RegNetB: Predicting Relevant Regulator-Gene Relationships in Localized Prostate Tumor Samples

Background: A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression. Results: The top regulatory relationships identified by RegNetB include the regulation of RLN1, RLN2, by PAX4, the regulation of ACPP (PAP) by JUN, BACH1 and BACH2, and the co-regulation of PGC and GDF15 by MAZ and TAF8. These target...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Angel Alvarez, Peter J. Woolf
• Keywords: Biometrics, BMCBI 2011, Gene Expression Data, Human Gene Expression, Prostate Cancer Progression