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通过单细胞RNA测序分析揭示新型双阴性前列腺癌亚型

Unveiling novel double-negative prostate cancer subtypes through single-cell RNA sequencing analysis.

作者信息

Cheng Siyuan, Li Lin, Yeh Yunshin, Shi Yingli, Franco Omar, Corey Eva, Yu Xiuping

机构信息

Department of Biochemistry and Molecular Biology, LSU Health Shreveport, Shreveport, LA, USA.

Feist-Weiller Cancer Center, LSU Health Shreveport, Shreveport, LA, USA.

出版信息

NPJ Precis Oncol. 2024 Aug 2;8(1):171. doi: 10.1038/s41698-024-00667-x.

DOI:10.1038/s41698-024-00667-x
PMID:39095562
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11297170/
Abstract

Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into cancer heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising publicly available cohorts and data generated by our research team, and established the Human Prostate Single cell Atlas (HuPSA) and Mouse Prostate Single cell Atlas (MoPSA) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution re-classified human PCa specimens, validating the presence of these novel subtypes. We then developed a user-friendly web application, "HuPSA-MoPSA" ( https://pcatools.shinyapps.io/HuPSA-MoPSA/ ), for visualizing gene expression across all newly established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.

摘要

单细胞RNA测序(scRNAseq)的最新进展促进了前列腺癌(PCa)中先前未被识别的亚型的发现,为癌症异质性和进展提供了新的见解。在本研究中,我们整合了来自多项研究的scRNAseq数据,包括公开可用的队列和我们研究团队生成的数据,并建立了人类前列腺单细胞图谱(HuPSA)和小鼠前列腺单细胞图谱(MoPSA)数据集。通过全面分析,我们鉴定出两种新的双阴性PCa群体:以KRT7表达升高为特征的KRT7细胞和以SOX2和FOXA2表达为标志的祖细胞样细胞,它们不同于神经内分泌PCa,并表现出干细胞/祖细胞特征。此外,基于HuPSA的反卷积对人类PCa标本进行了重新分类,验证了这些新亚型的存在。然后,我们开发了一个用户友好的网络应用程序“HuPSA-MoPSA”(https://pcatools.shinyapps.io/HuPSA-MoPSA/),用于可视化所有新建立数据集中的基因表达。我们的研究为PCa研究提供了全面的工具,并发现了可指导临床诊断和治疗策略的新型癌症亚型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/694353747f94/41698_2024_667_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/04cb21f8c16f/41698_2024_667_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/6c650a662b63/41698_2024_667_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/5b577f545057/41698_2024_667_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/438e97031081/41698_2024_667_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/694353747f94/41698_2024_667_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/04cb21f8c16f/41698_2024_667_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/6c650a662b63/41698_2024_667_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/5b577f545057/41698_2024_667_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/438e97031081/41698_2024_667_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd33/11297170/694353747f94/41698_2024_667_Fig5_HTML.jpg

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