Background:

The association of plasma homocysteine level (PHL) with gastric cancer risk was reported in observational studies. However, the causality is challenging due to confounding factors and the lack of evidence from well-designed cohort studies. Herein, we performed a two-sample Mendelian randomization (MR) analysis to investigate whether PHL is causally related to gastric cancer risk.

Methods:

We performed the MR analysis based on the results from genome-wide association studies consisting of 2,631 patients with gastric cancer and 4,373 controls. An externally weighted genetic risk score (wGRS) was constructed with 15 SNPs with well-established associations with PHL. We utilized logistic regression model to estimate associations of PHL-related SNPs and wGRS with gastric cancer risk in total population and in strata by sex, age, and study site, in addition to a series of sensitivity analyses.

Results:

High genetically predicted PHL was associated with an increased gastric cancer risk (per SD increase in the wGRS: OR = 1.07; 95% confidence interval, 1.01–1.12; P = 0.011), which was consistent in sensitivity analyses. Subgroup analyses provided evidence of a stronger association with gastric cancer risk in women than in men. MR-Egger and weighted median regression suggested that potentially unknown pleiotropic effects were not biasing the association between PHL and gastric cancer risk.

Conclusions:

These results revealed that genetically predicted high PHL was associated with an increased gastric cancer risk, suggesting that high PHL may have a causal role in the etiology of gastric cancer.

Impact:

These findings provide causal inference for PHL on gastric cancer risk, suggesting a causal role of high PHL in the etiology of gastric cancer.

Gastric cancer is the fifth most frequently diagnosed malignancies worldwide with over 1,000,000 new cases and 783,000 deaths estimated in 2018 (1). In China, gastric cancer is the second incident cancer and the second leading cause of cancer death with an estimated 679,100 new gastric cancer cases and an estimate of 498,000 deaths of gastric cancer in 2015 (2). Gastric cancer is a multifactorial disease, and both environmental and genetic factors play a role in its etiology. Helicobacter pylori infection is the most important cause of gastric cancer. Moreover, tobacco smoking, low consumption of fresh fruits and vegetables, and high intake of pickled and smoked foods are established risk factors for gastric cancer (3).

Homocysteine (Hcy), a key metabolite at the intersection of the methylation, remethylation, and transsulfuration pathways, is intrinsically related to cellular methylation status (4). Hcy can be recycled into methionine with the aid of vitamin B12, folic acid, or trimethylglycine or converted into cysteine with vitamin B6 as the cofactor (5). Therefore, genetic defects of the pathway above and nutritional deficiencies of folate, vitamin B6, and vitamin B12 can lead to elevation of Hcy concentrations. High plasma homocysteine level (PHL) has been recognized as an independent risk factor for cardiovascular diseases (6). However, the relationship is still controversial for gastric cancer. Several observational studies indicated a positive association between high PHL and increased gastric cancer risk (7, 8), whereas others found no statistically significant relationship (9–11). One limitation of these studies is that it is difficult to interpret the results with confounding and reverse causation.

Mendelian randomization (MR), an established epidemiological approach, provides an opportunity for causal inference within the framework of observational research design (12). It utilizes instrumental variables (IV) such as genetic variants that proxy for environmental, social, or behavioral factors to assess the causality of an observed association between a given exposure and an outcome (such as PHL and gastric cancer; ref. 13). This inference relies on the natural, random assortment of genetic variants during meiosis yielding a random distribution of genotypes in a population (14). Thus, MR is often robust to the issues of confounding and reverse causation inherent in observational epidemiologic studies. Recent genome-wide association studies (GWAS) have identified multiple loci associated with PHL (15–17), enabling investigation of a potentially causal role of PHL in gastric cancer risk using the MR approach.

In this study, we included both individual and summary data from 7,004 subjects of Chinese Han descent to perform a two-sample MR analysis (18) to investigate the causal relationship between PHL and risk of gastric cancer. A weighted genetic risk score (wGRS) incorporating 15 SNPs taken from a meta-analysis of GWAS on plasma homocysteine concentrations was employed as a proxy of PHL.

Study populations

Epidemiologic and genetic data were derived from 2,631 gastric cancer cases and 4,373 controls from three published GWAS (Nanjing-GWAS, Beijing-GWAS, and NCI-GWAS; refs. 19–21). The characteristics of the three datasets are summarized in Supplementary Table S1. The cases and cancer-free controls of each study were unrelated individuals of Chinese Han descent, and there was no overlap of participants between these studies. For Nanjing GWAS and Beijing GWAS, individuals were recruited from two separate case–control studies conducted in Nanjing (565 cases and 1,162 controls) and Beijing (468 cases and 1,123 controls), and all cases were histopathologically confirmed nonardia gastric cancer. For NCI GWAS, subjects were from Shanxi (1,368 cases and 1,650 controls) and Linxian (257 cases and 450 controls), and the cases contained both cardia and noncardia gastric cancer. We accessed the NCI GWAS data from the database of Genotype and Phenotypes (dbGaP, http://www.ncbi.nlm.nih.gov/gap) with the study accession number phs000361.v1.p1. Written informed consent was given by all participants, and studies were approved by the relevant institutional review boards.

