The Analysis Model Based On The Necroptosis-Related Gene Is Using To Study The Prognosis,Immunotherapy And Early Diagnosis In Gastric Cancer

Purposes: Gastric cancer(GC)is one of the most common malignant tumors in the world,with the fifth highest incidence rate and the third highest mortality rate.Necroptosis is a programmed necrosis different from apoptosis and pyroptosis,and its molecular mechanism has been described in inflammatory diseases.And necroptosis plays a huge role in the occurrence and development of GC,and affects the response to immunotherapy.Understanding the characteristics of necroptosis-related genes(NRGs)is the key to studying the occurrence and immunotherapy of GC.However,the current research on the characteristics of NRGs in the occurrence and the role of immunotherapy of GC is still unclear.Therefore,the role of necroptosis in the initiation and progression of GC is largely worth exploring.Methods: 1.Download GC-related data from Gene Expression Omnibus(GEO)and Cancer Genome Atlas(TCGA),and collect NRGs from related literature,Kyoto Encyclopedia of Genes and Genomes(KEGG)and human genome database.These NRGs were extracted and analyzed in TCGA-GC expression profile;2.Using single factor Cox regression,least absolute minimum shrinkage and selection operator(LASSO)Cox regression and multivariate Cox regression,the expression profiles of TCGA-GC were randomly grouped and the prognostic characteristics of NRGs in GC were analyzed;3.Analyzing the independent prognostic characteristics of prognostic risk score,construct nomogram and perform Gene Set Enrichment Analysis(GSEA);4.The relationship between risk score and immune microenvironment(TME),immune cell infiltration,immunotherapy and immune checkpoint inhibitors(ICIs) was further analyzed;5.Based on the genes of prognosis model,the early diagnosis characteristics of NRGs were analyzed by using UALCAN database and binary logistic regression;6.On the basis of prognosis model,early diagnosis model and Gene Set Variation Analysis(GSVA),the core genes were determined;7.Using R language,the relationship between core gene and TME,immune cells,immune checkpoint related genes and ICIs;8.In order to verify the result of differential expression of core gene in GEPIA database of GC,a real-time fluorescence quantitative polymerase chain reaction(q PCR)experiment is conducted to verify the results.Results: 1.A total of 781 NRGs were collected,and 740 NRGs were obtained in the GC-TCGA expression profile.Among them,36 NRGs were upregulated and 47 were downregulated;2.2-NRGs(CCT6A and FAP)and 4-NRGs(ZFP36,TP53I3,FAP and CCT6A)can effectively assess the early disease risk of GC patients(AUC = 0.943)and the prognostic risk of GC patients(AUC = 0.866);3.The nomogram can effectively evaluate the survival rate of patients;4.There is a significant correlation between risk score and TME,most immune cells and their functions,GC immunotherapy effect and ICIs(P < 0.05);5.In this study,the core gene FAP was identified,and it is found that the expression of FAP in tissues of GC patients was different from that of TME,immune cells and ICIs(P < 0.05),and there is also related to the immune checkpoints related genes;6.QCPR experimental data shows that the expression of the core gene FAP is upregulated in tissues of GC patients,which is consistent with the result of bioinformatics analysis.Conclusions: We conducted a comprehensive bioinformatics analysis,determined the NRGs-related early diagnosis and prognosis characteristics of GC,and determined the relationship between risk score,immunotherapy and ICIs.The relationship between core genes and ICIs was also determined.These findings can provide new targets for immunotherapy of GC and provide more effective treatment programs for GC patients.