• Molecules and Cells
• /
• 제45권4호
• /
• pp.202-215
• /
• 2022

The androgen receptor (AR) is an important therapeutic target for treating prostate cancer (PCa). Moreover, there is an increasing need for understanding the AR-independent progression of tumor cells such as neuroendocrine prostate cancer (NEPC). Menin, which is encoded by multiple endocrine neoplasia type 1 (MEN1), serves as a direct link between AR and the mixed-lineage leukemia (MLL) complex in PCa development by activating AR target genes through histone H3 lysine 4 methylation. Although menin is a critical component of AR signaling, its tumorigenic role in AR-independent PCa cells remains unknown. Here, we compared the role of menin in AR-positive and AR-negative PCa cells via RNAi-mediated or pharmacological inhibition of menin. We demonstrated that menin was involved in tumor cell growth and metastasis in PCa cells with low or deficient levels of AR. The inhibition of menin significantly diminished the growth of PCa cells and induced apoptosis, regardless of the presence of AR. Additionally, transcriptome analysis showed that the expression of many metastasis-associated genes was perturbed by menin inhibition in AR-negative DU145 cells. Furthermore, wound-healing assay results showed that menin promoted cell migration in AR-independent cellular contexts. Overall, these findings suggest a critical function of menin in tumorigenesis and provide a rationale for drug development against menin toward targeting high-risk metastatic PCa, especially those independent of AR.

• Genomics & Informatics
• /
• 제18권1호
• /
• pp.8.1-8.10
• /
• 2020

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/.