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CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients. MET...

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Autores principales: Zeng, Yuping, He, He, Zhang, Yu, Wang, Xia, Yang, Lidan, An, Zhenmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333053/
https://www.ncbi.nlm.nih.gov/pubmed/32685486
http://dx.doi.org/10.1155/2020/4612158
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author Zeng, Yuping
He, He
Zhang, Yu
Wang, Xia
Yang, Lidan
An, Zhenmei
author_facet Zeng, Yuping
He, He
Zhang, Yu
Wang, Xia
Yang, Lidan
An, Zhenmei
author_sort Zeng, Yuping
collection PubMed
description BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients. METHODS: Prognosis-related gene modules were first established by WGCNA. Venn diagrams obtained intersection genes of module genes and differentially expressed genes. The Kaplan-Meier overall survival curves and disease-free survival curves of intersection genes were further analyzed on the Gene Expression Profiling Interactive Analysis website. Chi-square tests were performed to explore the associations between prognostic gene expressions and clinicopathological features. RESULTS: CCNB2, TOP2A, and ASPM were identified as both prognosis-related genes and differentially expressed genes. TOP2A (HR: 1.7, P = 0.003) and ASPM (HR: 1.8, P < 0.001) exhibited a significant difference between the high- and low-expression groups in the overall survival analysis, while CCNB2 (HR: 1.4, P = 0.052) was not statistically significant. CCNB2 (HR: 1.5, P = 0.006), TOP2A (HR: 1.7, P < 0.001), and ASPM (HR: 1.6, P = 0.003) were all statistically significant in the disease-free survival analysis. All three genes were significantly associated with race and fetoprotein values (P < 0.05). CCNB2 expression was associated with tumor stage (P = 0.01), and ASPM expression was associated with new tumor events (P = 0.03). CONCLUSION: Overexpression of CCNB2, TOP2A, and ASPM are associated with poor prognosis, and these genes could serve as potential prognostic markers and therapeutic targets for HCC.
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spelling pubmed-73330532020-07-17 CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis Zeng, Yuping He, He Zhang, Yu Wang, Xia Yang, Lidan An, Zhenmei Biomed Res Int Research Article BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients. METHODS: Prognosis-related gene modules were first established by WGCNA. Venn diagrams obtained intersection genes of module genes and differentially expressed genes. The Kaplan-Meier overall survival curves and disease-free survival curves of intersection genes were further analyzed on the Gene Expression Profiling Interactive Analysis website. Chi-square tests were performed to explore the associations between prognostic gene expressions and clinicopathological features. RESULTS: CCNB2, TOP2A, and ASPM were identified as both prognosis-related genes and differentially expressed genes. TOP2A (HR: 1.7, P = 0.003) and ASPM (HR: 1.8, P < 0.001) exhibited a significant difference between the high- and low-expression groups in the overall survival analysis, while CCNB2 (HR: 1.4, P = 0.052) was not statistically significant. CCNB2 (HR: 1.5, P = 0.006), TOP2A (HR: 1.7, P < 0.001), and ASPM (HR: 1.6, P = 0.003) were all statistically significant in the disease-free survival analysis. All three genes were significantly associated with race and fetoprotein values (P < 0.05). CCNB2 expression was associated with tumor stage (P = 0.01), and ASPM expression was associated with new tumor events (P = 0.03). CONCLUSION: Overexpression of CCNB2, TOP2A, and ASPM are associated with poor prognosis, and these genes could serve as potential prognostic markers and therapeutic targets for HCC. Hindawi 2020-06-23 /pmc/articles/PMC7333053/ /pubmed/32685486 http://dx.doi.org/10.1155/2020/4612158 Text en Copyright © 2020 Yuping Zeng et al. http://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
Zeng, Yuping
He, He
Zhang, Yu
Wang, Xia
Yang, Lidan
An, Zhenmei
CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis
title CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis
title_full CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis
title_fullStr CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis
title_full_unstemmed CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis
title_short CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis
title_sort ccnb2, top2a, and aspm reflect the prognosis of hepatocellular carcinoma, as determined by weighted gene coexpression network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333053/
https://www.ncbi.nlm.nih.gov/pubmed/32685486
http://dx.doi.org/10.1155/2020/4612158
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