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Open Access

Peer-reviewed

Research Article

SEPP1 Influences Breast Cancer Risk among Women with Greater Native American Ancestry: The Breast Cancer Health Disparities Study

  • Andrew J. Pellatt,

    Affiliation University of Utah, Department of Medicine, Salt Lake City, Utah, United States of America

    ⨯
  • Roger K. Wolff,

    Affiliation University of Utah, Department of Medicine, Salt Lake City, Utah, United States of America

    ⨯
  • Esther M. John,

    Affiliations Cancer Prevention Institute of California, Fremont, California, United States of America, Division of Epidemiology, Department of Health Research and Policy and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, United States of America

    ⨯
  • Gabriela Torres-Mejia,

    Affiliation Instituto Nacional de Salud Pública, Centro de Investigación en Salud Poblacional, Ahuacatitlán, Cuernavaca Morelos, México

    ⨯
  • Lisa M. Hines,

    Affiliation University of Colorado at Colorado Springs, Department of Biology, Colorado Springs, Colorado, United States of America

    ⨯
  • Kathy B. Baumgartner,

    Affiliation Department of Epidemiology and Population Health, School of Public Health & Information Sciences, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, United States of America

    ⨯
  • Anna R. Giuliano,

    Affiliation Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America

    ⨯
  • Abbie Lundgreen,

    Affiliation University of Utah, Department of Medicine, Salt Lake City, Utah, United States of America

    ⨯
  • Martha L. Slattery

    Marty.Slattery@hsc.utah.edu

    Affiliation University of Utah, Department of Medicine, Salt Lake City, Utah, United States of America

    ⨯

Abstract

Selenoproteins are a class of proteins containing a selenocysteine residue, many of which have been shown to have redox functions, acting as antioxidants to decrease oxidative stress. Selenoproteins have previously been associated with risk of various cancers and redox-related diseases. In this study we evaluated possible associations between breast cancer risk and survival and single nucleotide polymorphisms (SNPs) in the selenoprotein genes GPX1, GPX2, GPX3, GPX4, SELS, SEP15, SEPN1, SEPP1, SEPW1, TXNRD1, and TXNRD2 among Hispanic/Native American (2111 cases, 2597 controls) and non-Hispanic white (NHW) (1481 cases, 1586 controls) women in the Breast Cancer Health Disparities Study. Adaptive Rank Truncated Product (ARTP) analysis was used to determine both gene and pathway significance with these genes. The overall selenoprotein pathway PARTP was not significantly associated with breast cancer risk (PARTP = 0.69), and only one gene, GPX3, was of borderline significance for the overall population (PARTP =0.09) and marginally significant among women with 0-28% Native American (NA) ancestry (PARTP=0.06). The SEPP1 gene was statistically significantly associated with breast cancer risk among women with higher NA ancestry (PARTP=0.002) and contributed to a significant pathway among those women (PARTP=0.04). GPX1, GPX3, and SELS were associated with Estrogen Receptor-/Progesterone Receptor+ status (PARTP = 0.002, 0.05, and 0.01, respectively). Four SNPs (GPX3 rs2070593, rsGPX4 rs2074451, SELS rs9874, and TXNRD1 rs17202060) significantly interacted with dietary oxidative balance score after adjustment for multiple comparisons to alter breast cancer risk. GPX4 was significantly associated with breast cancer survival among those with the highest NA ancestry (PARTP = 0.05) only. Our data suggest that SEPP1 alters breast cancer risk among women with higher levels of NA ancestry.

Citation: Pellatt AJ, Wolff RK, John EM, Torres-Mejia G, Hines LM, Baumgartner KB, et al. (2013) SEPP1 Influences Breast Cancer Risk among Women with Greater Native American Ancestry: The Breast Cancer Health Disparities Study. PLoS ONE 8(11): e80554. https://doi.org/10.1371/journal.pone.0080554

Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America

Received: September 10, 2013; Accepted: October 15, 2013; Published: November 20, 2013

Copyright: © 2013 Pellatt et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The Breast Cancer Health Disparities Study was funded by grant CA14002 from the National Cancer Institute to Dr. Slattery. The San Francisco Bay Area Breast Cancer Study was supported by grants CA63446 and CA77305 from the National Cancer Institute, grant DAMD17-96-1-6071 from the U.S. Department of Defense and grant 7PB-0068 from the California Breast Cancer Research Program. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute's Surveillance, Epidemiology and End Results Program under contract HHSN261201000036C awarded to the Cancer Prevention Institute of California; and the Centers for Disease Control and Prevention's National Program of Cancer Registries, under agreement #1U58 DP000807-01 awarded to the Public Health Institute. The 4-Corners Breast Cancer Study was funded by grants CA078682, CA078762, CA078552, and CA078802 from the National Cancer Institute. The research also was supported by the Utah Cancer Registry, which is funded by contract N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the New Mexico Tumor Registry, and the Arizona and Colorado cancer registries, funded by the Centers for Disease Control and Prevention National Program of Cancer Registries and additional state support. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. The Mexico Breast Cancer Study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Selenium is a trace element essential for essential health that has been suggested to play a preventive role for a variety of chronic diseases, including various cancers [1-6]. This is likely due to the role of selenium as a constituent of various proteins, known as the selenoproteins [2,7]. Selenoproteins are a class of approximately 25 proteins containing a selenocysteine (SEC) residue. SEC is a cysteine analogue, where sulfur has been replaced by a selenium atom, synthesized from serine bound to a tRNA [8]. The incorporation of SEC is a complex process requiring an in-frame UGA codon, occurring as a stop codon, which is recognized as a SEC codon with the aid of a stem loop called a SEC insertion sequence (SECIS) and several other trans-acting factors [8,9].

The known human selenoproteins include the glutathione peroxidases (GPX), thioredoxin reductases (TXNRD), iodothyronine deiodinases (DIO), and selenophosphate synthetases 2 (SPS2) [10]. The other identified selenoproteins include, but are not limited to, selenoprotein n (SEPN1), selenoprotein p (SEPP1), selenoprotein s (SELS), selenoprotein w (SEPW1), and a 15-kDA selenoprotein (SEP15) [4,10]. Not all identified selenoproteins have been characterized; however, many of the selenoproteins with known function (including the GPXs, TXNRDs, SPS2, and DIOs), have redox functions [10] and behave as antioxidants [1] to reduce oxidative stress.

In this study we evaluated single nucleotide polymorphisms (SNPs) in several selenoprotein coding genes for an association with breast cancer: glutathione peroxidase 1 (GPX1), glutathione peroxidase 2 (GPX2), glutathione peroxidase 3 (GPX3), glutathione peroxidase 4 (GPX4), SELS, SEP15, SEPN1, SEPP1, SEPW1, thioredoxin reductase 1 (TXNRD1), and thioredoxin reductase 2 (TXNRD2). These selenoproteins were selected for analysis because their functions have been characterized and many of them have been associated with risk of various types of cancer and/or oxidative stress [2-7,9,11-14]. The glutathione peroxidases (GPX1, GPX2, GPX3, and GPX4) primarily function to reduce oxidative stress by detoxifying hydrogen peroxide and other organic peroxides [2,13]. SELS is involved in inflammatory response [10]. SEP15 is a protein located primarily in the endoplasmic reticulum and plays a role in protein folding [2]. SEPN1 plays a role in redox homeostasis and protects against oxidative stress [15]. SEPP1 acts as a selenium transport protein, a heavy-metal chelator, and an antioxidant [10]. SEPW1 is a highly conserved protein that acts as an antioxidant protecting against oxidative stress [10]. The thioredoxin reductases (TXNRD1 and TXNRD2) are antioxidants that reduce the oxidized form of thioredoxin, an important regulator of redox-controlled cell functions and redox balance [12]. While many of these selenoprotein-coding genes have been associated with certain cancers, their association with breast cancer remains unclear.

In addition to evaluating SNPs in these genes for an association with breast cancer risk and survival, we also evaluated associations by level of Native American (NA) genetic ancestry. Higher levels of NA ancestry have been associated with reduced breast cancer risk [16,17] and serum selenium concentration has been shown to differ amongst racial and ethnic groups [18]. We also evaluated breast cancer associations by menopausal status and estrogen receptor (ER) and progesterone receptor (PR) status. Additionally, we evaluated breast cancer associations by dietary oxidative balance score (DOBS) since selenoprotein genes may interact with dietary factors that influence oxidative stress.