Genotyping and imputation

Nanjing/Beijing study used Affymetrix Genome-Wide Human SNP Array (V.6.0) for genotyping while the NCI study applied Illumina 660W-Quad microarray. Samples were excluded on the basis of excess autosomal heterozygosity, gender discrepancy, and outlier with identical-by-state clustering analysis. SNPs were excluded on the basis of call rate (<95%), minor allele frequency (MAF < 0.01), and departure from Hardy–Weinberg equilibrium (P-value < 1 × 10−6). Imputation was performed separately for the Nanjing/Beijing study and the NCI study using SHAPEIT (V.2) and Impute2 (V.2.2.2) software. All populations from the 1,000 Genomes Project Phase 3 were taken as the reference set (22). Qualified SNPs were restricted to those with MAF >1% and overall IMPUTE2 INFO score >0.4.

Selection of PHL-associated SNPs

A recent meta-analysis of GWAS including a total of 44,147 individuals identified 18 SNPs associated with PHL at genome-wide significance level (P < 5 × 10−8; ref. 15). For each GWAS-identified locus, a representative SNP with the lowest P-value in the original GWAS was selected (linkage disequilibrium r2 < 0.01, based on 1,000 Genome Phase 3 data; ref. 23). As a result, the remaining 15 SNPs were from independent loci.

Statistical analysis

A wGRS was constructed as a genetic proxy for PHL by adding up the dosages for PHL-raising alleles across the 15 variants in each individual following the formula: ${\rm{wGRS}} = \sum\nolimits_{i = 1}^{15} {{\beta _i}{\rm{SN}}{{\rm{P}}_i}}$⁠, where {\beta _i}$ is the beta coefficient of the i$th SNP for PHL from the published GWAS and SNPi is the imputed dosage of the individual variant (recoded as 0, 1 and 2, according to the number of PHL-increasing allele). Associations of PHL-related SNPs and wGRS with risk of gastric cancer were estimated using logistic regression model adjusted for age, sex, and study site, as well as the first principal component. We also categorized the PHL wGRS into four groups according to its quartile distribution in controls. The association between the PHL wGRS (as a continuous variable scaled per SD and as a categorical variable taken the bottom quarter as reference) and gastric cancer risk was assessed using logistic regression with potential confounders adjusted. We also performed stratified analyses based on sex, age, and study site.

In addition, we also applied an MR inverse-variance weighted (IVW) method (24) to estimate the causal effect using summary statistics from gastric cancer GWAS. IVW regression implemented in the MendelianRandomization package (0.3.0; ref. 25) in R was performed to assess the combined association of the 15 PHL-related variants with gastric cancer risk. The Wald estimator (26) and delta method (27) were used to calculate the causal effect and SE, respectively. MR Egger regression and weighted median regression were also performed to detect potential pleiotropy of the instruments, which were described elsewhere (28, 29). Briefly, MR-Egger is a modified form of standard IVW approach, using a weighted regression with an unconstrained intercept to relax the assumption that the effects of the IVs on the outcome are entirely mediated via the exposure. A significant, nonzero intercept implies directional bias among the genetic instruments. Weighted median, which provides valid estimates when up to 50% of the statistical weight comes from valid IVs, orders, and weights the MR estimates by the inverse of their variance. We also excluded four SNPs (rs1047891, rs1801222, rs234709, and rs838133) associated with other traits (P < 5 × 10−8) in GWAS Catalog to eliminate the potential pleiotropy. Analyses were conducted using PLINK (v 1.90) and R (v.3.3.2). Two-sided P values less than 0.05 were considered statistically significant.

Human rights statement and informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of the 1964 and later versions.

Informed consent

Informed consent to be included in the study, or the equivalent, was obtained from all patients.

Study populations and SNPs

A total of 2,631 gastric cancer cases and 4,373 cancer-free controls of Chinese Han descent were included in the current analyses, comprising 1,006 cases and 2,273 controls from the Nanjing/Beijing study, and 1,625 cases and 2,100 controls from the NCI study. Selected characteristics of cases and controls included in the final analyses are summarized in Supplementary Table S1.

We constructed the instruments based on 15 LD-independent SNPs that achieved genome-wide significance for PHL. All of these SNPs were genotyped or imputed, and the INFO values of imputed SNPs were more than 0.4 in all datasets. The information of these 15 SNPs were shown in Table 1.

