Interaction effect of PTEN and CDKN1B chromosomal regions on prostate cancer linkage

Landscape of CDKN1B Mutations in Luminal Breast Cancer and Other Hormone-Driven Human Tumors

The CDKN1B gene encodes for the p27Kip1 protein, firstly characterized as a cyclin dependent kinase (CDK)-inhibitor. Germline CDKN1B pathogenic variants have been described in hereditary tumors, such as multiple endocrine neoplasia (MEN)-like syndromes and familial prostate cancer. Despite its central role in tumor progression, for a long time it has been proposed that CDKN1B was very rarely somatically mutated in human cancer and that its expression levels were almost exclusively regulated at post-transcriptional level. Yet, the advent of massive parallel sequencing has partially subverted this general understanding demonstrating that, at least in some types of cancer, CDKN1B is mutated in a significant percentage of analyzed samples. Recent works have demonstrated that CDKN1B can be genetically inactivated and this occurs particularly in sporadic luminal breast cancer, prostate cancer and small intestine neuroendocrine tumors. However, a clear picture of the extent and significance of CDKN1B mutations in human malignances is still lacking. To fill this gap, we interrogated the COSMIC, ICGC, cBioPortal, and TRANSFAC data portals and current literature in PubMed, and reviewed the mutational spectrum of CDKN1B in human cancers, interpreting the possible impact of these mutations on p27Kip1 protein function and tumor onset and progression.

Assessing the joint effect of population stratification and sample selection in studies of gene-gene (environment) interactions

BACKGROUND

It is well known that the presence of population stratification (PS) may cause the usual test in case-control studies to produce spurious gene-disease associations. However, the impact of the PS and sample selection (SS) is less known. In this paper, we provide a systematic study of the joint effect of PS and SS under a more general risk model containing genetic and environmental factors. We provide simulation results to show the magnitude of the bias and its impact on type I error rate of the usual chi-square test under a wide range of PS level and selection bias.

RESULTS

The biases to the estimation of main and interaction effect are quantified and then their bounds derived. The estimated bounds can be used to compute conservative p-values for the association test. If the conservative p-value is smaller than the significance level, we can safely claim that the association test is significant regardless of the presence of PS or not, or if there is any selection bias. We also identify conditions for the null bias. The bias depends on the allele frequencies, exposure rates, gene-environment odds ratios and disease risks across subpopulations and the sampling of the cases and controls.

CONCLUSION

Our results show that the bias cannot be ignored even the case and control data were matched in ethnicity. A real example is given to illustrate application of the conservative p-value. These results are useful to the genetic association studies of main and interaction effects.

Gene-based interaction analysis by incorporating external linkage disequilibrium information

Gene-gene interactions have an important role in complex human diseases. Detection of gene-gene interactions has long been a challenge due to their complexity. The standard method aiming at detecting SNP-SNP interactions may be inadequate as it does not model linkage disequilibrium (LD) among SNPs in each gene and may lose power due to a large number of comparisons. To improve power, we propose a principal component (PC)-based framework for gene-based interaction analysis. We analytically derive the optimal weight for both quantitative and binary traits based on pairwise LD information. We then use PCs to summarize the information in each gene and test for interactions between the PCs. We further extend this gene-based interaction analysis procedure to allow the use of imputation dosage scores obtained from a popular imputation software package, MACH, which incorporates multilocus LD information. To evaluate the performance of the gene-based interaction tests, we conducted extensive simulations under various settings. We demonstrate that gene-based interaction tests are more powerful than SNP-based tests when more than two variants interact with each other; moreover, tests that incorporate external LD information are generally more powerful than those that use genotyped markers only. We also apply the proposed gene-based interaction tests to a candidate gene study on high-density lipoprotein. As our method operates at the gene level, it can be applied to a genome-wide association setting and used as a screening tool to detect gene-gene interactions.

RhoA and RhoC -siRNA inhibit the proliferation and invasiveness activity of human gastric carcinoma by Rho/PI3K/Akt pathway

AIM: To evaluate the effects of adenovirus-mediated gene transfer of RhoA siRNA and RhoC siRNA on proliferation and invasion of SGC7901 cells by Rho/ PI3K/Akt pathway.

METHODS: Plasmid of RhoA siRNA and RhoC siRNA were constructed and transfected into SGC7901 cells. siRNA and LY294002 (PI3K inhibitor) were designed as the control group. The mRNA and protein expressions of RhoA and RhoC were respectively detected with RT-PCR and western blotting. In order to find out the changes of proliferation and invasion power of SGC7901 cell lines, we analyzed the data by MTT, Boyden chamber and evaluated apoptosis of cell with flow cytometry. We treated BALB /C nude mice with RhoA and RhoC-siRNA, and tumor control rate (%) in nude mice was calculated.

RESULTS: RhoA and RhoC siRNA transfections specifically down-regulated the corresponding mRNA and protein levels in SGC7901 Cells.The experiment of permeated artificial basal membrane showed that the invasion power of SGC7901 cell lines are on the decline after treatment of Ad-RhoA and RhoC-siRNA (12.64 ±3.27 vs 87.38 ± 17.38, P < 0.05). The values of 490 nm wavelength light absorption were different in the five groups. The number of alive cells in the group of RhoA and RhoC-siRNA was lower than others in the 6^th d (0.71 ± 0.01 vs 3.82 ± 0,11 P < 0.05). The apoptosis rate of transfected RhoA and RhoC-siRNA group with FACS were 19.07% ± 1.78 and there were significant differences between treated and control groups (19.07 ± 1.78% vs 1.23 ± 0.11%, P < 0.01). The tumor transplantation experiment in BALB/C nude mice showed intratumoral injection of RhoA or RhoC siRNA can inhibit tumor growth.

CONCLUSION: RhoA and RhoC siRNA gene therapy mediated by adenovirus may be useful for inhibiting growth and invasion of SGC7901 through a PI3K/Akt pathway. These results provide a novel therapeutic target in preventing gastric cancer cell invasion and metastasis.

Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

Prostate cancer represents a significant worldwide public health burden. Epidemiological and genetic epidemiological studies have consistently provided data supporting the existence of inherited prostate cancer susceptibility genes. Segregation analyses of prostate cancer suggest that a multigene model may best explain familial clustering of this disease. Therefore, modeling gene-gene interactions in linkage analysis may improve the power to detect chromosomal regions harboring these disease susceptibility genes. In this study, we systematically screened for prostate cancer linkage by modeling two-locus gene-gene interactions for all possible pairs of loci across the genome in 426 prostate cancer families from Johns Hopkins Hospital, University of Michigan, University of Umeå, and University of Tampere. We found suggestive evidence for an epistatic interaction for six sets of loci (target chromosome-wide/reference marker-specific P< or =0.0001). Evidence for these interactions was found in two independent subsets from within the 426 families. While the validity of these results requires confirmation from independent studies and the identification of the specific genes underlying this linkage evidence, our approach of systematically assessing gene-gene interactions across the entire genome represents a promising alternative approach for gene identification for prostate cancer.