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. Author manuscript; available in PMC: 2010 Oct 28.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2009 May;18(5):1429–1438. doi: 10.1158/1055-9965.EPI-09-0001

Genetic polymorphisms in nitric oxide synthase genes modify the relationship between vegetable and fruit intake and risk of non-Hodgkin lymphoma

Xuesong Han 1, Tongzhang Zheng 1, Qing Lan 2, Yaqun Zhang 3, Briseis A Kilfoy 1, Qin Qin 4, Nathaniel Rothman 2, Shelia H Zahm 2, Theodore R Holford 1, Brian Leaderer 1, Yawei Zhang 1
PMCID: PMC2965355  NIHMSID: NIHMS246058  PMID: 19423521

Abstract

Oxidative damage caused by reactive oxygen species (ROS) and other free radicals is involved in carcinogenesis. It has been suggested that high vegetable and fruit intake may reduce the risk of non-Hodgkin lymphoma (NHL) as vegetables and fruit are rich in antioxidants. The aim of this study is to evaluate the interaction of vegetable and fruit intake with genetic polymorphisms in oxidative stress pathway genes and NHL risk. This hypothesis was investigated in a population-based case-control study of NHL and NHL histological subtype in Connecticut women including 513 histologically confirmed incident cases and 591 randomly selected controls. Gene-vegetable/fruit joint effects were estimated using unconditional logistic regression model. The false discovery rate method was applied to adjust for multiple comparisons. Significant interactions with vegetable and fruit intake were mainly found for genetic polymorphisms on nitric oxide synthase (NOS) genes among those with diffuse large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL). Two single nucleotide polymorphisms (SNPs) in the NOS1 gene were found to significantly modify the association between total vegetable and fruit intake and risk of NHL overall, as well as the risk of follicular lymphoma (FL). When vegetables, bean vegetables, cruciferous vegetables, green leafy vegetables, red vegetables, yellow/orange vegetables, fruit, and citrus fruit were examined separately, strong interaction effects were narrowed to vegetable intake among DLBCL patients. Our results suggest that genetic polymorphisms in oxidative stress pathway genes, especially in the nitric oxide synthase genes, modify the association between vegetable and fruit intake and risk of NHL.

Keywords: oxidative stress pathway, vegetable and fruit intake, non-Hodgkin lymphoma, nitric oxide synthase, genetic polymorphisms

Introduction

Non-Hodgkin lymphoma (NHL) presents a heterogeneous group of malignancies arising from lymphocytes throughout the body. It has been estimated that 66,120 individuals will be diagnosed with NHL and 19,160 will die from NHL in the United States in 2008. Although established risk factors such as immunodeficiency and viral infection may be responsible for a portion of the cases, the vast majority of the NHL cases remain unexplained.

Reactive oxygen species (ROS) and other free radicals can cause oxidative damage to all components of the cell (i.e., proteins, lipids, and DNA) and have been demonstrated to be involved in a number of pathological conditions including cancer (1-7). Vegetables and fruit are rich in antioxidants that protect cells from such damage. As such, high vegetable and fruit intake has been hypothesized to reduce the risk of NHL. However, results from epidemiologic studies have been inconsistent (8-22). Variation in genetic susceptibility within these populations under study could, in part, account for some of the conflicting findings in previous investigation into the role of fruit and vegetable intake.

It is biologically plausible that the role of antioxidant-rich vegetable and fruit intake in lymphomagenesis is modified by common genetic variation in oxidative stress pathway genes. Oxidative stress pathway genes can modify the impact of oxidative damage at many stages of carcinogenesis (4), including tumor initiation due to genotoxicity of ROS, tumor promotion by oxidants and free radicals, inflammatory responses mediated by oxide synthase and oxygenase, inhibition of intercellular communication regulating cellular proliferation and differentiation, and alteration of the extracellular matrix involved in tumor invasion and metastasis. We therefore investigated NHL risk in general and by subtype in relation to common genetic variation in the oxidative stress pathway in conjunction with vegetable and fruit intake in a population-based case-control study of females in Connecticut.

Materials and methods

Study population

The study population has been described in detail elsewhere (8, 23). Briefly, cases were histologically confirmed, incident NHL patients diagnosed in Connecticut between 1996 and 2000, restricted to women aged 21-84 at diagnosis, without previous diagnosis of cancer except nonmelanoma skin cancer, and alive at the time of interview. Out of 832 eligible NHL cases, 601 (72%) cases completed in-person interviews. Participants were slightly older than non-participants, with mean ages of 67 and 62, respectively. The race distribution was similar between participants and non-participants. Out of 601 cases, 518 provided a biosample (461 provided a blood sample and 57 provided a buccal cell sample); excluding 5 cases with dietary information missing, yielding 513 cases for final analysis.

