Neal, D. E. | Search bioRxiv

bioRxiv2017

Genome-Wide Differential Gene Network Analysis R Software And Its Application In LnCap Prostate Cancer

We present an R software package that performs at genome-wide level differential network analysis and infers only disease-specific molecular interactions between two different cell conditions. This helps revealing the disease mechanism and predicting most influential genes as potential drug targets or biomarkers of the disease condition of interest. As an exemplary analysis, we performed an application of the software over LNCaP datasets and, out of approximately 25000 genes, predicted CXCR7 and CXCR4 together as drug targets of LNCaP prostate cancer dataset. We further successfully validated them with our initial wet-lab experiments. The introduced software can be applied to all the diseases, especially cancer, with gene expression data of two different conditions (e.g. tumor vs normal) and thus has the potential of a global benefit. As a distinct remark, our software provide the causal disease mechanism with multiple potential drug-targets rather than a single independent target prediction. AvailabilityThe introduced R software package for the analysis is available in CRAN at https://cran.r-project.org/web/packages/dc3net and also at https://github.com/altayg/dc3net

bioinformatics↗

bioRxiv2016

A genetic risk score to guide age-specific, personalized prostate cancer screening

BackgroundProstate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patients risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa.\n\nMethodsGenotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given mans risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening.\n\nFindingsPHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set (p =10-53). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1st or 99th percentiles of genetic risk.\n\nInterpretationPolygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa.\n\nFundingDepartment of Defense #W81XWH-13-1-0391

genetics↗