Methods

Ethics Statement

All participants signed informed written consent prior to participation and each study was approved by the Institutional Review Board for Human Subjects at the participating institutions: University of Utah, University of Arizona, University of Colorado, University of New Mexico, Comisión de ética, and Institutional Review Board of the Cancer Prevention Institute of California.

Study Design

The Breast Cancer Health Disparities Study includes participants from three population-based case-control studies, the 4-Corners Breast Cancer Study, the Mexico Breast Cancer Study, and the San Francisco Bay Area Breast Cancer Study [17] who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction. In the 4- Corners Breast Cancer Study, participants were between 25 and 79 years of age with a histological confirmed diagnosis of in situ (n=341) or invasive (n=1492) cancer between October 1999 and May 2004; controls were selected from the target populations of cases living in Arizona, Colorado, New Mexico, and Utah and were frequency matched to cases on ethnicity and 5-year age distribution [19]. Participants from the Mexico Breast Cancer Study were between 28 and 74 years of age. Eligible cases in Mexico were women diagnosed with either a new histologically confirmed in situ or invasive breast cancer between January 2004 and December 2007 at 12 participating hospitals from three main health care systems; controls were randomly selected from the catchment area as the cases and frequency matched to cases based on 5-year age distribution, membership in health care institution, and place of residence. The San Francisco Bay Area Breast Cancer Study included women aged 35 to 79 years from the San Francisco Bay Area diagnosed with a first primary histologically confirmed invasive breast cancer between April 1995 and April 2002; controls were identified by random-digit dialing and frequency-matched to cases based on the expected race/ethnicity and 5-year age distribution [20,21].

Data Harmonization

Data were harmonized across all study centers and questionnaires as previously described [17]. Women were classified as either pre-menopausal or post-menopausal based on responses to questions on menstrual history. Women who reported still having periods during the referent year (defined as the year before diagnosis for cases or before selection into the study for controls) were classified as pre-menopausal. Center-specific definitions were used to define post-menopausal women. Women were classified as post-menopausal if they reported either a natural menopause or if they reported taking hormone therapy and were still having periods or were at or above the 95th percentile of age for those who reported having a natural menopause (i.e., > 12 months since their last period. This age at menopause was site-specific by ethnicity: 58 years for NHW and 56 for Hispanic/Native women from the 4-Corners’ Breast Cancer Study; 54 for the Mexico Breast Cancer Study; and 55 for NHW and 56 for Hispanic women from the San Francisco Bay Area Breast Cancer Study.

A dietary oxidative balance score (DOBS) that included nutrients with anti- or pro-oxidative balance properties was developed as previously reported [22]. Anti-oxidants included in the score were vitamin C, vitamin E, beta carotene (data for beta carotene were not available for Mexico), folic acid, and dietary fiber; alcohol was treated as a pro-oxidant. Nutrients per 1000 calories were evaluated and quartiles of intake were based on study-specific distributions; Long-term alcohol consumption was classified into three levels: the top 25th percentile of consumption, all other drinkers, and non-drinkers. Referent year alcohol consumption was used for those women who did not have long-term alcohol measurements. In creating the DOBS, participants were assigned values of zero for low levels (first quartile) of exposure to anti-oxidants or high exposure to pro-oxidants (fourth quartile), one for intermediate levels (second and third quartiles) of exposure, and two for high levels (fourth quartile) of exposure to anti-oxidants and low exposure (first quartile) to pro-oxidants.

Genetic Data

DNA was extracted from either whole blood (n=7287) or mouthwash (n=634) samples. Whole Genome Amplification (WGA) was applied to the mouthwash-derived DNA samples prior to genotyping. A tagSNP approach was used to characterize variation across candidate genes. TagSNPs were selected using the following parameters: linkage disequilibrium (LD) blocks were defined using a Caucasian LD map and an r2=0.8; minor allele frequency (MAF) >0.1; range= -1500 bps from the initiation codon to +1500 bps from the termination codon; and 1 SNP/LD bin. We distinguished European and NA ancestry in the study population by using 104 Ancestry Informative Markers (AIMs) [17]. A multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, California) was used for genotyping. A genotyping call rate of 99.93% was attained (99.65% for WGA samples). We included 132 blinded internal replicates representing 1.6% of the sample set.  The duplicate concordance rate was 99.996% as determined by 193,297 matching genotypes among sample pairs.  In the current analysis we evaluated GPX1 (2 SNPs), GPX2 (4 SNPs), GPX3 (3 SNPs), GPX4 (1 SNP), SEPP1 (2 candidate SNPs and 1 tagSNP), SELS (2 SNPs), SEP15 (4 SNPs), SEPN1 (5 SNPs), SEPW1 (3 SNPs), TXNRD1 (7 SNPs), and TXNRD2 (20 SNPs). A description of these genes and SNPs is shown in online Table S1. SEP15 rs9433110 was not analyzed since it was not in Hardy-Weinberg Equilibrium (HWE) among NHW participants. Online Table S2 shows minor allele frequency (MAF) and HWE by ancestry groups. It should be noted that in most instances a trend in a different prevalence of MAF across ancestry groups was noted and in some instances, such as SEPP1 rs6865453, we observed a reversal in the major and minor allele from the most European to the most Native ancestry groups.

Tumor Characteristics and Survival

Data for survival and ER/PR tumor status were available for cases from the 4-Corners Breast Cancer Study and the San Francisco Bay Area Breast Cancer Study.

Information on stage at diagnosis, months of survival after diagnosis, cause of death, and ER and PR status were available from cancer registries in Utah, Colorado, Arizona, New Mexico, and California. Information on ER and PR status of tumors was available for 1019 (69%) NHW and 977 (75%) Hispanic/NA cases. Surveillance Epidemiology and End Results (SEER) summary disease stage, based on three codes of local, regional, and distant, was used. Data on survival and ER and PR tumor status were not available from the Mexico Breast Cancer Study.

Statistical Methods

Genetic Ancestry Estimation.

The program STRUCTURE was used to compute individual ancestry for each study participant assuming two founding populations [23,24]. A three-founding population model was assessed but did not fit the population structure with the same level of repeatability and correlation among runs as the two-founding population model. Participants were classified by level of percent NA ancestry. Assessment across categories of ancestry was done using cut-points based on the distribution of genetic ancestry in the control population. These cut-points, 0-28%, >28-70%, and >70-100%, maximized power within all three ancestry groups to assess ancestry-specific associations. Genetic ancestry was used as a continuous variable when included in the models to adjust for possible confounding.

SNP Associations.

Genes and SNPs were assessed for their association with breast cancer risk by strata of menopausal status and genetic ancestry in the whole population and by ER/PR status for the 4-Corners Breast Cancer Study and the San Francisco Bay Area Study. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC) unless otherwise noted. Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for breast cancer risk associated with SNPs, adjusting for age, study center, genetic ancestry, body mass index (BMI of kg/m2) during referent year, and parity. The generalized logit link function was used when estimating breast cancer risk by ER/PR status. Associations with SNPs were assessed assuming co-dominant models. Based on the initial assessment, SNPs which appeared to have a dominant or recessive mode of inheritance were evaluated with those inheritance models in subsequent analyses. Stratified analyses tests for interactions were calculated using a 1 degree of freedom (df) Wald chi-square tests; p values based on 4-df Wald tests measure the overall SNP (treated as continuous) association with breast cancer risk by ER/PR status. Adjustments for multiple comparisons within the gene used the step-down Bonferroni correction (i.e., Holm method) taking into account the correlated nature of the data using the SNP spectral decomposition method proposed by Nyholt [25] and modified by Li and Ji [26].

Survival Analysis.

Survival months were calculated based on month and year of diagnosis and month and year of death or date of last contact by the cancer registries. Associations between SNPs and risk of dying of breast cancer among primary invasive cases were evaluated using Cox proportional hazards models to obtain multivariate hazard ratios (HR) and 95% CI among all cases and by genetic ancestry strata. The upper two ancestry strata were combined to evaluate survival by ancestry groups since survival data were not available for the Mexico study site. In the analysis of breast cancer survival, individuals were censored when they died of causes other than breast cancer or were lost to follow-up. In addition to the minimal adjustments for age, study center, genetic ancestry, BMI during referent year, and parity, models were also adjusted for SEER summary stage.

