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. 2019 Aug 20;11(8):1954. doi: 10.3390/nu11081954

Vitamin D-Related Genes, Blood Vitamin D Levels and Colorectal Cancer Risk in Western European Populations

Veronika Fedirko 1,2,*, Hannah B Mandle 1, Wanzhe Zhu 1, David J Hughes 3, Afshan Siddiq 4,5, Pietro Ferrari 6, Isabelle Romieu 6, Elio Riboli 7, Bas Bueno-de-Mesquita 8, Fränzel JB van Duijnhoven 8, Peter D Siersema 9, Anne Tjønneland 10, Anja Olsen 10, Vittorio Perduca 11,12,13, Franck Carbonnel 12,13,14, Marie-Christine Boutron-Ruault 12,13, Tilman Kühn 15, Theron Johnson 15, Aleksandrova Krasimira 16, Antonia Trichopoulou 17, Periklis Makrythanasis 17,18, Dimitris Thanos 17,18, Salvatore Panico 19, Vittorio Krogh 20, Carlotta Sacerdote 21, Guri Skeie 22, Elisabete Weiderpass 22,23,24,25,26, Sandra Colorado-Yohar 27,28,29, Núria Sala 30, Aurelio Barricarte 28,31, Maria-Jose Sanchez 28,32, Ramón Quirós 33, Pilar Amiano 28,34, Björn Gylling 35, Sophia Harlid 36, Aurora Perez-Cornago 37, Alicia K Heath 7, Konstantinos K Tsilidis 7,38, Dagfinn Aune 7,39,40, Heinz Freisling 6, Neil Murphy 6, Marc J Gunter 6, Mazda Jenab 6,*
PMCID: PMC6722852  PMID: 31434255

Abstract

Higher circulating 25-hydroxyvitamin D levels (25(OH)D) have been found to be associated with lower risk for colorectal cancer (CRC) in prospective studies. Whether this association is modified by genetic variation in genes related to vitamin D metabolism and action has not been well studied in humans. We investigated 1307 functional and tagging single-nucleotide polymorphisms (SNPs; individually, and by gene/pathway) in 86 vitamin D-related genes in 1420 incident CRC cases matched to controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. We also evaluated the association between these SNPs and circulating 25(OH)D in a subset of controls. We confirmed previously reported CRC risk associations between SNPs in the VDR, GC, and CYP27B1 genes. We also identified additional associations with 25(OH)D, as well as CRC risk, and several potentially novel SNPs in genes related to vitamin D transport and action (LRP2, CUBN, NCOA7, and HDAC9). However, none of these SNPs were statistically significant after Benjamini–Hochberg (BH) multiple testing correction. When assessed by a priori defined functional pathways, tumor growth factor β (TGFβ) signaling was associated with CRC risk (P ≤ 0.001), with most statistically significant genes being SMAD7 (PBH = 0.008) and SMAD3 (PBH = 0.008), and 18 SNPs in the vitamin D receptor (VDR) binding sites (P = 0.036). The 25(OH)D-gene pathway analysis suggested that genetic variants in the genes related to VDR complex formation and transcriptional activity are associated with CRC depending on 25(OH)D levels (interaction P = 0.041). Additional studies in large populations and consortia, especially with measured circulating 25(OH)D, are needed to confirm our findings.

Keywords: single nucleotide polymorphism (SNP), vitamin D, colorectal neoplasms, incidence

1. Introduction

Colorectal cancer (CRC) is the second most common cancer in men and women combined, with approximately 1.4 million new cases diagnosed in 2012 worldwide [1]. There is compelling observational evidence that low circulating vitamin D concentrations are associated with increased risk of incident CRC [2,3]. However, other human evidence is less convincing. A few Mendelian randomization (MR) studies did not support an association between vitamin D genetic score and CRC risk, but the genetic contribution to 25(OH)D is relatively small (7.5% as estimated based on genome-wide association studies (GWAS) on common SNPs [4]), possibly explaining the null findings [5,6]. Also, the relatively few randomized clinical trials (RCTs) of vitamin D supplementation and colorectal neoplasms have not shown statistically significant effects, but sample size, duration and timing of supplementation, issues with compliance and choice of study population, and the limited range of vitamin D exposures assessed may have contributed to the null results [7,8,9]. Finally, the benefits from vitamin D supplementation for the prevention of colorectal neoplasms may vary according to genetic variation in the vitamin D-related genes (e.g., vitamin D receptor (VDR) [10]).

Anti-neoplastic effects of vitamin D on colorectal tissue are also supported by the fact that the normal colorectal epithelium expresses the vitamin D receptor (VDR) and vitamin D metabolizing enzymes (CYP27B1 and CYP24A1) and, therefore, can locally produce and degrade the active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)2D), from 25-hydroxyvitamin D (25(OH)D) [11,12,13]. In the colorectum, the active metabolite of vitamin D, 1,25(OH)2D, exerts its anti-neoplastic effects by genomic (mediated by the VDR) and non-genomic mechanisms [14], including the regulation of over 200 vitamin D-responsive genes and rapid activation of intracellular signaling pathways, resulting in modulation of the cell cycle, bile acid degradation, immune response, growth factor signaling, and anti-inflammation [15].

Observational and RCT data suggest a potential vitamin D-colorectal neoplasms risk association is modified by polymorphisms in the vitamin D receptor (VDR) [10,16,17] and the vitamin D-binding protein gene (GC) [18]; however, only a few single nucleotide polymorphisms (SNPs) and a limited number of related pathways were considered. Novel evidence highlights a wide array of VDR binding sites across the human genome [19], and multiple pathways related to vitamin D effects [20]. Thus, it is plausible that the vitamin D–CRC risk association may be modulated by variation in a broad array of genes related to vitamin D metabolism (e.g. absorption, endogenous synthesis, transport, activation, and deactivation) and action (including transcriptional activity/post-transcriptional effects). All of these genes are polymorphic, but no studies to date have comprehensively investigated their individual and collective associations with CRC risk or circulating vitamin D levels. In consideration of these points, we investigated whether variation in genes related to vitamin D metabolism and transcriptional activity is related to circulating blood vitamin D levels, and whether genetic variation at the SNP, pathway and gene level, alone and in combination with circulating vitamin D levels, is associated with CRC risk in a large Western European prospective cohort study.

2. Materials and Methods

2.1. Study Population

We used a case-control design nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, a large prospective study with over 520,000 men and women aged 35–70 years enrolled from 23 centers in 10 Western European countries (Denmark, France, Greece, Germany, Italy, the Netherlands, Norway, Spain, Sweden, and United Kingdom). The methods of the EPIC study have been described in detail elsewhere [21,22]. Individuals who were eligible for the study were selected from the general population of a specific geographical area, town, or province. Exceptions included the French sub-cohort, which is based on members of the health insurance system or state-school employees, and the Utrecht (Netherlands) sub-cohort, which is based on women who underwent screening for breast cancer. Between 1992 and 1998, standardized lifestyle and personal history information, anthropometrics, and blood samples were collected from most participants at recruitment. Diet over the previous 1 year was measured at baseline by validated country-specific dietary questionnaires developed to ensure high compliance and better measures of local dietary habits [21]. Blood samples were stored at the International Agency for Research on Cancer (Lyon, France; −196 °C, in liquid nitrogen) for all countries except Denmark (−150 °C, in nitrogen vapor) and Sweden (in −80 °C freezers). The EPIC study was approved by the Ethical Review Board of the International Agency for Research on Cancer (IARC) and the Institutional Review Board of each participating EPIC center. Written consent was obtained from all EPIC participants at enrolment into the study.

2.2. Cancer Incidence and Vital Status Follow-Up

Cancer incidence was determined through record linkages with regional cancer registries (Denmark/Italy/the Netherlands/Norway/Spain/Sweden/United Kingdom; complete up to December 2006) or via a combination of methods, including the use of health insurance records, contacts with cancer and pathology registries, and active follow-up through study subjects and their next-of-kin (France/Germany/Naples/Greece; complete up to June 2010).

Vital status follow-up (98.5% complete) was collected by record linkage with regional and/or national mortality registries in all countries except France, Germany, and Greece, where data are collected through an active follow-up. Censoring dates for complete follow-up were between June 2005 and June 2009 in Denmark, the Netherlands, Spain, the United Kingdom, Sweden, Norway, and Italy. In Germany, Greece, and France follow-up was based on a combination of methods, including health insurance records, cancer and pathology registries, and active follow-up through study subjects and their next-of-kin. In these centers, the end of follow-up was defined as the last known date of contact, or the date of death whichever came first. The last update of endpoint information occurred between December 2007 and December 2009.

2.3. Nested Case-Control Design and Participant Selection

2.3.1. Case Ascertainment and Selection

CRC cases were selected among participants who developed colon (C18.0–C18.7, according to the ICD–10), rectum (C19–C20), and overlapping/unspecified origin tumors (C18.8 and C18.9). Cancers of the anus were excluded. CRC is defined as the combination of the colon and rectal cancers.

A total of 1420 first-time previously cancer-free colorectal cancer cases (colon cancer = 900; rectal cancer = 520) were identified. Cases were not selected from Norway (blood samples only recently collected; few colorectal cancers diagnosed after blood donation) and the Malmö center of Sweden. The number of cases for gene-environment analyses was 1176 because of missing, previously collected 25(OH)D measurements [23] (France = 6, Italy = 49, Spain = 30, UK = 27, The Netherlands = 8, Greece = 18, Germany = 21, and Sweden = 16).

2.3.2. Control Selection

Controls were selected (1:1) by incidence density sampling from all cohort members alive and not having a reported cancer at the time of diagnosis of the cases and were matched by age (±6 months at recruitment), sex, study center, time of the day at blood collection, and fasting status at the time of blood collection (less than three hours, three to six hours, and more than six hours). Women were further matched by menopausal status (pre-/post-/peri-menopausal, and unknown) and for pre-menopausal women, phase of menstrual cycle at time of blood collection and usage of postmenopausal hormone therapy at time of blood collection (yes/no, regardless of menopausal status). The additional matching criteria for women were required for other studies that were being carried out using the same matched case-control sets. One control sample failed the genotyping and was not included in the analysis, resulting in a total of 1419 controls. The number of controls for analyses involving 25(OH)D was 764 because of missing or unobtainable, previously collected 25(OH)D measurements [23] (France = 18, Italy = 69, Spain = 48, UK = 62, The Netherlands = 41, Greece = 23, Germany = 60, Sweden = 49, and Denmark = 328).

2.3.3. Blood 25-(OH)-Vitamin D Assessment

We previously measured blood concentrations of 25(OH)D using a commercially available enzyme immunoassay kit (OCTEIA 25-(OH)D Kit, Immuno Diagnostic Systems, Boldon, UK) at the Laboratory for Health Protection Research, National Institute for Public Health and the Environment, the Netherlands [23]. The kit is specific for 100% of 25-(OH)-vitamin D3 form and 75% of 25-(OH)-vitamin D2 form. The inter-assay coefficient of variation as determined with two kit control samples was minimal (5.9% at the level of 20.3 nmol/L and 5.4% at the level of 77.4 nmol/L). No significant between-day drift, time shifts, or other trends were observed and the percentage of variance attributable to batch-to-batch differences was 4.5%. For all analyses, laboratory technicians were blinded to the case-control status of the samples.

