Deciphering the protein–protein interaction network regulating hepatocellular carcinoma metastasis

AbstractHepatocellular carcinoma is one of the most common tumors in the world and has a high mortality rate. This study elucidates the mechanism of hepatocellular carcinoma- (HCC) related development. The HCC gene expression profile (GSE54238, GSE84004) was downloaded from Gene Expression Omnibus for comprehensive analysis. A total of 359 genes were identified, of which 195 were upregulated and 164 were downregulated. Analysis of the condensed results showed that “extracellular allotrope” is a substantially enriched term. “Cell cycle”, “metabolic pathway” and “DNA replication” are three significantly enriched Kyoto Encyclopedia of Genes and Genomespathways. Subsequently, a protein-protein interaction network was constructed. The most important module in the protein-protein interaction network was selected for path enrichment analysis. The results showed thatCCNA2, PLK1, CDC20, UBE2CandAURKAwere identified as central genes, and the expression of these five hub genes in liver cancer was significantly increased in The Cancer Genome Atlas. Univariate regression analysis was also performed to show that the overall survival and disease-free survival of patients in the high expression group were longer than in the expression group. In addition, genes in important modules are mainly involved in “cell cycle”, “DNA replication” and “oocyte meiosis” signaling pathways. Finally, through upstream miRNA analysis, mir-300 and mir-381-3p were found to coregulateCCNA2,AURKAandUBE2C. These results provide a set of targets that can help researchers to further elucidate the underlying mechanism of liver cancer.

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Analysis of hepatocellular carcinoma and metastatic hepatic carcinoma via functional modules in a protein-protein interaction network

Journal of Cancer Research and Therapeutics ◽

10.4103/0973-1482.145866 ◽

2014 ◽

Vol 10 (7) ◽

pp. 186 ◽

Cited By ~ 3

Author(s):

Yihui Sun ◽

Jun Pan ◽

Zhijie Cong ◽

Ming Zhong

Keyword(s):

Hepatocellular Carcinoma ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Functional Modules ◽

Hepatic Carcinoma ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

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Identification of Potential Biomarkers in Hepatocellular Carcinoma: A Network-Based Approach

10.21203/rs.3.rs-91501/v1 ◽

2020 ◽

Author(s):

Mehrdad Ameri ◽

Haniye Salimi ◽

Sedigheh Eskandari ◽

Navid Nezafat

Keyword(s):

Hepatocellular Carcinoma ◽

Protein Interaction ◽

Microarray Data ◽

Protein Interaction Network ◽

Interaction Network ◽

Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network ◽

Potential Biomarkers

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of death worldwide. Identification of potential therapeutic and diagnostic biomarkers can be helpful to screen cancer progress. This study implemented with the aim of discovering potential biomarkers for HCC within a network-based approach integrated with microarray data. Methods: Through downloading a gene expression profile GSE62232 differentially expressed genes (DEGs) were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for DEGs were performed utilizing enrichr server. Following reconstruction of protein-protein interaction network of DEGs with STRING, network visualization, analyses, and clustering into structural modules carried out using Cytoscape. Considering degree centrality, 15 hub genes were selected as early biomarker candidates for final validation. In order to validate hub genes, GEPIA server was used to perform overall survival (OS) and disease-free survival (DFS). Results: In our approach 1996 DEGs were identified including 995 up-regulated genes and 1001 down-regulated genes. KEGG pathway enrichment analysis shown that DEGs are associated with Chemical carcinogenesis, and Cell cycle. GO term enrichment analysis indicated the relation of DEGs with epoxygenase P450 pathway, arachidonic acid monooxygenase activity, and secretory granule lumen. Following analysis of protein-protein interaction network of DEGs top three structural modules and 15 early hub genes were selected. Validation of hub genes performed using GEPIA. Consequently, CDK1, CCNB1, CCNA2, CDC20, AURKA, MAD2L1, TOP2A, KIF11, BUB1B, TYMS, EZH2, and BUB1 were considered as our final proposed biomarkers. Conclusion: using an integrated network-based approach with microarray data our results revealed 12 final candidates with potential to considered as biomarkers in hepatocellular carcinoma.

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