ARTP Analysis.

We used the adaptive rank truncated product (ARTP) method that is based on a highly efficient permutation algorithm to determine the significance of association of each gene and of the pathway with breast cancer overall, by genetic ancestry, and by ER/PR strata. The gene p values were generated using the ARTP package in R, permuting outcome status 10,000 times while adjusting for age, BMI during referent year, and genetic ancestry [27,28]. The ARTP method was also applied to survival data using Cox proportional hazard models in R with an additional adjustment for SEER summary stage to generate p values based on likelihood ratio tests. The survival outcome (i.e., vital status and survival months) was permuted 10,000 times in R. We report both pathway and gene p values (PARTP).

Results

The overall selenoprotein pathway PARTP was not significantly associated with breast cancer risk (PARTP = 0.69), and the association with only one gene, GPX3, was of borderline significance for the entire population (PARTP =0.09). However, we observed several significant associations prior to adjustment for multiple comparisons between selenoprotein SNPs and breast cancer risk (Table 1). GPX3 rs8177447 (p=0.009, padj=0.02) was associated with breast cancer risk for the entire population. GPX3 was marginally significant among individuals with ≤28% NA ancestry (PARTP =0.06) and SEPP1 was significant among those with >28% NA ancestry (PARTP =0.002). The overall pathway was statistically significant (PARTP =0.04) among women with higher NA ancestry mainly because of the strong association with SEPP1 and breast cancer risk. GPX3 rs8177447 (p=0.014, padj =0.035) and SELS rs4965814 (p=0.046, padj =0.06) were significant among individuals of <28% NA ancestry; SEPP1 rs230812 (p=0.025, padj = 0.04), and SEPP1 rs6865453 (p=0.002, padj = 0.005) were associated with breast cancer risk in individuals of 28-70% NA ancestry; and SEPN1 rs718391 (p=0.02, padj =0.09) was marginally associated with breast cancer risk among women of >70% NA ancestry. Associations were similar for women with >28% NA ancestry for SEPP1 rs230812 and rs6865453; when combining the upper ancestry groups these two SNPs were statistically significant (OR=1.33, 95% CI 1.09,1.64 and OR=0.80, 95% CI 0.66,0.96, respectively). Despite apparent differences between SNP associations and breast cancer risk among different NA ancestry groups, only SEPN1 rs718391 (p=0.03 padj =0.10), SEPP1 rs230812 (p=0.005, padj = 0.008), and SEPP1 rs6865453 (p=<0.001, padj = 0.002) showed significant interaction by ancestry.

Everyone0 - 28% Native American Ancestry>28 - 70% Native American Ancestry>70 - 100% Native American AncestryInteraction P-value
ControlsCasesOR1(95% CI)PARTPControlsCasesOR1(95% CI)PARTPControlsCasesOR1(95% CI)PARTPControlsCasesOR1(95% CI)PARTP
GPX3 (rs8177447)0.090.060.670.530.38
CC/CT407134691.00179816751.00164813591.006254351.00
TT781001.50(1.11,2.03)44671.63(1.11,2.40)30311.34(0.80,2.24)420.72(0.13,4.05)
P-value (raw; adjusted)0.009, 0.020.014, 0.0350.27, 0.630.71, 1.00
SELS (rs4965814)0.700.430.310.580.14
TT20421833112161136161655912101381
TC166513620.98(0.89,1.08)5645220.99(0.86,1.14)8156260.88(0.75,1.03)2862141.21(0.91,1.61)
CC4433731.05(0.89,1.23)62831.41(1.00,1.98)2482050.93(0.75,1.17)133851.04(0.73,1.49)
P-value (raw; adjusted) 0.58, 0.980.050, 0.0670.55, 0.900.83, 1.00
SEPN1 (rs718391)0.460.270.730.160.034
CC/CG356230241.00148813691.00149612391.005784161.00
GG5855401.03(0.91,1.18)3543721.13(0.96,1.33)1811470.98(0.78,1.24)50210.54(0.31,0.92)
P-value (raw; adjusted) 0.62, 1.000.14, 0.290.89, 0.890.024, 0.09
SEPP1 (rs230812)20.160.570.010.15
AA145612221.004885011.006345041.003342171.000.005
AC189815740.96(0.86,1.06)9218190.87(0.75,1.02)7556030.99(0.84,1.16)2221521.08(0.82,1.42)
CC6055891.09(0.95,1.26)3603360.90(0.74,1.10)2002101.30(1.03,1.64)45431.45(0.91,2.30)
P-value (raw; adjusted) 0.21, 0.330.31. 0.790.025, 0.0390.12, 0.16
SEPP1 (rs6865453)3<.001
AA146313871.008978341.004814761.0085771.00
AC/CC240019370.90(0.81,0.99)8328021.03(0.90,1.18)10668110.78(0.66,0.91)5023240.73(0.52,1.04)
P-value (raw; adjusted) 0.037, 0.100.65, 1.000.002, 0.0050.08, 0.16
TXNRD1 (rs4964287)0.460.450.690.340.27
CC/CT388432861.00167215611.00159713031.006154221.00
TT2642831.20(1.00,1.43)1701811.13(0.91,1.42)81871.27(0.93,1.75)13151.52(0.70,3.30)
P-value (raw; adjusted) 0.046, 0.230.26, 0.790.14, 0.650.29, 0.54
TXNRD2 (rs2073750)0.250.690.540.330.99
GG216918681.00108710301.008096421.002731961.00
GA166414131.02(0.93,1.12)6516131.00(0.87,1.16)7326071.06(0.91,1.23)2811931.00(0.77,1.31)
AA3112841.13(0.95,1.35)104960.97(0.73,1.30)1321401.38(1.06,1.79)75481.00(0.66,1.52)
P-value (raw; adjusted) 0.17, 1.000.85, 1.000.018, 0.270.99, 1.00
TXNRD2 (rs5992493)0.64
AA/AG405734541.00178916761.00164613501.006224281.00
GG921151.42(1.08,1.88)52661.35(0.93,1.95)33401.43(0.89,2.30)791.63(0.59,4.48)
P-value (raw; adjusted) 0.013, 0.200.12, 1.000.14, 1.000.35, 1.00
TXNRD2 (rs3788314)0.33
GG11589411.005324891.004433401.001831121.00
GA207117701.06(0.95,1.18)9108380.98(0.84,1.15)8317131.12(0.94,1.33)3302191.09(0.81,1.47)
AA9188531.14(1.00,1.30)3994121.10(0.92,1.33)4033351.08(0.88,1.33)1161061.42(0.99,2.04)
P-value (raw; adjusted) 0.04, 0.610.31, 1.000.47, 1.000.055, 0.78
TXNRD2 (rs3788317)0.34
GG235619791.00109410171.009257441.003372181.00
GT155413551.04(0.95,1.15)6516331.03(0.90,1.19)6565391.03(0.88,1.20)2471831.15(0.88,1.49)
TT2392351.20(0.99,1.45)97921.02(0.75,1.37)971071.37(1.02,1.84)45361.23(0.76,1.99)
P-value (raw; adjusted) 0.07, 0.850.91, 1.000.039, 0.540.40, 1.00

Table 1. Associations between selenoprotein genes and breast cancer risk by genetic ancestry.

1Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study, BMI during referent year, parity, and genetic ancestry (continuous).
2Association for SEPP1 rs230812 was 1.33 (95% CI 1.09,1.64; p = 0.006) for the CC genotype for women with >28% NA ancestry. pathway PARTP is 0.04
3Association for SEPP1 rs6865453 was 0.78 (95%CI 0.67,0.90; p<0.001) for AC/CC genotype for women with >28% NA ancestry. SEPP1 PARTP for >28% NA ancestry is 0.002 and pathway PARTP is 0.04
CSV
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Assessment of SNP associations by ER/PR status indicated that some SNPs were associated with breast cancer risk by ER/PR status; several genes had statistically significant PARTP values (Table 2). GPX1, GPX3, and SELS were associated with ER-/PR+ status (PARTP = 0.002, 0.05, and 0.01, respectively). GPX1 rs1800668 was strongly associated with increased likelihood of ER-/PR+ phenotype p<0.001; padj=0.002), while rs3448 was associated with a decreased likelihood of this phenotype (p=0.04). GPX2 rs11623705 was associated with increased risk of ER-/PR+ (p=0.002; padj =0.006). GPX3 rs8177447 showed a significant association with an increased likelihood of an ER-/PR+ phenotype (p=0.025; padj =0.07) as did SELS rs9874 (p=0.041; padj=0.04) and SELS rs4965814 (p=0.01; padj=0.02). SELS was also significantly associated with ER+/PR- tumors (PARTP = 0.009); SELS rs4965814 was positively associated with ER+/PR- (p=0.004; padj=0.006). Multiple SNPs in SEP15 showed an association with increased likelihood of ER+/PR- tumors: rs5859 (p=0.025; padj=0.04), rs486133 (p=0.05; padj=0.05), and rs1407131 (p=0.002; padj=0.006). TXNRD2 rs2073750 was marginally inversely associated with ER-/PR+ tumors (p=0.005; padj= 0.08) with an overall gene PARTP of 0.08. None of the genes evaluated showed a significant association with ER-/PR- tumor phenotype according to ARTP.

ControlsER+ / PR+ER+ / PR-ER- / PR+ER- / PR-
NNOR1(95% CI)PARTPNOR(95% CI)PARTPNOR(95% CI)PARTPNOR(95% CI)PARTPMultinomial p-value
GPX1 (rs1800668)0.940.740.0020.540.007
CC16086381.001071.00121.001981.00
CT11174250.91(0.79,1.06)730.93(0.68,1.27)192.60(1.24,5.47)1280.92(0.73,1.17)
TT191921.11(0.85,1.46)201.43(0.86,2.39)65.85(2.08,16.47)190.81(0.49,1.33)
P-value (raw; adjusted)0.44, 0.840.17, 0.32<.001, 0.0020.40, 0.75
GPX1 (rs3448)0.31
CC19847961.001481.00341.002671.00
CT/TT11815021.01(0.89,1.16)870.96(0.72,1.27)90.46(0.22,0.96)1480.94(0.75,1.16)
P-value (raw; adjusted)0.83, 0.840.77, 0.770.039, 0.0390.55, 0.75
GPX2 (rs11623705)0.310.710.300.230.61
GG250010151.001921.00361.003401.00
GT6282711.05(0.89,1.23)360.74(0.51,1.08)40.44(0.16,1.26)740.87(0.67,1.14)
TT36110.72(0.36,1.43)72.35(1.02,5.42)37.19(2.06,25.06)10.22(0.03,1.63)
P-value (raw; adjusted)0.35, 0.810.045, 0.130.002, 0.0060.14, 0.26
GPX3 (rs8177447)0.560.300.0480.170.056
CC22709261.001801.00241.002971.00
CT8273230.92(0.79,1.07)480.71(0.51,0.98)161.86(0.98,3.56)1000.91(0.72,1.16)
TT68491.79(1.23,2.62)71.38(0.62,3.08)34.12(1.19,14.24)181.94(1.13,3.31)
P-value (raw; adjusted)0.003, 0.0070.43, 0.680.025, 0.0660.016, 0.04
SELS (rs9874)0.210.0090.010.710.07
AA23559931.001681.00261.003051.00
AG/GG8103050.88(0.76,1.03)671.15(0.86,1.55)171.91(1.03,3.54)1091.03(0.81,1.30)
P-value (raw; adjusted)0.10, 0.160.35, 0.350.04, 0.040.83, 0.99
SELS (rs4965814)0.005
TT17167191.001081.00141.002151.00
TC/CC14505791.02(0.89,1.17)1271.51(1.15,2.00)292.37(1.22,4.59)1991.06(0.85,1.31)
P-value (raw; adjusted)0.81, 0.810.004, 0.0060.011, 0.0170.62, 0.99
SEP15 (rs5859)0.87
CC18787211.000.371311.000.84231.000.772181.000.92
CT9253761.04(0.89,1.20)540.82(0.59,1.14)131.19(0.60,2.38)1111.03(0.81,1.31)
TT109531.23(0.88,1.73)151.93(1.09,3.42)10.85(0.11,6.37)131.05(0.58,1.89)
P-value (raw; adjusted)0.23, 0.630.025, 0.040.87, 1.000.88, 1.00
SEP15 (rs486133)0.86
TT21388621.001601.00301.002881.00
TC9253851.02(0.88,1.17)610.88(0.64,1.19)120.95(0.48,1.87)1140.93(0.74,1.17)
CC103511.19(0.84,1.69)141.80(1.00,3.24)10.76(0.10,5.64)130.97(0.53,1.75)
P-value (raw; adjusted)0.32, 0.630.05, 0.050.79, 1.000.91, 1.00
SEP15 (rs1407131)0.69
TT24789871.001871.00361.003241.00
TC6482931.11(0.95,1.30)390.79(0.55,1.12)60.64(0.27,1.54)871.04(0.80,1.33)
CC40181.14(0.65,2.01)93.21(1.52,6.79)11.81(0.24,13.69)40.78(0.28,2.21)
P-value (raw; adjusted)0.64, 0.650.002, 0.0060.575, 1.000.64, 1.00
TXNRD1 (rs5018287)0.120.760.760.940.15
GG9134141.00681.00111.001211.00
GA15716310.87(0.75,1.01)1190.98(0.72,1.34)211.11(0.53,2.32)1980.95(0.75,1.21)
AA6782520.81(0.67,0.98)480.92(0.62,1.35)111.39(0.60,3.23)961.09(0.82,1.45)
P-value (raw; adjusted)0.030, 0.150.66, 1.000.45, 0.870.56, 1.00
TXNRD1 (rs7962759)0.41
CC22338731.001631.00321.002891.00
CG8483751.09(0.94,1.26)661.05(0.77,1.42)70.59(0.26,1.37)1131.04(0.82,1.31)
GG85501.43(1.00,2.06)60.96(0.41,2.24)43.60(1.21,10.73)131.21(0.66,2.21)
P-value (raw; adjusted)0.05, 0.210.92, 1.000.02, 0.110.54, 1.00
TXNRD2 (rs1044732)0.360.930.080.800.20
AA22288841.001481.00351.002641.00
AG/GG6892710.96(0.82,1.13)521.11(0.80,1.55)20.19(0.04,0.78)810.98(0.75,1.28)
P-value (raw; adjusted)0.66, 1.000.54, 1.000.02, 0.300.88, 1.00
TXNRD2 (rs3788305)0.28
AA9444231.00701.00161.001291.00
AG/GG22208750.87(0.75,1.00)1651.00(0.75,1.34)270.71(0.38,1.33)2860.94(0.75,1.17)
P-value (raw; adjusted)0.045, 0.680.99, 1.000.28, 1.000.58, 1.00
TXNRD2 (rs2073750)0.09
GG17137071.001251.00321.002231.00
GA/AA14485901.01(0.89,1.16)1081.03(0.79,1.35)110.37(0.19,0.75)1910.99(0.80,1.22)
P-value (raw; adjusted)0.84, 1.000.81, 1.000.005, 0.080.91, 1.00

Table 2. Associations between selenoprotein genes and breast cancer risk by ER/PR tumor status.

1Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study, BMI during referent year, parity, and genetic ancestry (continuous); data from 4-CBCS and SFBCS
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Five SNPs, GPX3 rs2070593 (p=0.002), GPX4 rs2074451 (p=0.046), SELS rs9874 (p=0.029), TXNRD1 rs17202060 (p=0.007) and TXNRD2 rs7322262 (p=0.025), significantly interacted with DOBS to alter breast cancer risk (Table 3). Of these SNPs, only TXNRD2 rs732262 (padj =0.38) did not remain statistically significantly associated with breast cancer risk after multiple comparison adjustment.

