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Advances in neuroendocrine prostate cancer research: From model construction to molecular network analyses

Abstract

Prostate cancer is the most common cancer among men and has a high incidence and associated mortality worldwide. It is an androgen-driven disease in which tumor growth is triggered via ligand-mediated signaling through the androgen receptor (AR). Recent evidence suggests that the widespread use of effective AR pathway inhibitors may increase the occurrence of neuroendocrine prostate cancer (NEPC), an aggressive and treatment-resistant AR-negative variant; however, mechanisms controlling NEPC development remain to be elucidated. Various preclinical models have recently been developed to investigate the mechanisms driving the NEPC differentiation. In the present study, we summarized strategies for the development of NEPC models and proposed a novel method for model evaluation, which will help in the timely and accurate identification of NEPC by virtue of its ability to recapitulate the heterogeneity of prostate cancer. Moreover, we discuss the origin and the mechanism of NEPC. The understanding of the regulatory network mediating neuroendocrine differentiation presented in this review could provide valuable insights into the identification of novel drug targets for NEPC as well as into the causes of antiandrogenic drug resistance.

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Fig. 1: Proposed pathways related to NEPC.

Data availability

All data included in this study are available upon request by contact with the corresponding author.

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Funding

This study was funded by the National Natural Science Foundation Program of China [Nos. 32070532 and 31772546] and Shaanxi Natural Science Foundation Program [No. 2020FWPT-02].

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X.S., J.Z., and C.S.: conceptualization. X.S., R.X., C.Z., and H.M.: writing. X.S. and C.S.: revising. All authors approved the final version of the manuscript.

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Correspondence to Jumei Zhao or Changhong Shi.

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Shui, X., Xu, R., Zhang, C. et al. Advances in neuroendocrine prostate cancer research: From model construction to molecular network analyses. Lab Invest 102, 332–340 (2022). https://doi.org/10.1038/s41374-021-00716-0

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