AbstractWe 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
High-Throughput Transcriptomic and RNAi Analysis Identifies AIM1, ERGIC1, TMED3 and TPX2 as Potential Drug Targets in Prostate Cancer
