Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Hepatocellular Carcinoma.

Liver cancer is a major medical problem because of its high morbidity and mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Currently, the mechanism of HCC is unclear, and the prognosis is poor with limited treatment. The purpose of this study is to identify hub genes and potential therapeutic drugs for HCC. We used the GEO2R algorithm to analyze the differential expression of each gene in 4 gene expression profiles (GSE101685, GSE62232, GSE46408, and GSE45627) between HCC and normal hepatic tissues. Next, we screened out the differentially expressed genes (DEGs) by corresponding calculation data according to adjusted P-value < 0.05 and | log fold change (FC) | > 1.0. Subsequently, we used the DAVID software to analyze the DEGs by GO and KEGG enrichment analysis. Then, we carried out the protein-protein interaction (PPI) network analysis of DEGs using the STRING tool, and the PPI network was constructed by Cytoscape software. MCODE plugin was used for module analysis, and the hub genes were screened out by the Cyto- Hubba plugin. Meanwhile, we used The Kaplan-Meier plotter, GEPIA2 and HPA databases to exert survival analysis and verify the expression alternation of hub genes. Furthermore, we used ENCORI, TargetScan, miRDB and miRWalk database to predict the upstream regulated miRNA of hub genes and construct a miRNA-hub genes network by Cytoscape software. Finally, we selected potential therapeutic drugs for HCC through DGIdb databases. A total of 415 DEGs were screened in HCC, including 196 up-regulated DEGs and 219 down-regulated DEGs. The results of KEGG pathway analysis suggested that the up-regulated DEGs can regulate the cell cycle, and DNA replication signal pathway, while the down-regulated DEGs were associated with metabolic pathways. In this study, we identified 11 hub genes (AURKA, BUB1B, TOP2A, MAD2L1, CCNA2, CCNB1, BUB1, KIF11, CDK1, CCNB2 and TPX2), which were independent risk factors of HCCand all up-regulated DEGs. We verified the expression difference of hub genes through the GEPIA2 and HPA database, which was consistent with the results of GEO data. We found that those hub genes were mutations in HCC according to the cBioPortal database. Finally, we used the DGIdb database to select 32 potential therapeutic targeting drugs for hub genes. In summary, our study provided a new perspective for researching the molecular mechanism of HCC. Hub genes, miRNAs, and candidate drugs provide a new direction for the early diagnosis and treatment of HCC.

Su Q ，Li W ，Zhang X ，Wu R ，Zheng K ，Zhou T ，Dong Y ，He Y ，Wang D ，Ran J ... -  《-》

Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases.

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China. Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes' protein expression levels. Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC. Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

Zhang P ，Feng J ，Wu X ，Chu W ，Zhang Y ，Li P ... -  《-》

Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer.

Yang D ，He Y ，Wu B ，Deng Y ，Wang N ，Li M ，Liu Y ... -  《Journal of Ovarian Research》

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

Yang WX ，Pan YY ，You CG 《-》

Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis.

Zhou Z ，Li Y ，Hao H ，Wang Y ，Zhou Z ，Wang Z ，Chu X ... -  《-》