Investigation of Genes and Their Interactions in Liver Diseases Using Bioinformatics Algorithms

In this study, we considered progression of liver diseases. Particularly we considered Hepatocellular Carcinoma Cancer, HCC, whose patients have low survival rates. For this purpose, we researched molecular structures and protein interactions involved in the initiation and progression of HCC. We exploited microarray data samples and their gene expression profiles from literature. During analysis, we implemented statistical data analysis techniques and looked for Differentially Expressed Genes during the initiation and progression of HCC. As a result of this analysis we found 12 hub genes, where 3 of them (ANLN, TOP2A and ASPM) were upregulated and the others (CXCL14, LINC01093, OIT3, CLEC4G, THRSP, APOF, CLTRN and FCN3) were downregulated. By performing Gene Ontology Analysis, we classified genes with increased or decreased expressions in terms of cellular component, biological process, and molecular function. Subsequently, we executed protein-protein interaction network analysis and found important interactions between the hub genes. Results of data analysis concluded that these 12 genes and their interactions play a key role in the initiation and progression of significant liver diseases and can be used as a potential biomarker for disease progression. Furthermore, gene feature analysis showed that it is becoming more difficult to compensate functional deficiencies of the proteins encoded by these genes during biological processes. In particular, Gene Ontology Analysis denoted that TOP2A gene associates with many of the biological pathways and a change in the expression of this gene can cause decent problems in many cellular functions.