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Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network
BACKGROUND: Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536424/ https://www.ncbi.nlm.nih.gov/pubmed/34692847 http://dx.doi.org/10.1155/2021/9972011 |
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author | Nong, Shunqiang Chen, Xiaohao Wang, Zechen Xu, Guidan Wei, Wujun Peng, Bin Zhou, Lü Wei, Liuzhi Zhao, Jingjie Wei, Qiuju Deng, Yibin Meng, Lingzhang |
author_facet | Nong, Shunqiang Chen, Xiaohao Wang, Zechen Xu, Guidan Wei, Wujun Peng, Bin Zhou, Lü Wei, Liuzhi Zhao, Jingjie Wei, Qiuju Deng, Yibin Meng, Lingzhang |
author_sort | Nong, Shunqiang |
collection | PubMed |
description | BACKGROUND: Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC. METHODS: HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software. RESULT: A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app. CONCLUSION: The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC. |
format | Online Article Text |
id | pubmed-8536424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-85364242021-10-23 Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network Nong, Shunqiang Chen, Xiaohao Wang, Zechen Xu, Guidan Wei, Wujun Peng, Bin Zhou, Lü Wei, Liuzhi Zhao, Jingjie Wei, Qiuju Deng, Yibin Meng, Lingzhang Biomed Res Int Research Article BACKGROUND: Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC. METHODS: HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software. RESULT: A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app. CONCLUSION: The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC. Hindawi 2021-10-15 /pmc/articles/PMC8536424/ /pubmed/34692847 http://dx.doi.org/10.1155/2021/9972011 Text en Copyright © 2021 Shunqiang Nong 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 Nong, Shunqiang Chen, Xiaohao Wang, Zechen Xu, Guidan Wei, Wujun Peng, Bin Zhou, Lü Wei, Liuzhi Zhao, Jingjie Wei, Qiuju Deng, Yibin Meng, Lingzhang Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network |
title | Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network |
title_full | Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network |
title_fullStr | Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network |
title_full_unstemmed | Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network |
title_short | Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network |
title_sort | potential lncrna biomarkers for hbv-related hepatocellular carcinoma diagnosis revealed by analysis on coexpression network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536424/ https://www.ncbi.nlm.nih.gov/pubmed/34692847 http://dx.doi.org/10.1155/2021/9972011 |
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