title: The interaction of four genes in the inflammation pathway significantly predicts prostate cancer risk.
abstract: It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap, we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk. We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the CAPS (CAncer Prostate in Sweden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP, and TLR5) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and quickly evolving field.
authors.affiliation: Center for Human Genomics, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. jxu@wfubmc.edu
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Xu
authors.forename: James
authors.initials: J
authors.lastname: Lowey
authors.forename: Fredrik
authors.initials: F
authors.lastname: Wiklund
authors.forename: Jielin
authors.initials: J
authors.lastname: Sun
authors.forename: Fredrik
authors.initials: F
authors.lastname: Lindmark
authors.forename: Fang-Chi
authors.initials: FC
authors.lastname: Hsu
authors.forename: Latchezar
authors.initials: L
authors.lastname: Dimitrov
authors.forename: Baoli
authors.initials: B
authors.lastname: Chang
authors.forename: Aubrey R
authors.initials: AR
authors.lastname: Turner
authors.forename: Wennan
authors.initials: W
authors.lastname: Liu
authors.forename: Hans-Olov
authors.initials: HO
authors.lastname: Adami
authors.forename: Edward
authors.initials: E
authors.lastname: Suh
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: S Lilly
authors.initials: SL
authors.lastname: Zheng
authors.forename: William B
authors.initials: WB
authors.lastname: Isaacs
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Trent
authors.forename: Henrik
authors.initials: H
authors.lastname: Grönberg
mesh_terms: D016022:Case-Control Studies; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D007249:Inflammation; D008297:Male; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000209:etiology / Q000235:genetics* / Q000276:immunology*; D012042:Registries / Q000706:statistics & numerical data; D012307:Risk Factors; D015398:Signal Transduction; D051193:Toll-Like Receptors / Q000235:genetics*
chemical_list: D051193:Toll-Like Receptors