Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

BMC Medical Genomics

La Creis R Kidd⁵  David W Hein¹  Jason H Moore⁴  Kevin S Kimbro²  Guy N Brock³  Jie Zhang³  Ting Hu⁵  James Rudd²  Susan Yeyeodu²  Erica N Rogers¹  Nicole A Lavender¹ 

[1] Department of Pharmacology & Toxicology, School of Medicine, University of Louisville (UofL), Louisville, KY, USA;Department of Biology, The Julius L. Chambers Biomedical/Biotechnology Research Institute, North Carolina Central University, Durham, NC, USA;Department of Bioinformatics & Biostatistics, School of Public Health and Information Sciences, UofL, Louisville, KY, USA;Departments of Genetics and Community and Family Medicine, Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH, USA;505 South Hancock Street, Clinical & Translational Research Building, Room 306, Louisville, KY 40202, USA

关键词: Statistical epistasis networks (SEN);    Multifactor dimensionality reduction (MDR);    Gene-gene interactions;    Single nucleotide polymorphisms;    Apoptosis;    Prostate cancer;   

Others  :  1135019
DOI  :  10.1186/1755-8794-5-11

 received in 2011-05-10, accepted in 2012-04-30,  发布年份 2012

【 摘 要 】

Background

Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.

Methods

To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.

Results

Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between AKT3 rs2125230-PRKCQ rs571715 and disease aggressiveness using SEN-guided MDR (p = 0.011).

Conclusions

In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between AKT3-PRKCQ and aggressive PCA requires further validation using independent observational studies.

【 授权许可】

   
2012 Lavender et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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