Network Analysis in the Identification of Genes Conferring Metastatic Potential in Hepatocellular Carcinoma

Objectives: Hepatocellular carcinoma (HCC) is a common liver cancer accounting with high mortality rate owing to metastasis. Anti-metastatic treatment is scant while proposed mechanisms are in excess, yet specific molecular drivers of HCC remain at large. Therefore, our study aims to identify drivers of HCC metastasis using protein-protein interaction (PPI) networks to identify key driver genes associated with HCC metastasis. Methods: From differential expression genes (DEGs) analysis using GSE45114 microarray dataset, four main hub genes that correlated with patient survival were identified. The first hub gene, SERPINC1 had the highest centrality parameter in impeding HCC metastasis, implicating thrombin mediation through thrombin-induced tumor growth and angiogenesis. Results: Our study reveals that thrombin was not differentially expressed, hence, suggesting the involvement of other, less-well studied pathways in impeding metastasis, such as KNG1, PAH, AMBP, and TTR. Findings for CD44 were consistent with existing literature. Meanwhile, FGG and APOA5, both less studied genes in the context cancer metastasis studies, were found to be crucial in impeding HCC metastasis. Conclusion: This study identified four potential proteins (SERPINC1, CD44, FGG and APOA5) to be therapeutic targets or biomarkers and demonstrates the use of PPI networks for understanding HCC metastasis at a more profound level.