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Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma
The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were d...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283041/ https://www.ncbi.nlm.nih.gov/pubmed/35845962 http://dx.doi.org/10.1155/2022/2093437 |
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author | Wang, Jue Jin, Zongrui Wu, Guolin Wang, Jilong Xu, Banghao Zhu, Hai Guo, Ya Wen, Zhang |
author_facet | Wang, Jue Jin, Zongrui Wu, Guolin Wang, Jilong Xu, Banghao Zhu, Hai Guo, Ya Wen, Zhang |
author_sort | Wang, Jue |
collection | PubMed |
description | The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune-related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3-antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune-related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six-lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC. |
format | Online Article Text |
id | pubmed-9283041 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92830412022-07-15 Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma Wang, Jue Jin, Zongrui Wu, Guolin Wang, Jilong Xu, Banghao Zhu, Hai Guo, Ya Wen, Zhang Biomed Res Int Research Article The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune-related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3-antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune-related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six-lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC. Hindawi 2022-07-07 /pmc/articles/PMC9283041/ /pubmed/35845962 http://dx.doi.org/10.1155/2022/2093437 Text en Copyright © 2022 Jue Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Jue Jin, Zongrui Wu, Guolin Wang, Jilong Xu, Banghao Zhu, Hai Guo, Ya Wen, Zhang Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma |
title | Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma |
title_full | Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma |
title_fullStr | Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma |
title_full_unstemmed | Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma |
title_short | Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma |
title_sort | bioinformatics analysis for constructing a six-immune-related long noncoding rna signature as a prognostic model of hepatocellular carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283041/ https://www.ncbi.nlm.nih.gov/pubmed/35845962 http://dx.doi.org/10.1155/2022/2093437 |
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