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Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma
Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression. Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573973/ https://www.ncbi.nlm.nih.gov/pubmed/36263133 http://dx.doi.org/10.3389/fphar.2022.1019988 |
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author | Liu, Wangrui Zhao, Shuai Xu, Wenhao Xiang, Jianfeng Li, Chuanyu Li, Jun Ding, Han Zhang, Hailiang Zhang, Yichi Huang, Haineng Wang, Jian Wang, Tao Zhai, Bo Pan, Lei |
author_facet | Liu, Wangrui Zhao, Shuai Xu, Wenhao Xiang, Jianfeng Li, Chuanyu Li, Jun Ding, Han Zhang, Hailiang Zhang, Yichi Huang, Haineng Wang, Jian Wang, Tao Zhai, Bo Pan, Lei |
author_sort | Liu, Wangrui |
collection | PubMed |
description | Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression. Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC. Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming. Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients. |
format | Online Article Text |
id | pubmed-9573973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95739732022-10-18 Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma Liu, Wangrui Zhao, Shuai Xu, Wenhao Xiang, Jianfeng Li, Chuanyu Li, Jun Ding, Han Zhang, Hailiang Zhang, Yichi Huang, Haineng Wang, Jian Wang, Tao Zhai, Bo Pan, Lei Front Pharmacol Pharmacology Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression. Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC. Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming. Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients. Frontiers Media S.A. 2022-10-03 /pmc/articles/PMC9573973/ /pubmed/36263133 http://dx.doi.org/10.3389/fphar.2022.1019988 Text en Copyright © 2022 Liu, Zhao, Xu, Xiang, Li, Li, Ding, Zhang, Zhang, Huang, Wang, Wang, Zhai and Pan. 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 | Pharmacology Liu, Wangrui Zhao, Shuai Xu, Wenhao Xiang, Jianfeng Li, Chuanyu Li, Jun Ding, Han Zhang, Hailiang Zhang, Yichi Huang, Haineng Wang, Jian Wang, Tao Zhai, Bo Pan, Lei Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
title | Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
title_full | Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
title_fullStr | Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
title_full_unstemmed | Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
title_short | Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
title_sort | integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573973/ https://www.ncbi.nlm.nih.gov/pubmed/36263133 http://dx.doi.org/10.3389/fphar.2022.1019988 |
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