, FUMA, REVIGO, and the software R package ClusterProfiler 4.12.0 in R Studio. The genes listed in Table 1 compared to the reference genome (57,241 genes) were used in these analyses. Some insights about the main mechanisms and signaling pathways in which the 1CM-related genes are involved were obtained, as well as the main genetic and biochemical alterations in which they participate.
Cells phenomena with a significant p-value (<0.05, after false discovery rate (FDR) correction) in the enrichment analysis are shown in Figure 4. The pathways in which these genes are involved were as follows: one-carbon pool by folate, antifolate resistance, vitamin digestion, and absorption; glycine, serine, and threonine metabolism; cysteine and methionine metabolism; selecompounds metabolism; biosynthesis of amino acids; folate biosynthesis; biosynthesis of cofactors; glyoxylate and dicarboxylate metabolism; metabolic pathways; microRNAs (miRNAs) in cancer; and carbon metabolism. Regarding the results of the enrichment analysis, the interrogated genes were mainly related to the one-carbon pool by folate, showing a high association and enrichment value. Furthermore, the analysis performed revealed an association between the 1CM-related genes and the nitrogen metabolism via several amino acids’ metabolism and biosynthesis (serine, glycine, threonine, cysteine, and methionine) with a similar number of genes involved and −log10 FDR value. Additionally, it was observed that, despite a low proportion of participating genes, the term “metabolic pathways” had a high −log10 FDR value. This term is defined as reactions and mechanisms that transform molecules, including macro-molecular processes such as DNA repair, replication, and methylation, which are essential in the carcinogenic process.
Likewise, the 1CM-related genes may be involved in biochemical and genetic alterations that are the basis of various pathologies. According to the analysis carried out using the FUMA and REVIGO tools, the genes are related to a wide variety of these alterations. Regarding genes involved in the different pathways, a large proportion of them were involved in signaling pathways linked to carcinogenesis, mediated by MAPK, RAS, HOX11, TP53, and MYC [51,52,53]. There was also a high proportion of genes involved in processes related to different types of cancer. Furthermore, other cellular processes related to the interrogated genes are involved in pathways associated with cell adaptation to stress and hypoxia through the hypoxia-inducible factor 1-α, which is associated with breast cancer response to chemotherapy [54,55]. On the other hand, the results of the enrichment analysis also linked the genes of interest to mechanisms involved in other pathologies with glucocorticoid therapy (Figure 5). According to the FDR value obtained, the main mechanisms involving the interrogated genes were those responsible for nasopharyngeal carcinoma and colorectal cancer mediated by MYC overexpression (Figure 5).
Finally, Figure 5 shows information about the overlap of certain genes in the different cellular mechanisms. Processes with the biggest number of overlapped genes were related to uterus adaptation during pregnancy (MTHFD2, ATIC, SLC25A32, SHMT2, GART, MAT2A, MTRR, BHMT, DHFR, FPGS, and MTHFD1) [56], and nasopharyngeal carcinoma (MTR, MTHFD2, ATIC, SLC25A32, SHMT2, TYMS, AHCY, GART, MAT2A, MTRR, DHFR, and MTHFD2L) [52]. Additionally, the third pathway with the highest number of overlapping genes was found regarding the BCRA1-Pearson correlation coefficient (PCC) network [57], this model being potentially associated with breast cancer and the BRCA1 mutations effect. The overlapping genes in this network were MTR, MTHFD2, ATIC, SHMT2, TYMS, AHCY, GART, MAT2A, DHFR, GGH, and DNMT1. Furthermore, alterations related to carcinogenesis in the colon and rectum had a high proportion of overlapping genes in the gene-sets.
More detailed information about the number of background and overlapped genes for each genetic and biochemical alteration in the adjusted and non-adjusted p-value from the enrichment analysis can be consulted in Table S4.
On the other hand, enrichment analyses, according to the overall and disease-free survival of breast cancer, have been performed using the online tools GEPIA and UALCAN [58,59]. These online free-use platforms provide information about the expression of those genes involved in survival for a precise type of cancer according to the data from the TCGA Study (The Cancer Genome Atlas Program, National Cancer Institute, NCI) [60].
GEPIA results indicate that higher expression of the 1CM-participating gene TCN1, measured in TPM, is associated with a higher overall survival rate in patients with breast cancer (plogRank = 0.0007) (Figure 6). Inversely, concerning breast cancer free-disease survival, none of the 1CM genes showed any association. These results are in accordance with the ones obtained from the tool UALCAN, where a higher expression of TCN1 is observed in those patients diagnosed with breast invasive carcinoma and with a higher overall survival (p = 0.042) (Figure 7). Otherwise, UALCAN results indicate that expression of SLC25A32 and SHMT2 correlates with overall survival in breast invasive carcinoma. Indeed, a higher expression of SLC25A32 is associated with a poorer overall survival rate of the patients compared to those with a lower expression (p = 0.03) (Figure 8). Furthermore, results for SHMT2 indicate that a lower expression of the gene is associated with a low overall survival rate (p = 0.0065) (Figure 9).
