Identifification Of Pyroptosis-related Subtypes,Development Of A Prognostic Model,and Characterization Of Tumour Microenvironment Infifiltration In Gastric Cancer

Objective:To explore the Identifification of pyroptosis-related subtypes,development of a prognostic model,and characterization of tumour microenvironment infifiltration in gastric cancer.Methods:The Gene Expression Omnibus(GEO)https://www.ncbi.nlm.nih.gov/geo/)and The Cancer Genome Atlas(TCGA)(https://portal.gdc.cancer.gov/)databases were used to obtain the GSE84437,GSE38749,and TCGA GC cohorts for downloading GC gene expression(fragments per kilobase million,FPKM),gene mutation,and associated prognostic and clinical data,and the raw fifiles obtained were also normalized and annotated.Among them,All patients included in this study had no history of radiotherapy and chemotherapy,and all tissue samples were primary tumors.The “limma” package of the R language was used to convert the FPKM value of the TCGA data into the TPM value.Then,the three datasets were integrated using the “limma” and “sva” packages,and after excluding patients with a lack of OS data,a total of 886 GC patients were obtained for subsequent analysis.Results:The PRG interaction network results further showed that IRF1,GZMB,and CASP5 were most associated with prognosis.Finally,we classifified GC patients by consensus unsupervised cluster analysis of PRGs.The results showed that k = 2 was the best choice,with the highest sample correlation within the A and B subtypes.In addition,PCA also confifirmed this result;that is,GC samples can be divided into two subtypes,A and B,according to the expression levels of PRGs.The Sankey diagram shows the process of constructing the prognostic model and the distribution of GC patients in different subtypes.In addition,the results of RS difference analysis showed that in the two pyroptosis-related subtypes,the RS of subtype A with better prognosis was signifificantly lower than that of subtype B.Likewise,among the three DEG subtypes,RS had the highest value in the worst-prognosis subtype A and the lowest value in the bestprognosis subtype C.These results confifirmed that RS may be one of the factors affecting OS in GC patients.In addition,there were differences in the expression of PRGs in the high-and low-risk groups.CHMP6,IL-1A,CHMP4 B,PRKACA,GPX4,and IL-6 were highly expressed in the high-risk group,while 22 genes such as NLRP6,GZMB,CASP1,GZMA,and AIM2 were highly expressed in the low-risk group.Furthermore,the results of MSI difference analysis showed that RS was signifificantly correlated with MSI status.The proportions of microsatellite stability(MSS)and low microsatellite instability(MSI-L)in the lowrisk group were signifificantly lower than those in the high-risk group,and high microsatellite instability(MSI-H)was signifificantly higher than that in the high-risk group.These results are consistent with the RS differences among MSS,MSI-L and MSI-H.This suggests that patients in the low-risk group are more sensitive to immunotherapy.Finally,the correlation results between RS and CSCs showed that RS and CSCs were signifificantly negatively correlated,indicating that GC cells with lower RS exhibited more poorly differentiated stem cell characteristics.Conclusions:This study revealed mutations,CNVs,and TME multiomics characteristics of PRGs by comprehensively analysing the overall changes in the genomic and transcriptional levels of PRGs in GC and constructing a prognosis prediction model for GC patients.We found IL18 RAP,CTLA4,SLC2A3,IL1 A,KRT7,PEG10,IGFBP2,GPA33,and DES are key genes of gastric cancer and deeply explored the potential role in clinicopathological features,immune infifiltration,TME and prognosis in GC,providing new immunotherapy strategies and drug targets for the clinical treatment of GC patients.