| Background: Gastric cancer,as the third most common cancer death factor in the world,poses a serious threat to human survival and quality of life,but the pathogenesis of gastric cancer still needs to be explored.A number of studies have confirmed that autophagy is commonly expressed abnormally in gastric cancer,and it plays an important role in the occurrence and prognosis of gastric cancer,but the specific mechanism of action needs to be further explored.Bioinformatics technology has developed rapidly in recent years.The TCGA(The Cancer Genome Atlas)database records a variety of cancer gene data including gastric cancer.This study will screen the autophagy genes which is related to gastric cancer based on the TGGA database,and then further analyze the relationship between autophagy and gastric cancer.The correlation between the occurrence and prognosis of gastric cancer provides new markers for the early diagnosis and prognosis of gastric cancer.Objective: Firstly,this paper is to screen the differentially expressed autophagy-related genes in gastric cancer based on the TCGA database,to further clarify the possible role of the differential gene in the occurrence and prognosis of gastric cancer,and to verify the differential gene in gastric cancer tissue and more than 5cm from the edge of the cancer tissue by real-time fluorescence quantitative PCR The expression level in distant cancer tissues,which is combined with the overall analysis and comprehensive prediction of the relevant clinical characteristics of gastric cancer patients in the TCGA database.Secondly,its aim is to explore the possible mechanism of autophagy in the occurrence and prognosis of gastric cancer.Methods: 1.The author has downloaded 413 gastric cancer transcription data and449 clinical data from the TCGA database,among which,875 differential genes are screened out by R language.The String network is used to draw the protein interaction(PPI)network analysis diagram of the differentially expressed genes,and the cytoscape software is used to screen out 10 core genes.Furthermore,oncomine and survival analysis are combined to screen the differential genes of gastric cancer patients,and finally one target gene is obtained,and then all differential genes are enriched by GO and KEGG on the David website.2.232 autophagy-related genes are downloaded from the HADb website,merging them with the gastric cancer transcription data in TCGA,and R language is used to screen out 27 autophagy-related differential genes.Differential genes is used to construct a single-factor COX model to obtain 2 high-risk genes.After further merging functionally similar genes,an autophagy risk model containing one target gene is obtained,and the model is subjected to independent prognostic factor analysis and clinical correlation analysis.And all the differential genes are enriched by GO and KEGG on the David website.3.Real-time fluorescent quantitative PCR is used to detect the expression levels of the above two target genes: AGT and GRID2 in 10 patients with gastric cancer tissues and distant cancer tissues.Results: 1.The differential genes of gastric cancer patients were screened by TCGA combined with Oncomine,and the top 10 core genes in the protein interaction network diagram were screened out of the differential genes.There were no autophagy-related genes.Through Oncomine search,it was found that among the 10 core genes,the genes with research significance were: AGT,MFI2,and MSLN.The P value of AGT survival curve among the three genes was 0.0173(less than 0.05).In the differential gene GO enrichment analysis,45 functional analyses with FDR less than0.05 were obtained,and there were 35 signal pathways in the KEGG enrichment analysis(P<0.05).2.Screening autophagy genes related to the prognosis of gastric cancer patients by combined with TCGA,and constructing a single-factor COX model with differential genes to obtain two high-risk genes IRGM and GRID2.Using the high-risk gene risk value,the constructed COX regression model only contains the gene GRID2.The univariate and multivariate analysis of the COX risk model all indicate that it is related to prognosis(P<0.05).The AUC value of the COX risk model in the multi-index ROC curve is 0.616,which has predictive value(greater than 0.5).The genetic risk in the COX regression model Correlation analysis was performed between the value and clinical data.The autophagy risk model had no significant correlation with clinical traits such as grade,stage,T,N,and M(P>0.05).In the GO enrichment analysis of autophagy differential genes,8 data with P values less than 0.05 were obtained,and no KEGG enrichment analysis pathway was found.3.Validation experiment Collected 10 groups of gastric cancer tissues and distant cancer tissues.Real-time fluorescent quantitative PCR detected that the expression of genes AGT and GRID2 in the cancer tissues was low in the distant cancer tissues,and the difference was statistically significant(P<0.05).Conclusion:1.There is no autophagy-related gene in the screening of differential genes based on the TCGA database,indicating that in gastric cancer,under the screening conditions(FDR<0.05,?log FC?>2),and there is no significant difference in the expression of autophagy differential genes.On this basis,combined with the Oncomine database to screen,finally it is determined the target gene AGT.Although this gene is not an autophagy-related gene,there is a difference in expression between gastric cancer and normal tissues,and it is related to the survival time of gastric cancer patients,the related effects of gene AGT on gastric cancer can be further explored.2.Autophagy genes related to the prognosis of gastric cancer patients based on TCGA have been Screened.After merging autophagy genes with gastric cancer genes,the 27 autophagy-related genes with expression differences in gastric cancer were finally screened,and the survival time was combined to construct GRID2 genes.COX model,this model has no significant statistical significance for the prognosis of patients with gastric cancer.3.Genes AGT and GRID2 were detected by real-time fluorescence quantitative PCR,and their expression levels in gastric cancer tissues were lower than those in distant cancer tissues,which may be one of the relevant factors in the progression from adjacent gastric cancer tissues to gastric cancer tissues,but their specific mechanism needs further exploring. |
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