Table 1.

Effect estimates for associations of genetic instruments with PHL and gatsric cancer risk.

SNP–PHL associationSNP–GC association
ChrSNPNearby genePHL increasing alleleAllele frequencyaEffect on PHLaSEaPaOR (95% CI)bPb
rs1801133 MTHFR 0.34 0.158 0.007 4.34E−104 1.02 (1.00–1.03) 0.047 
rs2275565 MTR 0.79 0.054 0.009 1.96E−10 1.01 (0.99–1.03) 0.437 
rs4660306 MMACHC 0.33 0.044 0.007 2.33E−09 1.00 (0.97–1.02) 0.885 
rs1047891 CPS1 0.33 0.086 0.008 4.58E−27 1.00 (0.98–1.02) 0.914 
rs9369898 MUT 0.62 0.045 0.007 2.17E−10 1.00 (0.99–1.02) 0.616 
rs548987 SLC17A3 0.13 0.060 0.010 1.12E−08 0.98 (0.96–1.00) 0.100 
rs42648 GTPB10 0.6 0.040 0.007 1.97E−08 1.01 (0.99–1.02) 0.529 
10 rs1801222 CUBN 0.34 0.045 0.007 8.43E−10 0.99 (0.97–1.01) 0.224 
10 rs12780845 CUBN 0.65 0.053 0.009 7.80E−10 1.01 (0.99–1.02) 0.576 
11 rs7130284 NOX4 0.93 0.124 0.013 1.88E−20 1.01 (0.99–1.03) 0.201 
12 rs2251468 HNF1A 0.35 0.051 0.007 1.28E−12 1.03 (1.01–1.05) 2.38E−04 
16 rs154657 DPEP1 0.47 0.096 0.007 1.74E−43 1.01 (0.96–1.05) 0.794 
16 rs12921383 DPEP1/FANCA 0.13 0.090 0.014 8.22E−11 1.01 (0.98–1.03) 0.629 
19 rs838133 FUT2 0.45 0.042 0.007 7.48E−09 1.02 (0.97–1.08) 0.391 
21 rs234709 CBS 0.55 0.072 0.007 3.90E−24 0.99 (0.96–1.01) 0.406 
SNP–PHL associationSNP–GC association
ChrSNPNearby genePHL increasing alleleAllele frequencyaEffect on PHLaSEaPaOR (95% CI)bPb
rs1801133 MTHFR 0.34 0.158 0.007 4.34E−104 1.02 (1.00–1.03) 0.047 
rs2275565 MTR 0.79 0.054 0.009 1.96E−10 1.01 (0.99–1.03) 0.437 
rs4660306 MMACHC 0.33 0.044 0.007 2.33E−09 1.00 (0.97–1.02) 0.885 
rs1047891 CPS1 0.33 0.086 0.008 4.58E−27 1.00 (0.98–1.02) 0.914 
rs9369898 MUT 0.62 0.045 0.007 2.17E−10 1.00 (0.99–1.02) 0.616 
rs548987 SLC17A3 0.13 0.060 0.010 1.12E−08 0.98 (0.96–1.00) 0.100 
rs42648 GTPB10 0.6 0.040 0.007 1.97E−08 1.01 (0.99–1.02) 0.529 
10 rs1801222 CUBN 0.34 0.045 0.007 8.43E−10 0.99 (0.97–1.01) 0.224 
10 rs12780845 CUBN 0.65 0.053 0.009 7.80E−10 1.01 (0.99–1.02) 0.576 
11 rs7130284 NOX4 0.93 0.124 0.013 1.88E−20 1.01 (0.99–1.03) 0.201 
12 rs2251468 HNF1A 0.35 0.051 0.007 1.28E−12 1.03 (1.01–1.05) 2.38E−04 
16 rs154657 DPEP1 0.47 0.096 0.007 1.74E−43 1.01 (0.96–1.05) 0.794 
16 rs12921383 DPEP1/FANCA 0.13 0.090 0.014 8.22E−11 1.01 (0.98–1.03) 0.629 
19 rs838133 FUT2 0.45 0.042 0.007 7.48E−09 1.02 (0.97–1.08) 0.391 
21 rs234709 CBS 0.55 0.072 0.007 3.90E−24 0.99 (0.96–1.01) 0.406 

Abbreviation: GC, gastric cancer.

aThe allele frequency, effect size (β coefficient, measured as an SD change per additional PHL increasing allele), SE, and P for each SNP were obtained from the initial study.

bResults (OR, 95% CI, and P) were derived from pooled analysis of the Nanjing/Beijing study and the NCI study and were adjusted for age, sex, study site, and the first principle component.