2.3.4. SNP Selection, Genotyping, and Quality Control

Genomic DNA was extracted from whole blood samples using conventional methods. We used the custom GoldenGate Universal-32 3072-plex assay kit (Illumina, CA, USA) to genotype 1716 genetic variants within the genes known and proposed to be involved in (1) vitamin D metabolism (DHCR7, GC, CYP3A4, CYP2R1, CYP24A1, CUBN, and LRP2), (2) mineral homeostasis and endocrine regulations of 1,25(OH)2D synthesis (CASR, PTH, TRPV5, and TRPV6), (3) vitamin D genomic effects (VDR, RXRA, RXRB, and RXRG), (4) formation of the VDR complex (co-activators and co-regulators ACTL6A, ARID1A, BAZ1B, CARM1, CHAF1A, CREBBP, EP300, HDAC9, MED1, NCOA1, NCOA2, NCOA3, NCOA7, NCOR1, NCOR2, PCAF/KAT2B, PRMT1, SMARCA2, SMARCA4, SMARCC1, SMARCD1, SMARCE1, SNW1, SUPT16H, TOP2B, and TSC2), and (5) vitamin D post-transcriptional response (tumor growth factor β (TGFβ)-signaling, inflammation, oxidative stress, insulin growth factor (IGF) signaling, cell cycle, and VDR binding sites; please see Supplementary Table S1 for a complete list of genes and SNPs). The custom GoldenGate assay was designed using the Illumina online Assay Design Tool in May 2012. SNP genotype dataset for CEU population (Utah residents with Northern and Western European ancestry; HapMap Data Rel 28 Phase II + III, August 10, on NCBI B36 assembly, dbSNP b126) were loaded in the Haploview program (Broad Institute, MIT and Harvard, Cambridge, MA, USA) and SNPs with minor allele frequencies (MAFs) greater than 5% and the r2 linkage disequilibrium (LD) statistic of 0.8 were selected as tagging SNPs (tagSNPs). Additionally, we searched published literature for previously reported functional and regulatory SNPs in the genes of interest and included them in genotyping irrespective of MAFs or r2 with other SNPs. Genotyping was performed by the Genetics Laboratory at Imperial College London. After excluding 409 SNPs [247 (14.4%) that failed genotyping, 54 (3.1%) that failed to satisfy the Hardy-Weinberg criterion (Supplementary Table S1), 98 (5.7%) missing in more than 20% of genotyped samples, and 10 (0.6%) that were monomorphic], a total of 1307 SNPs were included in the analysis. All genotyping underwent standard quality control including concordance checks for blinded duplicates and examination of sample and SNP call rates. The lowest reproducibility frequency across 62 replicate samples was 0.98. The call rate was 95% for all samples and 95% for all SNPs.

2.3.5. Statistical Analysis

The season adjustment of 25(OH)D was carried out by the week of blood draw using the sine curve method [24]. The associations between season-adjusted 25(OH)D concentrations and genetic variants (coded as 0, 1, 2 corresponding to the number of minor alleles) were assessed among controls using linear regression models adjusted for age, sex, and center. Further adjustment for BMI, smoking status, and physical activity did not change the results substantially. We used unconditional logistic regression analysis to assess the association of individual SNPs with CRC risk, adjusting for age (continuous), sex, and study center. Results were similar when we used conditional logistic regression on 1331 complete case-matched sets. We assumed a log-additive genetic model, but also tested dominant and recessive models as the underlying genetic model for these SNPs is unknown. Further adjustment for body mass index (BMI; continuous), smoking status (never, former, current smokers, missing), physical activity (active, moderately active, moderately inactive, and inactive), alcohol intake (continuous), hormone therapy, and menopausal status did not substantially change the results, and thus these variables were not included in the final statistical model. Subgroup analyses were conducted by sex and tumor location (colon vs. rectum).

To examine the associations between genes (a combination of SNPs) and genetic pathways (a combination of genes) and CRC risk, we used the Adaptive Rank Truncated Product (ARTP) method [25] as implemented in the first step (no interaction) of the R package PIGE (http://cran.r-project.org/web/packages/PIGE/index.html). This method can combine associations of SNPs in each gene (or from the genes in a pathway) to provide a P-value at the gene or pathway level, respectively. Genetic markers in high LD (r2 ≥ 0.8) were excluded using the AdaJoint R package (https://cran.r-project.org/web/packages/ARTP2). To investigate the multiplicative interaction between the genes and genetic pathways with 25(OH)D on CRC risk, we used the modified ARTP method as implemented in the R package PIGE. The P-values at the SNP and the gene levels were corrected for multiple testing for the number of SNPs and for the number of genes, respectively, using the false discovery rate (Benjamini–Hochberg or BH) method [26]. Furthermore, we used traditional methods to assess potential interactions between SNPs and 25(OH)D stratifying by categories of 25(OH)D concentrations and assuming a log-additive model for genetic markers. Also, we assessed the association of 25(OH)D (per 24.96 nmoL = 10 ng/mL) with CRC risk by genotype.

All statistical tests were two-sided with P-values < 0.05 considered statistically significant (SAS software, version 9.2; SAS Institute, Cary/NC; R, R Foundation for Statistical Computing, Vienna/Austria).

3. Results

3.1. Baseline Characteristics of Cases and Controls

Selected baseline characteristics of the CRC cases and matched controls are shown in Table 1. The mean age at blood donation of cases and controls was 58 years. On average, CRC cases had 4 years between blood donation and the time of diagnosis. The dataset included 520 rectal cancer cases and 900 colon cancer cases.

Table 1.

Selected baseline characteristics of incident colorectal cancer (CRC) cases and their matched controls, the European Prospective Investigation into Cancer and Nutrition (EPIC) study, 1992–2003.

Baseline Characteristic Cases Controls
n = 1420 n = 1419
Women, N (%) 705 (49.6) 701 (49.4)
Mean age at blood collection, (SD) years 58.5 (7.3) 58.6 (7.3)
Mean years of follow-up, (SD) years 4.1 (2.3) ---
Smoking status, N (%) a
  Never 580 (40.8) 594 (41.9)
  Former 476 (33.5) 460 (32.4)
  Current 346 (24.4) 349 (24.6)
Physical activity, N (%)
  Inactive 202 (14.2) 183 (12.9)
  Moderately inactive 402 (28.3) 367 (25.9)
  Moderately active 583 (41.1) 612 (43.1)
  Active 130 (9.2) 148 (10.4)
BMI, (SD) kg/m2 26.8 (4.2) 26.3 (3.8)
25-(OH)-vitamin D measurement, N (%) 1,176 (82.8) 764 (53.8)
25-(OH)-vitamin D, mean (SD) nmol/L b 58.5 (25.6) 62.0 (25.4)
Country, N (%)
  France 28 (2.0) 29 (2.0)
  Italy 202 (14.2) 198 (14.0)
  Spain 146 (10.3) 141 (9.9)
  United Kingdom 240 (16.9) 250 (17.6)
  The Netherlands 153 (10.8) 158 (11.1)
  Greece 46 (3.2) 48 (3.4)
  Germany 179 (12.6) 169 (11.9)
  Sweden 88 (6.2) 86 (6.1)
  Denmark 338 (23.8) 340 (24.0)
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a Percent missing is not shown. Therefore the total percentages do not add up to 100%. b Season standardized using the sine-curve method [25].

3.2. SNPs in the Genes Related to Vitamin D Metabolism/Transcriptional Activity and 25(OH)D

Thirty-seven SNPs in the genes related to vitamin D metabolism, formation of the VDR complex, and VDR transcriptional activity were associated with season-adjusted 25(OH)D concentrations with unadjusted P ≤ 0.05 among controls (Supplementary Table S2). The top 10 SNPs are shown in Table 2. Of the 37, 17 SNPs were in the genes involved in vitamin D metabolism, and 20 SNPs in the genes involved in vitamin D transcriptional activity. None of these SNPs were statistically significantly associated with 25(OH)D after BH correction. The associations of all SNPs with 25(OH)D among controls only are shown in Supplementary Table S3A, and among cases and controls combined in Supplementary Table S3B.

Table 2.

Top 10 single-nucleotide polymorphisms (SNPs) in the genes related to vitamin D metabolism and transcriptional activity associated with season-adjusted 25(OH)D concentrations among controls only, the EPIC study, 1992–2003 a.

Gene b SNP N 25(OH)D, β (95% CI) P PBH c
VDR rs2239182 742 −3.82 (−6.15, −1.49) 0.001 0.949
LRP2 rs2673170 747 −4.43 (−7.27, −1.58) 0.002 0.949
NCOA7 rs579477 758 −3.57 (−6.07, −1.07) 0.005 0.949
GC rs1352844 747 5.26 (1.63, 8.88) 0.005 0.949
GC rs188812 752 5.38 (1.57, 9.20) 0.006 0.949
GC rs2298849 757 4.26 (1.11, 7.41) 0.008 0.949
CUBN rs4525114 750 7.11 (1.94, 12.29) 0.007 0.949
CYP27B1 rs4646536 751 3.65 (0.93, 6.37) 0.009 0.949
CYP27B1 rs10877013 764 3.42 (0.73, 6.12) 0.013 0.974
HDAC9 rs212669 753 −8.12 (−14.34, −1.90) 0.011 0.974
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a Adjusted for age at blood collection, sex, and center. b Genes related to vitamin D metabolism and transcriptional activity. c P after Benjamini–Hochberg (BH) multiple testing correction.

3.3. SNPs in the Genes Related to Vitamin D Metabolism/Function and CRC Risk

We examined the associations between SNPs in the genes involved in vitamin D metabolism (genes = 9, SNPs = 274), mineral homeostasis and endocrine regulation of 1,25(OH)2D synthesis (genes = 5, SNPs = 58), vitamin D genomic effects including the VDR complex co-activators and co-regulators (genes = 30, SNPs = 538), and two SNPs in the intergenic regions previously associated with circulating 25(OH)D [27] and CRC risk (Supplementary Table S4). In Table 3, we show the top fifteen statistically significant SNPs associated with CRC risk defined by Punadjusted < 0.01. However, after BH correction, none of the associations remained statistically significant (all PBH > 0.2). The results did not differ by tumor location (Table 3 and Supplementary Table S4) or sex (Supplementary Table S5).

Table 3.

Associations of SNPs with CRC risk overall and by tumor location (colon vs. rectum), the EPIC study, 1992–2003.