3. Discussion
In this study, the association between nutrients and genes involved in 1CM and cancer, specifically breast cancer, was investigated. Several computational tools and biological databases have been used to identify the molecular, metabolic, and cellular mechanisms linking the genes of interest to cancer.
The results of the literature search highlight the role of vitamins B2, B6, B9, and B12, as well as alcohol consumption, in the development of breast cancer. Current scientific evidence indicates that higher intakes of B2, B6, and folate decrease the risk of developing breast cancer. On the other hand, there is controversy about the effect of plasma levels of these nutrients. As for alcohol, it has been observed that any consumption favors the onset of the disease. The results of the in silico functional and enrichment analysis show that the genes of interest are up-regulated in the breast tissue compared to other tissues and cells. Precisely, genes AHCY, CD320, FPGS, MTHFD2, PRMT1, and TCN2 present an outstanding expression compared to the rest of the interrogated genes. Additionally, they are involved in a wide variety of cellular processes and metabolic pathways, 1CM being the main one, as well as the metabolism of several amino acids and the digestion and absorption of proteins, antifolate resistance, and miRNAs in cancer. Current scientific evidence indicates that miRNAs play an important role in cancer, being involved in the regulation of different cancer mechanisms such as apoptosis, TNFα signaling, hypoxia, or inflammatory response. Furthermore, a wide range of miRNAs has been related to breast cancer, either acting as tumor suppressors (e.g., MIR100, MIR1-1, or MIR114) or oncogenes (e.g., MIR115, MIR17, or MIR224) [61]. Furthermore, survival enrichment analyses between the 1CM-related genes and survival in breast cancer indicate that a higher expression of TCN1 correlates with a higher overall survival. Conversely, genes SLC25A32 and SHMT2 higher expression is associated with poorer overall survival. The above-mentioned mechanisms are closely related to cell proliferation and carcinogenesis. In addition, the 1CM genes are involved in various biochemical and genetic alterations that cause several pathologies, including cancer, mainly colorectal, nasopharyngeal, and breast cancer. Indeed, expression of 1CM-related genes as ALDH1L2 has been associated with the response to chemotherapy with 5-FU (5-fluorouracil, inhibitor of folate metabolism) in colorectal cancer patients [62]. Additionally, it has been reported that participating nutrients in 1CM (e.g., methionine and betaine) may influence colorectal cancer risk [63]. On the other hand, the role of the 1CM-related genes and nutrients has not been already explored, albeit the nasopharyngeal carcinoma network elucidates that the disease is closely related to mechanisms where 1CM plays a pivotal role, such as DNA repair [52]. Concerning breast cancer, the 1CM-involved genes are highly overlapped in the alterations derived from the breast cancer network, which uses BRCA1, BRCA2, CHEK2, and ATM as reference genes for the disease. The network connects a wide range of genes involved in breast cancer after PCC, where we can find 11 genes related to 1CM. Interestingly, the breast cancer reference gene ATM is also one of the 1CM-involved genes [57]. Thus, molecular variants in the genes studied may influence the development and survival of cancer and, specifically, breast cancer. Wu et al. (2016) measured genome stability and cell viability in vitro in lymphocytes from women with breast cancer and healthy controls as a function of the expression of the 1CM-related genes (SHMT, MTR, and MTRR) and vitamin B6. They observed a positive correlation between vitamin B6 and genome stability, with 48 nmol/L being the optimal vitamin B6 concentration. In addition, they indicated that SNPs located in the genes studied are involved in the stress to which the cell is subjected [64].
This is the first study on functional and enrichment analyses between genes involved in 1CM and cancer, focusing on breast cancer. Currently, few in silico analyses on the main potential possibly involved in breast cancer have been published. These functional analyses agree on the association of a few genes with breast cancer survival and prognosis, such as DLGAP5, NCAPG, and RRM2, which are not involved in 1CM [65,66].
Some previous studies have evaluated the role of B2, B6, B12, folate, folic acid, and alcohol consumption, as well as the influence of SNPs located in these genes on breast cancer.
The study conducted by Maruti et al. (2009) evaluated the role of the MTHFR rs1081133 polymorphism, as well as folate, B2, B6, B12, and alcohol intake in 318 breast cancer cases and 647 controls of European Caucasian origin, matched for age and race. The results of the study indicated that women carrying the TT genotype of these genes have a higher risk of developing breast cancer after menopause (OR = 1.62; 95%CI = 1.05–2.48; p < 0.05). Furthermore, lower folate intake combined with MTHFR rs1081133 contributed to an even higher risk [67], thus supporting an interaction between folate intake and this SNP of the MTHFR gene. On the other hand, the study by Ma et al. (2009), where the role of the SNPs MTHFR rs1081133 (G > A) and rs1081131, and MTR rs1805087, as well as the dietary intake of folate, B6 and B12 was explored among 458 women with breast cancer and 458 women without the disease from Brazil (mixed origin), showed that the GG genotype of the MTR rs1805087 increases the risk of overall breast cancer (OR = 1.99, 95%CI = 1.01–3.92; p = 0.01), while no association was observed for the MTHFR SNP rs1081133. Regarding the intake of the B-complex vitamins involved in 1CM, the results of this study do not agree with those of previous studies since women with a higher intake of folate had a higher risk of developing breast cancer, mostly among premenopausal women (OR = 2.17, 95%CI = 1.23–3.82, p = 0.01) [68].
