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

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. 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. 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). 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. Copyright © 2020 Yuping Zeng et al.

Citation

Yuping Zeng, He He, Yu Zhang, Xia Wang, Lidan Yang, Zhenmei An. CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis. BioMed research international. 2020;2020:4612158

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PMID: 32685486

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