MR analysis

Using wGRS with the 15 SNPs as IVs, per SD increase in PHL (μmol/L) was associated with a 7% increase in odds of gastric cancer [OR = 1.07; 95% confidence interval (CI), 1.01–1.12; P = 0.011; Table 2]. Compared with individuals in the bottom quartile of the PHL wGRS, those in the top quartile had an 19% (95% CI, 1.04–1.37) increased gastric cancer risk (Ptrend = 7.08 × 10−3; Table 3).

Table 2.

Estimates from MR methods for the association between PHL and gastric cancer.

Methodβ coefficient95% CIPa
wGRS 0.064 0.015–0.113 0.011 
Subset wGRSb 0.077 0.028–0.126 2.13E−03 
IVW MR 0.086 0.007–0.165 0.034 
MR-Egger estimate 0.085 −0.090–0.261 0.342 
MR-Egger intercept 0.000 −0.014–0.015 0.994 
Weighted median MR 0.102 0.016–0.188 0.020 
Methodβ coefficient95% CIPa
wGRS 0.064 0.015–0.113 0.011 
Subset wGRSb 0.077 0.028–0.126 2.13E−03 
IVW MR 0.086 0.007–0.165 0.034 
MR-Egger estimate 0.085 −0.090–0.261 0.342 
MR-Egger intercept 0.000 −0.014–0.015 0.994 
Weighted median MR 0.102 0.016–0.188 0.020 

aAdjusted for age, sex, study site, and the first principle component.

bFour SNPs were excluded for associations with other traits (P < 5 × 10−8) in GWAS Catalog.

Table 3.

Associations between PHL wGRS and gastric cancer risk.

wGRS categoriesCases, n (%)Controls, n (%)OR (95% CI)Pa
T1 614 (23.34) 1,095 (25.04) Ref. 
T2 625 (23.76) 1,090 (24.93) 1.03 (0.90–1.19) 0.652 
T3 672 (25.54) 1,093 (24.99) 1.11 (0.96–1.27) 0.157 
T4 720 (27.37) 1,095 (25.04) 1.19 (1.04–1.37) 0.012 
Ptrend    7.08E-03 
wGRS categoriesCases, n (%)Controls, n (%)OR (95% CI)Pa
T1 614 (23.34) 1,095 (25.04) Ref. 
T2 625 (23.76) 1,090 (24.93) 1.03 (0.90–1.19) 0.652 
T3 672 (25.54) 1,093 (24.99) 1.11 (0.96–1.27) 0.157 
T4 720 (27.37) 1,095 (25.04) 1.19 (1.04–1.37) 0.012 
Ptrend    7.08E-03 

aAdjusted for age, sex, study site, and the first principle component.

In addition, we also estimated the potential causal effect of PHL on gastric cancer using MR IVW with summary statistics from gastric cancer GWAS. As shown in Table 2, similar positive association of PHL with gastric cancer risk (OR = 1.09 per SD increase; 95% CI, 1.01–1.18; P = 0.034) was observed in comparison with that using wGRS.

Subgroup analysis

Subgroup analyses by sex showed different effect size for men (OR = 1.03; 95% CI, 0.97–1.09; P = 0.379) and for women [(OR = 1.17; 95% CI, 1.07–1.29; P = 0.001); Pheterogeneity = 0.023]. This result suggests that high PHL may have a stronger causal effect on gastric cancer risk in women than men. Stratification analyses by age group and study site did not show significant heterogeneity between different strata (Table 4).

Table 4.

Stratification analyses by sex, age, and study site.a

VariablesCases, N (%)Controls, N (%)OR (95% CI)PPheterogeneity
Sex     0.023 
 Men 1,974 (75.03) 3,126 (71.48) 1.03 (0.97–1.09) 0.379  
 Women 657 (24.97) 1,247 (28.52) 1.17 (1.07–1.29) 0.001  
Age     0.152 
 <60 1,279 (48.61) 2,020 (46.19) 1.11 (1.03–1.19) 0.005  
 ≥60 1,352 (51.39) 2,353 (53.81) 1.03 (0.96–1.11) 0.360  
Study site     0.097 
 Nanjing 550 (20.90) 1,155 (26.41) 1.05 (0.94–1.16) 0.375  
 Beijing 456 (17.33) 1,118 (25.57) 1.20 (1.07–1.35) 0.002  
 Shanxi 1,368 (52.00) 1,650 (37.73) 1.01 (0.94–1.08) 0.829  
 Linxian 257 (9.77) 450 (10.29) 1.03 (0.88–1.20) 0.699  
VariablesCases, N (%)Controls, N (%)OR (95% CI)PPheterogeneity
Sex     0.023 
 Men 1,974 (75.03) 3,126 (71.48) 1.03 (0.97–1.09) 0.379  
 Women 657 (24.97) 1,247 (28.52) 1.17 (1.07–1.29) 0.001  
Age     0.152 
 <60 1,279 (48.61) 2,020 (46.19) 1.11 (1.03–1.19) 0.005  
 ≥60 1,352 (51.39) 2,353 (53.81) 1.03 (0.96–1.11) 0.360  
Study site     0.097 
 Nanjing 550 (20.90) 1,155 (26.41) 1.05 (0.94–1.16) 0.375  
 Beijing 456 (17.33) 1,118 (25.57) 1.20 (1.07–1.35) 0.002  
 Shanxi 1,368 (52.00) 1,650 (37.73) 1.01 (0.94–1.08) 0.829  
 Linxian 257 (9.77) 450 (10.29) 1.03 (0.88–1.20) 0.699  

