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Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

Author(s):

Daniel Moore, Ricardo de Matos Simoes, Matthias Dehmer and Frank Emmert-Streib*   Pages 1 - 11 ( 11 )

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. Method: 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.

Keywords:

Gene regulatory network, Prostate cancer, Genomics, Systems biology, Network inference, Precision medicine, Data science

Affiliation:

School of Pharmacy, Queen’s University Belfast, Belfast, BT9 7BL, Department of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Harvard Medical School, Boston, MA 02115, Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria; College of Computer and Control Engineering, Nankai University, Tianjin, China; Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Predictive Medicine and Data Analytics Laboratory, Department of Signal Processing, Tampere University of Technology, Tampere 33720, Finland; Institute of Biosciences and Medical Technology, Tampere



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