Taking into account the development of breast cancer as a function of hormone receptors, in the study carried out by Wang et al. (2022) in 439 women with breast cancer (and 439 controls) of Asian origin, the influence of the MTR rs1805087, MTHFR rs1801133, ALDH1L1 rs2002287 (G > A), DNMT1 rs2228611, and DNMT3B rs2424908 (C > T) variants on breast cancer was examined. Results showed that the A allele of DNMT1 rs2228611 decreased the risk of overall breast cancer (OR = 0.74; 95%CI = 0.56–0.97; p = 0.03; GA + AA vs. GG). This SNP was also associated with a lower risk of ER+, PR+, and HER2 breast cancer. Similarly, the stratified analysis by hormone receptors indicated that carriers of the C allele of ALDH1L1 rs2002287 present an increased risk of developing breast cancer PR+ (OR = 1.54; 95%IC = 1.04–2.26; p = 0.03) [69]. Interestingly, this gene is also associated with alcohol metabolism, an established risk factor for breast cancer.
Similarly, studies have considered the adherence to a dietary pattern and the interaction with the 1CM-related SNPs to assess their influence on breast cancer. Cao et al. (2021) conducted this study on 818 breast cancer cases and 935 controls of Asian origin. The results showed no association between SNPs, studied individually, and breast cancer. However, taking into account the joint effect of all SNPs using a polygenic risk score or PRS, an increased risk of overall breast cancer was observed (OR = 2.09, 95%CI = 1.54–2.85, p < 0.001). On the other hand, greater adherence to the Mediterranean diet was associated with a lower risk of postmenopausal breast cancer (OR = 0.54, 95% CI = 0.38–0.78, p = 0.001). Considering the interaction between the Mediterranean diet and PRS, an increased risk of overall and postmenopausal breast cancer was observed [70]. This finding also highlights a potential interaction between dietary factors and 1CM-related SNPs.
On the other side, few studies have assessed the role of the 1CM-involved genes in breast cancer survival. The study developed by Xu et al. (2008) enrolled 1479 breast cancer cases where they assessed the role of B-complex vitamins dietary intake and 1CM SNPs on survival. Results showed that the altered allele of MTHFR rs1801133 (T) carriers have reduced all-cause mortality (HR = 0.69, 95%CI = 0.49–0.98) and breast cancer-specific mortality (HR = 0.58, 95%IC = 0.38–0.89) [71]. The association of 1CM with cancer survival is closely related to chemotherapy. Indeed, Zhang et al. (2024) assessed the role of 1CM in breast cancer response to treatment using the GSE20685 from the Gene Expression Omnibus (GEO) dataset. The risk score model was constructed and included the 1CM genes MAT2B, DNMT3B, CHDH, AHCY, and SHMT2. Kaplan–Meier analysis revealed that SHMT2 and DNMT3B can be labeled as risk factors for survival and were up-regulated in high-risk patients. By contrast, the analysis identified MAT2B, AHCY, and CHDH as protective factors, being down-regulated in high-risk patients [72].
The current study has limitations inherent to in silico studies, such as the lack of association of the genes studied with certain outcomes due to the limited information available in biological or genetic databases and repositories. In addition, we have not considered gene–environment interaction as a potential driver of breast cancer, which could have affected the final result. Furthermore, with respect to the observational studies, limitations that may have affected the conclusions drawn can be related to sample size, dietary assessment methods, participants’ ethnicity, and study design. Regarding the strengths of the study, we have used several sources and web tools to explore the relationship between genes and breast cancer, as well as their molecular activity and the metabolic pathways in which they participate. In addition, a selection has been made of those SNPs located in genes involved in 1CM and potentially associated with cancer, considering the previous scientific literature, the MAF in the Caucasian population, and the LD.
