Identification of Novel Biomarkers in Hepatocellular Carcinoma by Integrated Bioinformatical Analysis and Experimental Validation
Authors: Liao Chen , Wang Lanlan , Li Xiaoqiang , Bai Jinyu , Wu Jieqiong , Zhang Wei , Shi Hailong , Feng Xuesong , Chao Xu 
Published: June 2021

Background: Hepatocellular carcinoma (HCC) is one of the most common poorly prognosed virulent neoplasms of the digestive system. In this study, we identified novel biomarkers associated with the pathogenesis of HCC aiming to provide new diagnostic and therapeutic approaches for HCC. Methods: Gene expression profiles of GSE62232, GSE84402,GSE121248 and GSE45267 were obtained in GEO database. Differential expressed genes (DEGs) between HCC and normal samples were identified using the GEO2R tool and Venn diagram software.Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to carry out enrichment analysis on gene ontology (GO) and the Kyoto Encyclopaedia of Genes and Genomes pathway (KEGG). The protein-protein interaction (PPI) network of DEGs was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized by Cytoscape. Expressions and prognostic values of hub genes were validated through Kaplan-Meier plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas Database (HPA), western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, potential small molecule drugs were screened by Connectivity Map (CMAP). Results: A total of 100 overlapped DEGs were detected and results showed 23 of which were up-regulated with the rest being down-regulated. STRING screened the 70 edges and the 199 nodes in the PPI network. Survival analysis showed that aberrant mRNA expression of TOP2A, DTL, ANLN, CDKN3, BUB1B, CDK1, PBK, RRM2, RACGAP1, PRC1, NEK2, ECT2, CCNB1, HMMR, ASPM was significantly associated with a low survival rate. Results of WB and qRT-PCR showed that the expression levels of ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A were all increased in HCC tissues. Furthermore, CMAP predict suggest the 10 most vital small molecule drugs could reverse the progression of HCC. Conclusions: Core DEGs (ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A) with poor prognosis and candidate drugs for HCC treatment were identified through integrated bioinformatic analysis.This study will contribute to providing prognostic biomarker and therapeutic strategies in HCC. Background : Hepatocellular carcinoma (HCC) is one of the most common poorly prognosed virulent neoplasms of the digestive system. In this study, we identified novel biomarkers associated with the pathogenesis of HCC aiming to provide new diagnostic and therapeutic approaches for HCC. Methods : Gene expression profiles of GSE62232, GSE84402,GSE121248 and GSE45267 were obtained in GEO database. Differential expressed genes (DEGs) between HCC and normal samples were identified using the GEO2R tool and Venn diagram software.Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to carry out enrichment analysis on gene ontology (GO) and the Kyoto Encyclopaedia of Genes and Genomes pathway (KEGG). The protein-protein interaction (PPI) network of DEGs was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized by Cytoscape. Expressions and prognostic values of hub genes were validated through Kaplan-Meier plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas Database (HPA), western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, potential small molecule drugs were screened by Connectivity Map (CMAP). Results : A total of 100 overlapped DEGs were detected and results showed 23 of which were up-regulated with the rest being down-regulated. STRING screened the 70 edges and the 199 nodes in the PPI network. Survival analysis showed that aberrant mRNA expression of TOP2A, DTL, ANLN, CDKN3, BUB1B, CDK1, PBK, RRM2, RACGAP1, PRC1, NEK2, ECT2, CCNB1, HMMR, ASPM was significantly associated with a low survival rate. Results of WB and qRT-PCR showed that the expression levels of ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A were all increased in HCC tissues. Furthermore, CMAP predict suggest the 10 most vital small molecule drugs could reverse the progression of HCC. Conclusions : Core DEGs (ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A) with poor prognosis and candidate drugs for HCC treatment were identified through integrated bioinformatic analysis.This study will contribute to providing prognostic biomarker and therapeutic strategies in HCC.