Gene/SNP Genotype Colorectal Cancer Colon Cancer Rectal Cancer
Cases Controls OR (95% CI) a P PBH Cases OR (95% CI) a P PBH Cases OR (95% CI) a P PBH b
CUBN
rs12243895 GG 702 767 1.00 (ref) 0.009 0.569 435 1.00 (ref) 0.006 0.509 267 1.00 (ref) 0.261 0.969
GA 551 513 1.18 (1.00, 1.38) 354 1.20 (1.00, 1.44) 197 1.12 (0.90, 1.40)
AA 140 104 1.48 (1.12, 1.96) 94 1.59 (1.17, 2.16) 46 1.34 (0.91, 1.97)
Additive 1393 1384 1.20 (1.07, 1.35) 0.002 0.274 883 1.24 (1.08, 1.41) 0.002 0.254 510 1.14 (0.97, 1.34) 0.106 0.824
Dominant 1393 1384 1.22 (1.05, 1.43) 0.009 0.561 883 1.27 (1.07, 1.50) 0.007 0.571 510 1.16 (0.94, 1.43) 0.169 0.896
Recessive 1393 1384 1.38 (1.05, 1.80) 0.020 0.898 883 1.46 (1.08, 1.97) 0.013 0.827 510 1.27 (0.88, 1.85) 0.204 0.939
rs1801224 AA 601 669 1.00 (ref) 0.015 0.677 359 1.00 (ref) 0.004 0.473 242 1.00 (ref) 0.517 0.998
AC 614 582 1.18 (1.00, 1.38) 399 1.26 (1.05, 1.51) 215 1.04 (0.84, 1.30)
CC 180 144 1.40 (1.09, 1.80) 120 1.52 (1.15, 2.01) 60 1.22 (0.87, 1.73)
Additive 1395 1395 1.18 (1.06, 1.32) 0.004 0.374 878 1.24 (1.09, 1.41) 0.001 0.169 517 1.08 (0.93, 1.27) 0.308 0.926
Dominant 1395 1395 1.22 (1.05, 1.42) 0.010 0.561 878 1.31 (1.10, 1.56) 0.002 0.377 517 1.07 (0.87, 1.32) 0.494 0.965
Recessive 1395 1395 1.29 (1.02, 1.63) 0.035 0.898 878 1.35 (1.04, 1.76) 0.026 0.875 517 1.20 (0.86, 1.67) 0.276 0.986
rs7096079 CC 275 338 1.00 (ref) 0.023 0.677 159 1.00 (ref) 0.007 0.509 116 1.00 (ref) 0.587 0.998
CA 654 620 1.31 (1.08, 1.60) 417 1.43 (1.14, 1.80) 237 1.13 (0.87, 1.48)
AA 301 305 1.22 (0.97, 1.53) 194 1.36 (1.04, 1.77) 107 1.03 (0.75, 1.40)
Additive 1230 1263 1.10 (0.99, 1.24) 0.084 0.894 770 1.16 (1.02, 1.32) 0.025 0.712 460 1.02 (0.87, 1.18) 0.841 0.996
Dominant 1230 1263 1.28 (1.07, 1.54) 0.008 0.561 770 1.41 (1.13, 1.75) 0.002 0.377 460 1.10 (0.86, 1.41) 0.461 0.965
Recessive 1230 1263 1.02 (0.85, 1.22) 0.861 1.000 770 1.06 (0.86, 1.31) 0.578 0.991 460 0.95 (0.73, 1.22) 0.668 0.997
VDR
rs886441 AA 885 926 1.00 (ref) 0.024 0.677 563 1.00 (ref) 0.028 0.729 322 1.00 (ref) 0.179 0.963
AG 444 404 1.16 (0.98, 1.36) 273 1.12 (0.93, 1.35) 171 1.21 (0.97, 1.52)
GG 57 36 1.66 (1.08, 2.56) 40 1.83 (1.15, 2.93) 17 1.32 (0.73, 2.41)
Additive 1386 1366 1.20 (1.05, 1.38) 0.009 0.508 876 1.20 (1.03, 1.40) 0.020 0.670 510 1.19 (0.99, 1.44) 0.067 0.690
Dominant 1386 1366 1.20 (1.02, 1.40) 0.027 0.693 876 1.18 (0.98, 1.41) 0.078 0.896 510 1.22 (0.99, 1.52) 0.067 0.750
Recessive 1386 1366 1.59 (1.04, 2.43) 0.034 0.898 876 1.77 (1.11, 2.81) 0.016 0.827 510 1.24 (0.68, 2.25) 0.481 0.997
NCOA2
rs10087049 AA 393 472 1.00 (ref) 0.007 0.569 240 1.00 (ref) 0.003 0.448 153 1.00 (ref) 0.180 0.963
AG 724 665 1.32 (1.11, 1.56) 464 1.40 (1.15, 1.71) 260 1.17 (0.92, 1.48)
GG 173 182 1.14 (0.89, 1.46) 120 1.30 (0.98, 1.72) 53 0.88 (0.61, 1.26)
Additive 1290 1319 1.12 (1.00, 1.26) 0.054 0.848 824 1.19 (1.04, 1.36) 0.010 0.596 466 1.00 (0.85, 1.17) 0.959 0.996
Dominant 1290 1319 1.28 (1.08, 1.51) 0.004 0.469 824 1.38 (1.14, 1.67) 0.001 0.297 466 1.11 (0.88, 1.39) 0.385 0.963
NCOA7
rs10223441 CC 648 709 1.00 (ref) 0.007 0.569 399 1.00 (ref) 0.009 0.531 249 1.00 (ref) 0.153 0.939
CG 640 561 1.25 (1.07, 1.47) 413 1.30 (1.09, 1.55) 227 1.17 (0.95, 1.45)
GG 128 149 0.93 (0.72, 1.21) 84 0.98 (0.72, 1.31) 44 0.85 (0.59, 1.23)
Additive 1416 1419 1.06 (0.95, 1.19) 0.277 0.921 896 1.09 (0.96, 1.24) 0.183 0.920 520 1.01 (0.87, 1.18) 0.882 0.996
Dominant 1416 1419 1.19 (1.02, 1.38) 0.025 0.669 896 1.23 (1.04, 1.46) 0.017 0.685 520 1.10 (0.90, 1.35) 0.338 0.944
Recessive 1416 1419 0.84 (0.65, 1.08) 0.169 0.948 896 0.86 (0.65, 1.14) 0.295 0.991 520 0.79 (0.55, 1.13) 0.202 0.939
rs17292488 GG 594 639 1.00 (ref) 0.004 0.569 375 1.00 (ref) 0.019 0.659 219 1.00 (ref) 0.031 0.882
GA 657 575 1.23 (1.05, 1.44) 416 1.21 (1.01, 1.46) 241 1.26 (1.01, 1.57)
AA 148 185 0.86 (0.67, 1.10) 97 0.86 (0.65, 1.13) 51 0.85 (0.60, 1.21)
Additive 1399 1399 1.01 (0.90, 1.13) 0.841 0.998 888 1.00 (0.89, 1.14) 0.942 0.994 511 1.02 (0.88, 1.19) 0.802 0.996
Dominant 1399 1399 1.14 (0.98, 1.33) 0.087 0.876 888 1.13 (0.95, 1.34) 0.174 0.944 511 1.16 (0.94, 1.43) 0.155 0.896
Recessive 1399 1399 0.77 (0.61, 0.97) 0.028 0.898 888 0.77 (0.59, 1.01) 0.059 0.973 511 0.76 (0.54, 1.06) 0.102 0.939
NCOR2
rs10846670 AA 288 360 1.00 (ref) 0.021 0.677 169 1.00 (ref) 0.007 0.509 119 1.00 (ref) 0.676 0.998
AG 688 669 1.29 (1.07, 1.56) 441 1.39 (1.12, 1.74) 247 1.12 (0.86, 1.44)
GG 274 265 1.30 (1.03, 1.64) 177 1.42 (1.09, 1.86) 97 1.12 (0.81, 1.53)
Additive 1250 1294 1.15 (1.02, 1.29) 0.020 0.663 787 1.20 (1.05, 1.37) 0.007 0.564 463 1.06 (0.91, 1.24) 0.461 0.927
Dominant 1250 1294 1.29 (1.08, 1.55) 0.005 0.528 787 1.40 (1.13, 1.73) 0.002 0.377 463 1.12 (0.87, 1.43) 0.377 0.962
Recessive 1250 1294 1.09 (0.90, 1.32) 0.356 0.987 787 1.13 (0.91, 1.41) 0.256 0.991 463 1.04 (0.80, 1.36) 0.777 0.997
rs906304 GG 1032 1082 1.00 (ref) 0.010 0.569 666 1.00 (ref) 0.005 0.496 366 1.00 (ref) 0.025 0.827
GA 359 298 1.26 (1.06, 1.51) 220 1.20 (0.98, 1.47) 139 1.37 (1.08, 1.74)
AA 20 32 0.66 (0.38, 1.17) 6 0.30 (0.13, 0.74) 14 1.36 (0.71, 2.62)
Additive 1411 1412 1.12 (0.96, 1.31) 0.141 0.894 892 1.02 (0.85, 1.22) 0.822 0.990 519 1.30 (1.07, 1.59) 0.010 0.514
Dominant 1411 1412 1.21 (1.02, 1.43) 0.032 0.693 892 1.12 (0.92, 1.36) 0.268 0.954 519 1.37 (1.09, 1.73) 0.007 0.416
Recessive 1411 1412 0.63 (0.35, 1.11) 0.106 0.931 892 0.29 (0.12, 0.70) 0.006 0.714 519 1.26 (0.66, 2.40) 0.490 0.997
CHAF1A
rs243352 CC 410 369 1.00 (ref) 0.014 0.677 250 1.00 (ref) 0.240 0.977 160 1.00 (ref) 0.003 0.453
CA 695 673 0.93 (0.78, 1.11) 438 0.97 (0.79, 1.19) 257 0.85 (0.67, 1.08)
AA 285 346 0.74 (0.60, 0.91) 190 0.82 (0.65, 1.05) 95 0.60 (0.44, 0.80)
Additive 1390 1388 0.86 (0.78, 0.96) 0.006 0.417 878 0.91 (0.81, 1.03) 0.128 0.920 512 0.78 (0.67, 0.90) 0.001 0.132
Dominant 1390 1388 0.86 (0.73, 1.02) 0.087 0.876 878 0.92 (0.76, 1.11) 0.392 0.956 512 0.76 (0.61, 0.95) 0.018 0.629
Recessive 1390 1388 0.77 (0.65, 0.92) 0.005 0.648 878 0.84 (0.69, 1.03) 0.097 0.981 512 0.66 (0.51, 0.86) 0.002 0.729
rs9352 AA 461 417 1.00 (ref) 0.023 0.677 277 1.00 (ref) 0.315 0.995 184 1.00 (ref) 0.003 0.453
AG 648 681 0.86 (0.72, 1.02) 413 0.92 (0.76, 1.13) 235 0.75 (0.60, 0.95)
GG 254 307 0.74 (0.60, 0.92) 166 0.83 (0.65, 1.06) 88 0.61 (0.46, 0.83)
Additive 1363 1405 0.86 (0.78, 0.96) 0.006 0.417 856 0.91 (0.81, 1.03) 0.131 0.920 507 0.78 (0.67, 0.90) 0.001 0.132
Dominant 1363 1405 0.82 (0.70, 0.97) 0.018 0.597 856 0.89 (0.74, 1.08) 0.238 0.954 507 0.71 (0.57, 0.88) 0.002 0.243
Recessive 1363 1405 0.81 (0.68, 0.98) 0.032 0.898 856 0.87 (0.70, 1.07) 0.192 0.991 507 0.73 (0.56, 0.95) 0.018 0.939
HDAC9
rs2520361 AA 881 841 1.00 (ref) 0.021 0.677 556 1.00 (ref) 0.161 0.960 325 1.00 (ref) 0.033 0.882
AG 385 395 0.92 (0.78, 1.09) 240 0.93 (0.76, 1.13) 145 0.91 (0.72, 1.15)
GG 50 79 0.60 (0.41, 0.87) 36 0.68 (0.45, 1.02) 14 0.46 (0.26, 0.83)
Additive 1316 1315 0.85 (0.75, 0.97) 0.018 0.641 832 0.88 (0.75, 1.02) 0.086 0.920 484 0.81 (0.67, 0.98) 0.027 0.664
Dominant 1316 1315 0.87 (0.74, 1.02) 0.089 0.876 832 0.89 (0.74, 1.07) 0.197 0.946 484 0.84 (0.67, 1.05) 0.122 0.853
Recessive 1316 1315 0.61 (0.43, 0.88) 0.009 0.898 832 0.69 (0.46, 1.04) 0.079 0.973 484 0.48 (0.27, 0.85) 0.013 0.939
rs4141042 AA 1028 1072 1.00 (ref) 0.007 0.569 645 1.00 (ref) 0.006 0.509 383 1.00 (ref) 0.146 0.921
AG 366 304 1.26 (1.06, 1.50) 238 1.30 (1.07, 1.58) 128 1.23 (0.97, 1.57)
GG 18 31 0.60 (0.33, 1.09) 10 0.54 (0.26, 1.12) 8 0.71 (0.32, 1.56)
Additive 1412 1407 1.11 (0.95, 1.30) 0.176 0.916 893 1.13 (0.95, 1.35) 0.166 0.920 519 1.10 (0.90, 1.36) 0.346 0.926
Dominant 1412 1407 1.20 (1.01, 1.42) 0.039 0.752 893 1.23 (1.01, 1.49) 0.035 0.806 519 1.18 (0.93, 1.49) 0.164 0.896
Recessive 1412 1407 0.57 (0.32, 1.03) 0.062 0.916 893 0.51 (0.25, 1.05) 0.067 0.973 519 0.67 (0.30, 1.49) 0.328 0.986
SMARCC1
rs3755637 GG 661 605 1.00 (ref) 0.015 0.677 412 1.00 (ref) 0.026 0.729 249 1.00 (ref) 0.073 0.882
GA 520 601 0.79 (0.67, 0.93) 322 0.78 (0.64, 0.93) 198 0.81 (0.65, 1.02)
AA 132 141 0.86 (0.66, 1.12) 90 0.95 (0.70, 1.27) 42 0.70 (0.48, 1.03)
Additive 1313 1347 0.87 (0.78, 0.98) 0.023 0.712 824 0.90 (0.79, 1.03) 0.111 0.920 489 0.83 (0.70, 0.97) 0.023 0.650
Dominant 1313 1347 0.80 (0.69, 0.93) 0.005 0.517 824 0.81 (0.68, 0.96) 0.017 0.685 489 0.79 (0.64, 0.98) 0.030 0.679
Recessive 1313 1347 0.96 (0.75, 1.24) 0.758 1.000 824 1.07 (0.80, 1.41) 0.659 0.991 489 0.78 (0.54, 1.12) 0.174 0.939
TOP2B
rs1001647 AA 948 884 1.00 (ref) 0.022 0.677 612 1.00 (ref) 0.011 0.531 336 1.00 (ref) 0.460 0.993
AG 353 415 0.79 (0.66, 0.93) 220 0.74 (0.61, 0.90) 133 0.87 (0.68, 1.10)
GG 57 55 0.94 (0.64, 1.39) 37 0.91 (0.59, 1.41) 20 1.06 (0.61, 1.81)
Additive 1358 1354 0.86 (0.75, 0.99) 0.032 0.792 869 0.83 (0.71, 0.97) 0.017 0.650 489 0.93 (0.77, 1.13) 0.454 0.926
Dominant 1358 1354 0.80 (0.68, 0.95) 0.009 0.561 869 0.76 (0.63, 0.92) 0.004 0.442 489 0.89 (0.71, 1.11) 0.298 0.943
Recessive 1358 1354 1.02 (0.70, 1.49) 0.922 1.000 869 1.00 (0.65, 1.55) 0.982 0.999 489 1.11 (0.65, 1.90) 0.702 0.997
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a Unconditional logistic regression adjusted for age at blood collection, sex, and study center. b P of false discovery rate (BH; Benjamini–Hochberg) method.