4. Materials and Methods
4.1. Search Strategy to Review the Relationship between Folate, B2, B6, B12, and Alcohol with Breast Cancer
A bibliographic search of the scientific literature has been performed to review the relationship between the nutrients and diet components related to 1CM and breast cancer. We conducted searches in Medline (PubMed) (
Breast[title] AND (carcinoma* OR cancer* OR malign* OR neoplasm* OR tumor* OR tumour*)
AND
(“vitamin B2” OR B2 OR riboflavin)
AND
(“vitamin B6” OR B6 OR pyridoxine OR pyridoxamine OR pyridoxal)
AND
(“vitamin B9” OR B9 OR folate OR “folic acid”)
AND
(“vitamin B12” OR B12 OR cobalamin)
AND
alcohol
4.2. Annotation of the Genes Related to One-Carbon Metabolism and Single Nucleotide Polymorphisms Selection
To select the genes related to 1CM, we performed a search in the main scientific databases Medline (PubMed) (
“One-carbon metabolism”[title] AND (gene* OR genome*)
Information about the selected genes shown in Table 1 was collected by consultation of the public repositories GeneCards® 5.20 version (
In parallel, the SNPs related to 1CM and breast cancer were selected considering the scientific evidence about the SNPs and their relationship with any cancer-related outcome. For this purpose, we used the database GWAS Catalog (
Breast[title] AND (carcinoma* OR cancer* OR malign* OR neoplasm* OR tumor* OR tumour*)
AND
“one-carbon metabolism”
AND
(“single nucleotide” OR gene* OR genom* AND (polymorphism* OR variant* OR variation*))
Subsequently, we studied the MAF of the selected SNPs in the Caucasian population, according to the published data in Ensembl (
4.3. Biological Database Studies
We performed functional and enrichment analyses of the genes selected in the previous steps using online computational biology tools and R software packages (Clusterprofiler 4.12.0) [75,76], which are described below.
4.3.1. In Silico Functional Analyses and Enrichment Analyses
The bioinformatic tools used were FUMA (Functional Mapping and Annotation of Genome-Wide Association Studies), REVIGO, ShinyGO, GEPIA, and UALCAN. These databases contain genetic information from several sources such as Ensembl, Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG).
FUMA is an online platform to prioritize, visualize, and interpret GWAS results [77]. This tool provides useful functional biological information. Additionally, it performs functional and enrichment analyses based on genes, molecular pathways, and specific tissue information. The sources of information for FUMA are GO, WikiPathways, and MsigDB. Using the module GENE2FUNC (
ShinyGO (
REVIGO (
The Gene Expression Profiling Interactive Analysis (GEPIA) (
The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) (
4.3.2. Workflow of the Analyses
We started the analysis with a bibliographic search on the association between 1CM and breast cancer to annotate the genes involved in this pathway and consider the dietary components related to 1CM. To select those SNPs potentially associated with cancer, we looked for variants located in the genes involved in 1CM from the GWAS Catalog (p-value threshold 5 × 10−8) and other databases (PubMed and Scopus). Subsequently, we consulted Ensembl to study the MAF of the SNPs in the Caucasian population. Additionally, the LD of the selected molecular variants was examined. In addition, we carried out an in silico functional and enrichment analysis study using biological databases such as FUMA, REVIGO, and ShinyGO (Figure 10).
5. Conclusions
In the present study, genes and SNPs related to 1CM that may have a potential role in cancer development, focusing on breast cancer, have been analyzed using information available in public biological databases via in silico enrichment and functional analysis. Likewise, bibliographic research on the current evidence about the role of B group vitamins involved in 1CM and alcohol on breast cancer has been conducted. Genes related to 1CM have been poorly explored regarding their role in breast cancer despite their potential involvement in this disease, as evidenced in biological databases. This study unveils that 1CM-related genes participate in a wide range of cellular mechanisms closely related to carcinogenesis, such as amino acid metabolism and cell proliferation, with a notable impact on breast cancer. According to the dietary compounds, vitamins B2, B6, B9, and B12 might be involved in breast cancer as well. Further molecular and nutritional studies are needed to elucidate the underlying linking mechanisms and to explore their potential interplay. Additionally, studies should evaluate the role of the SNPs selected in this study, as well as their interaction with nutrients involved in 1CM or foods rich in these nutrients in the etiology and survival of breast cancer.
Conceptualization, J.M.G.-N., E.M.-M. and M.R.-T.; methodology, M.R.-B.; software, J.M.G.-N. and M.R.-B.; validation, E.M.-M., M.R.-T. and M.-J.S.; formal analysis, J.M.G.-N.; investigation, J.M.G.-N.; resources, J.M.G.-N. and E.M.-M.; data curation, M.R.-B.; writing—original draft preparation, J.M.G.-N., E.M.-M. and M.R.-T.; writing—review and editing, M.R.-B., M.-J.S. and Á.G.; visualization, all authors; supervision, M.R.-T., M.-J.S. and Á.G.; project administration, E.M.-M. and M.-J.S.; funding acquisition, M.-J.S. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data is contained within the article and
The results presented in this paper are part of the Ph.D. dissertation developed by José María Gálvez-Navas in the Nutrition and Food Sciences Ph.D. Program at the University of Granada (Spain), directed by Esther Molina-Montes and Miguel Rodríguez-Barranco.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. One-carbon metabolism pathway. DHF: dihydrofolate, THF: tetrahydrofolate, IMP: inosine monophosphate, Met: methionine, SAM: S-adenosylmethionine, SAH: S-adenosylhomocysteine, HCY: homocysteine, DMG: dimethylglycine, dATP: deoxyadenosine triphosphate, dGTP: deoxyguanosine triphosphate, dTMP: deoxythymidine monophosphate, dUMP: deoxyuridine monophosphate, Ser: serine.