Pathology slides or tissue blocks were obtained from the hospitals where the cases were diagnosed. The specimens were reviewed by two independent study pathologists. All NHL cases were classified according to the World Health Organization (WHO) classification system (24, 25), including 160 cases of diffuse large B-cell lymphoma (DLBCL), 117 cases of follicular lymphoma (FL), 59 cases of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), 35 cases of marginal zone B-cell lymphoma (MZBL), 39 cases of T/NK-cell lymphoma (T-cell), and 103 cases of other rare and unspecified subtypes.

Population-based controls with Connecticut addresses were recruited using random digit dialing methods (RDD) for those below age 65 years and files provided by the Centers for Medicare and Medicaid Service (CMS) were used to recruit those aged 65 years or older, and were frequency matched to cases by age (±5 years). The participation rates were 69% for RDD controls and 47% for CMS controls. The distribution of age and race between participants and non-participants was similar. Out of 717 controls, 597 provided a biosample (535 provided a blood sample and 62 provided a buccal cell sample); excluding 6 controls with dietary information missing, yielding 591 controls for final analysis.

The study was approved by the Human Subjects Research Review Committee at Yale University, the Connecticut Department of Public Health, and the National Cancer Institute. Written and informed consent was obtained from all subjects.

Exposure assessment

In-person interviews were conducted by trained personnel using a standardized questionnaire to collect demographic information and other major known or suspected NHL risk factors, such as family history of cancer, pesticide exposure, medical history, smoking, alcohol consumption, UV radiation and hair-coloring products use. Diet was assessed using a mailed self-administered semiquantitative food frequency questionnaire (FFQ) developed by the Fred Hutchinson Cancer Research Center (Seattle, Washington), in which subjects were asked to characterize their usual diet in the year prior to being interviewed. Reproducibility and validity studies indicated that fruits and vegetables on the FFQ reasonably reflect long-term dietary intake (26). Following completion, the FFQ was sent to the Fred Hutchinson Cancer Research Center for analysis. Average daily nutrient intakes were calculated using the University of Minnesota Nutrition Coding Center Nutrient Data System database.

The FFQ collects data on consumption frequency and portion size for approximately 120 foods including 19 vegetables and 11 fruits (Table 1). Besides overall vegetable intake, we individually analyzed bean vegetables, cruciferous vegetables, green leafy vegetables, red vegetables, and yellow/orange vegetables. In addition to total fruit intake, we individually analyzed citrus fruits.

Table 1.

Vegetables and fruit in Food Frequency Questionnaire

Category of food Composition of food categories
Vegetable Bean vegetables; cruciferous vegetables; green leafy vegetables; red vegetables; yellow/orange vegetables; avocado or guacamole; corn; summer squash such as zucchini; onions, leeks, including in cooking; mixed lettuce salad with vegetables, such as carrots, tomatoes, etc.
Fruit Apples, pears; bananas; peaches, nectarines or plums; cantaloupes; other melon, watermelon, honeydew, etc.; apricots; other dried fruit, raisins, prunes, etc.; strawberries; any other fruit (fruit cocktail, berries, applesauce, grapes, pineapple, etc.); citrus fruits.
Bean vegetables String beans, green beans; peas; beans, such as baked beans, pinto, kidney, lima beans, lentils.
Cruciferous vegetables Broccoli; coleslaw; cabbage, sauerkraut, brussel sprouts; cauliflower.
Green leafy vegetables Cooked greens (spinach, mustard greens, tuinip greens, collards, etc.); lettuce, plain lettuce salad.
Red vegetables Tomatoes, fresh or juice; tomatoes cooked, tomato sauce, salsa.
Yellow/orange vegetables Carrots, including in mixed dishes; winter squash, such as acorn, butternut; sweet potatoes, yams.
Citrus fruits Oranges, grapefruit or tangerines; orange juice, grapefruit juice, or vitamin C enriched fruit drinks.
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The methods for evaluating genotypes in our study population have been described previously by Lan et al. (27). Briefly, DNA was extracted for blood or buccal cell samples using phenol-chloroform extraction. 137 Tag SNPs from 18 candidate genes (Table 2) involved in oxidative stress pathway were chosen from the designable set of common SNPs (minor allele frequency >5%) genotyped in the Caucasian (CEU) population sample of the HapMap Project (Data Release 20/Phase II, NCBI Build 35 assembly, dpSNPb125) using the software Tagzilla (http://tagzilla.nci.nih.gov/), which implements a tagging algorithm based on the pairwise binning method of Carlson et al. (28). For each gene, SNPs within the region 20kb 5’ of the ATG-translation initiation codon and 10kb 3’ of the end of the last exon were binned using a binning threshold of r2 > 0.80. When there were multiple transcripts available for genes, the primary transcript was assessed. Genotyping was conducted at the National Cancer Institute Core Genotyping Facility (Advanced Technology Center, Gaithersburg, MD; http://snp500cancer.nci.nih.gov) (29) using a real-time PCR assay (18 SNPs) and a custom-designed GoldenGate assay (Illumina, www.illumina.com) (129 SNPs). Duplicate samples and replicate samples were genotyped for quality control, and blinded to laboratory personnel. The concordance rates were 99-100% for all assays.

