Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

The impact of genetic variants in inflammatory-related genes on prostate cancer risk among men of African Descent: a case control study

  • Dominique Z Jones1,
  • Camille Ragin2,
  • Nayla C Kidd1,
  • Rafael E Flores-Obando3,
  • Maria Jackson4,
  • Norma McFarlane-Anderson5,
  • Marshall Tulloch-Reid6,
  • Kevin S Kimbro7 and
  • LaCreis R Kidd1Email author
Hereditary Cancer in Clinical Practice201311:19

https://doi.org/10.1186/1897-4287-11-19

Received: 29 July 2013

Accepted: 3 December 2013

Published: 23 December 2013

Abstract

Purpose

Although case–control studies have evaluated the role of variant inflammatory-related loci in prostate cancer, their impact is virtually unknown among men of African descent. To address this, we evaluated the impact of inflammatory cytokine single nucleotide polymorphisms (SNPs) on prostate cancer risk for men of African descent.

Methods

Forty-four SNPs in inflammatory cytokine-associated genes were evaluated among 814 African-American and Jamaican men (279 prostate cancer cases and 535 controls) using Illumina’s Golden gate genotyping system. Individual SNP effects were evaluated using logistic regression analysis.

Results

Four SNPs were modestly associated with prostate cancer after adjusting for age. In the total population, inheritance of the IL1R2 rs11886877 AA, IL8RB rs11574752 AA, TNF rs1800629 GA + AA, and TNF rs673 GA genotypes modestly increased prostate cancer risk by 1.45 to 11.7-fold relative to the referent genotype. Among U.S. men, age-adjusted dominant, recessive and additive genetic models for the IL1R2 rs11886877 locus were linked to an increase in prostate cancer susceptibility. However, these main effects did not persist after adjusting for multiple hypothesis testing.

Conclusion

Our preliminary data does not strongly support the hypothesis that inflammatory-related sequence variants influence prostate cancer risk among men of African descent. However, further evaluation is needed to assess whether other variant inflammatory-related genes may contribute to prostate cancer risk and disease progression in larger and ethnically diverse multi-center studies.

Keywords

Prostate cancer Inflammatory-related sequence variants Single nucleotide polymorphisms

Introduction

Chronic inflammation is thought to predispose an individual to cancer development[1]. This relationship is supported by a number of studies involving inflammatory bowel disease, colon cancer, hepatitis, liver cancer, pancreatitis, and pancreatic cancer[26]. Through several lines of evidence from epidemiological, histopathological, animal, genetic and molecular pathological studies, chronic inflammation is also thought to play a major role in prostate cancer development[2, 3]. For example, prostatic infections have been implicated in prostate cancer either through direct or indirect promotion of the inflammatory process[13]. In addition, the use of non-steroidal anti-inflammatory drugs (NSAIDS) and other anti-inflammatory agents have been shown to reduce prostate cancer risk[4].

The production of cytokines can be influenced by single nucleotide polymorphisms (SNPs) detected within pro- and anti-inflammatory genes. Genetic variations in cytokine related genes may lead to alterations in the spectrum of cytokines expressed in an inflammatory environment or level of antitumor response[5]. Epidemiological studies have reported on the relationship between prostate cancer susceptibility and genes involved in the cytokine-cytokine receptor signaling pathway, such as interleukins and their receptors, ribonuclease L (RNASEL) and tumor necrosis factor (TNF)[613]. While men of African Descent suffer disproportionately from this disease[2, 3, 14, 15], there is limited information about the positive link between variant cytokine genes and prostate cancer development in this population[16, 17]. Therefore, additional studies are needed to investigate the role of inflammatory-related SNPs in the development of prostate cancer among individuals of African Descent.

The current study evaluated the impact of 44 inflammatory-related sequence variants in relation to prostate cancer risk among men of African Descent from the U.S. and Jamaica. Findings from our study will help to fill in the gaps in information pertinent to prostate cancer among men of African Descent.

Materials and methods

Study population

Our study population, 279 cases and 535 controls, was comprised of two independent case–control study sets. These studies include the Prostate Cancer Clinical Outcome Study (PC2OS) at the University of Louisville and the Prostate Cancer Study in Jamaica at the University of the West Indies, Mona Campus. For both study sets, all incident prostate cancer cases were histologically confirmed and the controls were assigned based on normal PSA levels, and normal DREs/biopsies. Descriptions of each contributing study have been previously described[18, 19]. Briefly, the PC2OS study included 170 incident prostate cancer cases and 433 controls recruited between 2001–2005 through the Howard University Hospital (HUH) Division of Urology or related prostate cancer screening programs. Enrolled participants were men of African descent from the Washington, D.C. and Columbia, S.C. areas. The racial subgroups included self-reported African Americans, East African Americans, West African Americans, and Caribbean Americans. The Prostate Cancer Study in Jamaica included consecutively enrolled 109 incident prostate cancer cases and 102 controls recruited from 2005–2007 through the Urology clinic at the University Hospital of the West Indies in Kingston Jamaica.

Criteria for inflammatory gene and SNP selection

Inflammatory-associated genes and SNPs were selected using one or more of the following criteria: (1) empirical evidence that supports a relationship between the SNP/gene and cancer or inflammatory/immune response related diseases; (2) commonly studied loci; (3) marked disparities in genotype frequency comparing men of African Descent to their Caucasian counterparts (i.e., ±10% change); (4) evidence demonstrating a link with alterations in mRNA expression/stability or protein expression/structure or function using in silico tools such as SNPinfo (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm) or published reports; and (5) a minor allele frequency ≥5% reported in the National Center for Biotechnology Information (NCBI) Entrez SNP, (http://www.ncbi.nlm.nih.gov/snp). According to NCBI, the selected SNPs had an average minor allele frequency of 22%. However, the IL1RN rs315951 SNP had an allele frequency of 2.1%. This rare non-synonymous sequence variant was included in the analysis to explore whether a rare SNP would lead to substantial gains in effect sizes (i.e., 2–3 fold increases in risk) and contribute to the missing genetic heritability[20, 21].

Genetic analysis of variant inflammatory-associated SNPs

Allelic discrimination of 44 inflammatory-associated sequence variants was performed using a custom Illumina GoldenGate Genotyping assay with VeraCode Technology and BeadXpress reader, according to the manufacturer’s instructions[22].

Statistical analysis

Evaluation of the relationship between variant inflammatory associated alleles and prostate cancer risk was performed using univariate and multivariate analyses. The chi-square test of heterogeneity was used to assess for significant differences in the distribution of homozygous major, heterozygous, or homozygous minor genotypes between prostate cancer cases and controls. Evaluation of the relationship between prostate cancer risk and selected polymorphic genes, expressed as odds ratios (ORs) and corresponding 95% confidence intervals (CIs), were estimated using unconditional multivariate LR models adjusted for age. The major or common genotype was used as the reference category for each LR model. Statistical significance was assessed using a Bonferroni Correction (α = 0.05/44 SNPs) cut-off of 0.001, in order to adjust for multiple comparisons. All statistical analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC) and SNP Variation Software 7.0 (Golden Helix, Bozeman, MT).

