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Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma

PURPOSE: Hepatitis B (HBV)-infected hepatocellular carcinoma is one of the most common cancers, and it has high incidence and mortality rates worldwide. The incidence of hepatocellular carcinoma has been increasing in recent years, and existing treatment modalities do not significantly improve progn...

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Detalles Bibliográficos
Autores principales: Wang, Xianmo, Yi, Huawei, Tu, Jiancheng, Fan, Wen, Wu, Jiahao, Wang, Li, Li, Xiang, Yan, Jinrong, Huang, Huali, Huang, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907517/
https://www.ncbi.nlm.nih.gov/pubmed/35280822
http://dx.doi.org/10.3389/fonc.2022.838845
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author Wang, Xianmo
Yi, Huawei
Tu, Jiancheng
Fan, Wen
Wu, Jiahao
Wang, Li
Li, Xiang
Yan, Jinrong
Huang, Huali
Huang, Rong
author_facet Wang, Xianmo
Yi, Huawei
Tu, Jiancheng
Fan, Wen
Wu, Jiahao
Wang, Li
Li, Xiang
Yan, Jinrong
Huang, Huali
Huang, Rong
author_sort Wang, Xianmo
collection PubMed
description PURPOSE: Hepatitis B (HBV)-infected hepatocellular carcinoma is one of the most common cancers, and it has high incidence and mortality rates worldwide. The incidence of hepatocellular carcinoma has been increasing in recent years, and existing treatment modalities do not significantly improve prognosis. Therefore, it is important to find a biomarker that can accurately predict prognosis. METHODS: This study was analyzed using the The Cancer Genome Atlas (TCGA) database and validated by the International Cancer Genome Consortium (ICGC) database. The STRING database was used to construct a gene co-expression network and visualize its functional clustering using Cytoscape. A prognostic signature model was constructed to observe high and low risk with prognosis, and independent prognostic factors for HBV-infected hepatocellular carcinoma were identified by Cox regression analysis. The independent prognostic factors were then analyzed for expression and survival, and their pathway enrichment was analyzed using gene set enrichment analysis (GSEA). RESULTS: 805 differentially expressed genes (DEGs) were obtained by differential analysis. Protein–protein interaction (PPI) showed that DEGs were mostly clustered in functional modules, such as cellular matrix response, cell differentiation, and tissue development. Prognostic characterization models showed that the high-risk group was associated with poor prognosis, while Cox regression analysis identified ASF1B as the only independent prognostic factor. As verified by expression and prognosis, ASF1B was highly expressed in HBV-infected hepatocellular carcinoma and led to a poor prognosis. GSEA showed that high ASF1B expression was involved in cell cycle-related signaling pathways. CONCLUSION: Bioinformatic analysis identified ASF1B as an independent prognostic factor in HBV-infected hepatocellular carcinoma, and its high expression led to a poor prognosis. Furthermore, it may promote hepatocellular carcinoma progression by affecting cell cycle-related signaling pathways.
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spelling pubmed-89075172022-03-11 Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma Wang, Xianmo Yi, Huawei Tu, Jiancheng Fan, Wen Wu, Jiahao Wang, Li Li, Xiang Yan, Jinrong Huang, Huali Huang, Rong Front Oncol Oncology PURPOSE: Hepatitis B (HBV)-infected hepatocellular carcinoma is one of the most common cancers, and it has high incidence and mortality rates worldwide. The incidence of hepatocellular carcinoma has been increasing in recent years, and existing treatment modalities do not significantly improve prognosis. Therefore, it is important to find a biomarker that can accurately predict prognosis. METHODS: This study was analyzed using the The Cancer Genome Atlas (TCGA) database and validated by the International Cancer Genome Consortium (ICGC) database. The STRING database was used to construct a gene co-expression network and visualize its functional clustering using Cytoscape. A prognostic signature model was constructed to observe high and low risk with prognosis, and independent prognostic factors for HBV-infected hepatocellular carcinoma were identified by Cox regression analysis. The independent prognostic factors were then analyzed for expression and survival, and their pathway enrichment was analyzed using gene set enrichment analysis (GSEA). RESULTS: 805 differentially expressed genes (DEGs) were obtained by differential analysis. Protein–protein interaction (PPI) showed that DEGs were mostly clustered in functional modules, such as cellular matrix response, cell differentiation, and tissue development. Prognostic characterization models showed that the high-risk group was associated with poor prognosis, while Cox regression analysis identified ASF1B as the only independent prognostic factor. As verified by expression and prognosis, ASF1B was highly expressed in HBV-infected hepatocellular carcinoma and led to a poor prognosis. GSEA showed that high ASF1B expression was involved in cell cycle-related signaling pathways. CONCLUSION: Bioinformatic analysis identified ASF1B as an independent prognostic factor in HBV-infected hepatocellular carcinoma, and its high expression led to a poor prognosis. Furthermore, it may promote hepatocellular carcinoma progression by affecting cell cycle-related signaling pathways. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC8907517/ /pubmed/35280822 http://dx.doi.org/10.3389/fonc.2022.838845 Text en Copyright © 2022 Wang, Yi, Tu, Fan, Wu, Wang, Li, Yan, Huang and Huang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wang, Xianmo
Yi, Huawei
Tu, Jiancheng
Fan, Wen
Wu, Jiahao
Wang, Li
Li, Xiang
Yan, Jinrong
Huang, Huali
Huang, Rong
Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma
title Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma
title_full Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma
title_fullStr Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma
title_full_unstemmed Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma
title_short Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma
title_sort comprehensive analysis identified asf1b as an independent prognostic factor for hbv-infected hepatocellular carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907517/
https://www.ncbi.nlm.nih.gov/pubmed/35280822
http://dx.doi.org/10.3389/fonc.2022.838845
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