Table 2.

Candidate genes in oxidative stress pathway

Candidate gene* (N=18) Chromosomal location* Gene name (aliases)* Total SNPs (N=137) SNP500 Alias** (dbSNP ID)
AKR1A1 1p33-p32 Aldo-keto reductase family 1, member A1(aldehyde reductase) 1 AKR1A1_02 (rs2088102)
AKR1C1 10p15-p14 Aldo-keto reductase family 1, member C1 (dihydrodiol dehydrogenase 1; 20-alpha (3-alpha)-hydroxysteroid dehydrogenase) 1 AKR1C1_02 (rs8483)
AKR1C3 10p15-p14 Aldo-keto reductase family 1, member C3 (3-alpha hydroxysteroid dehydrogenase, type II) 2 AKR1C3_01 (rs12529), AKR1C3_08 (rs2245191)
ATG9B 7q36.1 ATG9 autophagy related 9 homolog B (S. cerevisiae) 1 ATG9B_01 (rs2373929)
CYBA 16q24 Cytochrome b-245, alpha polypeptide 5 CYBA_01 (rs4673), CYBA_02 (rs1049255), CYBA_07 (rs7195830), CYBA_08 (rs12709102), CYBA_14 (rs3794624)
GPX1 3p21.3 Glutathione peroxidase 1 1 GPX1_01 (rs1050450)
MPO 17q23.1 Myeloperoxidase 5 MPO_02 (rs2333227), MPO_03 (rs2243828), MPO_04 (rs2071409), MPO_17 (rs12452417), MPO_18 (rs4401102)
NCF2 1q25 Neutrophil cytosolic factor 2 (65kDa, chronic granulomatous disease, autosomal 2) 7 NCF2_27 (rs10797888), NCF2_28 (rs11588654), NCF2_29 (rs12753665), NCF2_30 (rs3843293), NCF2_31 (rs3845466), NCF2_34 (rs11579965), NCF2_35 (rs2333686)
NCF4 22q13.1 Neutrophil cytosolic factor 4, 40kDa 18 NCF4_12 (rs3788523), NCF418 (rs729749), NCF4_33 (rs2072711), NCF4_34 (rs2075938), NCF4_35 (rs4821544), NCF4_36 (rs741998), NCF4_37 (rs746713), NCF4_38 (rs760519), NCF4_39 (rs10854693), NCF4_41 (rs1883113), NCF4_42 (rs4821542), NCF4_43 (rs5750326), NCF4_44 (rs5756372), NCF4_45 (rs5756381), NCF4_46 (rs5995355), NCF4_47 (rs6000462), NCF4_48 (rs8137602), NCF4_49 (rs9680849)
NOS1 12q24.2-q24.31 Nitric oxide synthase 1 (neuronal) 31 NOS1_01 (rs10850803), NOS1_02 (rs11068446), NOS1_03 (rs1123425), NOS1_04 (rs11611788), NOS1_05 (rs12578547), NOS1_06 (rs1353939), NOS1_07 (rs1552227), NOS1_08 (rs2291908), NOS1_09 (rs2293054), NOS1_10 (rs2293055), NOS1_12 (rs3782218), NOS1_13 (rs3782221), NOS1_14 (rs483589), NOS1_15 (rs532967), NOS1_16 (rs545654), NOS1_17 (rs6490121), NOS1_18 (rs7295972), NOS1_19 (rs7298903), NOS1_20 (rs816293), NOS1_21 (rs816351), NOS1_22 (rs816353), NOS1_23 (rs816361), NOS1_24 (rs884847), NOS1_25 (rs9658354), NOS1_26 (rs9658490), NOS1_27 (rs11068458), NOS1_28 (rs12424669), NOS1_29 (rs1879417), NOS1_30 (rs4767535), NOS1_31 (rs816347), NOS1_32 (rs9658282)
NOS2A 17q11.2-q12 Nitric oxide synthase 2A (inducible, hepatocytes) 22 NOS2A_01 (rs944722), NOS2A_02 (rs2297518), NOS2A_03 (rs9282799), NOS2A_07 (rs9282801), NOS2A_09 (rs12944039), NOS2A_11 (rs2255929), NOS2A_12 (rs2297516), NOS2A_13 (rs3729508), NOS2A_14 (rs3794763), NOS2A_15 (rs3794766), NOS2A_16 (rs4795067), NOS2A_17 (rs8072199), NOS2A_18 (rs944725), NOS2A_19 (rs9797244), NOS2A_20 (rs11080358), NOS2A_21 (rs12150211), NOS2A_22 (rs2248814), NOS2A_23 (rs2531863), NOS2A_25 (rs2779248), NOS2A_26 (rs2779252), NOS2A_28 (rs8081248), NOS2A_29 (rs9901734)
NOS3 7q36 Nitric oxide synthase 3 (endothelial cell) 8 NOS3_01 (rs1799983), NOS3_07 (rs1007311), NOS3_11 (rs2070744), NOS3_23 (rs743507), NOS3_38 (rs3918227), NOS3_39 (rs12703107), NOS3_40 (rs2373961), NOS3_41 (rs4496877)
OGG1 3p26.2 8-Oxoguanine DNA glycosylase 1 OGG1_04 (rs1052133)
RAC1 7p22 Ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1) 7 RAC1_05 (rs3729790), RAC1_06 (rs6463554), RAC1_07 (rs6967221), RAC1_08 (rs702484), RAC1_09 (rs4720672), RAC1_10 (rs836551), RAC1_11 (rs836554)
RAC2 22q13.1 Ras-related C3 botulinum toxin substrate 2 (rho family, small GTP binding protein Rac2) 15 RAC2_01 (rs1476002), RAC2_05 (rs6572), RAC2_06 (rs7390410, RAC2_09, RAC2_14 (rs2213430), RAC2_15 (rs2239773), RAC2_16 (rs2239774), RAC2_17 (rs2239775), RAC2_18 (rs5756573), RAC2_19 (rs8135343), RAC2_20 (rs9607432), RAC2_21 (rs12484031), RAC2_22 (rs4820274), RAC2_23 (rs7288979), RAC2_24 (rs739043)
SOD1 21q22.11 Superoxide dismutase 1, soluble (amyotrophic lateral sclerosis 1 (adult)) 3 SOD1_03 (rs10432782), SOD1_08 (rs202445), SOD1_16 (rs1041740)
SOD2 6q25.3 Superoxide dismutase 2, mitochondrial 5 SOD2_01 (rs4880), SOD2_16 (rs2758352), SOD2_20 (rs5746136), SOD2_29 (rs2758331), SOD2_30 (rs5746151)
SOD3 4p16.3-q21 Superoxide dismutase 3, extracellular 4 SOD3_16 (rs2695234), SOD3_26 (rs13434451), SOD3_27 (rs2284659), SOD3_28 (rs758946)
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Abbreviations: single nucleotide polymorphism (SNP).