Statistical power

Based on our sample size for the total population, U.S. and Jamaican men, we had >80% power to detect SNPs with odds ratios (ORs) of ≥1.4, ≥1.6, ≥1.8, respectively, for a co-dominant genetic model with 1 degree of freedom (df), a minor allele frequency of at least 22% and disease prevalence of 0.74%. Analyses were performed using Power for Genetic Association Version 2 Software[23].

Results

Prevalence of inflammatory-associated sequence variants among men of African Descent

Inheritance of variant inflammatory-related loci was fairly common among African-American men in the current study. Specifically, the minor allele frequencies of the 44 sequence variants ranged from approximately 2.6% to 48%, as depicted in Table 1. Notably, the observed genotype frequency distribution among controls did not significantly deviate from expected counts according to the Hardy Weinberg equilibrium. With the exception of four loci (IL1RN rs4251961, IL10RB rs999788, IL10RB rs283416, and ILR1 rs3917225), the observed genotype frequencies in the current study corroborated with values for individuals of African-American/African ancestry reported in the NCBI’s SNP entrez (P = 0.063-1.000), as shown in Table 1.
Table 1

Functional consequence and prevalence of inflammatory-associated sequence variants

dbSNPID

Gene

Location functional consequence

NCBI nucleotide change (major > minor allele)

NCBI minor allele frequency (MAF) for African-Americans

NCBI major/major genotype n (%) for African-Americans

NCBI major/minor genotype n (%) for African-Americans

NCBI minor/minor genotype n (%) for African-Americans

Current study nucleotide change (major > minor allele)

Current study MAF n(%) for African-Americans

Current study major/major genotype n (%) for African-Americans

Current study major/minor genotype n (%) for African-Americans

Current study minor/minor genotype n (%) for African-Americans

Overall χ2P-value comparing genotypes from individuals of African Descent as reported in NCBI versus the current study††

rs1058867 ǂ

IL10RB

UTR'3 miRNA

G > A

A = 37.9

26 (42.0)

25 (40.3)

11 (17.7)

G > A

A = 33.9

239 (44.6)

229 (42.9)

67 (12.5)

0.514

rs1071676 ǂ

IL1B

UTR'3 miRNA

G > C

C = 14.6

19 (79.2)

3 (12.5)

2 (8.3)

G > C

C = 16.1

378 (70.7)

142 (26.5)

15 (2.80)

0.106

rs11123902 ǂ

IL1R2

Intron 1

A > C

C = 31.8

10 (45.5)

10 (45.5)

2 (9.10)

A > C

C = 30.7

258 (48.2)

225 (42.1)

52 (9.70)

0.949

rs1126579 ǂ

IL8RB

UTR'3 miRNA

C > T

T = 14.5

46 (74.2)

14 (22.6)

2 (3.20)

G > A

A = 13.8

402 (75.1)

118 (22.1)

15 (2.80)

0.886

rs1143627 ǂ

IL1B

Near gene 5' TFBS

C > T

T = 37.5

12 (50.0)

6 (25.0)

6 (25.0)

G > A

A = 39.6

194 (36.3)

256 (48.2)

83 (15.5)

0.063

rs1143634 ǂ

IL1B

Exon 4 Splicing

C > T

T = 12.9

47 (75.8)

14 (22.6)

1 (1.60)

G > A

A = 15.5

381 (71.2)

142 (26.5)

12 (2.3)

0.833

rs11574752 ǂ

IL8RB

UTR'3 miRNA

G > A

A = 10.4

19 (79.2)

5 (20.8)

0(0.00)

G > A

A = 9.40

435 (81.3)

99 (18.5)

1 (0.20)

0.798

rs11886877

IL1R2

Intron 1

---

---

---

---

---

G > A

A = 35.8

211 (39.4)

265 (49.5)

59 (11.1)

 

rs12135247 ǂǂ

RNASEL

UTR'3 TFBS, miRNA

T > C

C = 16.3

33 (67.3)

16 (32.7)

0 (0.00)

A > G

G = 17.9

368 (68.8)

142 (26.5)

25 (4.70)

0.226

rs12328606 ǂǂ

IL1R2

Near gene 5' TFBS

C > T

T = 11.2

38 (77.6)

11 (22.4)

0 (0.00)

G > A

A = 13.5

405 (75.7)

116 (21.7)

14 (2.60)

0.798

rs1304037 ǂ

IL1A

UTR'3 miRNA

A > G

G = 39.6

8 (33.3)

13 (54.2)

3 (12.5)

A > G

G = 41.1

191 (35.7)

248 (46.4)

96 (17.9)

0.760

rs16944 ǂ

IL1B

Near gene 5' TFBS

A > G

G = 39.0

18 (30.5)

36 (61.0)

5 (8.50)

A > G

G = 45.1

156 (29.2)

275 (51.4)

104 (19.4)

0.108

rs17561 ǂ

IL1A

Exon 4 Splicing, nsSNP, benign

G > T

T = 15.3

44 (71.0)

17 (27.4)

1 (1.60)

C > A

A = 18.6

358 (66.9)

155 (29.0)

22 (4.10)

0.698

rs1799964 ǂ

TNF

Near gene 5' TFBS

T > C

C = 12.9

46 (74.2)

16 (25.8)

0 (0.00)

A > G

G = 16.5

374 (69.9)

145 (27.1)

16 (3.00)

0.492

rs1800587 ǂ

IL1A

UTR'5 TFBS, Splicing

C > T

T = 39.1

9 (39.1)

10 (43.5)

4 (17.4)

G > A

A = 41.8

183 (34.2)

257 (48.0)

95 (17.8)

0.885

rs1800629 ǂ

TNF

Near gene 5' TFBS

G > A

A = 13.7

46 (74.2)

15 (24.2)

1 (1.60)

G > A

A = 16.9

368 (68.8)

153 (28.6)

14 (2.60)

0.752

rs1800871 ǂ

IL10

Near gene 5' TFBS

C > T

T = 36.3

28 (45.2)

23 (37.1)

11 (17.7)

G > A

A = 40.7

188 (35.0)

258 (48.0)

89 (17.0)

0.219

rs1800872 ǂǂ

IL10

Near gene 5' TFBS

A > C

A = 50.0 C = 50.0

5 (21.7)

13 (56.6)

5 (21.7)

C > A

A = 40.7

188 (35.0)

258 (48.0)

89 (17.0)

0.874

rs1800893 ǂ

IL10

Near gene 5' TFBS

G > A

A = 37.1

22 (35.5)

34 (54.8)

6 (9.70)

G > A

A = 36.4

216 (40.4)

248 (46.4)

71 (13.2)

0.419

rs1800896 ǂ

IL10

Near gene 5' TFBS

A > G

G = 40.5

7 (33.3)

11 (52.4)

3 (14.3)

A > G

G = 33.3

243 (45.4)

228 (42.6)

64 (12.0)

0.491

rs2192752 ǂ

IL1R1

Near gene 5' TFBS

A > C

C = 4.80

56 (90.3)

6 (9.70)

0 (0.00)

A > C

C = 5.60

477 (89.1)

56 (10.5)

2 (0.40)

1.000

rs2227532 ǂ

IL8

Near gene 5' TFBS

T > C

C = 9.70

50 (80.6)

12 (19.4)

0 (0.00)

A > G

G = 8.60

448 (83.7)

82 (15.3)

5 (1.00)

0.690

rs2227538 ǂ

IL8

UTR'5 TFBS, Splicing

C > T

T = 17.7

41 (66.1)

20 (32.3)

1 (1.60)

G > A

A = 23.2

323 (60.4)

176 (32.9)

36 (6.70)

0.291

rs2227545 ǂ

IL8

UTR'3 miRNA

A > C

C = 8.70

19 (82.6)

4 (17.4)

0 (0.00)

A > C

C = 8.50

449 (83.9)

81 (15.1)

5 (1.00)

0.812

rs2229113 ǂ

IL10RA

Exon 7 nsSNP, probably damaging

G > A

A = 20.5

14 (63.7)

7 (31.8)

1 (4.50)

G > A

A = 18.8

355 (66.4)

159 (29.7)

21 (3.90)

0.782

rs2834167 ǂ

IL10RB

Exon 2 Splicing, nsSNP, benign

A > G

G = 16.9

44 (71.0)

15 (24.2)

3 (4.80)

A > G

G = 11.0

423 (79.1)

106 (19.8)

6 (1.10)

0.050

rs2856836 ǂ

IL1A

UTR'3 miRNA

T > C

C = 17.4

16 (69.6)

6 (26.1)

1 (4.30)

A > G

G = 18.6

358 (66.9)

155 (29.0)

22 (4.10)

1.000

rs3135932 ǂ

IL10RA

Exon 5 Splicing, nsSNP, benign

A > G

G = 2.10

23 (95.8)

1 (4.20)

0 (0.00)

A > G

G = 2.60

508 (95.0)

26 (4.80)

1 (0.20)

1.000

rs315951 ǂ

IL1RN

UTR'3 miRNA

C > G

G = 47.9

8 (33.3)

9 (37.5)

7 (29.2)

C > G

G = 48.0

144 (26.9)

268 (50.1)

123 (23.0)

0.482

rs3738579 ǂ

RNASEL

UTR'5 TFBS, Splicing

T > C

C = 16.7

14 (66.7)

7 (33.3)

0 (0.00)

A > G

G = 12.3

411 (76.8)

116 (21.7)

8 (1.50)

0.474

rs3917225 ǂ

IL1R1

Near gene 5' TFBS

A > G

G = 12.3

49 (80.3)

9 (14.8)

3 (4.90)

A > G

G = 9.10

438 (81.9)

97 (18.1)

0 (0.00)

0.001

rs4073 ǂ

IL8

Near gene 5' TFBS

A > T

T = 26.6

34 (54.8)

23 (37.1)

5 (8.10)

T > A

A = 20.9

335 (62.6)

176 (32.9)

24 (4.50)

0.262

rs4141134 ǂǂ

IL1R2

Near gene 5'

T > C

C = 11.2

39 (79.6)

9 (18.4)

1 (2.00)

A > G

G = 13.7

399 (74.6)

125 (23.4)

11 (2.00)

0.660

rs4251961 ǂ

IL1RN

Near gene 5' TFBS

T > C

C = 20.2

37 (59.7)

25 (40.3)

0 (0.00)

A > G

G = 17.9

367 (68.6)

145 (27.1)

23 (4.30)

0.035

rs4252243 ǂ

IL10RA

Near gene 5' TFBS

C > T

T = 32.5

9 (45.0)

9 (45.0)

2 (10.0)

G > A

A = 27.5

271 (50.7)

234 (43.7)

30 (5.60)

0.522

rs4674257 ǂǂ

IL8RB

Near gene 5' TFBS

G > A

A = 25.0

14 (58.3)

8 (33.3)

2 (8.30)

G > A

A = 20.1

347 (64.9)

161 (30.10)

27 (5.00)

0.468

rs4674259 ǂ

IL8RB

UTR'5 TFBS

A > G

G = 23.9

12 (52.2)

11 (47.8)

0 (0.00)

A > G

G = 20.0

349 (65.0)

158 (30.0)

28 (5.00)

0.176

rs486907 ǂ

RNASEL

Exon 1 nsSNP, benign

G > A

A = 16.7

16 (66.7)

8 (33.3)

0 (0.00)

G > A

A = 13.2

402 (75.1)

125 (23.4)

8 (1.50)

0.528

rs6726713 ǂǂ

IL1R2

Near gene 5' TFBS

C > T

T = 11.2

38 (77.6)

11 (22.4)

0 (0.00)

G > A

A = 12.1

417 (78.0)

106 (19.8)

12 (2.20)

0.689

rs673 ǂ

TNF

Near gene 5' TFBS

G > A

A = 13.7

45 (72.6)

17 (27.4)

0 (0.00)

G > A

A = 17.4

364 (68.0)

156 (29.2)

15 (2.80)

0.546

rs8178433 ǂ

IL10RB

Near gene 5' TFBS

T > G

G = 12.9

46 (74.2)

16 (25.8)

0 (0.00)

A > C

C = 12.4

408 (76.3)

121 (22.6)

6 (1.10)

0.811

rs949963 ǂ

IL1R1

Near gene 5' TFBS

G > A

A = 31.1

31 (50.8)

22 (36.1)

8 (13.1)

G > A

A = 33.1

249 (46.5)

218 (40.8)

68 (12.7)

0.771

rs9610 ǂ

IL10RA

UTR'3 miRNA

A > G

G = 41.9

20 (32.3)

32 (51.6)

10 (16.1)

A > G

G = 33.9

237 (44.3)

233 (43.5)

65 (12.2)

0.184

rs999788 ǂ

IL10RB

Near gene 5' TFBS

C > T

T = 19.5

40 (67.8)

15 (25.4)

4 (6.80)

G > A

A = 12.4

410 (76.6)

117 (21.9)

8 (1.50)

0.026

The nucleotide change may vary relative to that reported in NCBI depending on whether the genotyping was performed using the sense or anti-sense DNA strand.

†† The chi-square test was used to assess differences in the overall genotype frequencies comparing men of African Descent as reported in NCBI to those in the total population from the current study. P-values generated from the Fisher’s exact test (in italics) were used when expected genotype counts were < 5 for either cases or controls.

