Bibliographic Details
| Title: |
Unveiling novel double-negative prostate cancer subtypes through single-cell RNA sequencing analysis |
| Authors: |
Siyuan Cheng, Lin Li, Yunshin Yeh, Yingli Shi, Omar Franco, Eva Corey, Xiuping Yu |
| Source: |
npj Precision Oncology, Vol 8, Iss 1, Pp 1-10 (2024) |
| Publisher Information: |
Nature Portfolio, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2397-768X |
| Relation: |
https://doaj.org/toc/2397-768X |
| DOI: |
10.1038/s41698-024-00667-x |
| Access URL: |
https://doaj.org/article/93af65cbcce64ac1a1e9f0bb040c8afc |
| Accession Number: |
edsdoj.93af65cbcce64ac1a1e9f0bb040c8afc |
| Database: |
Directory of Open Access Journals |
