Abstract
To understand the heterogeneity of prostate cancer (PCa) and identify novel underlying drivers, we constructed integrative molecular Bayesian networks (IMBNs) for PCa by integrating gene expression and copy number alteration data from published datasets. After demonstrating such IMBNs with superior network accuracy, we identified multiple sub-networks within IMBNs related to biochemical recurrence (BCR) of PCa and inferred the corresponding key drivers. The key drivers regulated a set of common effectors including genes preferentially expressed in neuronal cells. NLGN4Y—a protein involved in synaptic adhesion in neurons—was ranked as the top gene closely linked to key drivers of myogenesis subnetworks. Lower expression of NLGN4Y was associated with higher grade PCa and an increased risk of BCR. We show that restoration of the protein expression of NLGN4Y in PC-3 cells leads to decreased cell proliferation, migration and inflammatory cytokine expression. Our results suggest that NLGN4Y is an important negative regulator in prostate cancer progression. More importantly, it highlights the value of IMBNs in generating biologically and clinically relevant hypotheses about prostate cancer that can be validated by independent studies.
Highlights
Prostate cancer (PCa) is the most frequently diagnosed cancer in American men [1]
To understand the heterogeneity of prostate cancer (PCa) and identify novel underlying drivers, we constructed integrative molecular Bayesian networks (IMBNs) for PCa by integrating gene expression and copy number alteration data from published datasets. After demonstrating such IMBNs with superior network accuracy, we identified multiple sub-networks within IMBNs related to biochemical recurrence (BCR) of PCa and inferred the corresponding key drivers
The reconstructed IMBNs recapitulated the known biology of PCa
Summary
Prostate cancer (PCa) is the most frequently diagnosed cancer in American men [1]. it is a very heterogeneous disease, with a phenotype ranging from indolent behavior lasting decades to highly aggressive metastatic cancer which can be lethal in just a few years. Large scale genomic studies have been conducted to uncover novel genetic drivers of aggressive PCa [2] through analyzing gene expression datasets [3,4,5,6,7], identifying copy number alterations (CNAs) [8,9,10] and gene fusions [11,12,13], and detecting somatic mutations [14] Most of these studies focused on only one type of data, and when multiple data types were profiled, analysis was generally conducted for individual genes separately or within known pathways [15]. Since multiple genes and www.impactjournals.com/oncotarget pathways are involved in cancer progression, systems level analysis is needed to understand how such genes interact with and/or regulate each other, and how multiple genes and pathways work together to determine clinically meaningful endpoints such as disease recurrence
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call