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Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer
Pancreatic duct adenocarcinoma (PDAC) is an aggressive and lethal malignancy. Pancreatic cancer stem cells (PCSCs) are assumed to contribute to the initiation and invasion of PDAC. In this study, we performed single-cell RNA sequencing (scRNA-seq) analysis of PDAC tumor samples from patients and con...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806718/ https://www.ncbi.nlm.nih.gov/pubmed/34474642 http://dx.doi.org/10.1080/21655979.2021.1962484 |
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author | Ren, Xuechen Zhou, Chengliang Lu, Yu Ma, Fulin Fan, Yong Wang, Chen |
author_facet | Ren, Xuechen Zhou, Chengliang Lu, Yu Ma, Fulin Fan, Yong Wang, Chen |
author_sort | Ren, Xuechen |
collection | PubMed |
description | Pancreatic duct adenocarcinoma (PDAC) is an aggressive and lethal malignancy. Pancreatic cancer stem cells (PCSCs) are assumed to contribute to the initiation and invasion of PDAC. In this study, we performed single-cell RNA sequencing (scRNA-seq) analysis of PDAC tumor samples from patients and control pancreas tissues to reveal the transformation process of cancer stem cell (CSC)-like ductal cells into ductal cells with invasive potential and we screened out CSC-related genes (CRGs). Subsequently, we applied LASSO and Cox regression models to identify five CRGs with potential prognostic values and constructed a risk prognostic model using the Cancer Genome Atlas datasets. The risk models were verified using Gene Expression Omnibus datasets. Patients in the high-risk group had a significantly poor overall survival (Pvalue<0.0001), as illustrated by the Kaplan-Meier survival curve, and the area under the curve confirmed the accuracy of predictions by our risk model. Tumor mutation burden variations were used to further explore the differences between the two risk cohorts. In addition, the Human Protein Atlas was used to investigate the protein expression of five hub CRGs. In brief, we utilized scRNA-seq to reveal the invasive trajectory of ductal cells and identified crucial CRGs in PDAC, which may help predict patient survival and provide potential clinical therapeutic targets against CSCs. |
format | Online Article Text |
id | pubmed-8806718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-88067182022-02-02 Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer Ren, Xuechen Zhou, Chengliang Lu, Yu Ma, Fulin Fan, Yong Wang, Chen Bioengineered Research Paper Pancreatic duct adenocarcinoma (PDAC) is an aggressive and lethal malignancy. Pancreatic cancer stem cells (PCSCs) are assumed to contribute to the initiation and invasion of PDAC. In this study, we performed single-cell RNA sequencing (scRNA-seq) analysis of PDAC tumor samples from patients and control pancreas tissues to reveal the transformation process of cancer stem cell (CSC)-like ductal cells into ductal cells with invasive potential and we screened out CSC-related genes (CRGs). Subsequently, we applied LASSO and Cox regression models to identify five CRGs with potential prognostic values and constructed a risk prognostic model using the Cancer Genome Atlas datasets. The risk models were verified using Gene Expression Omnibus datasets. Patients in the high-risk group had a significantly poor overall survival (Pvalue<0.0001), as illustrated by the Kaplan-Meier survival curve, and the area under the curve confirmed the accuracy of predictions by our risk model. Tumor mutation burden variations were used to further explore the differences between the two risk cohorts. In addition, the Human Protein Atlas was used to investigate the protein expression of five hub CRGs. In brief, we utilized scRNA-seq to reveal the invasive trajectory of ductal cells and identified crucial CRGs in PDAC, which may help predict patient survival and provide potential clinical therapeutic targets against CSCs. Taylor & Francis 2021-09-02 /pmc/articles/PMC8806718/ /pubmed/34474642 http://dx.doi.org/10.1080/21655979.2021.1962484 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Ren, Xuechen Zhou, Chengliang Lu, Yu Ma, Fulin Fan, Yong Wang, Chen Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
title | Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
title_full | Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
title_fullStr | Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
title_full_unstemmed | Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
title_short | Single-cell RNA-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
title_sort | single-cell rna-seq reveals invasive trajectory and determines cancer stem cell-related prognostic genes in pancreatic cancer |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806718/ https://www.ncbi.nlm.nih.gov/pubmed/34474642 http://dx.doi.org/10.1080/21655979.2021.1962484 |
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