Promoter variants in the MSMB gene associated with prostate cancer regulate MSMB/NCOA4 fusion transcripts.
ABSTRACT Beta-microseminoprotein (MSP)/MSMB is an immunoglobulin superfamily protein synthesized by prostate epithelial cells and secreted into seminal plasma. Variants in the promoter of the MSMB gene have been associated with the risk of prostate cancer (PCa) in several independent genome-wide association studies. Both MSMB and an adjacent gene, NCOA4, are subjected to transcriptional control via androgen response elements. The gene product of NCOA4 interacts directly with the androgen receptor as a co-activator to enhance AR transcriptional activity. Here, we provide evidence for the expression of full-length MSMB-NCOA4 fusion transcripts regulated by the MSMB promoter. The predominant MSMB-NCOA4 transcript arises by fusion of the 5'UTR and exons 1-2 of the MSMB pre-mRNA, with exons 2-10 of the NCOA4 pre-mRNA, producing a stable fusion protein, comprising the essential domains of NCOA4. Analysis of the splice sites of this transcript shows an unusually strong splice acceptor at NCOA4 exon 2 and the presence of Alu repeats flanking the exons potentially involved in the splicing event. Transfection experiments using deletion clones of the promoter coupled with luciferase reporter assays define a core MSMB promoter element located between -27 and -236 of the gene, and a negative regulatory element immediately upstream of the start codon. Computational network analysis reveals that the MSMB gene is functionally connected to NCOA4 and the androgen receptor signaling pathway. The data provide an example of how GWAS-associated variants may have multiple genetic and epigenetic effects.
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ABSTRACT: Benign prostatic hyperplasia (BPH) is the most common prostate disease in aging men. Microseminoprotein-beta (MSMB) is abundant in semen. In this study, we investigated association between single nucleotide polymorphisms (SNPs) at the promoter of the MSMB gene and the risk for developing BPH in a Korean population.International neurourology journal 06/2014; 18(2):63-7.
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ABSTRACT: Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men. Genome-wide association studies (GWAS) has been highly successful in discovering susceptibility loci for prostate cancer. Currently, more than twenty GWAS have identified more than fifty common variants associated with susceptibility with PCa. Yet with the increase in loci, voices from the scientific society are calling for more. In this review, we summarize current findings, discuss the common problems troubling current studies and shed light upon possible breakthroughs in the future. GWAS is the beginning of something wonderful. Although we are quite near the end of the beginning, post-GWAS studies are just taking off and future studies are needed extensively. It is believed that in the future GWAS information will be helpful to build a comprehensive system intergraded with PCa prevention, diagnosis, molecular classification, personalized therapy.Protein & Cell 08/2013; · 3.22 Impact Factor
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ABSTRACT: Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes. We can produce more useful models for stratifying prostate cancers into high-risk cancer and low-risk cancer categories by considering the range in each class through inner-class clustering. In this paper, we attempt to classify cancers into high-risk (aggressive) prostate cancer and low-risk (non-aggressive) prostate cancer using ICP (Inner-class Clustering and Prediction). Our model classified more efficiently than the models of the algorithms used for comparison. After discovering a number of genes linked to prostate cancer from the gene pairs used in our classification, we discovered that the proposed method can be used to find new unknown genes and gene pairs which distinguish between high-risk cancer and low-risk cancer.Computers in biology and medicine 10/2013; 43(10):1363-73. · 1.27 Impact Factor