Enlarge this image.Figure 2. Heat-map of the one-carbon metabolism-related genes average expression pattern in different tissues and cells. The expression profiles are based on GTEx v8 RNA-seq data for 54 tissue and cell types. Scale bar represents gene expression measured in TPM (Transcripts Per Million). Cells in darker red mean higher expression of the gene compared to a darker blue color. This allows for comparison across tissue/cell labels and genes. Source: FUMA.
Enlarge this image.Figure 3. Tissue specificity expression of one-carbon metabolism-related genes in different tissues. The upper diagram shows the up-regulation of the 1CM-related genes in different tissues; meanwhile, the second diagram refers to the tissues where the genes involved in 1CM are down-regulated. Finally, the bottom diagram shows the differential expression of the genes (DEG) of the interrogated genes in all the tissues. Input genes were tested against each of the DEG sets using a hypergeometric test. The background genes are those that have an average expression value > 1 in at least one of the tissue labels and exist in the selected background genes (all). Tissues with significant enrichment at Bonferroni corrected p-value ≤ 0.05 are colored in red. Bonferroni correction is performed for each of the up-regulated, down-regulated, and both-sided DEG sets separately. Source: FUMA.
Enlarge this image.Figure 4. Functional and enrichment analysis for the one-carbon metabolism-related genes in the cellular and molecular mechanisms from GeneOncology. In the diagram above is represented the association of the one-carbon-related genes and the different mechanisms in which they are involved after FDR correction. Dot size refers to the number of genes involved in the pathway. FDR value is defined through a color scale. Thus, the closer to red, the bigger the association is, and the reverse for blue. Fold enrichment is defined as the percentage of the selected genes involved in one pathway divided by the percentage of the reference genes. It assesses the enrichment magnitude, so a higher value means a stronger enrichment. Source: ShinyGO.
Enlarge this image.Figure 5. In silico enrichment analysis in the one-carbon metabolism-related genes on the main genetic and biochemical alterations (MsigDB). Figures were created according to the comparison between the interrogated genes and the reference genome through KEGG and MsigDB. The overlapping genes related to one-carbon metabolism (in yellow), the FDR p-value after the enrichment analysis (in blue), and the proportion of overlapping genes compared to the reference genome in each associated pathway (in red) are shown. Source: FUMA.
Enlarge this image.Figure 6. Kaplan–Meier plot of overall survival curves from in silico enrichment analysis according to TCN1 expression in breast cancer patients from TCGA Study. Source: GEPIA.
Enlarge this image.Figure 7. Kaplan–Meier plot of overall survival curves from in silico enrichment analysis according to TCN1 expression in patients diagnosed with invasive breast carcinoma. Source: UALCAN.
Enlarge this image.Figure 8. Kaplan–Meier plot of overall survival curves from in silico enrichment analysis according to SLC25A32 expression level in patients diagnosed with invasive breast carcinoma. Source: UALCAN.
Enlarge this image.Figure 9. Kaplan–Meier plot of overall survival curves from in silico enrichment analysis according to SHMT2 expression level in patients diagnosed with invasive breast carcinoma. Source: UALCAN.
Genes related to one-carbon metabolism: chromosomic location, gene size from the GRCh38.p14 genome version, the Ensembl ID, and function.
