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Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma

BACKGROUND: Growing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore g...

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Autores principales: Wang, Zheng, Zhu, Jie, Liu, Yongjuan, Liu, Changhong, Wang, Wenqi, Chen, Fengzhe, Ma, Lixian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011553/
https://www.ncbi.nlm.nih.gov/pubmed/32046766
http://dx.doi.org/10.1186/s12967-020-02255-6
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author Wang, Zheng
Zhu, Jie
Liu, Yongjuan
Liu, Changhong
Wang, Wenqi
Chen, Fengzhe
Ma, Lixian
author_facet Wang, Zheng
Zhu, Jie
Liu, Yongjuan
Liu, Changhong
Wang, Wenqi
Chen, Fengzhe
Ma, Lixian
author_sort Wang, Zheng
collection PubMed
description BACKGROUND: Growing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore gene signatures and prognostic values of immune-related genes in HCC. METHODS: We comprehensively integrated gene expression data acquired from 374 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) analysis and univariate Cox regression analysis were performed to identify DEGs that related to overall survival. An immune prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The correlation analysis between immune-related signature and immune cells infiltration were also investigated. Finally, the signature was validated in an external independent dataset. RESULTS: A total of 329 differentially expressed immune‐related genes were detected. 64 immune‐related genes were identified to be markedly related to overall survival in HCC patients using univariate Cox regression analysis. Then we established a TF-mediated network for exploring the regulatory mechanisms of these genes. Lasso and multivariate Cox regression analyses were applied to construct the immune-based prognostic model, which consisted of nine immune‐related genes. Further analysis indicated that this immune-related prognostic model could be an independent prognostic indicator after adjusting to other clinical factors. The relationships between the risk score model and immune cell infiltration suggested that the nine-gene signature could reflect the status of tumor immune microenvironment. The prognostic value of this nine-gene prognostic model was further successfully validated in an independent database. CONCLUSIONS: Together, our study screened potential prognostic immune-related genes and established a novel immune-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets, but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.
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spelling pubmed-70115532020-02-18 Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma Wang, Zheng Zhu, Jie Liu, Yongjuan Liu, Changhong Wang, Wenqi Chen, Fengzhe Ma, Lixian J Transl Med Research BACKGROUND: Growing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore gene signatures and prognostic values of immune-related genes in HCC. METHODS: We comprehensively integrated gene expression data acquired from 374 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) analysis and univariate Cox regression analysis were performed to identify DEGs that related to overall survival. An immune prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The correlation analysis between immune-related signature and immune cells infiltration were also investigated. Finally, the signature was validated in an external independent dataset. RESULTS: A total of 329 differentially expressed immune‐related genes were detected. 64 immune‐related genes were identified to be markedly related to overall survival in HCC patients using univariate Cox regression analysis. Then we established a TF-mediated network for exploring the regulatory mechanisms of these genes. Lasso and multivariate Cox regression analyses were applied to construct the immune-based prognostic model, which consisted of nine immune‐related genes. Further analysis indicated that this immune-related prognostic model could be an independent prognostic indicator after adjusting to other clinical factors. The relationships between the risk score model and immune cell infiltration suggested that the nine-gene signature could reflect the status of tumor immune microenvironment. The prognostic value of this nine-gene prognostic model was further successfully validated in an independent database. CONCLUSIONS: Together, our study screened potential prognostic immune-related genes and established a novel immune-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets, but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy. BioMed Central 2020-02-11 /pmc/articles/PMC7011553/ /pubmed/32046766 http://dx.doi.org/10.1186/s12967-020-02255-6 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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
Wang, Zheng
Zhu, Jie
Liu, Yongjuan
Liu, Changhong
Wang, Wenqi
Chen, Fengzhe
Ma, Lixian
Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
title Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
title_full Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
title_fullStr Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
title_full_unstemmed Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
title_short Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
title_sort development and validation of a novel immune-related prognostic model in hepatocellular carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011553/
https://www.ncbi.nlm.nih.gov/pubmed/32046766
http://dx.doi.org/10.1186/s12967-020-02255-6
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