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Increasing Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival.

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bioRxiv : the preprint server for biology 📖 저널 OA 100% 2025
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Liu X, Cortes E, Ji Y, Zhao K, Ho J, Liu YS, Davicioni E, Feng FY, Alumkal JJ, Spratt DE, Sweeney CJ, Yu H, Hu Q, Cheng Z, Zhang D, Chatta G, Nastiuk KL, Goodrich DW, Rycaj K, Jamroze A, Kirk JS, Puzanov I, Liu S, Wang J, Tang DG

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Cancer progression involves loss of differentiation and acquisition of stem cell-like traits, broadly referred to as "stemness".

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APA Liu X, Cortes E, et al. (2025). Increasing Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival.. bioRxiv : the preprint server for biology. https://doi.org/10.1101/2025.04.27.650697
MLA Liu X, et al.. "Increasing Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival.." bioRxiv : the preprint server for biology, 2025.
PMID 40458374 ↗

Abstract

Cancer progression involves loss of differentiation and acquisition of stem cell-like traits, broadly referred to as "stemness". Here, we test whether the level of stemness, assessed by a transcriptome-derived Stemness score, can quantitatively track prostate cancer (PCa) development, progression, therapy resistance, metastasis, plasticity, and patient survival. Integrative analysis of transcriptomic data from 87,183 samples across 26 datasets reveals a progressive increase in Stemness and decline in pro-differentiation androgen receptor activity (AR-A) along the PCa continuum, with metastatic castration-resistant PCa (mCRPC) exhibiting the highest Stemness and lowest AR-A. Both the general Stemness score and a newly developed 12-gene "PCa-Stem Signature" correlate with and predict poor clinical outcomes. Mechanistically, increased AR-A may promote Stemness in early-stage PCa while amplification and bi-allelic loss likely drive greatly elevated Stemness in mCRPC where AR-A is suppressed. Our findings establish Stemness as a robust quantitative measure of PCa aggressiveness and offer a scalable framework for PCa risk stratification.

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