Investigation of androgen receptor-dependent alternative splicing has identified a unique subtype of lethal prostate cancer / 亚洲男科学杂志(英文版)

A complete proteomics study characterizing active androgen receptor (AR) complexes in prostate cancer (PCa) cells identified a diversity of protein interactors with tumorigenic annotations, including known RNA splicing factors. Thus, we chose to further investigate the functional role of AR-mediated alternative RNA splicing in PCa disease progression. We selected two AR-interacting RNA splicing factors, Src associated in mitosis of 68 kDa (SAM68) and DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) to examine their associative roles in AR-dependent alternative RNA splicing. To assess the true physiological role of AR in alternative RNA splicing, we assessed splicing profiles of LNCaP PCa cells using exon microarrays and correlated the results to PCa clinical datasets. As a result, we were able to highlight alternative splicing events of clinical significance. Initial use of exon-mini gene cassettes illustrated hormone-dependent AR-mediated exon-inclusion splicing events with SAM68 or exon-exclusion splicing events with DDX5 overexpression. The physiological significance in PCa was investigated through the application of clinical exon array analysis, where we identified exon-gene sets that were able to delineate aggressive disease progression profiles and predict patient disease-free outcomes independently of pathological clinical criteria. Using a clinical dataset with patients categorized as prostate cancer-specific death (PCSD), these exon gene sets further identified a select group of patients with extremely poor disease-free outcomes. Overall, these results strongly suggest a nonclassical role of AR in mediating robust alternative RNA splicing in PCa. Moreover, AR-mediated alternative spicing contributes to aggressive PCa progression, where we identified a new subtype of lethal PCa defined by AR-dependent alternative splicing.

p-SAGE: Parametric Statistical Analysis of Gene Sets / 生物化学与生物物理进展

Tumor genesis and development often result from deregulation of important biological pathways at the gene expression level. Although there has been much work focused on searching gene sets using gene expression data or other prior information, proper statistical testing of the gene sets is still an open question. Most studies have expanded the testing method of a single gene into the gene sets. Parametric statistical analysis of gene sets ( p-SAGE ) was presented for determining the significant gene sets or pathways associated with a phenotype of interest. The method was applied to brain tumor experiments to identify many gene sets. Some of the newly discovered gene sets were related to signal transduction and immunity. This simple and effective method gives useful biologically meaningful results.