Abbreviations: MAF Minor Allele Frequency; UTR untranslated region; TFBS transcription factor binding site; nsSNP non-synonymous coding SNP; miRNA microRNA binding site; NCBI National Center for Biotechnology Information Entrez SNP.

ǂNCBI AFR1 or African American Population Panel.

ǂǂNCBI ASW Population Panel.

Relationship between inflammatory sequence variants and prostate cancer risk

Seven out of 44 sequence variants detected in inflammatory-related genes were modestly associated with prostate cancer risk among 814 men of African Descent (279 cases and 535 controls), as summarized in Table 2. For age-adjusted risk models, elevations in prostate cancer susceptibility were observed among carriers of IL1R2 rs1188687 7AA (OR = 1.92; 95%CI = 1.11, 3.32), IL8RB rs11574752 GA + AA (OR = 38.40; 95%CI = 3.86, 382.8), TNF rs1800629 GA + AA (OR = 1.53; 95%CI = 1.06, 2.20), and TNF rs673 GA (OR = 1.50; 95%CI = 1.04, 2.16) genotypes with risk estimates ranging from 1.50-38.4. The IL1R2 rs11886877 marker was the only genetic susceptibility factor significant under the additive genetic model (P-trend = 0.010), indicative of a significant dose–response effect in relation to the number of inherited minor alleles. The aforementioned markers were not classified as important prostate cancer risk indicators after adjusting for multiple comparisons bias using the Bonferroni correction, with a significance cut-off of ≤0.001.
Table 2

Relationship between inflammatory related sequence variants and prostate cancer risk among men of African Descent

Genes

dbSNP ID location predicted function

Genotype

Cases n (%)

Controls n (%)

Unadjusted OR (95%CI)

Adjusted OR (95%CI)

p-valueǂ

p trend

Bonferroni correction

IL1R2

rs11886877

GG

87 (31.2)

211 (39.4)

1.00 (referent)

1.00 (referent)

0.034

0.010

NS

 

Intron 1

GA

149 (53.4)

265 (49.5)

1.36 (0.99, 1.88)

1.35 (0.92,1.98)

0.058

  
  

AA

43 (15.4)

59 (11.1)

1.77 (1.11, 2.82)

1.92 (1.11, 3.32)

0.017

  
  

GA + AA

192 (68.8)

324 (60.6)

1.44 (1.06, 1.95)

1.46 (1.01, 2.10)

0.021

  
  

AA vs (GG + GA)

  

1.47 (0.96,2.24)

1.61 (0.98,2.63)

0.074

  

IL1A

rs17561

CC

195 (69.9)

358 (66.9)

1.00 (referent)

1.00 (referent)

0.025

0.108

NS

 

Exon 4

CA

82 (29.4)

155 (29.0)

0.97 (0.70,1.34)

1.01 (0.68,1.48)

0.858

  
 

Splicing

AA

2 (0.70)

22 (4.10)

0.17 (0.04, 0.72)

0.40 (0.08,1.83)

0.016

  
 

nsSNP

CA + AA

84 (30.1)

177 (33.1)

0.87 (0.64,1.20)

0.96 (0.66,1.40)

0.388

  
 

benign

AA vs (CC + CA)

  

0.17 (0.04, 0.72)

0.40 (0.09,1.82)

0.016

  

IL8RB

rs11574752

GG

230 (82.4)

435 (81.3)

1.00 (referent)

1.00 (referent)

0.011

0.784

NS

 

3′-UTR

GA

43 (15.4)

99 (18.5)

0.82 (0.55,1.21)

0.90 (0.56,1.40)

0.326

  
 

miRNA

AA

6 (2.20)

1 (0.20)

11.3 (1.36, 94.6)

38.4 (3.86, 382.8)

0.009

  
  

GA + AA

49 (17.6)

100 (18.7)

0.93 (0.64,1.35)

1.08 (0.69,1.70)

0.693

  
  

AA vs (GG + GA)

  

11.7 (1.40, 98.0)

39.2 (3.94, 390)

0.008

  

TNF

rs1800629

GG

171 (61.2)

368 (68.8)

1.00 (referent)

1.00 (referent)

0.047

0.087

NS

 

5′ near gene

GA

103 (37.0)

153 (28.6)

1.45 (1.06, 1.97)

1.54 (1.06, 2.24)

0.019

  
 

TFBS

AA

5 (1.80)

14 (2.60)

0.77 (0.27, 2.16)

1.30 (0.37,4.60)

0.619

  
  

GA + AA

108 (38.8)

167 (31.2)

1.39 (1.03, 1.90)

1.53 (1.06, 2.20)

0.032

  
  

AA vs (GG + GA)

  

0.68 (0.24,1.91)

1.13 (0.32,3.90)

0.462

  

TNF

rs673

GG

171 (61.3)

364 (68.0)

1.00 (referent)

1.00 (referent)

0.009

0.228

NS

 

5′ near gene

GA

106 (38.0)

156 (29.2)

1.45 (1.06, 2.00)

1.50 (1.04, 2.16)

0.018

  
 

TFBS

AA

2 (0.70)

15 (2.80)

0.28 (0.06, 1.26)

0.47 (0.09,2.40)

0.097

  
  

GA + AA

108 (39.1)

171 (32.0)

1.34 (0.99, 1.82)

1.43 (1.00, 2.05)

0.055

  
  

AA vs (GG + AG)

  

0.25 (0.06,1.10)

0.41 (0.08,2.07)

0.067

  

IL1A

rs2856836

AA

196 (70.3)

358 (66.9)

1.00 (referent)

1.00 (referent)

0.024

0.089

NS

 

3′-UTR

AG

81 (29.0)

155 (29.0)

0.96 (0.69,1.32)

0.99 (0.67,1.45)

0.776

  
 

miRNA

GG

2 (0.70)

22 (4.10)

0.17 (0.04, 0.71)

0.40 (0.09,1.82)

0.016

  
  

AG + GG

83 (29.7)

177 (33.1)

0.86 (0.63,1.17)

0.94 (0.65,1.36)

0.333

  
  

GG vs (AA + AG)

  

0.17 (0.04, 0.72)

0.40 (0.09,1.82)

0.016

  

IL10RA

rs4252243

GG

134 (48.4)

268 (50.4)

1.00 (referent)

1.00 (referent)

0.066

0.168

NS

 

5′ near gene

GA

115 (41.5)

234 (44.0)

0.98 (0.72,1.32)

0.83 (0.58,1.18)

0.893

  
 

TFBS

AA

28 (10.1)

30 (5.60)

1.86 (1.07, 3.24)

1.62 (0.82, 3.21)

0.028

  
  

GA + AA

143 (51.6)

264 (49.6)

1.08 (0.81,1.44)

0.91 (0.64,1.28)

0.605

  
  

AA vs (GG + GA)

  

1.88 (1.10, 3.21)

1.77 (0.91, 3.43)

0.021

  

ǂOn a separate line before the text regarding the chi-square test p-values state the following:

Boldface odd ratios (ORs) and 95% confidence interval (CI) indicate a significant relationship between the selected SNPs and prostate cancer risk.

