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.
journal_name
Curr Genomicsjournal_title
Current genomicsauthors
Moore D,Simoes RM,Dehmer M,Emmert-Streib Fdoi
10.2174/1389202919666181107122005subject
Has Abstractpub_date
2019-01-01 00:00:00pages
38-48issue
1eissn
1389-2029issn
1875-5488pii
CG-20-38journal_volume
20pub_type
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