3.4. SNPs in the Vitamin D-Responsive Genes and CRC Risk

We also examined the associations between 434 SNPs in the genes responsive to vitamin D, including the genes in the TGFβ and IGF signaling pathways, inflammation, oxidative stress, cell cycle, and 19 SNPs located in the VDR binding sites as previously published [19] (Supplementary Table S6). Twenty-five SNPs were significantly associated with CRC risk at P < 0.01. However, after BH correction, none of the associations (except for SMAD3 rs7180244; SMAD7 rs11874392, rs12953717 and rs4939827) remained statistically significant (Supplementary Table S7). Interestingly, three SNPs (rs3197999, rs3802842, rs762421) in previously identified VDR binding sites were associated with CRC risk. The results did not differ by tumor location (Supplementary Tables S6 and S7) or sex (Supplementary Table S8).

3.5. Vitamin D Genes/Pathways and CRC Risk

At the pathway level, the VDR binding sites and TGFβ signaling pathway were statistically significantly associated with CRC risk (P < 0.04; Table 4). For colon cancer, in addition to the VDR binding sites (P = 0.008) and TGFβ signaling pathway (P = 0.0001), an association with cell cycle pathway was observed (P = 0.03). The TGFβ (P = 0.0001) and IGF (P = 0.007) signaling pathways, but not the VDR binding sites (P = 0.256), were statistically significantly associated with rectal cancer risk.

Table 4.

P-values of pathway- and gene-level associations with CRC risk overall and by tumor location (colon vs. rectal) and of interactions with 25(OH)D concentrations (per 24.96 nmol/L), the EPIC study, 1992–2003.

Pathway/Gene No. of SNPs No. of SNPs Retained After Pruning Colorectal Cancer Colon Cancer Rectal Cancer
Gene or Pathway Only Gene- or Pathway-25(OH)D Interaction Gene or Pathway Only Gene- or Pathway-25(OH)D Interaction Gene or Pathway Only Gene- or Pathway-25(OH)D Interaction
P PBH a P PBH P PBH P PBH P PBH P PBH
Vitamin D metabolism 276 245 0.580 0.159 0.550 0.160 0.418 0.116
Identified in GWAS of 25(OH)D b 2 2 0.235 0.759 0.867 0.999 0.167 0.657 0.923 0.990 0.561 0.944 0.499 0.991
CUBN 116 106 0.173 0.741 0.764 0.999 0.130 0.657 0.896 0.990 0.083 0.470 0.490 0.991
CYP24A1 25 23 0.443 0.777 0.358 0.999 0.083 0.647 0.666 0.990 0.622 0.944 0.007 0.595
CYP27A1 5 5 0.500 0.777 0.299 0.999 0.488 0.819 0.086 0.522 0.968 0.968 0.256 0.991
CYP27B1 6 5 0.448 0.777 0.037 0.446 0.585 0.829 0.041 0.448 0.514 0.944 0.154 0.991
CYP2R1 12 9 0.115 0.741 0.811 0.999 0.368 0.815 0.921 0.990 0.044 0.459 0.727 0.991
CYP3A4 7 5 0.241 0.759 0.730 0.999 0.461 0.815 0.533 0.990 0.262 0.747 0.392 0.991
DHCR7 12 6 0.997 0.997 0.549 0.999 0.800 0.911 0.614 0.990 0.434 0.944 0.716 0.991
GC 24 20 0.484 0.777 0.018 0.406 0.912 0.954 0.026 0.442 0.241 0.747 0.316 0.991
LRP2 67 64 0.804 0.926 0.377 0.999 0.508 0.819 0.677 0.990 0.904 0.967 0.487 0.991
Mineral homeostasis 58 40 0.834 0.313 0.912 0.431 0.537 0.782
CASR 31 23 0.580 0.784 0.736 0.999 0.536 0.819 0.957 0.990 0.643 0.944 0.565 0.991
PTH 6 5 0.931 0.982 0.671 0.999 0.773 0.911 0.739 0.990 0.539 0.944 0.736 0.991
CALB1 2 2 0.489 0.777 0.741 0.999 0.400 0.815 0.847 0.990 0.882 0.967 0.819 0.991
TRPV5 9 7 0.657 0.846 0.054 0.456 0.920 0.954 0.081 0.522 0.337 0.818 0.225 0.991
TRPV6 10 3 0.263 0.777 0.880 0.999 0.520 0.819 0.954 0.990 0.112 0.595 0.713 0.991
VDR complex/Transcriptional Co-regulators and Co-activators 538 490 0.634 0.041 0.874 0.105 0.180 0.727
ACTL6A 3 3 0.239 0.759 0.395 0.999 0.262 0.815 0.497 0.990 0.506 0.944 0.613 0.991
ARID1A 8 7 0.408 0.777 0.032 0.446 0.306 0.815 0.048 0.448 0.133 0.628 0.068 0.924
BAZ1B 14 9 0.478 0.777 0.955 0.999 0.360 0.815 0.867 0.990 0.385 0.909 0.935 0.993
CARM1 4 4 0.641 0.839 0.006 0.406 0.831 0.929 0.022 0.442 0.290 0.747 0.120 0.991
CHAF1A 5 4 0.035 0.511 0.013 0.406 0.307 0.815 0.047 0.448 0.007 0.187 0.098 0.926
CREBBP 15 12 0.388 0.777 0.285 0.999 0.800 0.911 0.215 0.865 0.011 0.187 0.793 0.991
EP300 6 5 0.771 0.926 0.791 0.999 0.434 0.815 0.835 0.990 0.917 0.967 0.342 0.991
HDAC9 149 141 0.559 0.784 0.873 0.999 0.524 0.819 0.578 0.990 0.553 0.944 0.970 0.993
MED1 5 5 0.507 0.777 0.561 0.999 0.719 0.899 0.694 0.990 0.131 0.628 0.617 0.991
NCOA1 18 14 0.581 0.784 0.065 0.504 0.804 0.911 0.201 0.854 0.239 0.747 0.355 0.991
NCOA2 19 16 0.542 0.781 0.579 0.999 0.145 0.657 0.456 0.990 0.938 0.967 0.505 0.991
NCOA3 11 9 0.067 0.636 0.051 0.456 0.056 0.647 0.069 0.489 0.522 0.944 0.350 0.991
NCOA7 31 31 0.518 0.777 0.142 0.755 0.646 0.872 0.056 0.448 0.223 0.747 0.482 0.991
NCOR1 7 3 0.312 0.777 0.802 0.999 0.377 0.815 0.759 0.990 0.577 0.944 0.489 0.991
PCAF/KAT2B 31 31 0.801 0.926 0.891 0.999 0.470 0.815 0.865 0.990 0.938 0.967 0.797 0.991
PRMT1 4 4 0.346 0.777 0.351 0.999 0.407 0.815 0.184 0.823 0.618 0.944 0.624 0.991
RXRA 30 27 0.683 0.866 0.763 0.999 0.091 0.647 0.732 0.990 0.662 0.944 0.726 0.991
RXRB 7 3 0.824 0.926 0.074 0.526 0.445 0.815 0.058 0.448 0.617 0.944 0.356 0.991
RXRG 24 24 0.853 0.929 0.716 0.999 0.558 0.819 0.923 0.990 0.875 0.967 0.087 0.924
SMARCA2 1 1 0.506 0.777 0.019 0.406 0.381 0.815 0.012 0.442 0.944 0.967 0.307 0.991
SMARCA4 12 9 0.474 0.777 0.794 0.999 0.559 0.819 0.975 0.990 0.557 0.944 0.923 0.993
SMARCC1 4 4 0.103 0.741 0.893 0.999 0.344 0.815 0.824 0.990 0.082 0.470 0.164 0.991
SMARCD1 3 3 0.312 0.777 0.703 0.999 0.356 0.815 0.550 0.990 0.519 0.944 0.998 0.998
SMARCE1 4 4 0.048 0.582 0.197 0.881 0.191 0.706 0.390 0.990 0.083 0.470 0.059 0.924
SNW1 10 10 0.615 0.816 0.863 0.999 0.740 0.911 0.947 0.990 0.813 0.967 0.807 0.991
SUPT16H 7 6 0.809 0.926 0.990 0.999 0.696 0.899 0.960 0.990 0.663 0.944 0.841 0.991
TOP2B 6 5 0.192 0.741 0.965 0.999 0.097 0.647 0.327 0.990 0.281 0.747 0.400 0.991
NCOR2 62 61 0.701 0.877 0.960 0.999 0.770 0.911 0.609 0.990 0.459 0.944 0.935 0.993
VDR 30 28 0.270 0.777 0.652 0.999 0.372 0.815 0.707 0.990 0.477 0.944 0.706 0.991
TSC2 8 7 0.135 0.741 0.885 0.999 0.354 0.815 0.811 0.990 0.284 0.747 0.761 0.991
TGF-beta signaling 110 98 0.0001 0.616 0.0001 0.729 0.0001 0.342
RHPN2 25 23 0.452 0.777 0.722 0.999 0.144 0.657 0.871 0.990 0.956 0.967 0.701 0.991
SMAD7 23 18 0.0001 0.008 0.927 0.999 0.001 0.043 0.947 0.990 0.0005 0.021 0.627 0.991
SMAD3 39 38 0.0002 0.008 0.367 0.999 0.0001 0.009 0.514 0.990 0.0003 0.021 0.085 0.924
BMP2 5 5 0.016 0.346 0.081 0.527 0.014 0.298 0.224 0.865 0.072 0.470 0.049 0.924
BMP4 1 1 0.201 0.742 0.431 0.999 0.334 0.815 0.615 0.990 0.261 0.747 0.276 0.991
TGFB1 11 8 0.159 0.741 0.293 0.999 0.469 0.815 0.112 0.595 0.068 0.470 0.306 0.991
TGFBR1 4 3 0.950 0.982 0.741 0.999 0.306 0.815 0.838 0.990 0.446 0.944 0.851 0.991
SCG5/GREM1 2 2 0.141 0.741 0.412 0.999 0.170 0.657 0.371 0.990 0.323 0.808 0.656 0.991
Inflammation 133 97 0.888 0.479 0.620 0.156 0.200 0.784
ALOX5 22 11 0.839 0.926 0.264 0.999 0.801 0.911 0.593 0.990 0.782 0.967 0.165 0.991
IL10 13 8 0.462 0.777 0.030 0.446 0.854 0.931 0.006 0.442 0.009 0.187 0.419 0.991
IL10R 9 7 0.734 0.904 0.552 0.999 0.913 0.954 0.751 0.990 0.663 0.944 0.591 0.991
IL2/IL21 6 5 0.441 0.777 0.933 0.999 0.538 0.819 0.978 0.990 0.666 0.944 0.607 0.991
IL6 13 10 0.983 0.995 0.807 0.999 0.982 0.988 0.557 0.990 0.696 0.967 0.843 0.991
IL12B 18 17 0.128 0.741 0.870 0.999 0.079 0.647 0.796 0.990 0.791 0.967 0.761 0.991
IFNG 7 4 0.300 0.777 0.686 0.999 0.453 0.815 0.608 0.990 0.287 0.747 0.497 0.991
TNF 4 4 0.959 0.982 0.218 0.926 0.988 0.988 0.375 0.990 0.790 0.967 0.076 0.924
NFKB1 22 15 0.816 0.926 0.644 0.999 0.873 0.939 0.736 0.990 0.856 0.967 0.794 0.991
IL12A 1 1 0.523 0.777 0.168 0.795 0.452 0.815 0.159 0.770 0.814 0.967 0.706 0.991
IL18 4 4 0.348 0.777 0.514 0.999 0.613 0.840 0.766 0.990 0.182 0.737 0.290 0.991
IL1A/IL1B 11 8 0.951 0.982 0.124 0.701 0.846 0.931 0.054 0.448 0.761 0.967 0.711 0.991
IL8 1 1 0.153 0.741 0.978 0.999 0.060 0.647 0.593 0.990 0.811 0.967 0.669 0.991
RELA (p65) 2 2 0.921 0.982 0.835 0.999 0.938 0.961 0.962 0.990 0.922 0.967 0.690 0.991
Oxidative Stress 51 37 0.726 0.471 0.598 0.460 0.913 0.747
GSR 9 7 0.530 0.777 0.116 0.701 0.712 0.899 0.110 0.595 0.656 0.944 0.376 0.991
GPx2 15 8 0.834 0.926 0.772 0.999 0.713 0.899 0.748 0.990 0.412 0.944 0.915 0.993
TXNRD1 (TR1) 17 14 0.232 0.759 0.859 0.999 0.168 0.657 0.875 0.990 0.757 0.967 0.946 0.993
SOD2 10 8 0.530 0.777 0.374 0.999 0.546 0.819 0.599 0.990 0.896 0.967 0.281 0.991
Insulin growth factor (IGF) signaling 61 52 0.105 0.320 0.550 0.135 0.007 0.346
Associated with IGF levels in GWAS c 4 4 0.414 0.777 0.999 0.999 0.579 0.829 0.901 0.990 0.036 0.437 0.879 0.993
IGF1 17 15 0.131 0.741 0.452 0.999 0.113 0.657 0.668 0.990 0.225 0.747 0.655 0.991
IGF2BP2 3 1 0.303 0.777 0.923 0.999 0.596 0.830 0.580 0.990 0.156 0.698 0.658 0.991
IGFBP2/IGFBP5 24 21 0.187 0.741 0.052 0.456 0.692 0.899 0.018 0.442 0.029 0.411 0.607 0.991
IGFBP3 13 11 0.057 0.601 0.882 0.999 0.273 0.815 0.997 0.997 0.050 0.459 0.058 0.924
Cell Cycle 60 56 0.120 0.852 0.030 0.845 0.482 0.916
KRAS 13 13 0.511 0.777 0.498 0.999 0.262 0.815 0.436 0.990 0.182 0.737 0.925 0.993
FOS (c-fos) 10 9 0.389 0.777 0.425 0.999 0.230 0.815 0.518 0.990 0.764 0.967 0.574 0.991
JUN 7 7 0.569 0.784 0.407 0.999 0.411 0.815 0.163 0.770 0.917 0.967 0.811 0.991
C-MYC region d 13 13 0.010 0.295 0.843 0.999 0.051 0.647 0.857 0.990 0.054 0.459 0.959 0.993
CCND1 4 3 0.177 0.741 0.893 0.999 0.155 0.657 0.565 0.990 0.549 0.944 0.763 0.991
BCL2A1 2 2 0.118 0.741 0.587 0.999 0.083 0.647 0.574 0.990 0.250 0.747 0.915 0.993
BAX 6 5 0.308 0.777 0.892 0.999 0.155 0.657 0.748 0.990 0.922 0.967 0.988 0.998
CDKN1A 5 4 0.521 0.777 0.168 0.795 0.099 0.647 0.459 0.990 0.867 0.967 0.216 0.991
VDR binding sites 19 18 0.036 0.530 0.008 0.410 0.256 0.798
VDR binding sites e 19 18 0.036 0.511 0.530 0.999 0.008 0.227 0.410 0.990 0.256 0.747 0.798 0.991
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aP of false discovery rate (BH; Benjamini–Hochberg or BH) method. b rs10485165 and rs10507577 (Bejamin et al. 2007) [27]. c rs1245541, rs4234798, rs700752, and rs780094. d Chromosome 8q24 region. e SNPs located in the VDR binding sites relating to colorectal cancer and Crohn’s disease risk as previously published (Ramagopalan et al. 2010) [19].