From top to bottom within the column, the chi-square test p-values were used to determine the difference in the genotype frequencies between cases and controls for the overall, minor/major versus major/major genotypes, as well as the dominant (i.e., minor/minor versus major/major), co-dominant (minor/minor + major/minor versus major/major), and recessive genetic models (minor/minor versus major/major + major/minor). P-values generated from the Fisher’s Exact test (in italics) were calculated when expected genotype counts were < 5 for either cases or controls. Statistically significant p-values are marked in bold face.

Abbreviations: UTR, untranslated region; TFBS, transcription factor binding site; miRNA, microRNA binding site; NS, non-significant.

Upon stratification by sub-population, modestly significant prostate cancer biomarkers varied by racial/ethnic group in the age adjusted risk models. Possession of the RNASEL rs1213524 AG genotype was associated with a 2.17-fold increase in the risk of developing prostate cancer (OR = 2.10; 95%CI = 1.04, 4.24) among Jamaican men, as detailed in Table 3. However, this locus was not significant in the dominant, recessive or additive genetic models. Similar to the total population, inheritance of sequence variants in IL1R2, IL10RA and TNF among U.S. men were linked with a significant increase in prostate cancer risk. Among U.S. men, two inflammatory-related sequence variants, [IL1R2 rs11886877 (GA, GA + AA, AA) and IL10RA rs4252243 AA], were associated with a 1.82-2.49-fold increase in prostate cancer risk. Out of these 2 markers, the IL1R2 rs11886877 locus was significant for the dominant (OR = 2.75; 95%CI = 1.38, 5.50), co-dominant (OR = 1.82; 95%CI = 1.14, 2.88), recessive (OR = 2.05; 95%CI = 1.10, 3.80), and additive (P-trend value = 0.002) genetic models. None of the aforementioned markers survived correction for multiple hypotheses testing. Moreover, the IL10RA rs4252243 SNP was only significantly related to prostate cancer risk under the recessive genetic model (OR = 2.49; 95%CI = 1.08, 5.72).
Table 3

Relationship between inflammatory related sequence variants and prostate cancer risk among U.S. and Jamaican men

Genes

dbSNP ID location predicted function

Genotype

Unadjusted OR (95%CI) US men†

Age-adjusted OR (95%CI) US men†

Unadjusted OR (95%CI) Jamaican men†

Age-adjusted OR (95%CI) Jamaican men†

p-value US menǂ

p-value Jamaican menǂ

p-trend US men

p-trend Jamaican men

IL1B

rs1071676

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.050

0.550

0.022

0.276

 

UTR'3

GC

0.72 (0.48, 1.10)

0.70 (0.42, 1.14)

1.39 (0.71, 2.70)

1.28 (0.62, 2.64)

0.124

0.338

  
 

miRNA

CC

0.16 (0.02, 1.25)

0.19 (0.02, 2.00)

2.02 (0.18, 22.8)

1.15 (0.10, 14.6)

0.035

0.500

  
  

GC + CC

0.66 (0.44, 1.00)

0.66 (0.40, 1.10)

1.42 (0.74, 2.72)

1.26 (0.62, 2.60)

0.050

0.294

  
  

CC vs (GG + GC)

0.18 (0.02, 1.36)

0.21 (0.02, 2.18)

1.89 (0.16, 21.1)

1.10 (0.08, 13.7)

0.046

0.525

  

IL1B

rs1143634

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.051

0.447

0.016

0.203

 

Exon 4

GA

0.67 (0.44, 1.01)

0.65 (0.40, 1.06)

1.51 (0.76, 3.00)

1.37 (0.64, 2.90)

0.058

0.243

  
 

Splicing

AA

0.21 (0.02, 1.60)

0.24 (0.02, 2.86)

2.05 (0.18, 23.0)

1.16 (0.10, 14.6)

0.080

0.496

  
  

GA + AA

0.63 (0.42, 0.95)

0.62 (0.38, 1.02)

1.54 (0.78, 3.00)

1.36 (0.65, 2.82)

0.028

0.208

  
 

cds-synonymous

AA vs (GG + GA)

0.23 (0.02, 1.80)

0.27 (0.02, 3.20)

1.89 (0.16, 21.1)

1.10 (0.08, 13.7)

0.105

0.525

  

IL1R2

rs11886877

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.007

0.889

0.002

0.631

 

Intron 1

GA

1.60 (1.08, 2.40)

1.63 (1.00, 2.64)

0.92 (0.50, 1.68)

0.94 (0.48, 1.80)

0.020

0.782

  
  

AA

2.34 (1.31, 4.16)

2.75 (1.38, 5.50)

0.82 (0.36, 1.86)

0.94 (0.38, 2.30)

0.004

0.633

  
  

GA + AA

1.72 (1.18, 2.52)

1.82 (1.14, 2.88)

0.89 (0.50, 1.58)

0.94 (0.50, 1.74)

0.005

0.700

  
  

AA vs (GG + GA)

1.76 (1.04, 2.96)

2.05 (1.10, 3.80)

0.86 (0.40, 1.80)

0.97 (0.43, 2.20)

0.033

0.691

  

RNASEL

rs12135247

AA

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.800

0.025

0.909

0.216

 

UTR'3

AG

1.06 (0.72, 1.58)

1.14 (0.71, 1.84)

2.17 (1.14, 4.12)

2.10 (1.04, 4.24)

0.756

0.018

  
 

TFBS

GG

0.77 (0.30, 1.96)

0.70 (0.24, 2.10)

0.45 (0.08, 2.40)

0.28 (0.04, 1.70)

0.570

0.284

  
 

miRNA

AG + GG

1.02 (0.70, 1.50)

1.07 (0.68, 1.68)

1.81 (0.99, 3.30)

1.68 (0.88, 3.24)

0.906

0.053

  
  

GG vs (AG + AA)

0.76 (0.30, 1.92)

0.67 (0.22, 1.97)

0.36 (0.06, 1.91)

0.22 (0.04, 1.35)

0.555

0.196

  

TNF

rs1800629

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.101

0.782

0.113

0.549

 

5′ near gene

GA

1.50 (1.03, 2.20)

1.52 (0.96, 2.42)

1.21 (0.68, 2.12)

1.41 (0.78, 2.63)

0.034

0.518

  
 

TFBS

AA

0.90 (0.28, 2.80)

1.51 (0.36, 6.24)

1.00 (0.06, 16.3)

1.00 (0.06, 17.2)

0.551

0.752

  
  

GA + AA

1.44 (0.99, 2.10)

1.53 (0.97, 2.40)

1.20 (0.68, 2.10)

1.40 (0.75, 2.60)

0.050

0.525

  
  

AA vs (GG + GA)

0.78 (0.25, 2.42)

