Analysis Of Gene Expression Profile Based On Human Liver Cancer Gene Chip

Objective :In this study,Series GSE40367 gene chip of human Hepatocellular carcinoma was analyzed by using bioinformatics method to screen out differentially expressed Hepatocellular carcinoma gene molecules and perform biological function analysis,so as to further explore the molecular mechanism of the pathogenesis of Hepatocellular carcinoma.Method: The gene expression matrix of the Series GSE40367 human Hepatocellular carcinoma gene chips were obtained from the GEO database.The Limma R package was used for differential analysis,GO gene function annotation and KEGG signal pathway annotation were performed by David,and differential gene GO / KEGG enrichment was analyzed by using the clusterprofiler R package.After that,the STRING database was used to obtain the protein interactions relationship among different genes,Cytoscape software was used for visualization and MCODE gene hub module identification,and Metascape tool was used for functional annotation of gene modules for key module genes.Liver cancer expression profile data and survival data were downloaded from TCGA database,and differential gene analysis was performed on liver cancer expression data with limma package.With the intersection of different liver cancer genes in the GEO database,COX proportional risk regression model(using the Survival package of R language)was used to screen the different genes related to the prognosis and Survival of liver cancer,and the risk model was established.Results: By screening the differential expression values of the Series GSE40367 data for the liver cancer(| log FC |> 1.5)and using the corrected p-value FDR <0.05,a total of 154 differentially up-regulated genes or transcripts and 133 differentially down-regulated differential genes or transcripts were found.Through the enrichment analysis and redundancy removal of these differentially expressed genes and transcripts,it was found that biological functions related to cell proliferation and immune system processes were highly correlated to the occurrence of liver cancer.On the other hand,pathway enrichment analysis showed that PPAR,p53 and P450 signaling pathways were differential signaling pathways.By establishing a protein-protein interaction network and using the MCODE algorithm for hub module identification to identify four key pivot modules of the liver cancer,and the biological functions of these key modules were analyzed through enrichment analysis of module gene members.Limma data package analysis of liver cancer expression data produced differential genes.After taking the intersection,the differential genes were 2669.The risk model is Risksocre = RAD54 B *(0.050373)+ MEX3 A *(0.020813)+ CAD *(0.029519).Conclusions: 1.Lipid metabolism and metabolic pathways such as P53 signaling pathway,PPAR signaling pathway,and cytochrome P450 are involved in the process of tumorigenesis and development.2.RAD54 B,MEX3A,and CAD are potential biomarkers for HCC.