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Detalle de: Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression

Title Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression
Abstract

Androgen receptor (AR) plays a central role in driving prostate cancer (PCa) progression. How AR promotes this process is still not completely clear. Herein, we used single-cell transcriptome analysis to reconstruct the transcriptional network of AR in PCa. Our work shows AR directly regulates a set of signature genes in the ER-to-Golgi protein vesicle-mediated transport pathway. The expression of these genes is required for maximum androgen-dependent ER-to-Golgi trafficking, cell growth, and survival. Our analyses also reveal the signature genes are associated with PCa progression and prognosis. Moreover, we find inhibition of the ER-to-Golgi transport process with a small molecule enhanced antiandrogen-mediated tumor suppression of hormone-sensitive and insensitive PCa. Finally, we demonstrate AR collaborates with CREB3L2 in mediating ER-to-Golgi trafficking in PCa. In summary, our findings uncover a critical role for dysregulation of ER-to-Golgi trafficking expression and function in PCa progression, provide detailed mechanistic insights for how AR tightly controls this process, and highlight the prospect of targeting the ER-to-Golgi pathway as a therapeutic strategy for advanced PCa.

Apa Reference Hu, L., Chen, X., Narwade, N., Lim, M.G.L., Chen, Z., Tennakoon, C., Guan, P., Chan, U.I., Zhao, Z., Deng, M., Xu, X., Sung, W. & Cheung, E. (2021). Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression. Oncogene, 40(47), 6479-6493. 10.1038/s41388-021-02026-7
Doi 10.1038/s41388-021-02026-7
Publication Date 5 Oct 2021
Publication Type Article
Researcher Nitin Narwade
Ontology Transcriptomics, Multivariate statistics, Drug Discovery and design, Single-cell omics, Clinical data, Dimensionality reduction
Research Groups
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