1.32 (0.32, 5.40)

0.94 (0.06, 15.2)

0.88 (0.05, 15.0)

0.452

0.734

  

IL1A

rs1800587

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.088

0.450

0.028

0.224

 

UTR'5

GA

0.75 (0.50, 1.10)

0.68 (0.42, 1.08)

1.38 (0.76, 2.50)

1.42 (0.74, 2.72)

0.144

0.297

  
 

TFBS

AA

0.56 (0.32, 0.96)

0.78 (0.40, 1.53)

1.56 (0.69, 3.50)

1.64 (0.70, 4.00)

0.038

0.279

  
 

Splicing (ESE or

GA + AA

0.70 (0.48, 1.00)

0.70 (0.45, 1.10)

1.42 (0.80, 2.50)

1.47 (0.80, 2.72)

0.053

0.222

  
 

ESS)

AA vs (GG + GA)

0.66 (0.40, 1.10)

0.96 (0.52, 1.80)

1.30 (0.62, 2.70)

1.34 (0.60, 3.00)

0.105

0.478

  

IL10RA

rs4252243

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.062

0.620

0.275

0.329

 

5′ near gene

GA

0.92 (0.63, 1.33)

0.70 (0.44, 1.10)

1.24 (0.70, 2.20)

1.21 (0.64, 2.28)

0.648

0.448

  
 

TFBS

AA

2.02 (1.04, 3.95)

2.10 (0.90, 4.98)

1.50 (0.54, 4.26)

1.04 (0.34, 3.20)

0.038

0.436

  
  

GA + AA

1.03 (0.72, 1.50)

0.81 (0.52, 1.30)

1.28 (0.74, 2.21)

1.15 (0.64, 2.10)

0.863

0.328

  
  

AA vs (GG + GA)

2.11 (1.10, 4.02)

2.49 (1.08, 5.72)

1.37 (0.50, 3.74)

1.02 (0.40, 2.98)

0.023

0.539

  

TNF

rs673

GG

1.00 (referent)

1.00 (referent)

1.00 (referent)

1.00 (referent)

0.027

0.452

0.279

0.874

 

5′ near gene

GA

1.50 (1.02, 2.20)

1.46 (0.92, 2.30)

1.22 (0.70, 2.14)

1.41 (0.76, 2.62)

0.025

0.498

  
 

TFBS

AA

0.24 (0.03, 1.84)

0.54 (0.06, 4.44)

0.33 (0.03, 3.24)

0.40 (0.03, 4.70)

0.116

0.315

  
  

GA + AA

1.38 (0.95, 2.00)

1.40 (0.88, 2.20)

1.14 (0.66, 2.00)

1.33 (0.72, 2.46)

0.087

0.635

  
  

AA vs (GG + GA)

0.21 (0.02, 1.60)

0.47 (0.06, 3.91)

0.31 (0.03, 2.98)

0.35 (0.03, 4.08)

0.080

0.286

  

On a separate line before the text regarding the chi-square test p-values state the following:

†Boldface odd ratios (ORs) and 95% confidence interval (CI) indicate a significant relationship between the selected SNPs and prostate cancer risk.

ǂFrom top to bottom within the column, the chi-square test p-values were used to determine the difference in the genotype frequencies between cases and controls for the overall, minor/major versus major/major genotypes, as well as the dominant (i.e., minor/minor versus major/major), co-dominant (minor/minor + major/minor versus major/major), and recessive genetic models (minor/minor versus major/major + major/minor). P-values generated from the Fisher’s Exact test (in italics) were calculated when expected genotype counts were < 5 for either cases or controls. Statistically significant p-values are marked in bold face.

Abbreviations: UTR, untranslated region; TFBS, transcription factor binding site; cds-syn, synonymous SNP; miRNA, microRNA binding site.

Discussion

Chronic inflammation has been associated with tumor development and metastasis. Inflammatory response is regulated through a complex network of cytokines, cytokine receptors and downstream targets that synergistically regulate innate/humoral immune and inflammatory processes. Recent molecular and genetic epidemiology studies have demonstrated that chronic inflammation and susceptibilities in inflammatory-associated genes are related to the development of several cancers, including lymphoma, and gastric and prostate cancer[6, 16, 2426]. However, to our knowledge, there are few published reports on the impact of variant cytokine-related genes in relation to prostate cancer among men of African Descent. Therefore, the current study evaluated the individual effects of 44 inflammatory associated sequence variants on prostate cancer risk among 279 cases and 535 disease-free men of African Descent from the U.S and Jamaica. Our findings revealed a modest increase in prostate cancer risk for unadjusted and adjusted logistic regression models for IL1R2 rs11886877 among men of African Descent. The additive, dominant and recessive genetic models of this variant were significant even after adjusting for age. However, this relationship did not survive after accounting for multiple comparisons bias.

IL1R2 rs11886877 is about 2415 base pairs from the transcription start site, which suggest it may have a high likelihood of regulating IL1R2 gene expression. Currently, there are no published reports on the relationship between IL1R2 rs11886877 and prostate cancer for any population. Although there is no evidence of the impact of this sequence variant on prostate cancer risk among European and African American men, the relationship between the IL1R2 gene expression and prostate cancer has been demonstrated through published reports[2729]. Leshem and colleagues (2011) found that the promoter region of IL1R2 possesses putative binding motifs for the TMPRSS2/ERG fusion gene, which is highly expressed in aggressive prostate cancer[27]. When the expression of IL1R2 was knocked down using small interfering RNAs, it resulted in the reduction of ZEB2 mRNA expression in hTERT/shp53/CyclinD-CDK4 overexpressing cells exposed to TMPRSS2/ERG[25]. TMPRSS2/ERG fusion gene indirectly up-regulates ZEB2, a facilitator of the epithelial to mesenchymal transition (EMT), by binding to IL1R2 to increase prostate cancer tumorigenesis[30].

Out of 44 inflammatory-related sequence variants, 7 SNPs included in our study were evaluated in relation to prostate cancer outcomes within 4 independent observational studies[6, 7, 10, 11]. Commensurate with our findings, two observational studies demonstrated that sequence variants detected in IL10 (rs1800871, rs1800872) and IL8 rs4073 were not significantly related to prostate cancer risk[6, 11]. Inheritance of the TNF rs1800629 AA genotype was associated with a significant 1.8 fold increase in prostate cancer risk among Caucasian men in a small study (150 cases, 150 controls); however, this marker resulted in null findings in a larger study (468 cases, 468 controls)[6, 7]. In our preliminary analyses, inheritance of one or more TNF rs1800629 A alleles was marginally associated with a 1.5-fold increase in prostate risk; however, this relationship did not survive adjustment after multiple hypothesis testing. Lastly, IL10 rs1800896 G and IL1B rs1143627 C alleles had protective effects in two separate Caucasian sub-populations. However, neither of these markers were significantly related to prostate cancer among African-American men in the current study. Casey and colleagues (2002) showed a 2-fold increase in prostate cancer susceptibility linked to inheritance of the RNASEL rs486907 AA genotype among mostly men of European descent[10]. This locus was not related to prostate cancer risk among African-Americans in the current study. Racial/ethnic disparities in the aforementioned risk estimates may be attributed to differences in minor allele frequencies, failure to adjust findings for multiple hypothesis testing or inadequate sample size among men with African ancestry.

