Screening Of Differentially Expressed Genes In Ovarian Cancer And Its Effect On The Biological Behavior Of Ovarian Cancer Cells

Background:Ovarian cancer,as a malignant tumor of the female reproductive system,seriously threatens women’s health.Although the annual number of new cases of ovarian cancer is lower than that of cervical cancer and endometrial cancer,its mortality rate is higher than that of cervical cancer and endometrial cancer,and it is the malignant tumor with the worst prognosis of female reproductive system.Ovarian cancer is difficult to detect in the early stage and the prognosis is poor.Therefore,early screening of ovarian cancer is of great significance to ovarian cancer.In this study,ovarian cancer tumor markers were screened to find markers or targets with potential value in the diagnosis and treatment of ovarian cancer.PartⅠ:Screening and functional enrichment analysis of differentially expressed genes in ovarian tumor tissuesObjective:Compared with normal ovarian tissues,differentially expressed genes in tumor tissues of ovarian cancer patients were screened and their functions were analyzed.Methods:1.1.Tumor tissue and normal tissue were derived from 26-year-old patient with ovarian serous adenocarcinoma（the right ovary was tumor tissue;the left side is normal ovary）.Agilent G2565CA Microarray Scanner chip was used to detect the m RNA expression level of transcriptome,and Roche Nimble Gen’s 3×720K format methylation chip was used to detect gene methylation level.2.Screening genes with significant differences in m RNA expression levels and methylation levels in ovarian cancer tissues（Log₂FC>2,P<0.05,vs normal tissues）.Up-regulated genes:the intersection of up-regulated m RNA expression and down-regulated methylation genes.Down-regulated genes:The intersection of down-regulated m RNA expression and up-regulated methylation genes.3.DAVID（https://david.ncifcrf.gov/）)was used to analyze the functional enrichment（GO,KEGG）of differentially expressed genes in ovarian cancer,and R was used to visualize the results.Results:1.Compared with normal ovarian tissues,55 genes were up-regulated and 111 genes were down-regulated in ovarian cancer tissues.2.The results of functional analysis showed that the up-regulated genes in ovarian cancer tumor tissues were mainly involved in biological processes such as inflammatory response,angiogenesis,and cell-matrix adhesion.The down-regulated genes in ovarian cancer tissues are mainly involved in biological processes such as G-protein coupled receptor pathway and positive regulation of multicellular biological growth.PartⅡ:Verification of differentially expressed genes in ovarian cancer and bioinformatics analysis of related genesObjective:The first part of this study initially screened the differentially expressed genes in ovarian cancer.Due to the small number of cases detected by the chip,this part will be combined with 426 ovarian cancer tissues and 88 normal tissues in the tumor database such as GEPIA to perform bioinformatics verification of the genes screened in the above part,and the key genes in the progression of ovarian cancer will be obtained.The correlation between the expression level and prognosis,clinical stage,age and immune infiltration was analyzed.Methods:1.GEPIA2.0（http://Gepia2.cancer-pku.cn/）)is a large database based on TCGA.GEPIA was used to verify the differentially expressed genes in ovarian cancer tissues and normal tissues.|Log₂FC|>1 and P<0.01were considered statistically significant.2.Kaplan-Meier Plotter（The data sources of the http://www.Kmplot.com/）database include GEO and TCGA.Kaplan-Meier can evaluate the correlation between gene expression and survival of multiple tumors.Kaplan-Meier was used to analyze the prognostic value of differential genes in ovarian cancer.3.UALCAN（http://UALCAN.path.uab.edu.）)is a comprehensive and extensive online tumor data platform.UALCAN was used to analyze the correlation between differentially expressed genes and stage,age and grade of ovarian cancer patients.4.TIMER2.0（http://Timer.cistrome.org/）)can systematically and comprehensively analyze the immune infiltration of various tumors.TIMER was used to analyze the correlation between differentially expressed genes and immune cell infiltration and prognosis in ovarian cancer.5.Linked Omics（http://www.Linkedomics.org/login.php）)is