Salinomycin inhibits prostate cancer growth and migration via induction of oxidative stress

BACKGROUND: We have shown that a sodium ionophore monensin inhibits prostate cancer cell growth. A structurally related compound to monensin, salinomycin, was recently identified as a putative cancer stem cell inhibitor. METHODS: The growth inhibitory potential of salinomycin was studied in a panel of prostate cells. To get insights into the mechanism of action, a variety of assays such as gene expression and steroid profiling were performed in salinomycin-exposed prostate cancer cells. RESULTS: Salinomycin inhibited the growth of prostate cancer cells, but did not affect non-malignant prostate epithelial cells. Salinomycin impacted on prostate cancer stem cell functions as evidenced by reduced aldehyde dehydrogenase activity and the fraction of CD44(+) cells. Moreover, salinomycin reduced the expression of MYC, AR and ERG, induced oxidative stress as well as inhibited nuclear factor-κB activity and cell migration. Furthermore, profiling steroid metabolites revealed increased levels of oxidative stress-inducing steroids 7-ketocholesterol and aldosterone and decreased levels of antioxidative steroids progesterone and pregnenolone in salinomycin-exposed prostate cancer cells. CONCLUSION: Our results indicate that salinomycin inhibits prostate cancer cell growth and migration by reducing the expression of key prostate cancer oncogenes, inducing oxidative stress, decreasing the antioxidative capacity and cancer stem cell fraction

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment