Identification of cancer/testis antigen 2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis

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Abstract

The 5-year survival rate of hepatocellular carcinoma (HCC) is <20%; thus, identifying new potential therapeutic targets or novel biomarkers for prognosis prediction is crucial. The present study aimed to screen hub genes by constructing protein-protein interaction (PPI) subnetworks using topological analysis methods, as well as reveal their clinical significance through big data analytics and their association with the clinicopathological features. Firstly, the PPI subnetworks were constructed using four topological analysis methods, including the MCC, DMNC, MNC and degree methods, to obtain 6 hub genes. Subsequently, the hub gene cancer/testis antigen 2 (CTAG2), which affects the prognosis of HCC (overall survival, P=0.035), was acquired by analysing clinical data in The Cancer Genome Atlas database. Meanwhile, CTAG2 expression was significantly associated with the age at diagnosis (P=0.003), T stage (P=0.028), TNM stage (P=0.028) and α-fetoprotein (AFP) expression (P=0.045). Further immunohistochemical analysis of samples collected in our hospital revealed that the expression level of CTAG2 in 46 HCC tissues was significantly higher in comparison with that in paired adjacent tissues. The clinical data indicated that the expression of CTAG2 was significantly correlated with the hepatitis B virus status (P=0.010) and AFP expression (P=0.004). These results were then found to be consistent with the results of big data analytics. Furthermore, Gene Set Enrichment Analysis demonstrated that the function of CTAG2 in HCC may be associated with the cell cycle. Taken together, these findings suggest that CTAG2 may serve as a new potential therapeutic target for HCC patients.

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Liu, J., Yu, Z., Sun, M., Liu, Q., Wei, M., & Gao, H. (2019). Identification of cancer/testis antigen 2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis. Oncology Letters, 18(5), 4778–4788. https://doi.org/10.3892/ol.2019.10811

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