Dietary Oxidative Balance Score1
Quartile 1Quartile 2Quartile 3Quartile 4Interaction P-value
ControlsCasesOR2(95% CI)ControlsCasesOR(95% CI)ControlsCasesOR(95% CI)ControlsCasesOR(95% CI)raw, adjusted
GPX3 (rs2070593)0.002, 0.005
GG5805891.005965210.88(0.75, 1.04)6955420.79(0.67, 0.93)5064630.92(0.78, 1.10)
GA/AA3503691.08(0.89, 1.30)3373371.04(0.86, 1.26)5083810.79(0.66, 0.94)4212620.66(0.54, 0.80)
GPX4 (rs2074451)0.046, 0.046
GG/GT7417301.007596300.87(0.75, 1.01)9287380.83(0.72, 0.96)7055620.84(0.72, 0.97)
TT1441471.00(0.78, 1.29)1241431.13(0.87, 1.47)1991210.61(0.47, 0.78)1601100.68(0.52, 0.89)
SELS (rs9874)0.029, 0.048
AA6917401.007066560.89(0.76, 1.03)9267170.75(0.65, 0.86)7345580.73(0.62, 0.85)
AG2232160.88(0.71, 1.10)2151860.81(0.64, 1.01)2701920.67(0.54, 0.83)1801520.81(0.63, 1.03)
GG1660.34(0.13, 0.88)18190.95(0.49, 1.83)10161.45(0.65, 3.24)17181.02(0.52, 2.00)
TXNRD1 (rs17202060)0.007, 0.034
CC/CT7638411.007887210.85(0.74, 0.98)10187780.71(0.62, 0.82)7816050.72(0.62, 0.83)
TT1651210.68(0.52, 0.88)1521400.86(0.67, 1.11)1881470.76(0.60, 0.97)1501230.81(0.62, 1.05)
TXNRD2 (rs732262)0.025, 0.378
GG6636441.006815660.87(0.75, 1.02)8216330.82(0.70, 0.95)6125070.87(0.74, 1.02)
GA2072101.08(0.86, 1.35)1821871.14(0.90, 1.44)2762060.82(0.66, 1.02)2331500.73(0.58, 0.93)
AA15231.78(0.91, 3.46)20201.14(0.60, 2.16)30200.75(0.42, 1.34)20150.83(0.42, 1.64)

Table 3. Associations between dietary oxidative balance score (DOBS) and selenoprotein genes and risk of breast cancer.

1DOBS consists of alcohol (pro-oxidant), vitamins C and E, beta carotene, folic acid, and dietary fiber (anti-oxidants).
2Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, genetic ancestry, study center, BMI during referent year, and parity.
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Multiple genes and SNPs in our analysis showed an association with breast cancer survival (Table 4). GPX4 was significantly associated with better breast cancer survival among those with the highest NA ancestry (PARTP = 0.05). GPX4GT/TT rs2074451 showed a marginal inverse association with survival for individuals among women with >28% NA ancestry (p=0.055; padj = 0.055; p interaction=0.06). Several SNPs in TXNRD2 were associated with survival prior to adjustment for multiple comparisons. Additionally, TXNRD2AA rs3788314 and TXNRD2CT/TT rs4333017 showed significant interaction across genetic ancestry (pint=0.035 and pint=0.017, respectively).

Overall0 - 28% Native American Ancestry29 - 100% Native American Ancestry
Death/Person YearsHR1(95% CI)PARTPDeath/Person YearsHR(95% CI)PARTPDeath/Person YearsHR(95% CI) PARTPInteraction P-value
GPX4 (rs2074451)0.460.55d0.050.06
GG 69 / 5878 1.00 36 / 34761.00 33 / 24021.00
GT/TT 127 / 12780 0.90(0.67,1.21) 91 / 85141.13(0.76,1.67) 36 / 42650.62(0.39,1.01)
P-value (raw, adjusted) 0.50, 0.500.55, 0.550.06, 0.06
SEPW1 (rs3786777)0.280.060.810.13
TT 57 / 6219 1.00 26 / 3389 1.00 31 / 2830 1.00
TG/GG 169 / 15030 1.28(0.94,1.73) 107 / 9331 1.65(1.07,2.54) 62 / 5700 0.99(0.64,1.53)
P-value (raw, adjusted) 0.11, 0.210.02, 0.040.97, 0.97
TXNRD2 (rs9606173)0.240.760.090.63
AA 168 / 14566 1.00 105 / 9429 1.00 63 / 5137 1.00
AT/TT 61 / 6816 0.73(0.54,0.99) 30 / 3355 0.80(0.53,1.20) 31 / 3462 0.68(0.44,1.06)
P-value (raw, adjusted) 0.04, 0.570.28, 1.000.09, 0.97
TXNRD2 (rs732262)0.15
GG 164 / 14336 1.00 111 / 10086 1.00 53 / 4250 1.00
GA/AA 32 / 4322 0.63(0.43,0.94) 16 / 1905 0.87(0.52,1.48) 16 / 2417 0.47(0.27,0.83)
P-value (raw, adjusted) 0.02, 0.330.62, 1.000.01, 0.15
TXNRD2 (rs3788314)
GG 69 / 5595 1.00 39 / 3530 1.00 30 / 2065 1.000.035
GA 108 / 10911 0.86(0.63,1.16) 58 / 6296 0.86(0.57,1.30) 50 / 4614 0.84(0.52,1.34)
AA 51 / 4836 0.91(0.63,1.31) 38 / 2931 1.26(0.80,1.99) 13 / 1905 0.50(0.26,0.97)
P-value (raw, adjusted) 0.61, 1.00 .0.31, 1.000.04, 0.52
TXNRD2 (rs3788317)0.04
GG 133 / 11991 1.00 74 / 7409 1.00 59 / 4582 1.00
GT 84 / 8163 0.94(0.71,1.24) 51 / 4744 1.04(0.72,1.49) 33 / 3419 0.82(0.53,1.26)
TT 12 / 1228 0.86(0.48,1.56) 10 / 630 1.58(0.81,3.07) 2 / 598 0.27(0.06,1.10)
P-value (raw, adjusted) 0.62, 1.000.18, 1.000.07, 0.81
TXNRD2 (rs4333017)
CC 180 / 16994 1.00 107 / 9640 1.00 73 / 7354 1.000.017
CT/TT 49 / 4388 1.13(0.82,1.56) 28 / 3143 0.85(0.56,1.29) 21 / 1245 1.86(1.13,3.06)
P-value (raw, adjusted) 0.46, 1.000.43, 1.000.02, 0.22

Table 4. Association between selenoprotein genes and survival by ancestry.

1Hazard Ratios (HR) and 95% Confidence Intervals (CI) among primary invasive cases; adjusted for age, study, BMI during referent year, parity, genetic ancestry, and SEER summary stage. Includes data from the 4-CBCS and SFBCS.
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Discussion

Based on ARTP results, GPX3 was borderline statistically significantly associated with breast cancer risk for all women and for women with lower levels of NA ancestry specifically. SEPP1 showed a statistically significant interaction by NA ancestry, with a strong association with breast cancer risk among women with higher NA ancestry. Some differences in association were observed by ER/PR tumor status. GPX1, GPX3, and SELS were significantly associated with ER-/PR+ tumors and SELS was significantly associated with ER+/PR- tumors. GPX4 was significantly associated with survival among those with higher NA ancestry and SEPW1 was marginally associated with survival among women in the low NA ancestry group. Although we hypothesized that DOBS would modify associations with selenoprotein genes, only one SNP in GPX4, SELS, and TXNRD1 that interacted with DOBS remained statistically significantly associated with breast cancer after adjustment for multiple comparisons.

Among the genes evaluated in this study, only GPX3 showed a borderline significant association overall as determined by the PARTP, and GPX3 rs8177447 was statistically significant after adjustment for multiple comparisons. This SNP is in high LD with rs3792797 and has been associated with Barrett’s Esophagus [29] (see table 5 for comparison of findings of this study to other information on SNPs). GPX3 is one of multiple glutathione peroxidases, all of which are selenoproteins that play a role in catalyzing the reduction of hydrogen peroxides to minimize oxidative stress which can damage cells [10]. This enzyme acts as an efficient antioxidant in the plasma and has previously been linked to other diseases associated with oxidative stress [10]. Our study indicates that in addition to an important role in development of these cancers, GPX3 may also play a role in the development and progression of breast cancer.