aResults were derived from the unconditional multivariate logistic regression model.

Sensitivity analysis

The MR-Egger method suggested that no pleiotropy was present (the intercept was centered at 0.000 (95% CI, −0.014–0.015; P = 0.994; Table 2) and the MR-Egger slope was consistent with previous estimates. Results from the weighted median analyses were also consistent with the main findings, indicating a low probability that pleiotropy influenced the results (Fig. 1). In addition, we excluded four SNPs that were associated with other traits (P < 5 × 10−8) in GWAS Catalog and the results remained consistent (Table 2).

Figure 1.

Plots of the effect size of each SNP on PHL and gastric cancer risk. The x-axis plots the previously published β-estimate for the association of each SNP with PHL. The y-axis plots the β-estimate from the multivariate logistic regression model for the association of each SNP with gastric cancer risk in our study population. Lines represent causal estimates from the different methods.

Figure 1.

Plots of the effect size of each SNP on PHL and gastric cancer risk. The x-axis plots the previously published β-estimate for the association of each SNP with PHL. The y-axis plots the β-estimate from the multivariate logistic regression model for the association of each SNP with gastric cancer risk in our study population. Lines represent causal estimates from the different methods.

Close modal

Our study provides evidence that genetically elevated PHL is causally associated with an increased risk of gastric cancer, where per SD increase in PHL conferred a 7% increase in the odds of gastric cancer in Chinese Han descent. Multiple MR techniques and sensitivity analyses demonstrated consistent results, suggesting the robustness of our findings. The results from the summary data were in agreement with those from individual-level genotype data. The findings hint that elevated PHL may have a causal role in the etiology of gastric cancer. In addition, stratified analysis suggested sex-specific associations between PHL and gastric cancer risk.

We previously reported that PHL was positively associated with gastric cancer risk in an observational study (7). Using both individual and summary data of the identified SNPs associated with PHL, we further confirmed the causal relationship in an MR approach. Although some studies reported a null association (9–11), the positive association results from MR analysis may offer more robust evidence to evaluate the causal role of PHL in gastric cancer etiology, which was free from confounding and reverse causation of traditional observational epidemiological study designs. By applying the two-sample MR approach, we were able to increase statistical power by extracting data from large sample size GWAS for PHL (n = 44,147) and gastric cancer (n = 2.631 cases and 4.373 controls).

Although our MR analyses indicate a causal relationship between PHL and gastric cancer risk, the underlying mechanism remains to be fully understood. Several possible explanations have been proposed to offer some mechanistic insights. First, homocysteine can disrupt methionine cycle and change cytosine methylation in CpG islands of DNA resulting in the repression of tumor suppressor genes and activation of proto-oncogenes, which may promote cancer development (30). PHL was found to modulate the expression of tumor suppressor genes RASSF1 and BRCA1 and therefore played a role in the breast cancer initiation (31). Moreover, inflammatory remodeling of gastrointestinal tract due to high PHL increases production of reactive oxygen species (32), which can cause several disorders including carcinogenesis in excessive accumulation (33). Finally, homocysteine was reported to contribute to perturbations in the endoplasmic reticulum protein folding machinery via altered cytosolic redox metabolism and it was tested in models of gastrointestinal tract cancer, including gastric cancer (34).

In this study, we observed a significant association of genetically elevated PHL with gastric cancer risk in women, but not in men. Sex-specific relationships were also reported between PHL and risk of cardiovascular disease (35), bipolar disorder (36), and nonalcoholic fatty liver disease (37). A recent study suggested the PHL-related risk of ischemic stroke prone to be significant in women, even though the PHL were higher in men than in women (38). Zhong and colleagues found that elevated PHL could be an independent risk factor for prognosis of acute ischemic stroke only in women (39). PHL are usually higher in men than in women, and it mainly attributes to differences of estradiol and homocysteine metabolism pathway (40). Of interest, GWAS also reported sex-specific genetic effects on PHL. In African Americans, the CPS1 locus was associated with PHL only in women (41). The SNPs rs18011131 of MTHFR and rs838892 of SCARB1 were also associated with PHL for women only (42). Nevertheless, further studies are warranted to clarify the mechanism of sex-specific relationship between elevated PHL and gastric cancer risk.

