Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation

HCC (hepatocellular carcinoma) is a highly aggressive malignancy that cause a mass of deaths world widely. We chose gene expression datasets of GSE27635 and GSE28248 from GEO database to find out key genes and their interaction network during the progression and metastasis of HCC. GEO2R online tool was used to screen differentially expressed genes (DEGs) between tumor and peri-tumor tissues based on these two datasets. The identified differentially expressed genes were prepared for further analysis such as GO function, KEGG pathway, PPI network analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Retrieval of Interacting Genes (STRING). Two modules were constructed by MOCDE plugin in Cytoscape and 21 genes were selected as hub genes during this analysis. The expression heatmap and GO function of hub genes were performed using R pheatmap package and BiNGO plugin in Cytoscape respectively. Six hub genes including CDC25 A, CDK1, HMMR, MYBL2, TOP2A were recollected for survival analysis and their expression was validated using Kaplan Meier-plotter and GEPIA website. We also investigated the DEGs between metastasis and non-metastasis tissues and two genes (NQO1 and PTHLH) are highly associated with the metastasis in HCC. Further verification using woundhealing and transwell assay confirmed their ability to mediate cell migration and invasion. In summary, our results obtained by bioinformatic analysis and experimental validation revealed the dominant genes and their interaction networks that are associated with the progression and metastasis of HCC and might serve as potential targets for HCC therapy and diagnosis.