Objective: The occurrence and development of hepatocellular carcinoma (HCC) remain unclear. This study aimed to investigate potential diagnostic or prognostic markers for early HCC by applying bioinformatic analysis.
Methods: The gene expression profiles of early HCC and normal tissues from a TCGA dataset were used to identify differentially expressed genes (DEGs) and then analysed by weighted gene coexpression network analysis. The integrated genes were selected to construct the protein–protein interaction (PPI) network and determine the hub genes. The prognostic impact of the hub genes was then analysed.
Results: A total of 508 integrated genes were selected from the 615 DEGs and 8956 genes in the turquoise module. A PPI network was constructed, and the top 20 hub genes, including apolipoprotein A-IV (APOA4), fibrinogen gamma chain (FGG), vitamin K-dependent protein Z (PROZ), secreted phosphoprotein 24 (SPP2) and fetuin-B (FETUB), were identified. Only PROZ was significantly associated with the prognosis of early HCC.
Conclusion: In this study, we demonstrated that the expression of PROZ was decreased in early HCC compared with normal liver controls, and low PROZ expression might result in poor overall survival of early HCC.
Keywords: hepatocellular carcinoma, weighted gene coexpression network analysis, bioinformatics analysis, vitamin K-dependent protein Z