*

As defined by Entrez gene (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene).

Statistical analysis

Intake of vegetables and fruit, all vegetables, bean vegetables, cruciferous vegetables, green leafy vegetables, red vegetables, yellow/orange vegetables, fruit, and citrus fruits was dichotomized into high and low levels according to the median daily consumption (servings of medium portions per day) in controls. Odds ratios (OR) and 95% confidence intervals (CI) were calculated to estimate the relative risk of NHL and NHL subtypes in relation to the SNP genotype using unconditional logistic regression models in different vegetable and/or fruit strata (high and low). The homozygote of the most common allele was used as the referent group. Significance of gene-vegetable and/or fruit intake interaction was assessed by adding an interaction term in the logistic models. The reference groups with a homozygote wild-type genotype were coded as 0, and the heterozygote and homozygote variant genotypes were grouped together to increase power and coded as 1. Statistical analyses were performed using the SAS system, version 9.1 (SAS Institute, Cary, NC).

Haplotype analyses were conducted for all genes in which more than one SNP was genotyped. Haplotype block structure was evaluated with the program HaploView (Whitehead Institute, Cambridge, MA) in controls using the 4-gamete rule with a minimum frequency of 0.005 for the fourth gamete. Haplotypes were estimated using the estimation-maximization algorithm (30) in SAS Genetics (SAS Institue, Cary, NC). An unconditional logistic regression model was used to estimate the effect of individual haplotypes in different vegetable and/or fruit intake level strata.

All models were adjusted for age, race (white and other), family history of NHL, total energy intake (kcal), smoking (never/ever), alcohol consumption (gm/day), and body mass index (BMI) (kg/m2). Total energy intake was examined for extreme values and exclusion of these subjects did not result in material changes for the observed associations.

The false discovery rate (FDR) method (31) was applied to adjust for multiple comparisons. The FDR provides the expected ratio of erroneous rejections of the null hypothesis compared to the total number of rejected hypotheses. FDR values were calculated separately for each vegetable and/or fruit category from the results of the 137 tests (i.e. total number of SNPs studied) evaluating the association between each SNP-vegetable and/or fruit interaction and the risk of NHL. Interactions were deemed significant at an FDR level of 0.20.

Results

Selected demographic characteristics of cases and controls were compared (Table 3). Cases and controls were similar with respect to age, race, reported family history of NHL, smoking status and total energy intake. However, cases tended to consume less alcohol and have a higher BMI.

Table 3.

Selected characteristics of NHL cases and controls among women from Connecticut

Characteristic Case (N=513) Control (N=591) P-value
Age (years) 61.8 (± 13.6) 62.1 (± 14.2) 0.71a
Race 0.11b
White 492 (95.9) 554 (93.7)
Other 21 (4.1) 37 (6.3)
Family history of NHL 0.08c
Yes 9 (1.8) 3 (0.5)
No 504 (98.2) 588 (99.5)
Smoking 0.33b
Never 227 (44.2) 279 (47.2)
Ever 286 (55.8) 312 (52.8)
Alcohol consumption (gm/day) 3.4 (± 9.6) 4.7 (± 8.5) 0.05a
Total energy intake (kcal) 1708.3 (± 592.3) 1642.3 (± 596.4) 0.07a
Body mass index (Kg/m2) 26.1 (± 5.7) 25.4 (± 5.0) 0.03a
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Table values are mean±SD for continuous variables and n (column%) for categorical variables.

a

P-value is for T-test.

b

P-value is for Chi-Square test.

c

P-value is for Fisher's Exact test.

