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Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes. METHODS: We used single-cell RNA seque...

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Autores principales: Ye, Bicheng, Wang, Qi, Zhu, Xiaofeng, Zeng, Lingling, Luo, Huiyuan, Xiong, Yan, Li, Qin, Zhu, Qinmei, Zhao, Songyun, Chen, Ting, Xie, Jingen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433893/
https://www.ncbi.nlm.nih.gov/pubmed/37601684
http://dx.doi.org/10.3389/fonc.2023.1236435
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author Ye, Bicheng
Wang, Qi
Zhu, Xiaofeng
Zeng, Lingling
Luo, Huiyuan
Xiong, Yan
Li, Qin
Zhu, Qinmei
Zhao, Songyun
Chen, Ting
Xie, Jingen
author_facet Ye, Bicheng
Wang, Qi
Zhu, Xiaofeng
Zeng, Lingling
Luo, Huiyuan
Xiong, Yan
Li, Qin
Zhu, Qinmei
Zhao, Songyun
Chen, Ting
Xie, Jingen
author_sort Ye, Bicheng
collection PubMed
description BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes. METHODS: We used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR. RESULTS: In this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue. CONCLUSIONS: In summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.
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spelling pubmed-104338932023-08-18 Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma Ye, Bicheng Wang, Qi Zhu, Xiaofeng Zeng, Lingling Luo, Huiyuan Xiong, Yan Li, Qin Zhu, Qinmei Zhao, Songyun Chen, Ting Xie, Jingen Front Oncol Oncology BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes. METHODS: We used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR. RESULTS: In this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue. CONCLUSIONS: In summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients. Frontiers Media S.A. 2023-08-02 /pmc/articles/PMC10433893/ /pubmed/37601684 http://dx.doi.org/10.3389/fonc.2023.1236435 Text en Copyright © 2023 Ye, Wang, Zhu, Zeng, Luo, Xiong, Li, Zhu, Zhao, Chen and Xie https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Ye, Bicheng
Wang, Qi
Zhu, Xiaofeng
Zeng, Lingling
Luo, Huiyuan
Xiong, Yan
Li, Qin
Zhu, Qinmei
Zhao, Songyun
Chen, Ting
Xie, Jingen
Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
title Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
title_full Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
title_fullStr Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
title_full_unstemmed Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
title_short Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
title_sort single-cell rna sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433893/
https://www.ncbi.nlm.nih.gov/pubmed/37601684
http://dx.doi.org/10.3389/fonc.2023.1236435
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