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Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma
Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angioge...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408211/ https://www.ncbi.nlm.nih.gov/pubmed/37550755 http://dx.doi.org/10.1186/s12935-023-02975-x |
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author | Xie, Shicheng Zhong, Jinwei Zhang, Zhongjing Huang, Weiguo Lin, Xiaoben Pan, Yating Kong, Xiuyan Xia, Hongping Yu, Zhijie Ni, Haizhen Xia, Jinglin |
author_facet | Xie, Shicheng Zhong, Jinwei Zhang, Zhongjing Huang, Weiguo Lin, Xiaoben Pan, Yating Kong, Xiuyan Xia, Hongping Yu, Zhijie Ni, Haizhen Xia, Jinglin |
author_sort | Xie, Shicheng |
collection | PubMed |
description | Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-023-02975-x. |
format | Online Article Text |
id | pubmed-10408211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104082112023-08-09 Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma Xie, Shicheng Zhong, Jinwei Zhang, Zhongjing Huang, Weiguo Lin, Xiaoben Pan, Yating Kong, Xiuyan Xia, Hongping Yu, Zhijie Ni, Haizhen Xia, Jinglin Cancer Cell Int Research Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-023-02975-x. BioMed Central 2023-08-07 /pmc/articles/PMC10408211/ /pubmed/37550755 http://dx.doi.org/10.1186/s12935-023-02975-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xie, Shicheng Zhong, Jinwei Zhang, Zhongjing Huang, Weiguo Lin, Xiaoben Pan, Yating Kong, Xiuyan Xia, Hongping Yu, Zhijie Ni, Haizhen Xia, Jinglin Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma |
title | Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma |
title_full | Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma |
title_fullStr | Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma |
title_full_unstemmed | Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma |
title_short | Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma |
title_sort | novel risk model based on angiogenesis-related lncrnas for prognosis prediction of hepatocellular carcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408211/ https://www.ncbi.nlm.nih.gov/pubmed/37550755 http://dx.doi.org/10.1186/s12935-023-02975-x |
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