Significant effect modification was identified for 8 SNPs in nitric oxide synthase (NOS) genes, 1 SNP in Myeloperoxidase (MPO) gene and 1 SNP in Superoxide dismutase 3 (SOD3) gene, with total vegetable and fruit intake, vegetable intake, red vegetable intake, or yellow/orange vegetable intake among either NHL overall or the three most common subtypes DLBCL, FL and CLL/SLL (Table 4). No significant result was found for MZBL or T-cell lymphoma (data not shown).

Table 4.

Associations between genetic polymorphisms and NHL overall and histological subtypes risks by vegetable and fruit intake (svgs/d).

NHL overall
 DLBCL
 FL
 CLL/SLL
Low
 High
 Low
 High
 Low
 High
 Low
 High
ca\co OR (95% CI) ca\co OR (95% CI) ca OR (95% CI) ca OR (95% CI) ca OR (95% CI) ca OR (95% CI) ca OR (95% CI) ca OR (95% CI)
Vegetable and fruit
MPO (rs4401102)
CC 126 \ 141 1 108 \ 165 1 43 1 37 1 23 1 29 1 20 1 12 1
CT + TT 86 \ 115 0.9(0.6- 1.3) 121 \ 95 1.9(1.3- 2.8) 22 0.7(0.4- 1.3) 41 2.0(1.2- 3.4) 17 0.9(0.5- 1.8) 33 1.9 (1.1-3.5) 10 0.6(0.3- 1.3) 10 1.5(0.6- 3.7)
P-interaction 0.0026 0.0052 0.1173 0.1243
NOS1 (rs2293054)
GG 108 \ 144 1 130 \ 108 1 35 1 42 1 13 1 39 1 17 1 10 1
AG + AA 103 \ 112 1.2(0.8- 1.7) 98 \ 152 0.5(0.4- 0.8) 30 1.1(0.6- 1.9) 36 0.6(0.4- 1.0) 26 2.7(1.3- 5.6) 23 0.4(0.2- 0.7) 13 1.0(0.5- 2.1) 11 0.7(0.3- 1.9)
P-interaction 0.0024 0.1467 0.0001 0.6042
NOS1 (rs7298903)
TT 161 \ 215 1 187 \ 200 1 50 1 65 1 26 1 55 1 24 1 13 1
CT + CC 51 \ 41 1.7(1.1- 2.8) 42 \ 60 0.8(0.5- 1.2) 15 1.7(0.8- 3.4) 13 0.7(0.3- 1.3) 14 3.0(1.4- 6.3) 7 0.4(0.2- 1.0) 6 1.3(0.5- 3.5) 9 2.6(1.0- 6.7)
P-interaction 0.0121 0.0459 0.0003 0.3442
Vegetables
NOS1 (rs545654)
CC 68 \ 68 1 63 \ 66 1 30 1 12 1 11 1 22 1 10 1 7 1
CT + TT 153 \ 183 0.8(0.5- 1.2) 151 \ 192 0.8(0.6- 1.3) 42 0.4(0.2- 0.8) 57 1.9(0.9- 3.8) 31 1.0(0.4- 2.1) 37 0.6(0.3- 1.1) 19 0.8(0.3- 1.8) 16 0.8(0.3-2.0)
P-interaction 0.8548 0.0028 0.3679 0.9955
SOD3 (rs2284659)
GG 75 \ 103 1 87 \ 95 1 22 1 21 1 19 1 25 1 4 1 14 1
GT + TT 147 \ 148 1.5(1.0- 2.2) 126 \ 164 0.8(0.6- 1.2) 50 1.6(0.9- 2.9) 48 1.4(0.8- 2.4) 24 1.0(0.5- 2.0) 34 0.8(0.5-1.5) 25 4.6(1.5-13.7) 9 0.4(0.2-0.9)
P-interaction 0.0463 0.578 0.7281 0.0007
Red vegetables
NOS1 (rs11068446)
CC 174 \ 196 1 149 \ 185 1 51 1 57 1 38 1 37 1 23 1 1
CT + TT 66 \ 53 1.5(1.0- 2.3) 44 \ 73 0.8(0.5- 1.2) 22 1.7(0.9- 3.1) 10 0.4(0.2- 0.9) 17 1.9(1.0- 3.8) 9 0.6(0.3- 1.4) 10 1.7(0.7-3.8) 1.2(0.4- 3.5)
P-interaction 0.0353 0.0057 0.0377 0.7181
NOS1 (rs3782221)
