The effects of phenoxodiol on the cell cycle of prostate cancer cell lines

Background: Prostate cancer is associated with a poor survival rate. The ability of cancer cells to evade apoptosis and exhibit limitless replication potential allows for progression of cancer from a benign to a metastatic phenotype. The aim of this study was to investigate in vitro the effect of the isoflavone phenoxodiol on the expression of cell cycle genes. Methods: Three prostate cancer cell lines-LNCaP, DU145, and PC3 were cultured in vitro, and then treated with phenoxodiol (10 μM and 30 μM) for 24 and 48 h. The expression of cell cycle genes p21WAF1, c-Myc, Cyclin-D1, and Ki-67 was investigated by Real Time PCR. Results: Here we report that phenoxodiol induces cell cycle arrest in the G1/S phase of the cell cycle, with the resultant arrest due to the upregulation of p21WAF1 in all the cell lines in response to treatment, indicating that activation of p21WAF1 and subsequent cell arrest was occurring via a p53 independent manner, with induction of cytotoxicity independent of caspase activation. We found that c-Myc and Cyclin-D1 expression was not consistently altered across all cell lines but Ki-67 signalling expression was decreased in line with the cell cycle arrest. Conclusions: Phenoxodiol demonstrates an ability in prostate cancer cells to induce significant cytotoxicity in cells by interacting with p21WAF1 and inducing cell cycle arrest irrespective of p53 status or caspase pathway interactions. These data indicate that phenoxodiol would be effective as a potential future treatment modality for both hormone sensitive and hormone refractory prostate cancer

Network-Based Prediction and Analysis of HIV Dependency Factors

HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other