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

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.

Identification of differentially expressed genes in the endothelial precursor cells of patients with type 2 diabetes mellitus by bioinformatics analysis

“…A total of 12 topological analyses were provided by the cytoHubba plugin (25). In accordance with previous reports, a total of 3 most widely used topological analysis methods, including maximal clique centrality (MCC), maximum neighborhood component (MNC) and density of maximum neighborhood component (DMNC), were used to identify potential hub genes (25,26). The overlapping genes were selected as the hub genes using Venn diagrams.…”

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

“…The human interactome map has a higher coverage on proteins encoded by human genes; therefore, it can provide additional insights than those based only on pathway databases. Moreover, PPI networks have been used to infer candidate genes for different types of cancer (44)(45)(46)(47)(48). These networks are sensitive to disruption of high degree proteins (proteins with numerous interaction partners, also named hub genes) (49,50) since protein degree correlates with its gene essentiality for phenotype survival.…”

Diagnosis efficacy of CEUS for hepatic inflammatory lesions

“…Tumor markers are specific antigen used as a biomarker of malignant cellular transformation. [51][52][53][54][55] We included the clinically widely used tumor serum markers including AFP, CEA, and CA199 in this study and aimed to investigate whether tumor marker measurement would facilitate differential diagnosis of inflammatory lesion and malignant lesion. AFP was not found to be a significant factor as suggested by multivariable analysis; CEA and CA199 might be taken into account.…”