Disease mechanism, drug-target and biomarker prediction software: Application on prostate cancer and validation

Abstract

We introduce an R software package for condition-specific gene regulatory network analysis based on DC3NET algorithm. We also present an application of it on a real prostate dataset and demonstrate the benefit of the software. We performed genome-wide differential gene network analysis with the software on the LnCap androgen stimulated and deprived prostate cancer gene expression datasets (GSE18684) and inferred the androgen stimulated prostate cancer specific differential network. As an outstanding result, CXCR7 along with CXCR4 appeared to have the most important role in the androgen stimulated prostate specific genome-wide differential network. This blind estimation is strongly supported from the literature. The critical roles for CXCR4, a receptor over-expressed in many cancers, and CXCR7 on mediating tumor metastasis, along with their contributions as biomarkers of tumor behavior as well as potential therapeutic target were studied in several other types of cancers. In fact, a pharmaceutical company had already developed a therapy by inhibiting CXCR4 to block non-cancerous immuno-suppressive and pro-angiogenic cells from populating the tumor for disrupting the cancer environment and restoring normal immune surveillance functions. Considering this strong confirmation, our inferred regulatory network might reveal the driving mechanism of LnCap androgen stimulated prostate cancer. Because, CXCR4 appeared to be in the center of the largest subnetwork of our inferred differential network. Moreover, enrichment analyses for the largest subnetwork of it appeared to be significantly enriched in terms of axon guidance, fc gamma R-mediated phagocytosis and endocytosis. This also conforms with the recent literature in the field of prostate cancer.We demonstrate how to derive condition-specific gene targets from expression datasets on genome-wide level using differential gene network analysis. Our results showed that differential gene network analysis worked well in a prostate cancer dataset, which suggest the use of this approach as essential part of current expression data processing.Availability: The introduced R software package available in CRAN at https://cran.r-project.org/web/packages/dc3net and also at https://github.com/altayg/dc3net . CC-BY-NC 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/129742 doi: bioRxiv preprint first posted online Apr. 24, 2017; 2 BackgroundProstate cancer is the second most common cancer in the male population, with an estimated 417,000 new cases diagnosed each year in Europe (Ferlay, 2013). The activation of androgen receptor (AR) through androgens plays a crucial role in the development and progression of prostate cancer (Kaur, 2016; Anantharaman, 2015; Choudhary, 2011;Massie, 2011). For early detection of prostate cancer, prostate specific antigen (PSA) screening method has been used widely as a diagnostic tool (Karatas, 2015). However, PSA fails...