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Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma
BACKGROUND: Hepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune m...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254981/ https://www.ncbi.nlm.nih.gov/pubmed/34217320 http://dx.doi.org/10.1186/s12935-021-02049-w |
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author | Xu, Qianhui Xu, Hao Deng, Rongshan Wang, Zijie Li, Nanjun Qi, Zhixuan Zhao, Jiaxin Huang, Wen |
author_facet | Xu, Qianhui Xu, Hao Deng, Rongshan Wang, Zijie Li, Nanjun Qi, Zhixuan Zhao, Jiaxin Huang, Wen |
author_sort | Xu, Qianhui |
collection | PubMed |
description | BACKGROUND: Hepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure. METHOD: The mutation data was analyzed by employing “maftools” package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package “limma”. Gene ontology (GO) enrichment analysis was implemented with “clusterProfiler”, “enrichplot” and “ggplot2” packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3. RESULTS: TMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction. CONCLUSION: Collectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02049-w. |
format | Online Article Text |
id | pubmed-8254981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82549812021-07-06 Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma Xu, Qianhui Xu, Hao Deng, Rongshan Wang, Zijie Li, Nanjun Qi, Zhixuan Zhao, Jiaxin Huang, Wen Cancer Cell Int Primary Research BACKGROUND: Hepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure. METHOD: The mutation data was analyzed by employing “maftools” package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package “limma”. Gene ontology (GO) enrichment analysis was implemented with “clusterProfiler”, “enrichplot” and “ggplot2” packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3. RESULTS: TMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction. CONCLUSION: Collectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02049-w. BioMed Central 2021-07-03 /pmc/articles/PMC8254981/ /pubmed/34217320 http://dx.doi.org/10.1186/s12935-021-02049-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Primary Research Xu, Qianhui Xu, Hao Deng, Rongshan Wang, Zijie Li, Nanjun Qi, Zhixuan Zhao, Jiaxin Huang, Wen Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
title | Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
title_full | Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
title_fullStr | Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
title_full_unstemmed | Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
title_short | Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
title_sort | multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254981/ https://www.ncbi.nlm.nih.gov/pubmed/34217320 http://dx.doi.org/10.1186/s12935-021-02049-w |
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