At the gene level, several genes (CHAF1A, SMARCE1, SMAD7, SMAD3, BMP2, and C-MYC region) were associated with CRC risk at unadjusted P < 0.05. However, all of them except SMAD7 (PBH = 0.008) and SMAD3 (PBH = 0.008) were not statistically significant after BH correction. The SMAD7, SMAD3, BMP2, and C-MYC regions were associated with colon cancer; however, after BH correction, only SMAD7 (PBH = 0.04) and SMAD3 (PBH = 0.009) remained statistically significant. In addition to SMAD7 (P = 0.0005) and SMAD3 (P = 0.0003), several other genes or genetic regions (CYP2R1, CHAF1A, CREBBP, IL10, SNPs identified in genome-wide association studies (GWAS) to be associated with IGF levels, IGFBP2/IGFBP5, IGFBP3, and C-MYC region) were associated with rectal cancer. However, after BH correction, only SMAD7 (P = 0.02) and SMAD3 (P = 0.02) remained statistically significant.

3.6. 25. (OH)D-Gene and 25(OH)D Pathway Interactions and CRC Risk

At the pathway level, the VDR complex and its transcriptional co-regulators and co-activators demonstrated a potential interaction with 25(OH)D concentrations in the association with CRC risk (P = 0.04; Table 4). Within this pathway, the interaction P-values of <0.05 were observed for ARID1A, CARM1, CHAF1A, and SMARCA2, but none were statistically significant after BH correction. Similar associations were observed for colon cancer, but not rectal cancer (P for interaction for the VDR complex and its transcriptional co-regulators and co-activators were 0.105 and 0.727, respectively).

At the gene level, the interaction P-values of <0.05 were observed for CYP27B1 and GC (vitamin D metabolism) and IL10 (inflammation) for CRC and colon cancer. Also, the interaction between 25(OH)D and IGFBP2/IGFBP5 was statistically significant for colon cancer. For rectal cancer, the interaction P-values of <0.05 were observed for CYP24A1 (vitamin D metabolism) and BMP2 (TGF signaling). None of the gene-25(OH)D interactions were statistically significant after BH correction.

Next, we assessed the associations between 25(OH)D (per 24.96 nmol/L) with CRC risk, stratified by genotypes of SNPs in the genes that were identified in the step above as potentially modifying the association of 25(OH)D with CRC risk (CYP27B1, GC, ARID1A, CARM1, CHAF1A, SMARCA2, and IL10; Supplementary Table S9). Sixteen SNPs in these genes with P for interaction <0.05 are presented in Table 5. None were statistically significant after BH correction. Several SNPs had a very low number of minor allele homozygotes, with no effect estimates presented in the table.

Table 5.

Associations of season-adjusted 25(OH)D concentrations (per 24.96 nmol/L) with CRC risk by genotypes, the EPIC study, 1992–2003.

Gene/SNP Major Allele Homozygotes Heterozygotes Minor Allele Homozygotes P interaction b
Cases/Controls OR (95%CI) a Cases/Controls OR (95%CI) a Cases/Controls OR (95%CI) a
Vitamin D metabolism
CYP27B1 rs10877013 557/383 1.00 (0.86,1.17) 510/311 0.84 (0.73,0.97) 108/70 0.61 (0.40,0.93) 0.024
CYP27B1 rs4646536 551/381 1.00 (0.86,1.17) 485/300 0.85 (0.74,0.99) 110/70 0.62 (0.41,0.95) 0.034
GC rs1352846 500/319 0.96 (0.81,1.13) 406/286 0.79 (0.67,0.93) 104/68 0.58 (0.35,0.97) 0.049
GC rs16846876 530/331 1.05 (0.90,1.23) 477/337 0.75 (0.65,0.88) 144/81 0.88 (0.63,1.21) 0.017
GC rs2298850 584/384 1.00 (0.86,1.15) 445/293 0.80 (0.68,0.94) 86/61 0.56 (0.33,0.97) 0.038
GC rs3755967 569/359 0.99 (0.85,1.15) 436/297 0.78 (0.66,0.91) 101/64 0.66 (0.40,1.09) 0.034
GC rs842873 261/192 0.73 (0.58,0.91) 588/354 0.84 (0.73,0.97) 240/196 1.22 (0.97,1.52) 0.002
VDR complex/Transcriptional Co-regulators and Co-activators
ARID1A rs11247596 720/474 0.96 (0.85,1.09) 400/256 0.76 (0.63,0.92) 53/34 0.42 (0.20,0.86) 0.051
ARID1A rs12737946 998/640 0.83 (0.74,0.93) 168/116 1.25 (0.95,1.66) 9/8 - c 0.016
ARID1A rs12752833 998/641 0.83 (0.74,0.93) 165/114 1.22 (0.92,1.62) 9/8 - 0.025
CARM1 rs7254708 764/515 0.82 (0.73,0.93) 208/144 1.04 (0.81,1.32) 9/8 - 0.0001
CHAF1A rs243341 596/403 0.78 (0.68,0.90) 468/276 0.95 (0.80,1.13) 102/78 1.42 (0.97,2.07) 0.020
CHAF1A rs243365 607/434 0.80 (0.70,0.91) 408/257 1.02 (0.86,1.22) 50/44 1.64 (0.89,3.02) 0.027
SMARCA2 rs2296212 920/607 0.81 (0.72,0.91) 229/142 1.15 (0.89,1.48) 14/7 4.09 (0.29,58.01) 0.035
Inflammation
IL10 rs3024509 1015/657 0.92 (0.83,1.03) 132/100 0.62 (0.45,0.85) 3/2 - 0.024
IL10 rs6686931 747/476 0.80 (0.70,0.91) 373/243 1.02 (0.86,1.20) 49/38 1.42 (0.59,3.39) 0.029
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a Adjusted for age at blood collection, sex, and center. b Interactions between SNPs and 25(OH)D stratifying by categories of 25(OH)D concentrations and assuming a log-additive model for genetic markers. c Not estimatable due to small sample size.

4. Discussion

In this large European prospective case-control study nested within the EPIC cohort, we investigated whether genetic variation in the genes and pathways related to vitamin D metabolism and vitamin D genomic effects is associated with CRC risk, and whether these associations are modified by 25(OH)D concentrations.