In this study, we considered the strengths, limitations and future directions of the project. Forty-four sequence variants were evaluated in relation to prostate cancer risk among men of African Descent from the U.S. and Jamaica. Upon stratification by study center, the IL1R2 rs11886877 locus was marginally related to prostate cancer among men of African descent from the U.S. However, overall the inflammatory-related sequence variants were not robustly related to prostate cancer among our study participants. Despite this, we cannot eliminate the possibility that IL1R2 and other inflammatory-related sequence variants not included in this study may influence the risk of prostate cancer development or aggressive tumor behavior. In larger studies, the impact of individual or interaction among inflammatory cytokine-associated sequence variants in relation to prostate cancer tumor grade, biochemical or disease recurrence, and mortality using targeted sequencing, in vitro studies, in silico and bioinformatics tools. Such efforts will help to identify genetic markers linked to disproportionately high prostate cancer incidence, mortality, and morbidity rates among men of African Descent. Population admixture, which commonly occurs among men of African descent, may bias risk estimates. However, adjustment of risk estimates by West African Ancestry and/or age did not significantly modify the directionality of observed risk estimates among men from the U.S. (data not shown). Although, the sample size of this study population is small, there was ample statistical power to accurately detect risk estimates, ranging between 1.4-1.8 or 0.55-0.70. Our findings are important to genetic epidemiology research teams interested in pooling genetic and tumor characteristic data to determine whether other variant inflammatory-related cytokines contribute to prostate cancer susceptibility and disease prognosis. Although this study displays a modest association between inflammatory-related cytokine variant IL1R2 rs11886877 and prostate cancer risk, this relationship has yet to be tested biologically. The association of IL1R2 rs11886877 with prostate cancer risk may prove to be strong in a larger study population.

Conclusions

Chronic inflammation is an established risk factor of prostate cancer and many studies argue that it can lead to prostate cancer development. In this study, 44 inflammatory-related cytokine variants that may play a role in chronic inflammation were analyzed in relation to prostate cancer risk. Our preliminary data suggests that the possession of IL1R2 rs11886877 locus modifies prostate cancer susceptibility among individuals with African ancestry in the U.S. However, the association of the IL1R2 variant with prostate risk did not remain significant after adjust for multiple hypothesis testing. Future studies with ample statistical power to accommodate adjustment for multiple comparisons bias, will enable us to evaluate the impact of the IL1R2 variant or a combination of inflammatory cytokine SNPs in relation to prostate cancer risk, tumor grade, biochemical or disease recurrence, and mortality. These studies will lead to the identification of genetic markers that modify the susceptibility of individuals.

Abbreviations

SNP: 

Single nucleotide polymorphism

LR: 

Logistic regression.

Declarations

Acknowledgements

We thank Tiva T. VanCleave and Nicole A. Lavender for DNA sample preparation. We appreciate the contract services of Expression Analysis, Inc. (http://www.expressionanalysis.com) for the generation of genotype data.

We offer gratitude to Dr. Rick Kittles for the donation of DNA samples from prostate cancer patients.

Lastly, we value Peter Andrews for his service as a computer programming consultant on this project.

Grant/Research support: Clinical Translational Science Pilot Grant to LRK; the JGBCC Bucks for Brains “Our Highest Potential” in Cancer Research Endowment to LRK; P20-MD000175 NIH NCMHD to KSK.

Authors’ Affiliations

(1)
Department of Pharmacology & Toxicology, University of Louisville
(2)
Cancer Prevention and Control Program, Fox Chase Cancer Center
(3)
Molecular and Cellular Biology Program, State University of New York
(4)
Department of Community Health and Psychiatry, University of West Indies
(5)
Department of Basic Medical Sciences, University of the West Indies
(6)
Tropical Medicine Research Institute, University of the West Indies
(7)
Biomedical/Biotechnology Research Institute (BBRI), North Carolina Central University

