Differential Analysis Of Gene Expression Profiles Of Hepatocellular Carcinoma Based On Bioinformation

BackgroundHepatocellular carcinoma(HCC)is the major histological subtype of primary liver cancer,accounting for 90%of primary liver cancer.HCC is a common malignancy,and its mortality rate is the third highest among malignancies.The causes are closely related to chronic liver disease,mainly including chronic hepatitis viruses such as hepatitis B virus(HBV)and hepatitis C virus(HCV)infection,as well as liver disease caused by metabolic diseases such as obesity.The occult of HCC is strong and the early diagnosis rate of HCC is low.Most patients with HCC are already in the middle or late stages when they are found.The main treatments for HCC are surgical resection,transplantation,radiotherapy,chemotherapy and radiofrequency.Due to the characteristics of high aggressiveness,high metastasis and high recurrence,the poor prognosis of HCC patients has not been fundamentally changed.Therefore,it is of great clinical significance for the diagnosis and treatment of HCC to search for key indicators and mechanisms related to the occurrence and development of HCC.AimsIn this study,we try to identify differentially expressed genes(DEGs)between HCC and healthy human liver tissue by integrated analysis.Functional enrichment analysis including the Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)were performed for the screened DEGs to investigate the biological function.A protein ? protein interaction(PPI)network was subsequently established to identify hub genes related to HCC.Finally,the survival analysis of these hub genes was performed using the online database GEPIA.Our study may be helpful in finding potential as diagnostic biomarkers or aiding the development of novel therapeutics.Materials and methods1.Gene expression datasets were retrieved from the Gene Expression Omnibus(GEO)database,all selected datasets consisted of genome-wide expression data from of hepatocellular carcinoma patients with HCC and/or normal samples.2.The DEGs was calculated using the GEO2R online analysis tool.The adjusted P-value and |logFC| were calculated from GEO2R.Analyzed each dataset,the threshold for DEGs identification was set as P<0.05 and |logFC|?1.0.The Venn diagram webtool was used to obtain intersection DEGs among all three datasets.3.The Database for Annotation,Visualization and Integrated Discovery(DAVID)was performed for GO analysis and KEGG pathway enrichment of DEGs.FDR<0.01 was identified as statistically significant.The GO analysis and KEGG enrichment analysis were visualized by the R date software package.4.The Search Tool for the Retrieval of Interacting Genes(STRING)database was used to gain insight into the interaction between DEGs and proteins.The PPI network was subsequently visualized using Cytoscape software 3.7.2.Cyhubba was used to determine the degree of between each protein node.The top ten genes were considered as hub genes.5.The Gene Expression Profiling Interactive Analysis(GEPIA)database was applied to evaluate the prognostic values of hub genes in hepatocellular carcinoma patients,and the Kaplan-Meier survival scurve and prognosis was drawn.P<0.05 was considered to indicate a statistically significant result.Results1.Four gene expression profiles(GSE62232,GSE57957,GSE121248 and GSE39791)were downloaded from the data portal of the GEO database.And 26 upregulated genes and 124 downregulated genes were screened out through analysis of the four data sets.2.The GO analysis includes biological process,molecular function,and cellular component.GO analysis showed that the enrichment of "oxidation-reduction process","extracellular exosome" and "heme binding" was the most significant.KEGG pathway analysis showed that "Metabolic pathways","Carbon metabolism","Retinol metabolism","Mineral absorption" and "Complement and coagulation cascades"were important pathways.3.The top ten genes were FTCD,MBL2,C8A,ALDH8A1,CYP3A4,TAT,KLKB1,ApoA5,PLG,and ASS1.4.Survival analysis showed that CYP3A4?TAT were associated with overall survival of HCC patients,and the lower the expression level,the shorter the survival time of liver cancer patients.FTCD?MBL2?C8A?ALDH8A1?KLKB1?APOA5?PLG and ASS1 had no relation to the overall survival of HCC patients.Conclusion1.There are different genes between HCC tissue and normal liver tissue.2.CYP3A4 and TAT are involved in the occurrence and development of HCC,and they may be biomarkers for the diagnosis or prognosis of HCC.