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Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma

BACKGROUND: The global burden of hepatocellular carcinoma (HCC) is increasing, negatively impacting social health and economies. The discovery of novel and valuable biomarkers for the early diagnosis and therapeutic guidance of HCC is urgently needed. METHODS: Extracellular matrix (ECM)-related gene...

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Autores principales: Wu, Guozhi, Yang, Yuan, Ye, Rong, Yue, Hanxun, Zhang, Huiyun, Huang, Taobi, Liu, Min, Zheng, Ya, Wang, Yuping, Zhou, Yongning, Guo, Qinghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531523/
https://www.ncbi.nlm.nih.gov/pubmed/36195857
http://dx.doi.org/10.1186/s12885-022-10049-w
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author Wu, Guozhi
Yang, Yuan
Ye, Rong
Yue, Hanxun
Zhang, Huiyun
Huang, Taobi
Liu, Min
Zheng, Ya
Wang, Yuping
Zhou, Yongning
Guo, Qinghong
author_facet Wu, Guozhi
Yang, Yuan
Ye, Rong
Yue, Hanxun
Zhang, Huiyun
Huang, Taobi
Liu, Min
Zheng, Ya
Wang, Yuping
Zhou, Yongning
Guo, Qinghong
author_sort Wu, Guozhi
collection PubMed
description BACKGROUND: The global burden of hepatocellular carcinoma (HCC) is increasing, negatively impacting social health and economies. The discovery of novel and valuable biomarkers for the early diagnosis and therapeutic guidance of HCC is urgently needed. METHODS: Extracellular matrix (ECM)-related gene sets, transcriptome data and mutation profiles were downloaded from the Matrisome Project and The Cancer Genome Atlas (TCGA)-LIHC datasets. Coexpression analysis was initially performed with the aim of identifying ECM-related lncRNAs (r > 0.4, p < 0.001). The screened lncRNAs were subjected to univariate analysis to obtain a series of prognosis-related lncRNA sets, which were incorporated into least absolute selection and shrinkage operator (LASSO) regression for signature establishment. Following the grouping of LIHC samples according to risk score, the correlations between the signature and clinicopathological, tumour immune infiltration, and mutational characteristics as well as therapeutic response were also analysed. lncRNA expression levels used for modelling were finally examined at the cellular and tissue levels by real-time PCR. All analyses were based on R software. RESULTS: AL031985.3 and MKLN1-AS were ultimately identified as signature-related lncRNAs, and both were significantly upregulated in HCC tissue samples and cell lines. The prognostic value of the signature reflected by the AUC value was superior to that of age, sex, grade and stage. Correlation analysis results demonstrated that high-risk groups exhibited significant enrichment of immune cells (DCs, macrophages and Tregs) and increased expression levels of all immune checkpoint genes. Prominent differences in clinicopathological profiles, immune functions, tumour mutation burden (TMB) and drug sensitivity were noted between the two risk groups. CONCLUSIONS: Our signature represents a valuable predictive tool in the prognostic management of HCC patients. Further validation of the mechanisms involved is needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10049-w.
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spelling pubmed-95315232022-10-05 Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma Wu, Guozhi Yang, Yuan Ye, Rong Yue, Hanxun Zhang, Huiyun Huang, Taobi Liu, Min Zheng, Ya Wang, Yuping Zhou, Yongning Guo, Qinghong BMC Cancer Research BACKGROUND: The global burden of hepatocellular carcinoma (HCC) is increasing, negatively impacting social health and economies. The discovery of novel and valuable biomarkers for the early diagnosis and therapeutic guidance of HCC is urgently needed. METHODS: Extracellular matrix (ECM)-related gene sets, transcriptome data and mutation profiles were downloaded from the Matrisome Project and The Cancer Genome Atlas (TCGA)-LIHC datasets. Coexpression analysis was initially performed with the aim of identifying ECM-related lncRNAs (r > 0.4, p < 0.001). The screened lncRNAs were subjected to univariate analysis to obtain a series of prognosis-related lncRNA sets, which were incorporated into least absolute selection and shrinkage operator (LASSO) regression for signature establishment. Following the grouping of LIHC samples according to risk score, the correlations between the signature and clinicopathological, tumour immune infiltration, and mutational characteristics as well as therapeutic response were also analysed. lncRNA expression levels used for modelling were finally examined at the cellular and tissue levels by real-time PCR. All analyses were based on R software. RESULTS: AL031985.3 and MKLN1-AS were ultimately identified as signature-related lncRNAs, and both were significantly upregulated in HCC tissue samples and cell lines. The prognostic value of the signature reflected by the AUC value was superior to that of age, sex, grade and stage. Correlation analysis results demonstrated that high-risk groups exhibited significant enrichment of immune cells (DCs, macrophages and Tregs) and increased expression levels of all immune checkpoint genes. Prominent differences in clinicopathological profiles, immune functions, tumour mutation burden (TMB) and drug sensitivity were noted between the two risk groups. CONCLUSIONS: Our signature represents a valuable predictive tool in the prognostic management of HCC patients. Further validation of the mechanisms involved is needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10049-w. BioMed Central 2022-10-04 /pmc/articles/PMC9531523/ /pubmed/36195857 http://dx.doi.org/10.1186/s12885-022-10049-w Text en © The Author(s) 2022 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 Research
Wu, Guozhi
Yang, Yuan
Ye, Rong
Yue, Hanxun
Zhang, Huiyun
Huang, Taobi
Liu, Min
Zheng, Ya
Wang, Yuping
Zhou, Yongning
Guo, Qinghong
Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
title Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
title_full Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
title_fullStr Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
title_full_unstemmed Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
title_short Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
title_sort development and validation of an ecm-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531523/
https://www.ncbi.nlm.nih.gov/pubmed/36195857
http://dx.doi.org/10.1186/s12885-022-10049-w
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