A novel predicted NFκB pathway specific to prostate cancer.

<p>(A) A pathway of putative molecular activities surrounding NFκB as predicted by our computational framework (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.g001" target="_blank">Fig 1</a>): the pathway results from a genome-wide functional interaction specific to the NFκB pathway in human prostate cancer and contains 50 genes connected by 112 mechanism-specific interactions. To generate this novel NFκB pathway, we extracted a high-confidence subnetwork from the genome-wide functional interaction network (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>) around 18 query genes in total (blue type), including five NFκB complex genes (NFκB1, NFκB2, REL, RELA, RELB), five NFκB specific inhibitors (NFκBIA, NFκBIE, IκBKB, IκBKG, CHUK), and eight genes found to be differentially expressed between lethal and indolent prostate cancer (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.t001" target="_blank">Table 1</a>). (B) We recovered all known molecular interaction mechanisms between NFκB complex members and their inhibitors (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.s024" target="_blank">S17 Table</a>).</p

Mechanistic interaction networks specific to prostate cancer and the NFκB pathway.

<p>(A) We constructed an interaction ontology containing seven interaction types: a functional relationship can be a physical interaction (two genes directly bind to each other and interact physically), a complex (two genes form a protein binding complex), a covalent modification (a kinase posttranslationally modifies a substrate), a phosphorylation (a kinase adds a phosphate group to a substrate at a phosphorylation site), a shared pathway (two genes react in the same pathway which can be an indirect regulation), a regulatory interaction (a gene is activating or inhibiting another gene), or a synthetic interaction (two genes simultaneously regulate another gene, whereas the two genes individually would not regulate the third gene). (B) The performance (AUC values) of the seven trained mechanistic interaction networks after performing a 10-fold gene-holdout-based cross-validation of each of the networks revealed that the mechanistic interaction networks are generally more accurate than the global functional network.</p

NEDD9 / ZFP36 co-immunoprecipitation supports predicted physical interaction and knockdown regulates cell proliferation in a prostate cancer line.

<p>(A) Anti-ZFP36 was used to co-immunoprecipitate ZFP36-NEDD9 complex, confirming the presence of NEDD9 by western blot; IgG-rabbit antibody was included as a negative control. (B) LAPC4 cells were transfected with NEDD9 and control siRNAs (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>), with knockdown efficiency verified by western blot. (C) Cell proliferation rate measured after NEDD9 knockdown by WST-1 optical density (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>). Depletion of NEDD9 transcript consistently downregulates proliferation rates and suggests a possible role in growth regulation.</p

List of genes significantly upregulated in lethal prostate cancer and highly confidently associcated with NFκB in multiple biological contexts.

<p>We integrated 860 total datasets (651 gene expression datasets and 225 gene interaction networks) using a Bayesian framework in different biological contexts (including cell death, cell differentiation, cell cycle, cell proliferation, cell migration, and NFκB regulation; <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.s010" target="_blank">S3 Table</a>). From such context-specific networks, we extracted the subnetworks of genes most confidently associated with NFκB (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.s012" target="_blank">S5 Table</a>), which were subsequently analyzed in a differential expression study for significant (FDR corrected <i>p<0</i>.<i>05)</i> changes between lethal and indolent prostate cancer (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>); this resulted in eight total genes.</p

HIV prevalence and change in incidence for leading cancers.

<p>Note: IP, inverse probability; 95%CI, 95% confidence interval</p><p>^a Quadratic term was significant for non-Hodgkin’s lymphoma among HIV-infected individuals—4.5% (95%CI -10.1 to 23.9%) per year and -9.45% (95%CI -19.5 to -1.2%) per year².</p><p>HIV prevalence and change in incidence for leading cancers.</p

Trend in standardized incidence ratio (SIR) of cancer comparing HIV infected and HIV uninfected populations during ART expansion.

<p>Analyses utilized the IPW population. Note: ART, combination antiretroviral therapy.</p

Annual number of cancer diagnoses among HIV-infected and HIV-uninfected in Botswana.

<p>Analyses used the IPW population.</p

Overall cancer age-adjusted incidence among HIV-infected (solid) and HIV-uninfected (dotted) individuals.

<p>Analyses utilized the IPW population.</p

HIV prevalence and change in incidence for leading cancers.

<p>Note: IP, inverse probability; 95%CI, 95% confidence interval</p><p>^a Quadratic term was significant for non-Hodgkin’s lymphoma among HIV-infected individuals—4.5% (95%CI -10.1 to 23.9%) per year and -9.45% (95%CI -19.5 to -1.2%) per year².</p><p>HIV prevalence and change in incidence for leading cancers.</p

ART treatment coverage and median CD4 at ART initiation during the study period.

<p>Note: ART, combination antiretroviral therapy.</p