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The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk

¹ Center for Human Genomics and ² Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina; ³ Translational Genomics Research Institute, Phoenix, Arizona; ⁴ Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, New Hampshire; ⁵ Department of Urology, Johns Hopkins Medical Institutions, Baltimore, Maryland; ⁶ Oncology, Department of Radiation Sciences, University of Umeå, Umeå; and ⁷ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

Requests for reprints: Jianfeng Xu, Center for Human Genomics, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157. Phone: 336-713-7500; Fax: 336-713-7566. E-mail: jxu{at}wfubmc.edu

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.

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