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Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunot...

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Autores principales: Wang, Lin, Bi, Jia, Li, Xueping, Wei, Minjie, He, Miao, Zhao, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545682/
https://www.ncbi.nlm.nih.gov/pubmed/33046974
http://dx.doi.org/10.7150/jca.47877
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author Wang, Lin
Bi, Jia
Li, Xueping
Wei, Minjie
He, Miao
Zhao, Lin
author_facet Wang, Lin
Bi, Jia
Li, Xueping
Wei, Minjie
He, Miao
Zhao, Lin
author_sort Wang, Lin
collection PubMed
description Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment. Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT. Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352). Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.
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spelling pubmed-75456822020-10-11 Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer Wang, Lin Bi, Jia Li, Xueping Wei, Minjie He, Miao Zhao, Lin J Cancer Research Paper Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment. Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT. Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352). Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic. Ivyspring International Publisher 2020-09-21 /pmc/articles/PMC7545682/ /pubmed/33046974 http://dx.doi.org/10.7150/jca.47877 Text en © The author(s) This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Wang, Lin
Bi, Jia
Li, Xueping
Wei, Minjie
He, Miao
Zhao, Lin
Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer
title Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer
title_full Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer
title_fullStr Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer
title_full_unstemmed Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer
title_short Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer
title_sort prognostic alternative splicing signature reveals the landscape of immune infiltration in pancreatic cancer
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545682/
https://www.ncbi.nlm.nih.gov/pubmed/33046974
http://dx.doi.org/10.7150/jca.47877
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