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Background The role of Th17 cells in prostate cancer is not fully understood. The transcription factor BATF controls the differentiation of Th17 cells. Mice deficient in Batf do not produce Th17 cells. Methods In this study, we aimed to characterize the role of Batf -dependent Th17 cells in prostate cancer by crossbreeding Batf knockout mice with mice conditionally mutant for Pten. Results We found that Batf knockout mice had changes in the morphology of prostate epithelial cells compared with normal mice, and Batf knockout mice deficient in Pten (called Batf-) had smaller prostate size and developed fewer invasive prostate adenocarcinomas than Pten -deficient mice with Batf expression (called Batf+). The prostate tumors in Batf- mice showed reduced proliferation, increased apoptosis, decreased angiogenesis and inflammatory cell infiltration, and activation of nuclear factor–κB signaling. Moreover, Batf- mice showed significantly reduced interleukin 23 (IL-23)-IL-23R signaling. In the prostate stroma of Batf- mice, IL-23R–positive cells were decreased considerably compared with Batf + mice. Splenocytes and prostate tissues from Batf- mice cultured under Th17 differentiation conditions expressed reduced IL-23/IL-23R than cultured cells from Batf + mice. Anti–IL-23p19 antibody treatment of Pten -deficient mice reduced prostate tumors and angiogenesis compared with control immunoglobulin G–treated mice. In human prostate tumors, BATF messenger RNA level was positively correlated with IL-23A and IL-23R but not RORC. Conclusion Our novel findings underscore the crucial role of IL-23-IL-23R signaling in mediating the function of Batf -dependent Th17 cells, thereby promoting prostate cancer initiation and progression. This finding highlights the BATF–IL-23R axis as a promising target for the development of innovative strategies for prostate cancer prevention and treatment. [ABSTRACT FROM AUTHOR]

The mapping of quantitative trait loci (QTL) is an important research question in animal and human studies. Missing data are common in such study settings, and ignoring such missing data may result in biased estimates of the genotypic effect and thus may eventually lead to errant results and incorrect inferences. In this article, we developed an expectation-maximization (EM)-likelihood-ratio test (LRT) in QTL mapping. Simulation studies based on two different types of phylogenetic models revealed that the EM-LRT, a statistical technique that uses EM-based parameter estimates in the presence of missing data, offers a greater statistical power compared with the ordinary analysis-of-variance (ANOVA)-based test, which discards incomplete data. We applied both the EM-LRT and the ANOVA-based test in a real data set collected from [F.sub.2] intercross studies of inbred mouse strains. It was found that the EM-LRT makes an optimal use of the observed data and its advantages over the ANOVA F-test are more pronounced when more missing data are present. The EM-LRT method may have important implications in QTL mapping in experimental crosses.

The way that smoking during pregnancy affects a baby's birth weight may depend on which genes the woman has. CYP1A1 and GSTT1 are two genes involved in the metabolism of chemicals in cigarette smoke. However, their structure is highly variable within the human population., Context: Little is known about genetic susceptibility to cigarette smoke in relation to adverse pregnancy outcomes. Objective: To investigate whether the association between maternal cigarette smoking and infant birth weight differs by polymorphisms of 2 maternal metabolic genes: CYP1A1 and GSTT1. Design, Setting, and Participants: Case-control study conducted in 1998-2000 among 741 mothers (174 ever smokers and 567 never smokers) who delivered singleton live births at Boston Medical Center. A total of 207 cases were preterm or low-birth-weight infants and 534 were non-low-birth-weight, full-term infants (control). Main Outcome Measure: Birth weight, gestation, fetal growth by smoking status and CYP1A1 Mspl (AA vs Aa and aa, where Aa and aa were combined because of small numbers of aa and similar results), and GSTT1 (present vs absent) genotypes. Results: Without consideration of genotype, continuous maternal smoking during pregnancy was associated with a mean reduction of 377 g (SE, 89 g) in birth weight (odds ratio [OR], 2.1; 95% confidence interval [CI], 1.2-3.7). When CYP1A1 genotype was considered, the estimated reduction in birth weight was 252 g (SE, 111 g) for the AA genotype group (n=75; OR, 1.3; 95% CI, 0.6-2.6), but was 520 g (SE, 124 g) for the Aa/aa genotype group (n=43 for Aa, n=6 for aa; OR, 3.2; 95% CI, 1.6-6.4). When GSTT1 genotype was considered, the estimated reduction in birth weight was 285 g (SE, 99 g) (OR, 1.7; 95% CI, 0.9-3.2) and 642 g (SE, 154 g) (OR, 3.5; 95% CI, 1.5-8.3) for the present and absent genotype groups, respectively. When both CYP1A1 and GSTT1 genotypes were considered, the greatest reduction in birth weight was found among smoking mothers with the CYP1A1 Aa/aa and GSTT1 absent genotypes (-1285 g; SE, 234 g; P Conclusions: In our study, maternal CYP1A1 and GSTT1 genotypes modified the association between maternal cigarette smoking and infant birth weight, suggesting an interaction between metabolic genes and cigarette smoking.

A genome scan for linkage has been conducted with sib pairs exhibiting extreme phenotypes in Anqing, China. The sample included 207 discordant, 258 high concordant and 99 low concordant sib pairs along with data from all but one of their parents. A total of 367 polymorphic markers were examined. In the primary analysis, five markers had maximum LOD-score values greater than 2.0. Secondary analyses identified additional candidate regions.