The major strength of this study was the availability of individual-level data in gastric cancer cases and controls, allowing us to perform analysis to control the effect of potential confounders. Furthermore, we adjusted for principal components, which accounted for potential confounding by population stratification. There are several limitations to this study. First, the potentially residual pleiotropy could not be fully tested while multiple sensitivity analyses were applied to evaluate pleiotropy. However, consistency across these approaches, and the fact that the MR-Egger intercept was centered at the origin, suggest that our results were not substantially influenced by pleiotropy. Second, this study only includes individuals of Chinese ancestry, which means our results may not apply to other races. However, this also avoids the potential confounding by population stratification. Third, we have used all PHL-related SNPs reported by the meta-analysis from different populations to make the causal inference. Some of these SNPs may be not appropriate instrument variables for Chinese, but so far there was no GWAS on PHL in Chinese population.

In summary, our findings provide evidence supporting a causal role for elevated PHL in gastric cancer etiology. Further investigations are warranted to uncover the underlying biological mechanisms for the associations observed in this study.

No potential conflicts of interest were disclosed.

Conception and design: T. Wang, J. Dai, Y. Ding, G. Jin

Development of methodology: T. Wang

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): C. Ren, C. Yan

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): T. Wang, C. Ren, J. Ni, H. Ding, Q. Qi

Writing, review, and/or revision of the manuscript: T. Wang, H. Ding, Q. Qi, Y. Ding, G. Jin