GeneSNPLocation/Coding type   Functional SNPLD1 r2
GPX3rs8177447intronic enhancerrs4958872 [6,29] 0.67
rs3792797 [6]0.95
rs3828599 [47] NA
rs3805435 [47]NA
GPX4rs20744513' downstreamrs713041 [3,40,42,48,49] 0.59
rs2075710 [36] 0.27
rs2074452 [36] 0.34
rs757229 [42] 0.72
SELSrs4965814intronicrs4965373 [40] 0.08
rs34713741 [4] 0.001
SEPP1 rs2308123' downstreamrs3877899[3,34,47,48] NS0.31
rs7579 [3,34,49] 0.34
rs230813 [4,38] 1.00
rs230819 [4,38]0.70
rs11959466 [35] 0.04
rs13168440 [35]0.19
rs3797310 [36] 0.32
rs12055266 [37,47] 0.32
rs68654535' upstreamrs3877899 NS0.14
promoterrs75790.96
/regulatory regionrs230813 0.32
rs2308190.42
rs119594660.12
rs131684400.09
rs37973101.00
rs12055266 0.92
SEPW1rs3786777intronic enhancer NA
TXNRD1rs4964287splicing regulationrs1128446 [36] 0.71
coding; synonymousrs4964785 [36]0.52
rs7310505 [3] 0.08
TXNRD2rs9606173intronic enhancerrs9605031 [40]0.02
rs1139793 [50] NS0.07
rs5992495 NS0.006
rs5748469 NS0.003
rs732262intronic enhancerrs9605031 0.11
rs1139793 NS 0.13
rs5992495 NS0.12
rs5748469 NS0.18
rs3788314intronicrs9605031 0.27
rs1139793 NS<0.001
rs5992495 NS0.28
rs5748469 NS0.18
rs3788317intronicrs9605031 0.10
rs1139793 NS0.03
rs5992495 NS0.53
rs5748469 NS0.001
rs5992493intronic enhancerrs9605031 0.05
rs1139793 NS0.12
rs5992495 NS1.00
rs5748469 NS0.12
rs4333017intronicrs9605031 0.003
rs1139793 NS0.006
rs5992495 NS0.04
rs5748469 NS0.001

Table 5. LD comparison of SNPs associated with breast cancer risk or with survival to other SNPs reported in literature as associated with cancer, serum markers, gene expression, or identified as being non-synonymous (NS) coding SNPs.

1LD determined for SNPs using SNAP, from the Broad Institute, with the 1000 genome data option; in some instance LD results were not available (NA)
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Other studies have indicated that glutathione peroxidases may be associated with breast cancer risk, specifically GPX1 [14,30], a cytosolic antioxidant [31]. A meta-analysis of six case-control studies of the Pro198Leu polymorphism (rs1050450) in GPX1, did not see an association between breast cancer risk in Caucasians, although they did see a strong increased risk of breast cancer among African women [32]. Likewise, Cox and colleagues did not see an association between this SNP and breast cancer risk [33]. In a recent study by Meplan, GPX1 rs1050450 was shown to interact with hormone therapy to alter risk of breast cancer [34]. Our study did not show an association with breast cancer risk and GPX1. We did however, detect an association between GPX1 and ER-/PR+ breast tumors; however this represents a small group of women and could be a chance finding.

SEPP1 SNPs have been associated with a variety of cancers, including prostate [35,36], lung [2], and colorectal [3,37] cancer. Therefore, we evaluated three candidate SEPP1 SNPs that have been associated with oxidative stress and cancer [6,38] and are in high LD with other functional SNPs (see Table 5). SEPP1 is the major selenoprotein in plasma, acting as a selenium transport protein [13]. SEPP1 has been shown to behave as an antioxidant [13] and estrogen has been shown to increase hepatic SEPP1 concentrations [39], providing biological support for the observed associations between SEPP1 SNPs and cancer risk. Support for SEPP1 as an antioxidant comes from earlier findings that in human plasma the SEPP1 protein is involved in the degradation of peroxynitrite, which plays a role in inflammatory toxicity [31]. Additionally, associations between serum selenium levels and thioredoxin reductase activity have been correlated with SEPP1 rs3877899 [40], thereby establishing a further link between SEPP1 and the antioxidant activities of selenoproteins. Our analysis failed to find an association between our candidate SNP, SEPP1 rs3877899, that was previously linked to breast cancer [26] and is a non-synonymous coding SNP. In our analysis of two other SEPP1 SNPs, rs230812 and rs6865453, we found an association with breast cancer risk among women with higher NA ancestry. SEPP1 rs230812 is in high LD with rs230813 and rs230819 (see Table 5) which have been associated with oxidative stress [6,38]. SEPP1 was the only significant gene associated with breast cancer risk among women with greater NA ancestry which was significantly different than the risk observed for women with low NA ancestry. In this study, a large percentage of women with greater NA ancestry were part of the Mexico City Breast Cancer Study and it is possible that differences in selenium levels in food could exist between those women and women in the United States. If women from Mexico had lower serum selenium it is possible that SEPP1 could have a greater effect on risk.

We found that some selenoproteins were associated with tumor ER/PR status. Based on our analysis of gene PARTP, GPX1, GPX3, and SELS were associated with ER-/PR+ tumor status, while SELS was also associated with ER+/PR- tumors. Additionally, several individual SNPs were associated with ER/PR status after adjusting for multiple comparisons. An earlier study found that glutathione peroxidase expression was associated with PR- status, as well as increased patient mortality [41]. The differences in results may be explained by the fact that the earlier study looked solely at expression levels of glutathione peroxidases, while our study looked at gene and SNP interactions without looking at expression of proteins. Other studies have shown links between selenoproteins and estrogen. GPX1 messenger RNA has been shown to be up-regulated in the presence of estrogen [42]. TXNRD1 has been shown to be an important modular of estrogen signaling through the estrogen receptor response elements [43]

We also observed associations between selenoprotein SNPs and survival. Notably, GPX4 rs2074451 showed marginally significant interaction with NA ancestry and having a T allele was associated with decreased likelihood of dying from breast cancer among women with >28% NA ancestry. This is in agreement with study by Udler and colleagues [42], where they reported that GPX4 rs757229 and rs713041 were associated with a greater risk of all-cause mortality after diagnosis with breast cancer. GPX4 rs2074451 is highly correlated with these SNPs (see Table 5). Additionally, three SNPs in TXNRD2 (rs3788314, rs3788317, and rs4333017) showed significant differences in survival by ancestry. TXNRD2 has been associated with oxidative stress and our previous analysis of dietary factors of oxidative stress found the strongest associations among women with higher NA ancestry [22]. Udler did not observe a significant association between any of the TXNRD1 or TXNRD2 SNPs and breast cancer survival [42].

Given their role as antioxidants and mediators of oxidative stress, we evaluated selenoprotein SNPs for interactions with DOBS and found that GPX3 rs2070593, GPX4 rs2074451, SELS rs9874, and TXNRD1 rs17202060 showed significant interactions with DOBS after adjusting for multiple comparisons. Oxidative stress and high levels of reactive oxidative species have been suggested to play an important role in breast cancer development because free radicals damage DNA, thereby decreasing genomic integrity [44]. Our observed interactions with DOBS indicate that high DOBS may reduce breast cancer risk in individuals with high-risk genotypes.

The selenoprotein genes analyzed in this study were selected due to previous studies reporting on their roles in regulating oxidative stress and/or carcinogenesis; however, the majority of the genes and SNPs had not been studied in relation to breast cancer. Table 5 compares SNPs associated with breast cancer risk and survival in our study, to those reported in the literature in alter cancer risk, influence oxidative stress, or influence gene expression. Selenium levels have been associated with breast cancer [45], along with SNPs in GPX1 [14] and GPX4 [46]. We did not find evidence for an association with breast cancer risk for these particular glutathione peroxidases, yet we found that GPX3 was associated marginally with breast cancer risk and SEPP1 was associated with risk among women with higher NA ancestry. GPX4 was associated with breast cancer survival. Glutathione peroxidases carry out similar functions and have similar mechanisms; they contain a conserved catalytic triad of Sec, Gln, and Trp that acts by sequential oxidation and reduction of the Sec residue during catalysis [10]. The primary difference between the different glutathione peroxidases appears to be tissue distribution and cellular location; therefore, it is highly likely that multiple glutathione peroxidases participate in the antioxidant defense system against oxidative damage.

