Meta-analysis Of MRNA Expression Profiles To Identify Hub Genes And Pathways In Differentiated Ovarian Cancer

Objectives:Meta-analyze the mRNA expression profile of ovarian cancer tissue to identify the pivotal genes and pathways of differentiated ovarian cancer.Methods:Using The results of GEO(Gene Expression Omnibus)database platform,TCGA(The Cancer Genome Atlas)team and Tothill et al.’s study on ovarian Cancer subtypes,we used neural network model to establish an accurate ovarian Cancer subtype classifier.Differentiated HGS-OVCA genes(DEGs)were identified by META analysis based on mRNA expression profile of ovarian cancer tissue,and GO functional annotation,KEGG pathway enrichment analysis and protein-protein interaction were performed on DEGs.To screen out the key genes associated with differentiated ovarian cancer and evaluate the relationship between the key genes and survival.Results:Finally,5 data sets were included in the analysis,namely GSE6008,GSE18520,GSE26712,GSE27651 and GSE9891.A total of 573 differentially expressed genes were identified by comparing differentiated high-grade serous ovarian cancer and undifferentiated high-grade serous ovarian cancer,which were divided into 218up-regulated genes and 355 down-regulated genes.The GO function annotation and KEGG pathway enrichment analysis of the up-regulated genes and down-regulated genes show that the up-regulated genes are involved in the P53 signaling pathway,cell tight junctions,cell adhesion molecules,pathogenic E.coli infection,and Fcγ-r mediation.Significantly enriched in phagocytosis,and down-regulated genes are involved in cancer polyglycoprotein,signal pathways that regulate stem cell pluripotency,complement system,JAK-STAT signal pathway and cGMP-PKG signal pathway.Use MCODE software to analyze the protein-protein interaction of DEGs,use the(PPI)network to detect 4 important gene modules and screen out 10 pivot genes with a degree of centrality> 40,among which CDH1,SNAI2,CDK1(CDC2),KDR(VEGFR)and DCN survival analysis suggests that it is related to the survival and prognosis of the disease.Conclusions:Through the TCGA and GEO database platforms,a cross-platform ovarian cancer data classifier was established.And identified 573 differentially expressed genes and 10 pivot genes of differentiated HGS-OvCa,among which CDH1,SNAI2,CDK1(CDC2),KDR(VEGFR)and DCN as pivot genes may become the future molecular targeted therapy of differentiated ovarian cancer potential targets.