We identified several genes related to vitamin D metabolism, the VDR complex, and VDR transcriptional activity associated with 25(OH)D concentrations, with an unadjusted P < 0.01 before BH correction among controls. We confirmed two genes related to vitamin D metabolism, CYP27B1 and GC, and one in the VDR, which were identified in previous GWAS studies [28,29,30,31,32]. We also identified other genes in our study including 1) two genes that encode the transcription-related factors HDAC9 and NCOA7, involved in vitamin D transcriptional activity and VDR complex formation, and 2) two genes that encode the vitamin D-related transporters LRP2 and CUBN [33,34]. LRP2, commonly known as megalin, is responsible for the endocytosis of the 25(OH)D vitamin D binding protein complex [35]. CUBN is an important co-receptor in the megalin-mediated endocytic pathway and patients without functioning CUBN were found to have abnormal 25(OH)D metabolism [33].

The genes associated with circulating 25(OH)D concentrations were also associated with CRC risk at unadjusted P < 0.01 before BH correction. HDAC9 is located in a region on chromosome 7p21 [36] in which chromosomal gains were observed in primary CRC [37]. Furthermore, HDAC9 has been observed via chromatin immunoprecipitation (ChIP) assay in human osteosarcoma tissues to suppress p53 transcription and, thereby, promote cell proliferation [38]. An association of CUBN with CRC was previously reported in a meta-analysis of six GWAS studies [39], while no studies have investigated a possible association of LRP2 with CRC. LRP2 is expressed in multiple epithelial cell lines, including colon [35,40] and is often co-expressed with CUBN [34]. Additionally, there are no previous GWAS regarding CRC and NCOA7, although Higginbotham et al., found statistically significant associations of NCOA7 gene variants with reduced breast cancer risk across three different study cohorts [41]. The NCOA7 SNPs identified in our study, however, differed from those identified by Higginbotham suggesting a possible novel CRC susceptibility locus.

Three VDR binding site SNPs were associated with CRC risk in our study population, but the associations were not statistically significant after BH correction. We a priori selected these VDR binding sites for genotyping based on the results of a previous study that used ChIP followed by DNA sequencing to identify 2776 VDR binding sites in lymphoblastoid cell lines treated with calcitriol, an active form of vitamin D [19]. The study found a statistically significant 4-fold increase in the enrichment of VDR binding sites located in genes associated with CRC, and a 3.5-fold increase in enrichment located in genes associated with Crohn’s disease [19]. Our findings suggest that genetic variation in these VDR binding sites, upregulated in response to treatment with vitamin D and relevant to colorectal carcinogenesis and inflammatory bowel diseases, may be associated with CRC risk.

TGFβ has an important role in the regulation of cell proliferation, differentiation, migration and apoptosis [42], and may be modulated by vitamin D [43]. SMAD7 and SMAD3, in the TGFβ signaling pathway, were statistically significantly associated with CRC risk after BH correction for multiple testing. SMAD7 SNPs were previously identified to be associated with CRC risk in several different populations [44,45,46] as well as in a meta-analysis of 2906 cases and 3416 controls from four previous GWAS studies [47]. SMAD7 is transcriptionally induced by cytokines from the TGFβ family and regulates the TGFβ signaling pathway via a negative feedback loop [42]; therefore, the overexpression of SMAD7 inhibits the pathway and its associated anti-neoplastic effects [42]. The active form of vitamin D was shown to inhibit SMAD7 in experimental models [48]. The role of SMAD3 in the development of CRC is less understood and somatic tumor mutations in this gene have been observed in only 4.3% of CRC cases [49]. SMAD3 has been identified to interact with VDR and mediate a cross-talk between TGFβ and vitamin D signaling pathways [50]. An animal model found that SMAD3 may also play an important role in the TGFβ response to inflammation and bacteria-induced colon carcinogenesis [51]. Inflammation is further associated with CRC risk in our study as indicated by the interaction between circulating 25(OH)D concentrations, which has anti-inflammatory properties [52,53], and genetic variation in the IL10 gene encoding anti-inflammatory cytokine interleukin (IL)-10 involved in immune response to pathogens [54]. The induction of IL-10 is mediated by 1,25(OH)2D and is repressed with SMAD3 inhibition [48].

Chromosome 8q24 polymorphisms in the cell cycle pathway were previously identified to be strongly associated with CRC risk [47,55,56]. However, they were not statistically significant after BH correction. Although the 8q24 region is described as a gene desert, it is closely located to the region encoding c-MYC oncogene [57]. C-MYC controls processes related to cell growth regulation, metabolism and proliferation and is not only activated by numerous oncogenic pathways but also stimulates metabolic changes which can lead to malignant transformation [58]. Multiple studies have identified long-range physical interaction of the 8q24 region with c-MYC via enhancer elements and chromatin loops [57,59,60]. In an experimental study, 1,25(OH)2D and the VDR were shown to affect the c-MYC/MXD1 pathway leading to inhibition of c-MYC protein expression [61]. Using 8q24 SNPs as a proxy, our results confirm an association between c-MYC and CRC risk, but do not indicate a potential modification by 25(OH)D despite a previously reported possible interaction for fatal prostate cancer risk [62].

The IGF signaling pathway plays a key role in cell growth [63]. In our study, IGF-related genetic variation was associated with rectal cancer risk at the pathway level as well as for several individual genes before BH correction. Contrary to our results, IGF genetic variants [64,65] in addition to high circulating IGF peptides [66] have been previously associated more strongly with increased colon versus rectal cancer risk.

The strengths of our study include its prospective design and high follow-up rate. The hypothesis-driven selection of pathways, genes, and SNPs, and relatively large samples size within a large cohort study allowed an extensive investigation of vitamin D–related and –responsive genetic variation and the effect modification by established biomarker of vitamin D status with CRC risk. We used the detailed data from EPIC to address potential confounding by body size and other factors; with our careful analyses suggesting no or little confounding. However, we cannot altogether discount the possibility of residual confounding nor changes in lifestyle habits between enrolment into the cohort and the eventual cancer diagnosis. Although our study was large, most interaction and stratified analyses had limited power, especially by sex and tumor location. Our power analyses (Supplementary Table S10) showed that we have sufficient power (80%) to detect the effect associations in the range of 1.17 to 1.27 for relatively common SNPs with MAF between 40 and 10%, respectively, using our full data set (n = 1419 matched case-control sets). In addition, most of our results were not statistically significant after BH correction for multiple testing. As to the selection of genes and pathways, we were limited by published literature on vitamin D at the time of genotyping, so we may have not included all vitamin D-responsive genes. Additional experimental studies are needed to understand the biological mechanisms of the identified associations.

5. Conclusions

This large and comprehensive study has confirmed genetic variations in several previously identified vitamin D-related pathways associated with CRC risk in European populations, and has suggested potential new pathways related to vitamin D genomic effects and colorectal carcinogenesis.

Acknowledgments

The authors would like to thank B. Hemon for his assistance in database preparation.

Abbreviations

CRC—colorectal cancer; MR—Mendelian randomization; RCT—randomized control trial; VDR—vitamin D receptor; 1,25(OH)2D—1,25-dihydroxyvitamin D; 25(OH)D—25-hydroxyvitamin D; SNP—single nucleotide polymorphism; EPIC—European Prospective Investigation into Cancer and Nutrition; IGF—insulin growth factor; TGFβ—tumor growth factor β; MAF—minor allele frequency; tagSNPs—tagging SNPs; BMI—body mass index; ARTP—Adaptive Rank Truncated Product; BH—Benjamini–Hochberg; ChIP—chromatin immunoprecipitation.

Supplementary Materials

The following are available online at https://www.mdpi.com/2072-6643/11/8/1954/s1, Table S1: Characteristics of SNPs used in the study, Table S2: Vitamin D metabolism and transcriptional activity-related SNPs associated (unadjusted P-value < 0.05) with season-adjusted 25(OH)D concentrations among controls, the EPIC study, 1992–2003, Table S3A: Associations between SNPs in the genes involved in the vitamin D metabolism, mineral homeostasis/endocrine regulation of 1,25(OH)2D synthesis, and vitamin D transcriptional activity with season-adjusted 25(OH)D concentrations among controls, Table S3B: Associations between SNPs in the genes involved in the vitamin D metabolism, mineral homeostasis/endocrine regulation of 1,25(OH)2D synthesis, and vitamin D transcriptional activity with season-adjusted 25(OH)D concentrations among cases and controls combined, Table S4: Associations of SNPs with CRC risk overall and by tumor location (colon vs. rectum) using unconditional logistic regression with adjustment for age at recruitment, study center and sex, the EPIC study, 1992–2003, Table S5 Associations of SNPs with CRC risk overall and by tumor location (colon vs. rectum) among men and women using unconditional logistic regression with adjustment for age at recruitment, study center and sex, the EPIC study, 1992–2003, Table S6: Associations of SNPs in vitamin D-responsive genes with CRC risk overall and by tumor location (colon vs. rectum) using unconditional logistic regression with adjustment for age at recruitment, study center and sex, the EPIC study, 1992-2003, Table S7: Statistically significant associations (unadjusted p < 0.01) of SNPs in vitamin D-responsive genes with CRC risk overall and by tumor location (colon vs. rectum) using unconditional logistic regression with adjustment for age at recruitment, study center and sex, the EPIC study, 1992–2003, Table S8: Associations of SNPs in vitamin D-responsive genes with CRC risk overall and by tumor location (colon vs. rectum) among men and women using unconditional logistic regression with adjustment for age at recruitment, study center and sex, the EPIC study, 1992–2003, Table S9: Associations of season-adjusted 25(OH)D concentrations (per 24.96 nmol/L) with CRC risk by genotypes, the EPIC study, 1992–2003, Table S10: SNP-Only Minimal Detectable Effect Associations by Minor Allele Frequency for 80% Power and N = 1419 matched cases and controls, the EPIC study, 1992–2003.

Click here for additional data file. (4.1MB, xlsx)

Author Contributions

Conceptualization, V.F.; Data curation, M.J.; Formal analysis, W.Z.; Funding acquisition, V.F. and M.J.; Investigation, A.S., P.F., I.R., E.R., B.B.-d.-M., F.J.B.v.D., A.T. (Anne Tjønneland), A.O., V.P., F.C., M.-C.B.-R., T.K., T.J., A.K., A.T. (Antonia Trichopoulou), P.M., D.T., S.P., V.K., C.S., G.S., E.W., S.C.-Y., N.S., A.B., M.-J.S., R.Q., P.A., B.G., S.H., A.P.-C., A.K.H., K.K.T., A.D., H.F., N.M. and M.J.G.; Project administration, V.F.; Supervision, V.F. and M.J.; Visualization, H.B.M. and W.Z.; Writing—original draft, V.F., H.B.M. and M.J.; Writing—review and editing, V.F., H.B.M., D.J.H., A.S., P.F., I.R., E.R., B.B.-d.-M., F.J.B.v.D., P.D.S., A.T. (Anne Tjønneland), A.O., V.P., F.C., M.-C.B.-R., T.K., T.J., A.K., A.T. (Antonia Trichopoulou), P.M., D.T., S.P., V.K., C.S., G.S., E.W., S.C.-Y., N.S., A.B., M.-J.S., R.Q., P.A., B.G., S.H., A.P.-C., A.K.H., K.K.T., A.D., H.F., N.M., M.J.G. and M.J.

Funding

Funding for this particular study was obtained from Wereld Kanker Onderzoek Fonds (WKOF) [Grant Number WCRF 2011-443; PI: M. Jenab], as part of the World Cancer Research Fund International grant programme. The EPIC study was supported by “Europe Against Cancer” Programme of the European Commission (SANCO); Ligue contre le Cancer; Institut Gustave Roussy; Mutuelle Générale de l’Education Nationale; Institut National de la Santé et de la Recherche Médicale (INSERM); German Cancer Aid; German Cancer Research Center; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; the CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; ISCIII RETIC (RD06/0020); Spanish Regional Governments of Andalusia, Asturias, Basque Country, Murcia (No 6236) and Navarra and the Catalan Institute of Oncology; Cancer Research UK; Medical Research Council, UK; The Hellenic Health Foundation; Italian Association for Research on Cancer; Italian National Research Council; Compagnia di San Paolo; Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Swedish Cancer Society; Swedish Scientific Council; Regional Governments of Skane and Vasterbotten, Sweden; and Nordforsk centre of excellence programme HELGA. Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 for EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (UK). DJH was supported by the Health Research Board of Ireland health research award HRA-PHS-2015-1142.