Our study has two primary limitations. First, we only evaluated a subset of the known selenoproteins and did not evaluate any DIOs or SPS2s. Since these selenoproteins, along with others, play a role in limiting oxidative stress it is possible that they may be associated with breast cancer risk. Our second limitation was in our analysis of ER/PR status and survival where we were unable to include data from Mexico. While our study population was large, the sample sizes for the different ER/PR subtypes were small, thereby decreasing the statistical power of our analysis. Nevertheless, our study has multiple strengths: our analytic approach that evaluated the pathway as a whole, and our analysis of genes beyond individual SNPs via PARTP. These strengths allowed us to show that both GPX3 and SEPP1 were associated with breast cancer; these associations warrant further study in other populations. An additional strength is our genetically admixed population that allowed us to evaluate associations across the spectrum of European to Native ancestry.

While we observed few significant associations between selenoprotein genes and breast cancer risk, GPX3 was marginally significant among women with lower NA ancestry and SEPP1 was statistically significant among women with higher NA ancestry. Additionally, several genes were associated with ER/PR status. While we hypothesized that selenoprotein genes would interact with DOBS, only four SNPs significantly interacted with DOBS after adjustment for multiple comparisons. In conclusion, this study provides limited support for an association between selenoprotein genes and risk of breast cancer.

Supporting Information

Table S1.

List of SNPs assessed.

https://doi.org/10.1371/journal.pone.0080554.s001

(DOCX)

Table S2.

MAF and HWE by level of Native American ancestry.

https://doi.org/10.1371/journal.pone.0080554.s002

(DOCX)

Acknowledgments

We would like to acknowledge the contributions of the following individuals to the study: Sandra Edwards for data harmonization oversight; Jennifer Herrick for data management and data harmonization; Erica Wolff and Michael Hoffman for laboratory support; Carolina Ortega for her assistance with data management for the Mexico Breast Cancer Study, Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study; Dr. Tim Byers for his contribution to the 4-Corners Breast Cancer Study; and Dr. Josh Galanter for assistance in selection of AIMs markers.

Author Contributions

Conceived and designed the experiments: MLS EMJ RKW. Performed the experiments: AJP RKW. Analyzed the data: AL MLS. Contributed reagents/materials/analysis tools: MLS EMJ GT KBB ARG LH. Wrote the manuscript: AJP. Edited and reviewed manuscript: MLS EMJ GT RKW LH KBB ARG AL.

