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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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