| Gene | Chromosomic Location | Ensembl ID | Function |
|---|---|---|---|
| FOLR1 | 11q13.4 | ENSG00000110195 | The coding protein is representative of the folate receptors family. Folate and folic acid bind to these receptors to enter the cell |
| GGH | 8q12.3 | ENSG00000137563 | The gene codifies for the γ-glutamyl hydrolase. It is responsible for hydrolyzing the polyglutamates from the diet where folate is bound |
| FOLH1 | 11p11.12 | ENSG00000086205 | The folate hydrolase 1 is a transmembrane glycoprotein with glutamate carboxypeptidase activity for dietary folate |
| SLC19A1 | 21q22.3 | ENSG00000173638 | The gene encodes a membrane protein that acts as a transporter of folate. The protein is involved in the regulation of folate intracellular concentration |
| SLC25A32 | 8q22.3 | ENSG00000164933 | This gene leads to a member of the mitochondrial carrier family transport proteins. The encoded protein transports folate across the inner mitochondrial membrane |
| SLC46A1 | 17q11.2 | ENSG00000076351 | This gene encodes a transmembrane proton-coupled folate transporter protein that facilitates the movement of folate and antifolate substrates across the cellular membrane |
| DHFR | 5q14.1 | ENSG00000228716 | The enzyme codified is the dihydrofolate reductase, which is involved in THF synthesis. Its deficiency has been related to megaloblastic anemia |
| TYMS | 18p11.32 | ENSG00000176890 | This gene codifies for the enzyme thymidylate synthase, the one in charge of maintaining the dTMP pool needed for DNA replication and repair |
| MTHFD1 | 14q23.3 | ENSG00000120254 | The methyl-tetrahydrofolate dehydrogenase 1 presents three different enzymatic activities participating in the methionine, thymidylate, and purine synthesis |
| MTHFD1L | 6q25.1 | ENSG00000120254 | The resulting protein is involved in the THF biosynthesis in the mitochondrion. Several isoforms have been identified for this gene |
| MTHFD2 | 2p13.1 | ENSG00000065911 | This gene codifies an enzyme with mitochondrial activity, which is involved in methionine and thymidylate synthesis. The enzyme is characterized for requiring Mg2+ and inorganic phosphate |
| MTHFD2L | 4q13.3 | ENSG00000163738 | This gene encodes a trifunctional enzyme that participates in the THF interconversion. It is predicted to be located in the mitochondrial matrix |
| MTHFS | 15q25.1 | ENSG00000136371 | The enzyme encoded by this gene catalyzes the conversion of 5-formyl-THF to 5,10-methenyl-THF. Two isoforms have been found for this gene |
| SHMT1 | 17p11.2 | ENSG00000176974 | The gene codifies for the serine hydroxymethyltransferase 1, which participates in the methionine, thymidylate, and purines synthesis in cytosol |
| SHMT2 | 12q13.3 | ENSG00000182199 | This gene encodes for the serine hydroxymethyltransferase 2 in the mitochondrion. Enzyme activity has been suggested to be the primary source of intracellular glycine |
| MTHFR | 1p36.22 | ENSG00000177000 | This is a paralogue gene of MTR. It codifies for the methyl-tetrahydrofolate reductase that catalyzes the conversion of 5,10-mTHF of 5-mTHF, which is a co-substrate of homocysteine re-methylation to methionine |
| MTR | 1q43 | ENSG00000116984 | The gene codifies for the enzyme methionine synthase, which depends on cobalamin. It is in charge of catalyzing the final step of methionine biosynthesis |
| MTRR | 5p15.31 | ENSG00000124275 | The gene codifies for the 5-mTHF-homocysteine methyltransferase reductase, which is involved in methionine synthesis, regenerating the enzyme methionine synthase to a functional state |
| MAT1A | 10q22.3 | ENSG00000151224 | This gene leads to the enzyme methionine adenosyl-transferase, which is in charge of transferring adenosyl groups for methyl group generation |
| MAT2A | 2p11.2 | ENSG00000168906 | The protein encoded by this gene catalyzes the production of S-adenosylmethionine from methionine and ATP, the key methyl donor in cellular processes |
| MAT2B | 5q34 | ENSG00000038274 | The protein encoded in this gene belongs to the methionine adenosyl-transferase (MAT) family, which catalyzes the biosynthesis of S-adenosylmethionine |
| GNMT | 6p21.1 | ENSG00000124713 | The enzyme codified if the glycine-N-methyltransferase acts in the cytoplasmatic synthesis of S-adenosyl-homocysteine from glycine and S-adenosyl-methionine |
| AHCY | 20q11.22 | ENSG00000101444 | The S-adenosyl-homocysteine hydrolase regulates the S-adenosyl-homocysteine concentration through its hydrolyzation |
| BHMT | 5q14.1 | ENSG00000145692 | It gives rise to the cytosolic enzyme betaine-homocysteine-S-methyltransferase, which catalyzes the conversion of betaine and homocysteine to dimethylglycine and methionine |
