Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data
Daniel Moore1, Ricardo de Matos Simoes1, Matthias Dehmer1
11School 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.
This study used a network-based approach to analyze prostate cancer gene interactions. The findings offer insights into cancer hallmarks and potential new therapies.
Area of Science:
- Systems biology
- Genomics
- Cancer research
Background:
- Prostate cancer etiology is complex.
- Understanding genetic causation is crucial.
Purpose of the Study:
- To gain insights into prostate cancer genetic associations.
- Utilize a network-based systems approach with the BC3Net algorithm.
Main Methods:
- Inferred a prostate cancer Gene Regulatory Network (GRN).
- Used 333 patient RNA-seq profiles from The Cancer Genome Atlas (TCGA).
Main Results:
- Analyzed functional components by extracting subnetworks based on biological processes.
- Interpreted the role of known cancer genes within processes.
- Investigated the landscape of prostate cancer genes and discussed pathological associations.
Conclusions:
- Presented a practical systems biology approach for prostate cancer gene interactions.
- Enabled interpretation of biological activity related to cancer hallmarks.
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