GG 126 \ 154 1 113 \ 134 1 31 1 43 1 29 1 28 1 18 1 3 1
AG + AA 112 \ 96 1.4(1.0- 2.1) 80 \ 121 0.8(0.5- 1.2) 42 2.2(1.3- 3.9) 24 0.6(0.3- 1.1) 24 1.5(0.8- 2.8) 18 0.7(0.4- 1.3) 14 1.3(0.6- 2.8) 15 6.3(1.7-22.7)
P-interaction 0.0243 0.0011 0.1128 0.0448
NOS1 (rs545654)
CC 80 \ 63 1 50 \ 70 1 31 1 10 1 17 1 16 1 10 1 7 1
CT + TT 158 \ 183 0.6(0.4- 0.9) 140 \ 185 1.1(0.7- 1.7) 41 0.4( 0.2- 0.7) 56 2.4(1.1- 5.0) 37 0.6(0.3- 1.2) 30 0.8(0.4- 1.5) 23 0.8(0.4- 1.8) 11 0.7(0.2- 1.8)
P-interaction 0.0593 0.0002 0.718 0.6200
NOS1 (rs7298903)
TT 186 \ 210 1 158 \ 197 1 53 1 60 1 40 1 40 1 25 1 12 1
CT + CC 55 \ 40 1.6(1.0- 2.6) 35 \ 61 0.7(0.5- 1.2) 20 2.1(1.1- 3.9) 7 0.4(0.2- 0.9) 15 2.3(1.1- 4.7) 6 0.5(0.2- 1.2) 8 1.9(0.8- 4.5) 6 1.7(0.6-4.7)
P-interaction 0.0172 0.0015 0.0101 0.9399
NOS1 (rs12424669)
CC 201 \ 194 1 149 \ 202 1 64 1 47 1 40 1 35 1 29 1 16 1
CT + TT 39 \ 55 0.6(0.4- 1.0) 44 \ 55 1.1(0.7- 1.7) 9 0.4(0.2- 0.9) 20 1.7(0.9- 3.2) 15 1.2(0.6- 2.3) 11 1.2(0.5- 2.5) 3 0.4(0.1- 1.2) 2 0.5(0.1- 2.3)
P-interaction 0.0961 0.0073 0.9057 0.7909
NOS2A (rs3729508)
CC 76 \ 103 1 82 \ 94 1 20 1 30 1 13 1 19 1 14 1 7 1
CT + TT 161 \ 145 1.5(1.0- 2.2) 111 \ 161 0.8(0.5- 1.2) 53 2.0(1.1- 3.7) 37 0.7( 0.4- 1.2) 40 2.2(1.1- 4.4) 27 0.8(0.4- 1.6) 18 0.9(0.4- 1.9) 11 0.9(0.3- 2.5)
P-interaction 0.021 0.0086 0.0445 0.9647
Yellow/orange vegetables
NOS1 (rs11068446)
CC 155 \ 205 1 166 \ 183 1 49 1 59 1 34 1 40 1 22 1 12 1
CT + TT 60 \ 50 1.7(1.1- 2.7) 49 \ 75 0.7(0.5- 1.1) 23 2.1(1.1- 3.8) 10 0.4(0.2- 0.9) 12 1.6(0.8- 3.4) 13 0.9(0.4- 1.7) 9 1.9(0.8- 4.5) 7 1.5(0.5- 4.0)
P-interaction 0.0068 0.001 0.2339 0.709
NOS1 (rs1552227)
CC 93 \ 128 1 113 \ 117 1 25 1 40 1 20 1 25 1 12 1 12 1
CT + TT 122 \ 128 1.3(0.9- 2.0) 103 \ 141 0.8(0.5- 1.1) 47 2.1(1.2- 3.7) 29 0.6(0.3- 1.0) 26 1.4(0.7- 2.6) 28 1.0(0.5- 1.8) 19 1.4(0.6- 3.1) 7 0.5(0.2- 1.3)
P-interaction 0.0195 0.0014 0.3022
NOS1 (rs7298903)
TT 163 \ 213 1 179 \ 200 1 50 1 63 1 36 1 43 1 23 13
CT + CC 52 \ 43 1.7(1.1- 2.8) 37 \ 58 0.7( 0.5- 1.2) 22 2.3(1.2- 4.3) 6 0.3(0.1- 0.8) 10 1.5(0.7- 3.3) 10 0.8(0.4- 1.8) 8 2.1(0.8- 5.2) 6 1.8(0.6- 4.9)
P-interaction 0.0104 0.0005 0.3115 0.8504
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Abbreviations: non-Hodgkin lymphoma (NHL); diffuse large B-cell lymphoma (DLBCL); follicular lymphoma (FL); chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL); marginal zone B-cell lymphoma (MZBL); T/NK-cell lymphoma (T-cell); odd ratio (OR); confidence interval (CI); cases (ca); controls (co).