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): B. Deng, G. Li

Study supervision: Y. Ding, G. Jin

The authors thank all the participants of the GWAS from Nanjing/Beijing and the NCI studies. This work was supported by grants from the National Natural Science Foundation of China (81872702 and 81521004); National Major Research and Development Program (2016YFC1302703); Key Research and Development Program of Jiangsu Province (BE2019698); Key Grant of Natural Science Foundation of Jiangsu Higher Education Institutions (15KJA330002); Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (PPZY2015A067); Jiangsu Province "333" project (BRA2018057); and Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Bray
F
,
Ferlay
J
,
Soerjomataram
I
,
Siegel
RL
,
Torre
LA
,
Jemal
A
. 
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
.
CA Cancer J Clin
2018
;
68
:
394
424
.
2.
Chen
W
,
Zheng
R
,
Baade
PD
,
Zhang
S
,
Zeng
H
,
Bray
F
, et al
Cancer statistics in China, 2015
.
CA Cancer J Clin
2016
;
66
:
115
32
.
3.
Van Cutsem
E
,
Sagaert
X
,
Topal
B
,
Haustermans
K
,
Prenen
H
. 
Gastric cancer
.
Lancet
2016
;
388
:
2654
64
.
4.
Schalinske
KL
,
Smazal
AL
. 
Homocysteine imbalance: a pathological metabolic marker
.
Adv Nutr
2012
;
3
:
755
62
.
5.
Lehmann
DJ
,
Cortina-Borja
M
. 
Genetic influence of plasma homocysteine on Alzheimer's disease
.
Neurobiol Aging
2019
;
76
:
217
8
.
6.
Jensen
MK
,
Bertoia
ML
,
Cahill
LE
,
Agarwal
I
,
Rimm
EB
,
Mukamal
KJ
. 
Novel metabolic biomarkers of cardiovascular disease
.
Nat Rev Endocrinol
2014
;
10
:
659
72
.
7.
Wang
L
,
Ke
Q
,
Chen
W
,
Wang
J
,
Tan
Y
,
Zhou
Y
, et al
Polymorphisms of MTHFD, plasma homocysteine levels, and risk of gastric cancer in a high-risk Chinese population
.
Clin Cancer Res
2007
;
13
:
2526
32
.
8.
Zacho
J
,
Yazdanyar
S
,
Bojesen
SE
,
Tybjaerg-Hansen
A
,
Nordestgaard
BG
. 
Hyperhomocysteinemia, methylenetetrahydrofolate reductase c.677C>T polymorphism and risk of cancer: cross-sectional and prospective studies and meta-analyses of 75,000 cases and 93,000 controls
.
Int J Cancer
2011
;
128
:
644
52
.
9.
Miranti
EH
,
Stolzenberg-Solomon
R
,
Weinstein
SJ
,
Selhub
J
,
Mannisto
S
,
Taylor
PR
, et al
Low vitamin B12 increases risk of gastric cancer: a prospective study of one-carbon metabolism nutrients and risk of upper gastrointestinal tract cancer
.
Int J Cancer
2017
;
141
:
1120
9
.
10.
Chang
SC
,
Goldstein
BY
,
Mu
L
,
Cai
L
,
You
NC
,
He
N
, et al
Plasma folate, vitamin B12, and homocysteine and cancers of the esophagus, stomach, and liver in a Chinese population
.
Nutr Cancer
2015
;
67
:
212
23
.
11.
Vollset
SE
,
Igland
J
,
Jenab
M
,
Fredriksen
A
,
Meyer
K
,
Eussen
S
, et al
The association of gastric cancer risk with plasma folate, cobalamin, and methylenetetrahydrofolate reductase polymorphisms in the European Prospective Investigation into Cancer and Nutrition
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2416
24
.
12.
Sekula
P
,
Del Greco
MF
,
Pattaro
C
,
Kottgen
A
. 
Mendelian randomization as an approach to assess causality using observational data
.
J Am Soc Nephrol
2016
;
27
:
3253
65
.
13.
Smith
GD
,
Ebrahim
S
. 
‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?
Int J Epidemiol
2003
;
32
:
1
22
.
14.
Emdin
CA
,
Khera
AV
,
Kathiresan
S
. 
Mendelian randomization
.
JAMA
2017
;
318
:
1925
6
.
15.
van Meurs
JB
,
Pare
G
,
Schwartz
SM
,
Hazra
A
,
Tanaka
T
,
Vermeulen
SH
, et al
Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease
.
Am J Clin Nutr
2013
;
98
:
668
76
.
16.
Tanaka
T
,
Scheet
P
,
Giusti
B
,
Bandinelli
S
,
Piras
MG
,
Usala
G
, et al
Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations
.
Am J Hum Genet
2009
;
84
:
477
82
.
17.
Williams
SR
,
Yang
Q
,
Chen
F
,
Liu
X
,
Keene
KL
,
Jacques
P
, et al
Genome-wide meta-analysis of homocysteine and methionine metabolism identifies five one carbon metabolism loci and a novel association of ALDH1L1 with ischemic stroke
.
PLos Genet
2014
;
10
:
e1004214
.
18.
Haycock
PC
,
Burgess
S
,
Wade
KH
,
Bowden
J
,
Relton
C
,
Davey Smith
G
. 
Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies
.
Am J Clin Nutr
2016
;
103
:
965
78
.
19.
Wang
Z
,
Dai
J
,
Hu
N
,
Miao
X
,
Abnet
CC
,
Yang
M
, et al
Identification of new susceptibility loci for gastric non-cardia adenocarcinoma: pooled results from two Chinese genome-wide association studies
.
Gut
2017
;
66
:
581
7
.
20.
Abnet
CC
,
Freedman
ND
,
Hu
N
,
Wang
Z
,
Yu
K
,
Shu
XO
, et al
A shared susceptibility locus in PLCE1 at 10q23 for gastric adenocarcinoma and esophageal squamous cell carcinoma
.
Nat Genet
2010
;
42
:
764
7
