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A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis

BACKGROUND: As a tumor type with high mortality and poor therapeutic effect, the pathogenesis of pancreatic cancer is still unclear. It is necessary to explore the significance of necroptosis in pancreatic cancer. METHODS: Pancreatic cancer transcriptome data were obtained from the TCGA database, IC...

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Autores principales: Chen, Liang, Zhang, Xueming, Zhang, Qixiang, Zhang, Tao, Xie, Jiaheng, Wei, Wei, Wang, Ying, Yu, Hongzhu, Zhou, Hongkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585241/
https://www.ncbi.nlm.nih.gov/pubmed/36275722
http://dx.doi.org/10.3389/fimmu.2022.1022420
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author Chen, Liang
Zhang, Xueming
Zhang, Qixiang
Zhang, Tao
Xie, Jiaheng
Wei, Wei
Wang, Ying
Yu, Hongzhu
Zhou, Hongkun
author_facet Chen, Liang
Zhang, Xueming
Zhang, Qixiang
Zhang, Tao
Xie, Jiaheng
Wei, Wei
Wang, Ying
Yu, Hongzhu
Zhou, Hongkun
author_sort Chen, Liang
collection PubMed
description BACKGROUND: As a tumor type with high mortality and poor therapeutic effect, the pathogenesis of pancreatic cancer is still unclear. It is necessary to explore the significance of necroptosis in pancreatic cancer. METHODS: Pancreatic cancer transcriptome data were obtained from the TCGA database, ICGC database, and GSE85916 in the GEO database. The TCGA cohort was set as a training cohort, while the ICGC and GSE85916 cohort were set as the validation cohorts. Single-cell sequencing data of pancreatic cancer were obtained from GSE154778 in the GEO database. The genes most associated with necroptosis were identified by weighted co-expression network analysis and single-cell sequencing analysis. COX regression and Lasso regression were performed for these genes, and the prognostic model was established. By calculating risk scores, pancreatic cancer patients could be divided into NCPTS_high and NCPTS_low groups, and survival analysis, immune infiltration analysis, and mutation analysis between groups were performed. Cell experiments including gene knockdown, CCK-8 assay, clone formation assay, transwell assay and wound healing assay were conducted to explore the role of the key gene EPS8 in pancreatic cancer. PCR assays on clinical samples were further used to verify EPS8 expression. RESULTS: We constructed the necroptosis-related signature in pancreatic cancer using single-cell sequencing analysis and transcriptome analysis. The calculation formula of risk score was as follows: NCPTS = POLR3GL * (-0.404) + COL17A1 * (0.092) + DDIT4 * (0.007) + PDE4C * (0.057) + CLDN1 * 0.075 + HMGA2 * 0.056 + CENPF * 0.198 +EPS8 * 0.219. Through this signature, pancreatic cancer patients with different cohorts can be divided into NCPTS_high and NCPTS_low group, and the NCPTS_high group has a significantly poorer prognosis. Moreover, there were significant differences in immune infiltration level and mutation level between the two groups. Cell assays showed that in CAPAN-1 and PANC-1 cell lines, EPS8 knockdown significantly reduced the viability, clonogenesis, migration and invasion of pancreatic cancer cells. Clinical PCR assay of EPS8 expression showed that EPS8 expression was significantly up-regulated in pancreatic cancer (*P<0.05). CONCLUSION: Our study can provide a reference for the diagnosis, treatment and prognosis assessment of pancreatic cancer.
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spelling pubmed-95852412022-10-22 A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis Chen, Liang Zhang, Xueming Zhang, Qixiang Zhang, Tao Xie, Jiaheng Wei, Wei Wang, Ying Yu, Hongzhu Zhou, Hongkun Front Immunol Immunology BACKGROUND: As a tumor type with high mortality and poor therapeutic effect, the pathogenesis of pancreatic cancer is still unclear. It is necessary to explore the significance of necroptosis in pancreatic cancer. METHODS: Pancreatic cancer transcriptome data were obtained from the TCGA database, ICGC database, and GSE85916 in the GEO database. The TCGA cohort was set as a training cohort, while the ICGC and GSE85916 cohort were set as the validation cohorts. Single-cell sequencing data of pancreatic cancer were obtained from GSE154778 in the GEO database. The genes most associated with necroptosis were identified by weighted co-expression network analysis and single-cell sequencing analysis. COX regression and Lasso regression were performed for these genes, and the prognostic model was established. By calculating risk scores, pancreatic cancer patients could be divided into NCPTS_high and NCPTS_low groups, and survival analysis, immune infiltration analysis, and mutation analysis between groups were performed. Cell experiments including gene knockdown, CCK-8 assay, clone formation assay, transwell assay and wound healing assay were conducted to explore the role of the key gene EPS8 in pancreatic cancer. PCR assays on clinical samples were further used to verify EPS8 expression. RESULTS: We constructed the necroptosis-related signature in pancreatic cancer using single-cell sequencing analysis and transcriptome analysis. The calculation formula of risk score was as follows: NCPTS = POLR3GL * (-0.404) + COL17A1 * (0.092) + DDIT4 * (0.007) + PDE4C * (0.057) + CLDN1 * 0.075 + HMGA2 * 0.056 + CENPF * 0.198 +EPS8 * 0.219. Through this signature, pancreatic cancer patients with different cohorts can be divided into NCPTS_high and NCPTS_low group, and the NCPTS_high group has a significantly poorer prognosis. Moreover, there were significant differences in immune infiltration level and mutation level between the two groups. Cell assays showed that in CAPAN-1 and PANC-1 cell lines, EPS8 knockdown significantly reduced the viability, clonogenesis, migration and invasion of pancreatic cancer cells. Clinical PCR assay of EPS8 expression showed that EPS8 expression was significantly up-regulated in pancreatic cancer (*P<0.05). CONCLUSION: Our study can provide a reference for the diagnosis, treatment and prognosis assessment of pancreatic cancer. Frontiers Media S.A. 2022-10-07 /pmc/articles/PMC9585241/ /pubmed/36275722 http://dx.doi.org/10.3389/fimmu.2022.1022420 Text en Copyright © 2022 Chen, Zhang, Zhang, Zhang, Xie, Wei, Wang, Yu and Zhou 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 Immunology
Chen, Liang
Zhang, Xueming
Zhang, Qixiang
Zhang, Tao
Xie, Jiaheng
Wei, Wei
Wang, Ying
Yu, Hongzhu
Zhou, Hongkun
A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
title A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
title_full A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
title_fullStr A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
title_full_unstemmed A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
title_short A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
title_sort necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585241/
https://www.ncbi.nlm.nih.gov/pubmed/36275722
http://dx.doi.org/10.3389/fimmu.2022.1022420
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