OR (95% CI) were adjusted for age, race (white, other), family history of NHL, total energy intake (kcal), smoking (ever/never), alcohol consumption (gm/day) and body mass index.

Italic P-values <0.05, and italic odd ratios’ 95% confidence intervals do not include 1; Bold P-values are significant after FDR adjustment for multiple comparisons.

Significant effect modification was identified for SNPs in MPO and NOS1 genes, with total vegetable and fruit intake, were found for NHL overall and FL. Carriers of the variant allele for MPO (rs4401102) (CT or TT) had a 1.9-fold increased risk of NHL overall and FL in the high vegetable and fruit intake group, but not in the low intake group. Carriers of the variant allele for NOS1 (rs2293054) (AG or AA) had a 50% reduced risk of NHL and a 60% reduced risk of FL in the high vegetable and fruit intake group, while a 2.7-fold increased risk of FL in the low intake group. Carriers of the variant allele for another NOS1 (rs7298903) (CT or CC) had a 1.7-fold increased risk of NHL and a 3.0-fold increased risk of FL in the low vegetable and fruit intake group but a 60% reduced risk of FL in the low intake group.

When vegetable intake was investigated alone, significant interactions with SNPs in NOS1 and SOD3 genes were found for DLBCL and CLL/SLL. Carriers of the variant allele for NOS1 (rs545654) (CT or TT) had a 60% reduced risk of DLBCL in the low vegetable intake groups but not in high intake group. Carriers of the variant allele for SOD3 (rs2284659) (GT or TT) had a 4.6-fold increased risk of CLL/SLL in the low intake group while a 60% reduced risk of CLL/SLL was observed in the high intake group.

When different types of vegetables were investigated separately, no significant interaction was found for bean vegetables, cruciferous vegetables or green leafy vegetables after adjusting for multiple comparisons. For red vegetable intake, significant interactions with five SNPs in the NOS1 gene and one SNP in the NOS2A gene were found for DLBCL. Those with variant alleles for NOS1 (rs11068446) (CT or TT), NOS1 (rs3782221) (AG or AA), NOS1 (rs7298903) (CT or CC) and NOS2A (rs3729508) (CT or TT) had a 1.7-2.2-fold increased risk of DLBCL in the low red vegetable intake group, while those in the high intake group had a 30%-60% reduced risk of DLBCL. Those with variant alleles for NOS1 (rs545654) (CT or TT) and NOS1 (rs12424669) (CT or TT) had a 60% reduced risk of DLBCL in the low red vegetable intake group and a 1.7-2.4-fold increased risk of DLBCL in the high intake group.

For yellow/orange vegetable intake, significant interactions with three SNPs in the NOS1 gene were found for DLBCL. Those with variant alleles for NOS1 (rs11068446) (CT or TT), NOS1 (rs1552227) (CT or TT) and NOS1 (rs7298903) (CT or CC) had a 2.1-2.3 fold increased risk of DLBCL in the low yellow/orange vegetable intake group though it reduces the risk by 40%-70% in the high intake group.

No significant interaction was found when fruit intake overall, or citrus fruit was investigated.

There were a total of 26 haplotype structures identified in the investigated genes (16 2-SNP haplotypes, 7 3-SNP haplotypes, 1 4-SNP haplotype, and 2 5-SNP haplotypes). No significant interaction with haplotype was found for NHL overall or for any histological subtype.

The results were similar when the analyses were limited to Caucasian subjects.

Discussion

Our study offers the first comprehensive analysis of the interaction between vegetable and fruit intake, genetic polymorphisms in oxidative stress pathway genes and NHL risk overall and by histological subtype. We observed that the risk of NHL differs by vegetable and fruit intake when considered in conjunction with genetic variation in NOS1, NOS2A, MPO and SOD3 with majority findings in NOS1, especially for the most prevalent histological subtypes DLBCL and FL.

The interaction between vegetable intake and polymorphisms in 28 genes and the risk of NHL overall recently studied (32). We shared 6 common SNPs (GPX P200L rs1050450, NOS2A S608L rs2297518, NOS3 D298E rs1799983, NOS3-762C>T rs2070744, OGG S326C rs1052133 and SOD2V16A rs4880). Our results are consistent with the previous findings as no interaction was observed for any of overlapping SNPs and vegetable intake.

High vegetable and fruit intake is believed to be protective for many types of cancers as vegetables and fruit are good sources of vitamins and minerals, carotenoids and other antioxidants, and various phytochemicals, each of which may play a role in reducing cancer risk. It is likely a combination of these factors, and other factors not yet explored, that confers protection (33). Vegetable and fruit intake have been previously examined as protective factors for NHL (8-22). Although the majority of studies indicate that a protective effect is found when vegetables and fruit are combined, or for specific types of vegetables and fruits, the evidence from prospective data has been weak (10, 19, 22), and inconsistencies have been observed. Potential explanations for the inconsistency in findings may be due to gender differences (34), lack of examination by NHL histological subtypes, and the issue of multiple comparisons. However, the gene-diet interactions identified in our multiple comparison-adjusted analyses by NHL subtype could address the inconsistencies observed in previous epidemiological studies.