.
21.
Shi
Y
,
Hu
Z
,
Wu
C
,
Dai
J
,
Li
H
,
Dong
J
, et al
A genome-wide association study identifies new susceptibility loci for non-cardia gastric cancer at 3q13.31 and 5p13.1
.
Nat Genet
2011
;
43
:
1215
8
.
22.
1000 Genomes Project Consortium
,
Abecasis
GR
,
Auton
A
,
Brooks
LD
,
DePristo
MA
,
Durbin
RM
, et al
An integrated map of genetic variation from 1,092 human genomes
.
Nature
2012
;
491
:
56
65
.
23.
Mao
Y
,
Yan
C
,
Lu
Q
,
Zhu
M
,
Yu
F
,
Wang
C
, et al
Genetically predicted high body mass index is associated with increased gastric cancer risk
.
Eur J Hum Genet
2017
;
25
:
1061
6
.
24.
Burgess
S
,
Butterworth
A
,
Thompson
SG
. 
Mendelian randomization analysis with multiple genetic variants using summarized data
.
Genet Epidemiol
2013
;
37
:
658
65
.
25.
Yavorska
OO
,
Burgess
S
. 
MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data
.
Int J Epidemiol
2017
;
46
:
1734
9
.
26.
Palmer
TM
,
Sterne
JA
,
Harbord
RM
,
Lawlor
DA
,
Sheehan
NA
,
Meng
S
, et al
Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses
.
Am J Epidemiol
2011
;
173
:
1392
403
.
27.
Thomas
DC
,
Lawlor
DA
,
Thompson
JR
. 
Re: estimation of bias in nongenetic observational studies using “Mendelian triangulation” by Bautista et al
.
Ann Epidemiol
2007
;
17
:
511
3
.
28.
Bowden
J
,
Davey Smith
G
,
Burgess
S
. 
Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression
.
Int J Epidemiol
2015
;
44
:
512
25
.
29.
Bowden
J
,
Davey Smith
G
,
Haycock
PC
,
Burgess
S
. 
Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator
.
Genet Epidemiol
2016
;
40
:
304
14
.
30.
Warnecke
PM
,
Bestor
TH
. 
Cytosine methylation and human cancer
.
Curr Opin Oncol
2000
;
12
:
68
73
.
31.
Naushad
SM
,
Reddy
CA
,
Kumaraswami
K
,
Divyya
S
,
Kotamraju
S
,
Gottumukkala
SR
, et al
Impact of hyperhomocysteinemia on breast cancer initiation and progression: epigenetic perspective
.
Cell Biochem Biophys
2014
;
68
:
397
406
.
32.
Skovierova
H
,
Vidomanova
E
,
Mahmood
S
,
Sopkova
J
,
Drgova
A
,
Cervenova
T
, et al
The molecular and cellular effect of homocysteine metabolism imbalance on human health
.
Int J Mol Sci
2016
;
17
.
doi: 10.3390/ijms17101733
.
33.
Prasad
S
,
Gupta
SC
,
Tyagi
AK
. 
Reactive oxygen species (ROS) and cancer: role of antioxidative nutraceuticals
.
Cancer Lett
2017
;
387
:
95
105
.
34.
Battle
DM
,
Gunasekara
SD
,
Watson
GR
,
Ahmed
EM
,
Saysell
CG
,
Altaf
N
, et al
Expression of the endoplasmic reticulum oxidoreductase Ero1alpha in gastro-intestinal cancer reveals a link between homocysteine and oxidative protein folding
.
Antioxid Redox Signal
2013
;
19
:
24
35
.
35.
Kuo
HK
,
Yen
CJ
,
Bean
JF
. 
Levels of homocysteine are inversely associated with cardiovascular fitness in women, but not in men: data from the National Health and Nutrition Examination Survey 1999–2002
.
J Intern Med
2005
;
258
:
328
35
.
36.
Sanchez-Autet
M
,
Arranz
B
,
Safont
G
,
Sierra
P
,
Garcia-Blanco
A
,
de la Fuente
L
, et al
Gender differences in C-reactive protein and homocysteine modulation of cognitive performance and real-world functioning in bipolar disorder
.
J Affect Disord
2018
;
229
:
95
104
.
37.
Won
BY
,
Park
KC
,
Lee
SH
,
Yun
SH
,
Kim
MJ
,
Park
KS
, et al
Sex difference in the association between serum homocysteine level and non-alcoholic fatty liver disease
.
Korean J Fam Med
2016
;
37
:
242
7
.
38.
Wang
C
,
Han
L
,
Wu
Q
,
Zhuo
R
,
Liu
K
,
Zhao
J
, et al
Association between homocysteine and incidence of ischemic stroke in subjects with essential hypertension: a matched case-control study
.
Clin Exp Hypertens
2015
;
37
:
557
62
.
39.
Zhong
C
,
Xu
T
,
Xu
T
,
Peng
Y
,
Wang
A
,
Wang
J
, et al
Plasma homocysteine and prognosis of acute ischemic stroke: a gender-specific analysis from CATIS randomized clinical trial
.
Mol Neurobiol
2017
;
54
:
2022
30
.
40.
Fukagawa
NK
,
Martin
JM
,
Wurthmann
A
,
Prue
AH
,
Ebenstein
D
,
O'Rourke
B
. 
Sex-related differences in methionine metabolism and plasma homocysteine concentrations
.
Am J Clin Nutr
2000
;
72
:
22
9
.
41.
Paré
G
,
Chasman
DI
,
Parker
AN
,
Zee
RR
,
Mälarstig
A
,
Seedorf
U
, et al
Novel associations of CPS1, MUT, NOX4, and DPEP1 with plasma homocysteine in a healthy population: a genome-wide evaluation of 13 974 participants in the Women's Genome Health Study
.
Circ Cardiovasc Genet
2009
;
2
:
142
50
.
42.
Clifford
AJ
,
Chen
K
,
McWade
L
,
Rincon
G
,
Kim
SH
,
Holstege
DM
, et al
Gender and single nucleotide polymorphisms in MTHFR, BHMT, SPTLC1, CRBP2, CETP, and SCARB1 are significant predictors of plasma homocysteine normalized by RBC folate in healthy adults
.
J Nutr
2012
;
142
:
1764
71
.