References

  1. 1. Ferguson LR, Karunasinghe N (2011) Nutrigenetics, nutrigenomics, and selenium. Front Genet 2: 15. PubMed: 22303312.
  2. 2. Gresner P, Gromadzinska J, Jablonska E, Kaczmarski J, Wasowicz W (2009) Expression of selenoprotein-coding genes SEPP1, SEP15 and hGPX1 in non-small cell lung cancer. Lung Cancer 65: 34-40. doi:https://doi.org/10.1016/j.lungcan.2008.10.023. PubMed: 19058871.
  3. 3. Méplan C, Hughes DJ, Pardini B, Naccarati A, Soucek P et al. (2010) Genetic variants in selenoprotein genes increase risk of colorectal cancer. Carcinogenesis 31: 1074-1079. doi:https://doi.org/10.1093/carcin/bgq076. PubMed: 20378690.
  4. 4. Sutherland A, Kim DH, Relton C, Ahn YO, Hesketh J (2010) Polymorphisms in the selenoprotein S and 15-kDa selenoprotein genes are associated with altered susceptibility to colorectal cancer. Genes Nutr 5: 215-223. doi:https://doi.org/10.1007/s12263-010-0176-8. PubMed: 21052528.
  5. 5. Zhuo P, Goldberg M, Herman L, Lee BS, Wang H et al. (2009) Molecular consequences of genetic variations in the glutathione peroxidase 1 selenoenzyme. Cancer Res 69: 8183-8190. doi:https://doi.org/10.1158/0008-5472.CAN-09-1791. PubMed: 19826042.
  6. 6. Takata Y, King IB, Lampe JW, Burk RF, Hill KE et al. (2012) Genetic variation in GPX1 is associated with GPX1 activity in a comprehensive analysis of genetic variations in selenoenzyme genes and their activity and oxidative stress in humans. J Nutr 142: 419-426. doi:https://doi.org/10.3945/jn.111.151845. PubMed: 22259188.
  7. 7. Irons R, Tsuji PA, Carlson BA, Ouyang P, Yoo MH et al. (2010) Deficiency in the 15-kDa selenoprotein inhibits tumorigenicity and metastasis of colon cancer cells. Cancer Prev Res (Phila) 3: 630-639. doi:https://doi.org/10.1158/1940-6207.CAPR-10-0003.
  8. 8. Moghadaszadeh B, Beggs AH (2006) Selenoproteins and their impact on human health through diverse physiological pathways. Physiology (Bethesda) 21: 307-315. doi:https://doi.org/10.1152/physiol.00021.2006. PubMed: 16990451.
  9. 9. Cheng Q, Sandalova T, Lindqvist Y, Arnér ES (2009) Crystal structure and catalysis of the selenoprotein thioredoxin reductase 1. J Biol Chem 284: 3998-4008. PubMed: 19054767.
  10. 10. Papp LV, Lu J, Holmgren A, Khanna KK (2007) From selenium to selenoproteins: synthesis, identity, and their role in human health. Antioxid Redox Signal 9: 775-806. doi:https://doi.org/10.1089/ars.2007.1528. PubMed: 17508906.
  11. 11. Ganther HE (1999) Selenium metabolism, selenoproteins and mechanisms of cancer prevention: complexities with thioredoxin reductase. Carcinogenesis 20: 1657-1666. doi:https://doi.org/10.1093/carcin/20.9.1657. PubMed: 10469608.
  12. 12. Zhu X, Huang C, Peng B (2011) Overexpression of thioredoxin system proteins predicts poor prognosis in patients with squamous cell carcinoma of the tongue. Oral Oncol 47: 609-614. doi:https://doi.org/10.1016/j.oraloncology.2011.05.006. PubMed: 21652258.
  13. 13. Zhuo P, Diamond AM (2009) Molecular mechanisms by which selenoproteins affect cancer risk and progression. Biochim Biophys Acta 1790: 1546-1554. doi:https://doi.org/10.1016/j.bbagen.2009.03.004. PubMed: 19289153.
  14. 14. Ravn-Haren G, Olsen A, Tjønneland A, Dragsted LO, Nexø BA et al. (2006) Associations between GPX1 Pro198Leu polymorphism, erythrocyte GPX activity, alcohol consumption and breast cancer risk in a prospective cohort study. Carcinogenesis 27: 820-825. PubMed: 16287877.
  15. 15. Arbogast S, Beuvin M, Fraysse B, Zhou H, Muntoni F et al. (2009) Oxidative stress in SEPN1-related myopathy: from pathophysiology to treatment. Ann Neurol 65: 677-686. doi:https://doi.org/10.1002/ana.21644. PubMed: 19557870.
  16. 16. Pellatt AJ, Wolff RK, Torres-Mejia G, John EM, Herrick JS et al. (2013) Telomere length, telomere-related genes, and breast cancer risk: The breast cancer health disparities study. Genes Chromosomes Cancer 52: 595-609. PubMed: 23629941.
  17. 17. Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Herrick JS et al. (2012) Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis 33: 1512-1521. doi:https://doi.org/10.1093/carcin/bgs163. PubMed: 22562547.
  18. 18. Vogt TM, Ziegler RG, Patterson BH, Graubard BI (2007) Racial differences in serum selenium concentration: analysis of US population data from the Third National Health and Nutrition Examination Survey. Am J Epidemiol 166: 280-288. doi:https://doi.org/10.1093/aje/kwm075. PubMed: 17557900.
  19. 19. Slattery ML, Sweeney C, Edwards S, Herrick J, Baumgartner K et al. (2007) Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res Treat 102: 85-101. doi:https://doi.org/10.1007/s10549-006-9292-y. PubMed: 17080310.
  20. 20. John EM, Horn-Ross PL, Koo J (2003) Lifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer study. Cancer Epidemiol Biomarkers Prev 12: 1143-1152. PubMed: 14652273.
  21. 21. John EM, Phipps AI, Davis A, Koo J (2005) Migration history, acculturation, and breast cancer risk in Hispanic women. Cancer Epidemiol Biomarkers Prev 14: 2905-2913. doi:https://doi.org/10.1158/1055-9965.EPI-05-0483. PubMed: 16365008.
  22. 22. Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Lewinger JP et al. (2013) Angiogenesis genes, dietary oxidative balance, and breast cancer risk and progression: The breast cancer health disparities study. Int J Cancer.
  23. 23. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567-1587. PubMed: 12930761.
  24. 24. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945-959. PubMed: 10835412.
  25. 25. Nyholt DR (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 74: 765-769. doi:https://doi.org/10.1086/383251. PubMed: 14997420.
  26. 26. Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb) 95: 221-227. doi:https://doi.org/10.1038/sj.hdy.6800717. PubMed: 16077740.
  27. 27. Yu K, Li Q, Bergen AW, Pfeiffer RM, Rosenberg PS et al. (2009) Pathway analysis by adaptive combination of P-values. Genet Epidemiol 33: 700-709. doi:https://doi.org/10.1002/gepi.20422. PubMed: 19333968.
  28. 28. Kai Yu OL, Wheeler W (2011). ARTP Gene and Pathway p-values computed using the Adaptive Rank Truncated Product. 2.0.0 ed. pp. R package.
    • 29. Takata Y, Kristal AR, Santella RM, King IB, Duggan DJ et al. (2012) Selenium, selenoenzymes, oxidative stress and risk of neoplastic progression from Barrett's esophagus: results from biomarkers and genetic variants. PLOS ONE 7: e38612. doi:https://doi.org/10.1371/journal.pone.0038612. PubMed: 22715394.
    • 30. Hu YJ, Diamond AM (2003) Role of glutathione peroxidase 1 in breast cancer: loss of heterozygosity and allelic differences in the response to selenium. Cancer Res 63: 3347-3351. PubMed: 12810669.
    • 31. Behne D, Kyriakopoulos A (2001) Mammalian selenium-containing proteins. Annu Rev Nutr 21: 453-473. doi:https://doi.org/10.1146/annurev.nutr.21.1.453. PubMed: 11375445.
    • 32. Hu J, Zhou GW, Wang N, Wang YJ (2010) GPX1 Pro198Leu polymorphism and breast cancer risk: a meta-analysis. Breast Cancer Res Treat 124: 425-431. doi:https://doi.org/10.1007/s10549-010-0841-z. PubMed: 20306294.
    • 33. Cox DG, Hankinson SE, Kraft P, Hunter DJ (2004) No association between GPX1 Pro198Leu and breast cancer risk. Cancer Epidemiol Biomarkers Prev 13: 1821-1822. PubMed: 15533915.
    • 34. Meplan C, Dragsted LO, Ravn-Haren G, Tjonneland A, Vogel U et al. (2013) Association between Polymorphisms in Glutathione Peroxidase and Selenoprotein P Genes, Glutathione Peroxidase Activity, HRT Use and Breast Cancer Risk. PLOS ONE 8: e73316. doi:https://doi.org/10.1371/journal.pone.0073316.
    • 35. Penney KL, Li H, Mucci LA, Loda M, Sesso HD et al. (2013) Selenoprotein P genetic variants and mrna expression, circulating selenium, and prostate cancer risk and survival. Prostate 73: 700-705. doi:https://doi.org/10.1002/pros.22611. PubMed: 23129481.
    • 36. Geybels MS, Hutter CM, Kwon EM, Ostrander EA, Fu R et al. (2013) Variation in selenoenzyme genes and prostate cancer risk and survival. Prostate 73: 734-742. doi:https://doi.org/10.1002/pros.22617. PubMed: 23143801.
    • 37. Peters U, Chatterjee N, Hayes RB, Schoen RE, Wang Y et al. (2008) Variation in the selenoenzyme genes and risk of advanced distal colorectal adenoma. Cancer Epidemiol Biomarkers Prev 17: 1144-1154. doi:https://doi.org/10.1158/1055-9965.EPI-07-2947. PubMed: 18483336.
    • 38. Gentschew L, Bishop KS, Han DY, Morgan AR, Fraser AG et al. (2012) Selenium, selenoprotein genes and Crohn's disease in a case-control population from Auckland, New Zealand. Nutrients 4: 1247-1259. doi:https://doi.org/10.3390/nu4091247. PubMed: 23112913.
    • 39. Zhou X, Smith AM, Failla ML, Hill KE, Yu Z (2012) Estrogen status alters tissue distribution and metabolism of selenium in female rats. J Nutr Biochem 23: 532-538. doi:https://doi.org/10.1016/j.jnutbio.2011.02.008. PubMed: 21684133.
    • 40. Karunasinghe N, Han DY, Zhu S, Yu J, Lange K et al. (2012) Serum selenium and single-nucleotide polymorphisms in genes for selenoproteins: relationship to markers of oxidative stress in men from Auckland, New Zealand. Genes Nutr 7: 179-190. doi:https://doi.org/10.1007/s12263-011-0259-1. PubMed: 22139612.
    • 41. Jardim BV, Moschetta MG, Leonel C, Gelaleti GB, Regiani VR et al. (2013) Glutathione and glutathione peroxidase expression in breast cancer: An immunohistochemical and molecular study. Oncol Rep 30: 1119–1128. PubMed: 23765060.
    • 42. Udler M, Maia AT, Cebrian A, Brown C, Greenberg D et al. (2007) Common germline genetic variation in antioxidant defense genes and survival after diagnosis of breast cancer. J Clin Oncol 25: 3015-3023. doi:https://doi.org/10.1200/JCO.2006.10.0099. PubMed: 17634480.
    • 43. Damdimopoulos AE, Miranda-Vizuete A, Treuter E, Gustafsson JA, Spyrou G (2004) An alternative splicing variant of the selenoprotein thioredoxin reductase is a modulator of estrogen signaling. J Biol Chem 279: 38721-38729. doi:https://doi.org/10.1074/jbc.M402753200. PubMed: 15199063.
    • 44. Chua PJ, Yip GW, Bay BH (2009) Cell cycle arrest induced by hydrogen peroxide is associated with modulation of oxidative stress related genes in breast cancer cells. Exp Biol Med (Maywood) 234: 1086-1094. doi:https://doi.org/10.3181/0903-RM-98. PubMed: 19596828.
    • 45. Chen YC, Prabhu KS, Mastro AM (2013) Is selenium a potential treatment for cancer metastasis? Nutrients 5: 1149-1168. doi:https://doi.org/10.3390/nu5041149. PubMed: 23567478.
    • 46. Rayman MP (2009) Selenoproteins and human health: insights from epidemiological data. Biochim Biophys Acta 1790: 1533-1540. doi:https://doi.org/10.1016/j.bbagen.2009.03.014. PubMed: 19327385.
    • 47. Méplan C, Hesketh J (2012) The influence of selenium and selenoprotein gene variants on colorectal cancer risk. Mutagenesis 27: 177-186. doi:https://doi.org/10.1093/mutage/ger058. PubMed: 22294765.
    • 48. Steinbrecher A, Méplan C, Hesketh J, Schomburg L, Endermann T et al. (2010) Effects of selenium status and polymorphisms in selenoprotein genes on prostate cancer risk in a prospective study of European men. Cancer Epidemiol Biomarkers Prev 19: 2958-2968. doi:https://doi.org/10.1158/1055-9965.EPI-10-0364. PubMed: 20852007.
    • 49. Gautrey H, Nicol F, Sneddon AA, Hall J, Hesketh J (2011) A T/C polymorphism in the GPX4 3'UTR affects the selenoprotein expression pattern and cell viability in transfected Caco-2 cells. Biochim Biophys Acta 1810: 584-591. PubMed: 21459128.
    • 50. Edvardsen H, Landmark-Høyvik H, Reinertsen KV, Zhao X, Grenaker-Alnaes GI et al. (2013) SNP in TXNRD2 associated with radiation-induced fibrosis: a study of genetic variation in reactive oxygen species metabolism and signaling. Int J Radiat Oncol Biol Phys 86: 791-799. doi:https://doi.org/10.1016/j.ijrobp.2013.02.025. PubMed: 23597419.