| CHDH | 3p21.1 | ENSG00000016391 | The protein codified is the choline dehydrogenase, which acts in the synthesis of betaine from choline at mitochondria |
| DNMT1 | 19p13.2 | ENSG00000130816 | This gene codes for the enzyme DNA methyltransferase 1, which is responsible for transferring methyl groups to the cytosine nucleotides of genomic DNA. It is the most active enzyme in maintaining the DNA methylation patterns |
| DNMT3A | 2p23.3 | ENSG00000119772 | It codes for the enzyme DNA methyltransferase 3A, which is responsible for de novo methylations of the CpG sites in genomic DNA |
| DNMT3B | 20q11.21 | ENSG00000088305 | It codes for the enzyme DNA methyltransferase 3B, which is responsible for de novo methylations of the CpG sites in genomic DNA |
| CBS | 21q22.3 | ENSG00000160200 | The synthesized protein, the cystathionine-β-synthase, is a tetramer that converts homocysteine to cystathionine. It initiates the trans-sulphurization pathway |
| CTH | 1p31.1 | ENSG00000116761 | This gene codes for the cytoplasmic enzyme γ-cystathionine lyase that converts methionine-derived cystathionine to cysteine |
| PRMT1 | 19q13.33 | ENSG00000126457 | The enzyme arginine-N-methyltransferase is involved in the regulation of several biological processes by methylating the amino-terminal groups of arginine. High expression of the enzyme is associated with various types of cancer |
| ALDH1L1 | 3q21.3 | ENSG00000144908 | The enzyme belongs to the aldehyde dehydrogenase family and catalyzes the conversion of 10-mTHF, NADP+, and water to THF, NADPH, and carbon dioxide |
| ALDH1L2 | 12q23.3 | ENSG00000136010 | It is the mitochondrial form of FDH, which plays an essential role in the distribution of one-carbon groups |
| AMT | 3p21.31 | ENSG00000145020 | It encodes one of the main components of the glycine cleavage system, which participates in the catalysis of glycine |
| ATIC | 2q35 | ENSG00000138363 | The gene encodes a bifunctional enzyme that catalyzes the last two steps of the de novo purine biosynthesis pathway |
| CD320 | 19p13.2 | ENSG00000167775 | This gene encodes the transcobalamin receptor, which is expressed in the cell surface. It mediates the cellular uptake of transcobalamin-bound cobalamin (vitamin B12) |
| CUBN | 10p13 | ENSG00000107611 | This gene encodes for the cubilin. This protein acts as a receptor for intrinsic factor–vitamin B12 complexes. The protein is located within the epithelium of intestine and kidney |
| DMGDH | 5q14.1 | ENSG00000132837 | The gene encodes an enzyme involved in the catabolism of choline, catalyzing the oxidative demethylation of dimethylglycine to form sarcosine at the mitochondria |
| FPGS | 9q34.11 | ENSG00000136877 | The resulting enzyme is the folylpolyglutamate synthetase. It has a central role in establishing and maintaining both cytosolic and mitochondrial folylpolyglutamate levels |
| FTCD | 21q22.3 | ENSG00000281775 | This gene encodes a bifunctional enzyme that channels one-carbon units from the histidine degradation pathway to the folate pool |
| GART | 21q22.11 | ENSG00000262473 | The result of this gene translation is a trifunctional polypeptide that is required for the de novo purine biosynthesis |
| CBLIF | 11q12.1 | ENSG00000134812 | The resulting protein is a member of the cobalamin transport family. The protein is secreted in the parietal cells, and it is required for adequate absorption of vitamin B12 |
| MMAB | 12q24.11 | ENSG00000139428 | This gene encodes a protein that catalyzes the final step in the conversion of vitamin B12 into adenosyl-cobalamin |
| MTFMT | 15q22.31 | ENSG00000103707 | The resulting enzyme is the mitochondrial methionyl-TRNA formyltransferase, which catalyzes the formylation of methionyl-tRNA |
| SARDH | 9q34.2 | ENSG00000123453 | This gene codifies for an enzyme localized in the mitochondrial matrix, the sarcosine dehydrogenase. It catalyzes the oxidative demethylation of sarcosine |
| TCN1 | 11q12.1 | ENSG00000134827 | This gene encodes a member of the vitamin B12-binding protein family. The protein facilitates the transport of cobalamin into cells |
| TCN2 | 22q12.2 | ENSG00000185339 | The resulting protein is a member of the vitamin B12-binding protein family. This plasma protein binds cobalamin and mediates its transport into cells |
5-mTHF: 5-methyl-tetrahydrofolate; 10-mTHF: 10-methyl-tetrahydrofolate; 5,10-mTHF: 5,10-methyl-tetrahydrofolate; dTMP: thymidine-5′ monophosphate; bp: base pair; THF: tetrahydrofolate; ATP: adenosine triphosphate; NADP+/NADPH: nicotinamide adenine dinucleotide phosphate; FDH: 10-formyltetrahidrofolate dehydrogenase.
Single nucleotide polymorphisms located in the genes related to one-carbon metabolism and potentially associated with cancer (GRCh38.p14 genome-built version).
