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.
Furthermore, we investigate the local landscape of prostate cancer genes and discuss pathological
associations 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.
Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data