Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

Daniel Moore; Ricardo de Matos Simoes; Matthias Dehmer; Frank Emmert-Streib

doi:10.2174/1389202919666181107122005

Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

Curr Genomics. 2019 Jan;20(1):38-48. doi: 10.2174/1389202919666181107122005.

Authors

Daniel Moore¹, Ricardo de Matos Simoes¹, Matthias Dehmer¹, Frank Emmert-Streib¹

Affiliation

¹ 1School of Pharmacy, Queen's University Belfast, Belfast, BT9 7BL, UK; 2Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, MA02115, USA; 3Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria; 4College of Computer and Control Engineering, Nankai University, Tianjin, China; 5Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria; 6Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of Technology, Tampere33720, Finland; 7Institute of Biosciences and Medical Technology, Tampere, Finland.

Abstract

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.

Keywords: Data science; Gene regulatory network; Genomics; Network inference; Precision medicine; Prostate cancer; Systems biology.