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Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma

BACKGROUND: There is ample evidence that macrophages play a crucial role in the biological processes of hepatocellular carcinoma (HCC). This study was designed to establish a novel macrophage-associated prognostic model for HCC. METHODS: Long non-coding RNA (lncRNA) microarrays and clinical data in...

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Autores principales: Chen, Guan-Yu, Wang, Duo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8765715/
https://www.ncbi.nlm.nih.gov/pubmed/35058716
http://dx.doi.org/10.2147/CMAR.S340574
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author Chen, Guan-Yu
Wang, Duo
author_facet Chen, Guan-Yu
Wang, Duo
author_sort Chen, Guan-Yu
collection PubMed
description BACKGROUND: There is ample evidence that macrophages play a crucial role in the biological processes of hepatocellular carcinoma (HCC). This study was designed to establish a novel macrophage-associated prognostic model for HCC. METHODS: Long non-coding RNA (lncRNA) microarrays and clinical data in The Cancer Genome Atlas (TCGA) database were analysed using a univariate Cox proportional regression model to select macrophage-associated prognostic lncRNAs. Multivariate Cox proportional regression models and survival analysis were used to establish a prognosis index (PI) model. Furthermore, to better understand the biological functions of differentially expressed macrophage-associated lncRNAs (MALs) in HCC, enrichment analysis was performed. Finally, the correlation between MALs and clinical features was further analysed in HCC. RESULTS: We identified eight MALs with significant prognostic values for HCC. Next, a PI model for HCC was developed, and patients were classified into the high-risk or low-risk group based on risk scores. The overall survival (OS) of high-risk patients was significantly shorter than that of low-risk patients (P < 0.001). Univariate and multivariate factors indicate that risk scores can be used as independent prognostic factors for patients with HCC. Multiple receiver operating characteristic (ROC) plots show that the area under the ROC curve (AUC) of the risk score is higher than that of other clinical features. The C-index of our nomogram was 0.768. CONCLUSION: The PI model has a prognostic efficacy superior to that of other clinical features.
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spelling pubmed-87657152022-01-19 Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma Chen, Guan-Yu Wang, Duo Cancer Manag Res Original Research BACKGROUND: There is ample evidence that macrophages play a crucial role in the biological processes of hepatocellular carcinoma (HCC). This study was designed to establish a novel macrophage-associated prognostic model for HCC. METHODS: Long non-coding RNA (lncRNA) microarrays and clinical data in The Cancer Genome Atlas (TCGA) database were analysed using a univariate Cox proportional regression model to select macrophage-associated prognostic lncRNAs. Multivariate Cox proportional regression models and survival analysis were used to establish a prognosis index (PI) model. Furthermore, to better understand the biological functions of differentially expressed macrophage-associated lncRNAs (MALs) in HCC, enrichment analysis was performed. Finally, the correlation between MALs and clinical features was further analysed in HCC. RESULTS: We identified eight MALs with significant prognostic values for HCC. Next, a PI model for HCC was developed, and patients were classified into the high-risk or low-risk group based on risk scores. The overall survival (OS) of high-risk patients was significantly shorter than that of low-risk patients (P < 0.001). Univariate and multivariate factors indicate that risk scores can be used as independent prognostic factors for patients with HCC. Multiple receiver operating characteristic (ROC) plots show that the area under the ROC curve (AUC) of the risk score is higher than that of other clinical features. The C-index of our nomogram was 0.768. CONCLUSION: The PI model has a prognostic efficacy superior to that of other clinical features. Dove 2022-01-14 /pmc/articles/PMC8765715/ /pubmed/35058716 http://dx.doi.org/10.2147/CMAR.S340574 Text en © 2022 Chen and Wang. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Chen, Guan-Yu
Wang, Duo
Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma
title Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma
title_full Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma
title_fullStr Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma
title_full_unstemmed Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma
title_short Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma
title_sort prognostic value of macrophage-associated long non-coding rna expression for hepatocellular carcinoma
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8765715/
https://www.ncbi.nlm.nih.gov/pubmed/35058716
http://dx.doi.org/10.2147/CMAR.S340574
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