Conflicts of Interest

The authors declare no conflict of interest.

References

  • 1.Ferlay J., Soerjomataram I., Dikshit R., Eser S., Mathers C., Rebelo M., Parkin D.M., Forman D., Bray F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer. 2015;136:E359–E386. doi: 10.1002/ijc.29210. [DOI] [PubMed] [Google Scholar]
  • 2.Touvier M., Chan D.S., Lau R., Aune D., Vieira R., Greenwood D.C., Kampman E., Riboli E., Hercberg S., Norat T. Meta-analyses of vitamin D intake, 25-hydroxyvitamin D status, vitamin D receptor polymorphisms, and colorectal cancer risk. Cancer Epidemiol. Biomark. Prev. 2011;20:1003–1016. doi: 10.1158/1055-9965.EPI-10-1141. [DOI] [PubMed] [Google Scholar]
  • 3.McCullough M.L., Zoltick E.S., Weinstein S.J., Fedirko V., Wang M., Cook N.R., Eliassen A.H., Zeleniuch-Jacquotte A., Agnoli C., Albanes D., et al. Circulating Vitamin D and Colorectal Cancer Risk: An International Pooling Project of 17 Cohorts. J. Natl. Cancer Inst. 2018;111:158–169. doi: 10.1093/jnci/djy087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Jiang X., O’Reilly P.F., Aschard H., Hsu Y.-H., Richards J.B., Dupuis J., Ingelsson E., Karasik D., Pilz S., Berry D., et al. Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels. Nat. Commun. 2018;9:260. doi: 10.1038/s41467-017-02662-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Dimitrakopoulou V.I., Tsilidis K.K., Haycock P.C., Dimou N.L., Al-Dabhani K., Martin R.M., Lewis S.J., Gunter M.J., Mondul A., Shui I.M., et al. Circulating vitamin D concentration and risk of seven cancers: Mendelian randomisation study. BMJ. 2017;359:j4761. doi: 10.1136/bmj.j4761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.He Y., Timofeeva M., Farrington S.M., Vaughan-Shaw P., Svinti V., Walker M., Zgaga L., Meng X., Li X., Spiliopoulou A., et al. Exploring causality in the association between circulating 25-hydroxyvitamin D and colorectal cancer risk: A large Mendelian randomisation study. BMC Med. 2018;16:142. doi: 10.1186/s12916-018-1119-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wactawski-Wende J., Kotchen J.M., Anderson G.L., Assaf A.R., Brunner R.L., O’Sullivan M.J., Margolis K.L., Ockene J.K., Phillips L., Pottern L., et al. Calcium plus vitamin D supplementation and the risk of colorectal cancer. N. Engl. J. Med. 2006;354:684–696. doi: 10.1056/NEJMoa055222. [DOI] [PubMed] [Google Scholar]
  • 8.Baron J.A., Barry E.L., Mott L.A., Rees J.R., Sandler R.S., Snover D.C., Bostick R.M., Ivanova A., Cole B.F., Ahnen D.J., et al. A Trial of Calcium and Vitamin D for the Prevention of Colorectal Adenomas. N. Engl. J. Med. 2015;373:1519–1530. doi: 10.1056/NEJMoa1500409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Manson J.E., Cook N.R., Lee I.M., Christen W., Bassuk S.S., Mora S., Gibson H., Gordon D., Copeland T., D’Agostino D., et al. Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease. N. Engl. J. Med. 2018;380:33–44. doi: 10.1056/NEJMoa1809944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Barry E.L., Rees J.R., Peacock J.L., Mott L.A., Amos C.I., Bostick R.M., Figueiredo J.C., Ahnen D.J., Bresalier R.S., Burke C.A., et al. Genetic Variants in CYP2R1, CYP24A1, and VDR Modify the Efficacy of Vitamin D3 Supplementation for Increasing Serum 25-Hydroxyvitamin D Levels in a Randomized Controlled Trial. J. Clin. Endocrinol. Metab. 2014;99:E2133–E2137. doi: 10.1210/jc.2014-1389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Matusiak D., Murillo G., Carroll R.E., Mehta R.G., Benya R.V. Expression of vitamin D receptor and 25-hydroxyvitamin D3-1α-hydroxylase in normal and malignant human colon. Cancer Epidemiol. Biomark. Prev. 2005;14:2370–2376. doi: 10.1158/1055-9965.EPI-05-0257. [DOI] [PubMed] [Google Scholar]
  • 12.Tangpricha V., Flanagan J.N., Whitlatch L.W., Tseng C.C., Chen T.C., Holt P.R., Lipkin M.S., Holick M.F. 25-hydroxyvitamin D-1alpha-hydroxylase in normal and malignant colon tissue. Lancet. 2001;357:1673–1674. doi: 10.1016/S0140-6736(00)04831-5. [DOI] [PubMed] [Google Scholar]
  • 13.Matusiak D., Benya R.V. CYP27A1 and CYP24 expression as a function of malignant transformation in the colon. J. Histochem. Cytochem. 2007;55:1257–1264. doi: 10.1369/jhc.7A7286.2007. [DOI] [PubMed] [Google Scholar]
  • 14.Ball G.F.M. Vitamins: Their Role in the Human Body. Blackwell Science; Ames, IA, USA: 2004. [Google Scholar]
  • 15.Feldman D., Krishnan A.V., Swami S., Giovannucci E., Feldman B.J. The role of vitamin D in reducing cancer risk and progression. Nat. Rev. Cancer. 2014;14:342–357. doi: 10.1038/nrc3691. [DOI] [PubMed] [Google Scholar]
  • 16.Slatter M.L., Yakumo K., Hoffman M., Neuhausen S. Variants of the VDR gene and risk of colon cancer (United States) Cancer Causes Control. 2001;12:359–364. doi: 10.1023/A:1011280518278. [DOI] [PubMed] [Google Scholar]
  • 17.Poynter J.N., Jacobs E.T., Figueiredo J.C., Lee W.H., Conti D.V., Campbell P.T., Levine A.J., Limburg P., Le Marchand L., Cotterchio M., et al. Genetic variation in the vitamin D receptor (VDR) and the vitamin D-binding protein (GC) and risk for colorectal cancer: Results from the Colon Cancer Family Registry. Cancer Epidemiol. Biomark. Prev. 2010;19:525–536. doi: 10.1158/1055-9965.EPI-09-0662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Gibbs D.C., Fedirko V., Um C., Gross M.D., Thyagarajan B., Bostick R.M. Associations of Circulating 25-Hydroxyvitamin D3 Concentrations With Incident, Sporadic Colorectal Adenoma Risk According to Common Vitamin D-Binding Protein Isoforms. Am. J. Epidemiol. 2018;187:1923–1930. doi: 10.1093/aje/kwy102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Ramagopalan S.V., Heger A., Berlanga A.J., Maugeri N.J., Lincoln M.R., Burrell A., Handunnetthi L., Handel A.E., Disanto G., Orton S.M., et al. A ChIP-seq defined genome-wide map of vitamin D receptor binding: Associations with disease and evolution. Genome Res. 2010;20:1352–1360. doi: 10.1101/gr.107920.110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gil A., Plaza-Diaz J., Mesa M.D. Vitamin D: Classic and Novel Actions. Ann. Nutr. Metab. 2018;72:87–95. doi: 10.1159/000486536. [DOI] [PubMed] [Google Scholar]
  • 21.Riboli E., Hunt K.J., Slimani N., Ferrari P., Norat T., Fahey M., Charrondiere U.R., Hemon B., Casagrande C., Vignat J., et al. European Prospective Investigation into Cancer and Nutrition (EPIC): Study populations and data collection. Public Health Nutr. 2002;5:1113–1124. doi: 10.1079/PHN2002394. [DOI] [PubMed] [Google Scholar]
  • 22.Bingham S., Riboli E. Diet and cancer—the European Prospective Investigation into Cancer and Nutrition. Nat. Rev. Cancer. 2004;4:206–215. doi: 10.1038/nrc1298. [DOI] [PubMed] [Google Scholar]
  • 23.Jenab M., Bueno-de-Mesquita H.B., Ferrari P., van Duijnhoven F.J., Norat T., Pischon T., Jansen E.H., Slimani N., Byrnes G., Rinaldi S., et al. Association between pre-diagnostic circulating vitamin D concentration and risk of colorectal cancer in European populations:a nested case-control study. BMJ. 2010;340:b5500. doi: 10.1136/bmj.b5500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gail M.H., Wu J., Wang M., Yaun S.S., Cook N.R., Eliassen A.H., McCullough M.L., Yu K., Zeleniuch-Jacquotte A., Smith-Warner S.A., et al. Calibration and seasonal adjustment for matched case-control studies of vitamin D and cancer. Stat. Med. 2016;35:2133–2148. doi: 10.1002/sim.6856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yu K., Li Q., Bergen A.W., Pfeiffer R.M., Rosenberg P.S., Caporaso N., Kraft P., Chatterjee N. Pathway analysis by adaptive combination of P-values. Genet. Epidemiol. 2009;33:700–709. doi: 10.1002/gepi.20422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Benjamini Y., Hochberg Y. Controlling the False Discovery Rate—A Practical and Powerful Approach to Multiple Testing. J. Roy. Stat. Soc. B Methodol. 1995;57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x. [DOI] [Google Scholar]
  • 27.Benjamin E.J., Dupuis J., Larson M.G., Lunetta K.L., Booth S.L., Govindaraju D.R., Kathiresan S., Keaney J.F., Jr., Keyes M.J., Lin J.P., et al. Genome-wide association with select biomarker traits in the Framingham Heart Study. BMC Med. Genet. 2007;8(Suppl. 1):S11. doi: 10.1186/1471-2350-8-S1-S11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Hibler E.A., Klimentidis Y.C., Jurutka P.W., Kohler L.N., Lance P., Roe D.J., Thompson P.A., Jacobs E.T. CYP24A1 and CYP27B1 Polymorphisms, Concentrations of Vitamin D Metabolites, and Odds of Colorectal Adenoma Recurrence. Nutr. Cancer. 2015;67:1131–1141. doi: 10.1080/01635581.2015.1068818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ahn J., Albanes D., Berndt S.I., Peters U., Chatterjee N., Freedman N.D., Abnet C.C., Huang W.-Y., Kibel A.S., Crawford E.D., et al. Vitamin D-related genes, serum vitamin D concentrations and prostate cancer risk. Carcinogenesis. 2009;30:769–776. doi: 10.1093/carcin/bgp055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wang T.J., Zhang F., Richards J.B., Kestenbaum B., van Meurs J.B., Berry D., Kiel D., Streeten E.A., Ohlsson C., Koller D.L., et al. Common genetic determinants of vitamin D insufficiency: A genome-wide association study. Lancet. 2010;376:180–188. doi: 10.1016/S0140-6736(10)60588-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Bu F.X., Armas L., Lappe J., Zhou Y., Gao G., Wang H.W., Recker R., Zhao L.J. Comprehensive association analysis of nine candidate genes with serum 25-hydroxy vitamin D levels among healthy Caucasian subjects. Hum. Genet. 2010;128:549–556. doi: 10.1007/s00439-010-0881-9. [DOI] [PubMed] [Google Scholar]
  • 32.Mokry L.E., Ross S., Ahmad O.S., Forgetta V., Smith G.D., Goltzman D., Leong A., Greenwood C.M., Thanassoulis G., Richards J.B. Vitamin D and Risk of Multiple Sclerosis: A Mendelian Randomization Study. PLoS Med. 2015;12:e1001866. doi: 10.1371/journal.pmed.1001866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Nykjaer A., Fyfe J.C., Kozyraki R., Leheste J.-R., Jacobsen C., Nielsen M.S., Verroust P.J., Aminoff M., de la Chapelle A., Moestrup S.K., et al. Cubilin dysfunction causes abnormal metabolism of the steroid hormone 25(OH) vitamin D3. Proc. Natl. Acad. Sci. USA. 2001;98:13895–13900. doi: 10.1073/pnas.241516998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Kaseda R., Hosojima M., Sato H., Saito A. Role of megalin and cubilin in the metabolism of vitamin D(3) Ther. Apher. Dial. 2011;15(Suppl. 1):14–17. doi: 10.1111/j.1744-9987.2011.00920.x. [DOI] [PubMed] [Google Scholar]
