Polymorphisms in the mTOR gene and risk of sporadic prostate cancer in an Eastern Chinese population.
BACKGROUND: The mTOR gene regulates cell growth by controlling mRNA translation, ribosome biogenesis, autophagy, and metabolism. Abnormally increased expression of mTOR was associated with carcinogenesis, and its functional single nucleotide polymorphisms (SNPs) may regulate the expression of mTOR and thus contribute to cancer risk. METHODOLOGY/PRINCIPAL FINDINGS: In a hospital-based case-control study of 1004 prostate cancer (PCa) cases and 1051 cancer-free controls, we genotyped six potentially functional SNPs of mTOR (rs2536 T>C, rs1883965 G>A, rs1034528 G>C, rs17036508 T>C, rs3806317 A>G, and rs2295080 T>G) and assessed their associations with risk of PCa by using logistic regression analysis. CONCLUSIONS/SIGNIFICANCES: In the single-locus analysis, we found a significantly increased risk of PCa associated with mTOR rs2536 CT/CC and rs1034528 CG/CC genotypes [adjusted OR = 1.42 (1.13-1.78), P = 0.003 and 1.29 (1.07-1.55), P = 0.007), respectively], compared with their common homozygous genotypes, whereas mTOR rs2295080 GT/GG genotypes were associated with a decreased risk of PCa [adjusted OR = 0.76 (0.64-0.92), P = 0.003], compared with wild-type TT genotypes. In the combined analysis of the six SNPs, we found that individuals carrying two or more adverse genotypes had an increased risk of PCa [adjusted OR = 1.24 (1.04-1.47), P = 0.016], compared with individuals carrying less than two adverse genotypes. In the multiple dimension reduction analysis, body mass index (BMI) was the best one-factor model with the highest CVC (100%) and the lowest prediction error (42.7%) among all seven factors. The model including an interaction among BMI, rs17036508, and rs2536 was the best three-factor model with the highest CVC (100%) and the lowest prediction error of 41.9%. These findings suggested that mTOR SNPs may contribute to the risk of PCa in Eastern Chinese men, but the effect was weak and needs further validation by larger population-based studies.
Li, Q; Gu, C; Zhu, Y; Wang, M; Yang, Y; Wang, J; Jin, L; Zhu, M-L; Shi, T-Y; He, J; Zhou, X; Ye, D-W; Wei, Q
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