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 |
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