  • 35.Rowling M.J., Kemmis C.M., Taffany D.A., Welsh J. Megalin-Mediated Endocytosis of Vitamin D Binding Protein Correlates with 25-Hydroxycholecalciferol Actions in Human Mammary Cells. J. Nutr. 2006;136:2754–2759. doi: 10.1093/jn/136.11.2754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Mahlknecht U., Schnittger S., Will J., Cicek N., Hoelzer D. Chromosomal organization and localization of the human histone deacetylase 9 gene (HDAC9) Biochem. Biophys. Res. Commun. 2002;293:182–191. doi: 10.1016/S0006-291X(02)00193-6. [DOI] [PubMed] [Google Scholar]
  • 37.Aragane H., Sakakura C., Nakanishi M., Yasuoka R., Fujita Y., Taniguchi H., Hagiwara A., Yamaguchi T., Abe T., Inazawa J., et al. Chromosomal aberrations in colorectal cancers and liver metastases analyzed by comparative genomic hybridization. Int. J. Cancer. 2001;94:623–629. doi: 10.1002/ijc.1522. [DOI] [PubMed] [Google Scholar]
  • 38.Zhao Y.-X., Wang Y.-S., Cai Q.-Q., Wang J.-Q., Yao W.-T. Up-regulation of HDAC9 promotes cell proliferation through suppressing p53 transcription in osteosarcoma. Int. J. Clin. Exp. Med. 2015;8:11818–11823. [PMC free article] [PubMed] [Google Scholar]
  • 39.Al-Tassan N.A., Whiffin N., Hosking F.J., Palles C., Farrington S.M., Dobbins S.E., Harris R., Gorman M., Tenesa A., Meyer B.F., et al. A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer. Sci. Rep. 2015;5:10442. doi: 10.1038/srep10442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Ternes S.B., Rowling M.J. Vitamin D Transport Proteins Megalin and Disabled-2 Are Expressed in Prostate and Colon Epithelial Cells and Are Induced and Activated by All-Trans-Retinoic Acid. Nutr. Cancer. 2013;65:900–907. doi: 10.1080/01635581.2013.805422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Higginbotham K.S.P., Breyer J.P., Bradley K.M., Schuyler P.A., Plummer W.D., Freudenthal M.E., Trentham-Dietz A., Newcomb P.A., Sanders M.E., Page D.L., et al. A multi-stage association study identifies a breast cancer genetic locus at NCOA7. Cancer Res. 2011;71:3881–3888. doi: 10.1158/0008-5472.CAN-10-2653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Yan X., Liu Z., Chen Y. Regulation of TGF-beta signaling by Smad7. Acta Biochim. Biophys. Sin. 2009;41:263–272. doi: 10.1093/abbs/gmp018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Tu H., Flanders W.D., Ahearn T.U., Daniel C.R., Gonzalez-Feliciano A.G., Long Q., Rutherford R.E., Bostick R.M. Effects of Calcium and Vitamin D(3) on Transforming Growth Factors in Rectal Mucosa of Sporadic Colorectal Adenoma Patients: A Randomized Controlled Trial. Mol. Carcinog. 2015;54:270–280. doi: 10.1002/mc.22096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Jiang X., Castelao J.E., Vandenberg D., Carracedo A., Redondo C.M., Conti D.V., Paredes Cotore J.P., Potter J.D., Newcomb P.A., Passarelli M.N., et al. Genetic variations in SMAD7 are associated with colorectal cancer risk in the colon cancer family registry. PLoS ONE. 2013;8:e60464. doi: 10.1371/journal.pone.0060464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zhang B., Jia W.H., Matsuo K., Shin A., Xiang Y.B., Matsuda K., Jee S.H., Kim D.H., Cheah P.Y., Ren Z., et al. Genome-wide association study identifies a new SMAD7 risk variant associated with colorectal cancer risk in East Asians. Int. J. Cancer. 2014;135:948–955. doi: 10.1002/ijc.28733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Kirac I., Matosevic P., Augustin G., Simunovic I., Hostic V., Zupancic S., Hayward C., Antoljak N., Rudan I., Campbell H., et al. SMAD7 variant rs4939827 is associated with colorectal cancer risk in Croatian population. PLoS ONE. 2013;8:e74042. doi: 10.1371/journal.pone.0074042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Peters U., Hutter C.M., Hsu L., Schumacher F.R., Conti D.V., Carlson C.S., Edlund C.K., Haile R.W., Gallinger S., Zanke B.W., et al. Meta-analysis of New Genome-wide Association Studies of Colorectal Cancer Risk. Hum. Genet. 2012;131:217–234. doi: 10.1007/s00439-011-1055-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Nanduri R., Mahajan S., Bhagyaraj E., Sethi K., Kalra R., Chandra V., Gupta P. The Active Form of Vitamin D Transcriptionally Represses Smad7 Signaling and Activates Extracellular Signal-regulated Kinase (ERK) to Inhibit the Differentiation of a Inflammatory T Helper Cell Subset and Suppress Experimental Autoimmune Encephalomyelitis. J. Biol. Chem. 2015;290:12222–12236. doi: 10.1074/jbc.M114.621839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Fleming N.I., Jorissen R.N., Mouradov D., Christie M., Sakthianandeswaren A., Palmieri M., Day F., Li S., Tsui C., Lipton L., et al. SMAD2, SMAD3 and SMAD4 mutations in colorectal cancer. Cancer Res. 2013;73:725–735. doi: 10.1158/0008-5472.CAN-12-2706. [DOI] [PubMed] [Google Scholar]
  • 50.Yanagisawa J., Yanagi Y., Masuhiro Y., Suzawa M., Watanabe M., Kashiwagi K., Toriyabe T., Kawabata M., Miyazono K., Kato S. Convergence of transforming growth factor-beta and vitamin D signaling pathways on SMAD transcriptional coactivators. Science. 1999;283:1317–1321. doi: 10.1126/science.283.5406.1317. [DOI] [PubMed] [Google Scholar]
  • 51.Maggio-Price L., Treuting P., Zeng W., Tsang M., Bielefeldt-Ohmann H., Iritani B.M. Helicobacter infection is required for inflammation and colon cancer in SMAD3-deficient mice. Cancer Res. 2006;66:828–838. doi: 10.1158/0008-5472.CAN-05-2448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Lamprecht S.A., Lipkin M. Chemoprevention of colon cancer by calcium, vitamin D and folate: Molecular mechanisms. Nat. Rev. Cancer. 2003;3:601–614. doi: 10.1038/nrc1144. [DOI] [PubMed] [Google Scholar]
  • 53.Bostick R.M., Goodman M., Sidelnikov E. Calcium and vitamin D. In: Potter J.D., Lindor N.M., editors. Genetics of Colorectal Cancer. Springer Science + Business Media, LLC; New York, NY, USA: 2009. pp. 277–296. [Google Scholar]
  • 54.Iyer S.S., Cheng G. Role of Interleukin 10 Transcriptional Regulation in Inflammation and Autoimmune Disease. Crit. Rev. Immunol. 2012;32:23–63. doi: 10.1615/CritRevImmunol.v32.i1.30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Hutter C.M., Slattery M.L., Duggan D.J., Muehling J., Curtin K., Hsu L., Beresford S.A., Rajkovic A., Sarto G.E., Marshall J.R., et al. Characterization of the association between 8q24 and colon cancer: Gene-environment exploration and meta-analysis. BMC Cancer. 2010;10:670. doi: 10.1186/1471-2407-10-670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Wang Y.P., Zhang J., Zhu H.Y., Qian C.L., Liu H., Ji F., Shen Z.Y. Common variation rs6983267 at 8q24.1 and risk of colorectal adenoma and cancer: Evidence based on 31 studies. Tumour Biol. 2014;35:4067–4075. doi: 10.1007/s13277-013-1532-2. [DOI] [PubMed] [Google Scholar]
  • 57.Pomerantz M.M., Ahmadiyeh N., Jia L., Herman P., Verzi M.P., Doddapaneni H., Beckwith C.A., Chan J.A., Hills A., Davis M., et al. The 8q24 cancer risk variant rs6983267 demonstrates long-range interaction with MYC in colorectal cancer. Nat. Genet. 2009;41:882–884. doi: 10.1038/ng.403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Miller D.M., Thomas S.D., Islam A., Muench D., Sedoris K. c-Myc and Cancer Metabolism. Clin. Cancer Res. 2012;18:5546–5553. doi: 10.1158/1078-0432.CCR-12-0977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Ahmadiyeh N., Pomerantz M.M., Grisanzio C., Herman P., Jia L., Almendro V., He H.H., Brown M., Liu X.S., Davis M., et al. 8q24 prostate, breast, and colon cancer risk loci show tissue-specific long-range interaction with MYC. Proc. Natl. Acad. Sci. USA. 2010;107:9742–9746. doi: 10.1073/pnas.0910668107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Wright J.B., Brown S.J., Cole M.D. Upregulation of c-MYC in cis through a Large Chromatin Loop Linked to a Cancer Risk-Associated Single-Nucleotide Polymorphism in Colorectal Cancer Cells. Mol. Cell. Biol. 2010;30:1411–1420. doi: 10.1128/MCB.01384-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Salehi-Tabar R., Nguyen-Yamamoto L., Tavera-Mendoza L.E., Quail T., Dimitrov V., An B.-S., Glass L., Goltzman D., White J.H. Vitamin D receptor as a master regulator of the c-MYC/MXD1 network. Proc. Natl. Acad. Sci. USA. 2012;109:18827–18832. doi: 10.1073/pnas.1210037109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Dimitrakopoulou V.I., Travis R.C., Shui I.M., Mondul A., Albanes D., Virtamo J., Agudo A., Boeing H., Bueno-de-Mesquita H.B., Gunter M.J., et al. Interactions Between Genome-Wide Significant Genetic Variants and Circulating Concentrations of 25-Hydroxyvitamin D in Relation to Prostate Cancer Risk in the National Cancer Institute BPC3. Am. J. Epidemiol. 2017;185:452–464. doi: 10.1093/aje/kww143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Vigneri P.G., Tirrò E., Pennisi M.S., Massimino M., Stella S., Romano C., Manzella L. The Insulin/IGF System in Colorectal Cancer Development and Resistance to Therapy. Front. Oncol. 2015;5:230. doi: 10.3389/fonc.2015.00230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Wong H.L., Delellis K., Probst-Hensch N., Koh W.P., Van Den Berg D., Lee H.P., Yu M.C., Ingles S.A. A new single nucleotide polymorphism in the insulin-like growth factor I regulatory region associates with colorectal cancer risk in singapore chinese. Cancer Epidemiol. Biomark. Prev. 2005;14:144–151. [PubMed] [Google Scholar]
  • 65.Simons C.C.J.M., Schouten L.J., Godschalk R.W.L., van Engeland M., van den Brandt P.A., van Schooten F.J., Weijenberg M.P. Genetic Variants in the Insulin-like Growth Factor Pathway and Colorectal Cancer Risk in the Netherlands Cohort Study. Sci. Rep. 2015;5:14126. doi: 10.1038/srep14126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Chi F., Wu R., Zeng Y.C., Xing R., Liu Y. Circulation insulin-like growth factor peptides and colorectal cancer risk: An updated systematic review and meta-analysis. Mol. Biol. Rep. 2013;40:3583–3590. doi: 10.1007/s11033-012-2432-z. [DOI] [PubMed] [Google Scholar]

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