The gene-diet interactions identified in our study confer support for prior findings that oxidative stress pathway genes and alter NHL risk (27, 35, 36). The genes with polymorphisms shown to impact risk include AKR1A1 and CYBA (27), CPX1 (35), NOS2A and SOD2 (35, 36). However, in an investigation of the same study population, Lan et al. (27) did not find an association between genetic polymorphisms in MPO or NOS2A and susceptibility to NHL and its major subtypes. Our results suggest that the dietary differences in vegetables and fruit might have masked the role of these genes in the susceptibility of NHL in the previous analyses.

We observed vegetable and/or fruit intake interactions with 8 SNPs in NOS genes (7 in NOS1 and 1 in NOS2A). NOS are isoenzymes that catalyze the synthesis of nitric oxide (NO), a free radical whose role in tumor biology is still controversial (37). On the one hand, NO can favor tumor growth and development by stimulating angiogenesis (38) and cause immunosuppression (39, 40); on the other hand, NO could play a role in tumor regression through its ability to induce apoptosis (41) and facilitate an immune response/rejection of the tumor (42). Studies have showed that certain antioxidants in vegetables and fruits regulate NOS expression and NO production. For example, polyphenols(43-46), which are widely distributed in fruit and vegetables, lutein (47), primarily found in dark-green leafy vegetables and fruits, and tomatoes, inhibit NOS expression and decrease NO production. Soyasaponins (48), which are found in soybeans, garlic and its derivatives (49, 50), dietary soy isoflavones (51), and onion (52) activate NOS expression leading to an increase in NO. Sulforaphane, which is rich in cruciferous vegetables, inhabits Hypoxia-inducible factor 1 which regulates NOS2 expression in cancer cells (53).

In addition to the components of vegetables and fruit, other factors such as contamination during cooking or processing, could also modulate NOS expression and impact NHL risk. For example, 3-monochloro-1,2-propanediol, a contaminant of acid-hydrolyzed vegetables can be formed during the cooking process, was recently found to inhabit NOS1 expression and increase NOS2 expression (54). Our finding of an interaction between NOS1 and NOS2A SNPs and red vegetables (including fresh tomato, cooked tomatoes and tomato sauce) support this explanation.

Our finding of the risk associated with NOS1 SNPs suggests that in addition to the known role of NOS1 in cellular communication, it may be also involved in lymphomagenesis and development, and its role could be strongly modified by the intake of vegetables, especially tomatoes, carrots and other yellow/orange vegetables. NOS1 are constitutively expressed in neurons and endothelial cells (37) and polymorphisms in NOS1 have previously been associated with many diseases and disorders in nervous system, such as Parkinson's disease (55), achalasia (56), restless legs syndrome (57), depression (58), schizophrenia (59), and suicidal behavior (60), while demonstrating few associations with cancer. Moreover, none of the risk NOS1 polymorphisms we identified were functional, implying a need for further exploration on this gene's role in tumor biology. There is, however, evidence of an association between the NOS2 isoform and NHL. Investigators have previously shown that the isoform NOS2 is inducible and expressed in many cell types including macrophage (61), B-lymphocytes (39), lymphoid neoplasms (62-64) and other tumor cells (65-68).

To our knowledge, there is no functional evidence for any of the risk SNPs we identified. NOS1 (rs2293054) is a synonymous mutation on Exon13, although theoretically, this type of mutation may impact splicing and protein structure and function, no evidence is shown for this specific SNP. All the other SNPs are intron variations or found in the cap or tail; which was generally thought to be “junk DNA”. Our findings support the dispute against it, and suggest that more exploration and investigation is needed for these mutations. Specifically, our results suggest that these mutations might affect human health by interacting with common environmental exposure or lifestyle factors such as diet, and were subsequently maintained through evolution.

We did not include specific antioxidant nutrients in our analysis, since the major source of antioxidant for human beings is natural plants other than supplements. And more likely, it is the combination of the antioxidants in vegetables and fruits confer a protection against cancer.

Although we adjusted for multiple comparisons, it is still possible that some of the interactions we identified are due to chance. Our results should be confirmed in future studies with large sample sizes. Three NOS1 SNPs (rs545654, rs7298903 and rs11068446) were significant in the multiple vegetable and/or fruit comparison groups, suggesting a strong likelihood that the association is true and should be investigated in future studies.

In summary, our study support a role for the modification from oxidative stress pathway genetic variations on the association between vegetable and fruit intake and NHL subtypes. Our results warrant replication in further studies and suggest that future research on the role of nonfunctional variants in lymphoma tumor biology should be explored.

Acknowledgement

This research was supported by the NIH grant CA62006, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute, and the National Institutes of Health Fogarty training grant 1D43TW007864-01. This publication was made possible by CTSA Grant number UL1 RR024139 from the National Center for Research Resources (NCRR), a component of the NIH, and NHL roadmap for medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR.

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