References

  1. Coussens LM, Werb Z: Inflammation and cancer. Nature 2002,420(6917):860–867. 10.1038/nature01322View ArticlePubMedPubMed CentralGoogle Scholar
  2. Vasto S, Carruba G, Candore G, Italiano E, Di Bona D, Caruso C: Inflammation and prostate cancer. Future Oncol 2008,4(5):637–645. 10.2217/14796694.4.5.637View ArticlePubMedGoogle Scholar
  3. Sfanos KS, De Marzo AM: Prostate cancer and inflammation: the evidence. Histopathology 2012,60(1):199–215. 10.1111/j.1365-2559.2011.04033.xView ArticlePubMedPubMed CentralGoogle Scholar
  4. Veitonmaki T, Tammela TL, Auvinen A, Murtola TJ: Use of aspirin, but not other non-steroidal anti-inflammatory drugs is associated with decreased prostate cancer risk at the population level. Eur J Cancer 2013,49(4):938–945. 10.1016/j.ejca.2012.09.030View ArticlePubMedGoogle Scholar
  5. Feghali CA, Wright TM: Cytokines in acute and chronic inflammation. Front Biosci 1997, 2: d12-d26.View ArticlePubMedGoogle Scholar
  6. Dluzniewski PJ, Wang MH, Zheng SL, De Marzo AM, Drake CG, Fedor HL, Partin AW, Han M, Fallin MD, Xu J, et al.: Variation in IL10 and other genes involved in the immune response and in oxidation and prostate cancer recurrence. Cancer Epidemiol Biomarkers Prev 2012,21(10):1774–1782. 10.1158/1055-9965.EPI-12-0458View ArticlePubMedPubMed CentralGoogle Scholar
  7. Berhane N, Sobti RC, Melesse S, Mahdi SA, Kassu A: Significance of Tumor necrosis factor alpha-308 (G/A) gene polymorphism in the development of prostate cancer. Mol Biol Rep 2012,39(12):11125–11130. 10.1007/s11033-012-2020-2View ArticlePubMedGoogle Scholar
  8. Lin DW, FitzGerald LM, Fu R, Kwon EM, Zheng SL, Kolb S, Wiklund F, Stattin P, Isaacs WB, Xu J, et al.: Genetic variants in the LEPR, CRY1, RNASEL, IL4, and ARVCF genes are prognostic markers of prostate cancer-specific mortality. Cancer Epidemiol Biomarkers Prev 2011,20(9):1928–1936. 10.1158/1055-9965.EPI-11-0236View ArticlePubMedPubMed CentralGoogle Scholar
  9. Alvarez-Cubero MJ, Saiz M, Martinez-Gonzalez LJ, Alvarez JC, Lorente JA, Cozar JM: RNASEL study of genetics of prostate cancer and its relation to clinical staging. Actas Urol Esp 2012,36(5):306–311.View ArticlePubMedGoogle Scholar
  10. Casey G, Neville PJ, Plummer SJ, Xiang Y, Krumroy LM, Klein EA, Catalona WJ, Nupponen N, Carpten JD, Trent JM, et al.: RNASEL Arg462Gln variant is implicated in up to 13% of prostate cancer cases. Nat Genet 2002,32(4):581–583. 10.1038/ng1021View ArticlePubMedGoogle Scholar
  11. Kesarwani P, Ahirwar DK, Mandhani A, Singh AN, Dalela D, Srivastava AN, Mittal RD: IL-10–1082 G > A: a risk for prostate cancer but may be protective against progression of prostate cancer in North Indian cohort. World J Urol 2009,27(3):389–396. 10.1007/s00345-008-0361-1View ArticlePubMedGoogle Scholar
  12. Tindall EA, Severi G, Hoang HN, Southey MC, English DR, Hopper JL, Giles GG, Hayes VM: Interleukin-6 promoter variants, prostate cancer risk, and survival. Prostate 2012,72(16):1701–1707. 10.1002/pros.22557View ArticlePubMedGoogle Scholar
  13. Tindall EA, Severi G, Hoang HN, Ma CS, Fernandez P, Southey MC, English DR, Hopper JL, Heyns CF, Tangye SG, et al.: Comprehensive analysis of the cytokine-rich chromosome 5q31.1 region suggests a role for IL-4 gene variants in prostate cancer risk. Carcinogenesis 2010,31(10):1748–1754. 10.1093/carcin/bgq081View ArticlePubMedGoogle Scholar
  14. American Cancer S: Cancer Facts and Figures 2012. American Cancer Society: Atlanta, Georgia; 2012.Google Scholar
  15. American Cancer S: Cancer Facts & Figures for African Americans 2011–2012. American Cancer Society: Atlanta; 2011.Google Scholar
  16. Zabaleta J, Lin HY, Sierra RA, Hall MC, Clark PE, Sartor OA, Hu JJ, Ochoa AC: Interactions of cytokine gene polymorphisms in prostate cancer risk. Carcinogenesis 2008,29(3):573–578.View ArticlePubMedGoogle Scholar
  17. Zabaleta J, Su LJ, Lin HY, Sierra RA, Hall MC, Sartor AO, Clark PE, Hu JJ, Ochoa AC: Cytokine genetic polymorphisms and prostate cancer aggressiveness. Carcinogenesis 2009,30(8):1358–1362. 10.1093/carcin/bgp124View ArticlePubMedPubMed CentralGoogle Scholar
  18. Kidd LR, Jones DZ, Rogers EN, Kidd NC, Beache S, Rudd JE, Ragin C, Jackson M, McFarlane-Anderson N, Tulloch-Reid M, et al.: Chemokine Ligand 5 (CCL5) and chemokine receptor (CCR5) genetic variants and prostate cancer risk among men of African Descent: a case–control study. Hereditary Cancer Clin Prac 2012,10(1):16. 10.1186/1897-4287-10-16View ArticleGoogle Scholar
  19. Jackson MD, Walker SP, Simpson-Smith CM, Lindsay CM, Smith G, McFarlane-Anderson N, Bennett FI, Coard KC, Aiken WD, Tulloch T, et al.: Associations of whole-blood fatty acids and dietary intakes with prostate cancer in Jamaica. Cancer Causes Control 2012,23(1):23–33. 10.1007/s10552-011-9850-4View ArticlePubMedGoogle Scholar
  20. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al.: Finding the missing heritability of complex diseases. Nature 2009,461(7265):747–753. 10.1038/nature08494View ArticlePubMedPubMed CentralGoogle Scholar
  21. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008,9(5):356–369. 10.1038/nrg2344View ArticlePubMedGoogle Scholar
  22. Steemers FJ, Gunderson KL: Whole genome genotyping technologies on the Bead Array platform. Biotechnol J 2007,2(1):41–49. 10.1002/biot.200600213View ArticlePubMedGoogle Scholar
  23. Menashe I, Rosenberg PS, Chen BE: PGA: power calculator for case–control genetic association analyses. BMC Genet 2008, 9: 36.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Song N, Han S, Lee KM, Choi JY, Park SK, Jeon S, Lee Y, Ahn HS, Shin HY, Kang HJ, et al.: Genetic variants in interleukin-2 and risk of lymphoma among children in Korea. Asian Pac J Cancer Prev 2012,13(2):621–623. 10.7314/APJCP.2012.13.2.621View ArticlePubMedGoogle Scholar
  25. Gonzalez CA, Sala N, Capella G: Genetic susceptibility and gastric cancer risk. Int J Cancer 2002,100(3):249–260. 10.1002/ijc.10466View ArticlePubMedGoogle Scholar
  26. Lin HC, Liu CC, Kang WY, Yu CC, Wu TT, Wang JS, Wu WJ, Huang CH, Wu MT, Huang SP: Influence of cytokine gene polymorphisms on prostate-specific antigen recurrence in prostate cancer after radical prostatectomy. Urol Int 2009,83(4):463–470. 10.1159/000251189View ArticlePubMedGoogle Scholar
  27. Leshem O, Madar S, Kogan-Sakin I, Kamer I, Goldstein I, Brosh R, Cohen Y, Jacob-Hirsch J, Ehrlich M, Ben-Sasson S, et al.: TMPRSS2/ERG promotes epithelial to mesenchymal transition through the ZEB1/ZEB2 axis in a prostate cancer model. PLoS One 2011,6(7):e21650. 10.1371/journal.pone.0021650View ArticlePubMedPubMed CentralGoogle Scholar
  28. Chang SY, Su PF, Lee TC: Ectopic expression of interleukin-1 receptor type II enhances cell migration through activation of the pre-interleukin 1alpha pathway. Cytokine 2009,45(1):32–38. 10.1016/j.cyto.2008.10.013View ArticlePubMedGoogle Scholar
  29. Ricote M, Garcia-Tunon I, Bethencourt FR, Fraile B, Paniagua R, Royuela M: Interleukin-1 (IL-1alpha and IL-1beta) and its receptors (IL-1RI, IL-1RII, and IL-1Ra) in prostate carcinoma. Cancer 2004,100(7):1388–1396. 10.1002/cncr.20142View ArticlePubMedGoogle Scholar
  30. King JC, Xu J, Wongvipat J, Hieronymus H, Carver BS, Leung DH, Taylor BS, Sander C, Cardiff RD, Couto SS, et al.: Cooperativity of TMPRSS2-ERG with PI3-kinase pathway activation in prostate oncogenesis. Nat Genet 2009,41(5):524–526. 10.1038/ng.371View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Jones et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.