a multigenomic clinical database of 32 tumors,including 11,158 patients,derived from the Cancer Genome Atlas（TCGA）project.Linked Omics was used to analyze the related genes of differentially expressed genes in ovarian cancer（Top50）.6.DAVID（https://David.ncifcrf.gov/）)was used to perform functional enrichment（GO,KEGG）analysis on the related genes（Top50）of differentially expressed genes in ovarian cancer,and R was used to visualize the results.Results:1.The results of GEPIA2.0 verification showed that compared with normal tissues,the expression of ANO1,ARHGEF10L,CPSF3L,EME2,FLNA,MYO7A,PKD1,SLC45A4,TAGLN,TMEM8A and TTYH3 increased in ovarian cancer tissues,while the expression of CECR2 and S100A1 decreased in ovarian cancer tissues.2.Kaplan-Meier Plotter prognostic analysis showed that the high expression of TMEM8A,TTYH3,SLC45A4 and CECR2 in ovarian cancer was associated with poor prognosis.3.UALCAN results showed that the expression of TMEM8 A and TTYH3 in ovarian cancer was negatively correlated with the clinical stage of patients.The expression of SLC45A4 in stage III ovarian cancer was lower than that in stage II and IV,and the expression of CECR2 in ovarian cancer was different in stage III and IV.The expression of TMEM8 A,TTYH3 and CECR2 in ovarian cancer was negatively correlated with the age of patients.4.The results of TIMER2.0 showed that the expression of TMEM8A,TTYH3,SLC45A4 and CECR2 in ovarian cancer was positively correlated with the infiltration of neutrophils,tumor-associated fibroblasts and endothelial cells.It is negatively correlated with cell infiltration such as plasmacytoid dendritic cells.5.The positive correlation genes of TMEM8A,TTYH3,SLC45A4 and CECR2 are mainly involved in protein ubiquitination,negative regulation of angiogenesis,transcriptional regulation of RNA polymerase II promoter,receptor transmembrane protein tyrosine kinase pathway and other biological processes;negatively correlated genes are mainly involved in biological processes such as cytoplasmic translation,cell division,and inflammatory response.PartⅢBiological effects of TMEM8A,TTYH3,SLC45A4 and CECR2 on ovarian cancer cellsObjective:The effects of TMEM8A,TTYH3,SLC45A4 and CECR2 on the proliferation,cycle,apoptosis and migration of ovarian cancer cells were studied by cell experiments.Methods:1.The si RNA sequences of TMEM8A,TTYH3,SLC45A4,CECR2 and the NC sequence of the control group were designed and synthesized to identify the knockdown efficiency.2.Ovarian cancer cells OVCAR3 and SKOV3 were transfected with si RNA,and cell proliferation was detected by CCK8 assay.3.The cell cycle was detected by flow cytometry 36 hours after si RNA transfection.4.The apoptosis of ovarian cancer cells was detected by flow cytometry after 36 hours of si RNA transfection.5.After si RNA was transfected into ovarian cancer cells for 24 hours,the cell migration ability was detected by scratch test.Results:1.Compared with the control group,the proliferation ability of OVCAR3 cells with TMEM8A knockdown decreased at 48 hours.The proliferation ability of OVCAR3 cells with TTYH3 knockdown decreased at 24 and 48 hours.The proliferation ability of SLC45A4 knockdown OVCAR3 cells decreased at 24 and 48 hours.The proliferation ability of OVCAR3 cells with CECR2 knockdown did not change.The proliferation ability of SKOV3 cells with TMEM8A and TTYH3 knockdown increased at 48 hours.The proliferation ability of SKOV3 cells with SLC45A4 knockdown decreased at 24 and 48 hours.The proliferation ability of SKOV3 cells with CECR2 knockdown did not change.2.The proportion of OVCAR3 cells with TMEM8A and TTYH3 knockdown in S phase was less.The proportion of TTYH3 knockdown OVCAR3 cells in G2 phase was more,and the proportion of CECR2 knockdown OVCAR3 cells in G2 phase was less.The proportion of SKOV3 cells with knockdown of TMEM8 A and TTYH3 in G1 phase was less,and the proportion of SKOV3 cells with knockdown of CECR2 in G1 phase was more.SKOV3 cells with TTYH3 knockdown were more in S phase,and SKOV3 cells with CECR2 knockdown were less in G2 phase.3.The proportion of apoptotic cells in OVCAR3 cells with TMEM8 A and CECR2 knockdown increased.The proportion of apoptotic cells in SKOV3 cells with TMEM8 A and TTYH3 knockdown increased.4.The migration distance of SKOV3 cells with SLC45A4 and CECR2 knockdown decreased at 24 hours.