Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data.

Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity.

Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm.

Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database.

Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Fur-thermore, we investigate the local landscape of prostate cancer genes and discuss pathological associa-tions that may be relevant in the development of new targeted cancer therapies.

Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.