| Gene | rsID | Human Genome Variation | Type of | Nucleotide Change |
|---|---|---|---|---|
| GGH | rs719235 | NC_000008.11:g.63039122C>A | 5′ UTR | C>A |
| FOLH1 | rs10839234 | NC_000011.10:g.49163972C>T | Intronic | C>T |
| SLC19A1 | rs9977637 | NC_000021.9:g.45494728A>G | 3′ UTR | A>G |
| rs17004785 | NC_000021.9:g.45512704G>A | 3′ UTR | G>C | |
| SLC46A1 | rs2239910 | NC_000017.11:g.28396647C>A | 3′ UTR | C>A |
| DHFR | rs1650697 | NC_000005.10:g.80654962A>G | Missense | A>G |
| TYMS | rs2124616 | NC_000018.10:g.661917G>A | Intronic | G>A |
| rs11664283 | NC_000018.10:g.650968G>A | NCEV a | G>A | |
| MTHFD1 | rs2236225 | NC_000014.9:g.64442127G>A | Missense | G>A |
| MTHFD1L | rs803446 | NC_000006.12:g.150944078G>A | Intronic | G>A |
| rs12660161 | NC_000006.12:g.151140796G>A | Intronic | G>A | |
| MTHFD2 | rs12469365 | NC_000002.12:g.74189113G>A | Intronic | G>A |
| MTHFD2L | rs7683181 | NC_000004.12:g.74237959C>T | Intronic | C>T |
| rs7686861 | NC_000004.12:g.74132767C>T | Intronic | C>T | |
| SHMT1 | rs2168781 | NC_000017.11:g.18337432C>G | Intronic | C>G |
| MTHFR | rs1801131 | NC_000001.11:g.11794419T>G | Missense | T>G |
| MTHFS | rs4778734 | NC_000015.10:g.79918509A>G | Intronic | A>G |
| MTR | rs1805087 | NC_000001.11:g.236885200A>G | Missense | A>G |
| MTRR | rs1801394 | NC_000005.10:g.7870860A>G | Missense | A>G |
| MAT1A | rs10887718 | NC_000010.11:g.80282868C>T | Intronic | C>T |
| MAT2A | rs2028900 | NC_000002.12:g.85540612C>T | Intronic | C>T |
| GNMT | rs10948059 | NC_000006.12:g.42960723C>T | RRV | C>T |
| AHCY | rs6087571 | NC_000020.11:g.34324285A>G | RRV | A>G |
| BHMT | rs3733890 | NC_000005.10:g.79126136G>A | Missense | A>G |
| CHDH | rs6801605 | NC_000003.12:g.53842191A>G | Intronic | A>G |
| DNMT1 | rs2228611 | NC_000019.10:g.10156401T>A | SV | T>A |
| DNMT3A | rs11890065 | NC_000002.12:g.25264508C>T | Intronic | C>T |
| rs7581217 | NC_000002.12:g.25302075T>C | Intronic | T>C | |
| rs752208 | NC_000002.12:g.25232520G>T | 3′ UTR | G>T | |
| DNMT3B | rs6141813 | NC_000020.11:g.32778437A>G | Intronic | A>G |
| CTH | rs672203 | NC_000001.11:g.70421416A>G | Intronic | A>G |
| rs1021737 | NC_000001.11:g.70439117G>T | Missense | G>T | |
| PRMT1 | rs10415880 | NC_000019.10:g.49685899G>A | NCEV a | G>A |
| ALDH1L1 | rs6792028 | NC_000003.12:g.126117830G>C | Intronic | G>C |
| ALDH1L2 | rs7954946 | NC_000012.12:g.105070258C>T | Intronic | C>T |
| CD320 | rs2232775 | NC_000019.10:g.8308268T>C | Missense | T>C |
| CUBN | rs1801222 | NC_000010.11:g.17114152A>G | Missense | A>G |
| rs61841503 | NC_000010.11:g.16977560A>G | Intronic | A>G | |
| rs796667 | NC_000010.11:g.16904820G>T | Intronic | G>T | |
| DMGDH | rs4512118 | NC_000005.10:g.79056268C>G | Intronic | C>G |
| FTCD | rs725976 | NC_000021.9:g.46137966G>A | Intronic | G>A |
| CBLIF (GIF) | rs7117509 | NC_000011.10:g.59822371A>G | RRV | A>G |
| MMAB | rs9593 | NC_000012.12:g.109557065A>T | Missense | A>T |
| SARDH | rs2519125 | NC_000009.12:g.133670195A>G | Intronic | A>G |
| rs2073817 | NC_000009.12:g.133694338C>T | Missense | C>T | |
| rs476835 | NC_000009.12:g.133738370C>G | Intronic | C>G | |
| TCN1 | rs34324219 | NC_000011.10:g.59855905C>A | Missense | C>A |
| TCN2 | rs4820023 | NC_000022.11:g.30634794C>T | TFBS | C>T |
Information on the genes to which these variants belong, the chromosomic location, the type of genetic variant, and the nucleotide change. 3′ UTR: 3′ untranslated region; 5′ UTR: 5′ untranslated region; NCEV: non-coding exonic variant; RRV: regulating region variant; SV: synonymous variant; TFBS: transcription factor binding site. a The NCEV polymorphism is not located in 3′ nor 5′ UTRs.
Supplementary Materials
The following supporting information can be downloaded at
References
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; Molina-Montes, Esther 2
; Rodríguez-Barranco, Miguel 3
; Ramírez-Tortosa, MCarmen 4
; Gil, Ángel 5
; María-José Sánchez 3
1 Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain;
2 Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain;
3 Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain;
4 Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Campus Universitario de Cartuja, 18011 Granada, Spain;
5 Instituto de Investigación Biosanitaria ibs. GRANADA, Av. de Madrid, 18012 Granada, Spain;