Establishment And Significance Of Pyroptosis-related Genes Prognostic Model In Breast Cancer

Objectives:In this study,bioinformatics methods were used to mine The Cancer Genome Atlas（TCGA）,and constructed a prognostic model related to pyroptosis genes.Survival analysis was used to evaluate the predictive value of the prognostic model,and functional analysis was used to explore the role of this prognostic model in the immune microenvironment of breast cancer Methods:We obtained gene expression profiles from TCGA of 1144 breast cancer tissues,including 111 normal tissues and 1033 tumor tissues.And important clinical information such as age,survival time,and survival status were downloaded.By reading the review article,38 genes related to pyroptosis were obtained,and the candidate genes were compared with the TCGA information to obtain the differential genes expression profiles of breast cancer.Furthermore,univariate factor analysis was used to screen out genes related to prognosis,we validated the differential expression of prognostic genes by using the Gene Expression Omnibus（GEO）data set gene expression matrix of breast cancer.Then,multivariate Cox regression analysis was performed on the prognostic-related genes expression profiles to obtain a pyroptosis gene-related prognostic model.Based on the median risk score obtained from the model,the samples were divided into high-and low-risk groups.The Kaplan-Meier survival analysis was used to verify the survival difference between the two groups of samples,and the multivariate ROC curve and independent prognostic analysis were used to explore the prognostic value of this prognostic model.The gene expression matrices of the two risk groups were subjected to differential analysis,and the differential genes obtained were subjected to functional analysis by using Gene Ontology（GO）and Kyoto Encyclopedia of Genes and Genomes（KEGG）.Based on this prognostic model of pyroptosis genes,single-sample gene enrichment analysis was used to analyze the infiltration levels of 10 immune cells and the activity level of 10 immune pathways in the high-and low-risk groups,the expression matrices of two common immune checkpoints in the high-and low-risk groups were extracted and differentially analyzed.Results:1.The pyroptosis-related genes were compared with the TCGA dataset to extract 12 differentially expressed genes in breast cancer,and 4 genes related to the prognosis were screened out by univariate Cox analysis.The difference analysis of the breast cancer gene expression matrix in the GEO dataset is the same as the results obtained in the TCGA dataset,which further confirmed that the four pyroptosis-related genes are differentially expressed in breast cancer2.A 4-genes prognostic model was established through multivariate regression and a risk score calculation formula was obtained.AIM2,GBP5,and IL18 were identified as protective genes with HR<1,and GSDMC was a risk gene with HR>1.3.We used the risk scoring formula to divide the patients into high-and low-risk groups based on the median scores,and the Kaplan-Meier curve found that the overall survival time of patients in the low-risk group was significantly longer than that in the high-risk group（P=0.0091）.The area under the ROC curve（Area under the curve,AUC）is 0.671,which indicated that the model has a certain predictive value.Univariate Cox regression showed that the risk score as an independent risk factor shows significant statistical differences（HR = 1.637;p=0.007）.After the multivariate Cox regression analysis,the risk score was still an independent prognostic factor（HR = 1.464;p =0.040）.4.KEGG analysis found that this prognostic model is mainly related to hematopoietic cell lineage,graft-versus-host disease,antigen processing and presentation,and primary immunodeficiency.GO analysis shows that this model is mainly related to lymphocyte-mediated immunity,adaptive immune response based on immune body cell recombination,immunoglobulin complexes,outer plasma membrane,antigen binding,and immunoglobulin receptor binding.5.Based on this risk model,ten common immune cells in the immune system（B cells,CD8+ T cells,macrophages,NK cells,p DC,Tfh,TIL,Treg,neutrophils,and DC）have higher levels of immune cell infiltration in the low-risk group samples in the two cohorts.Ten pathways involved in the immune pathways（APC＿co＿inhibition,APC＿co＿stimulation,Check-point,Cytolytic＿activity,Inflammation-promoting,MHC＿class＿I,T＿cell＿co-inhibition,T＿cell＿co-stimulation,Type＿I＿IFN＿Reponse and Type＿II＿IFN＿Reponse）have higher activity of immune cell infiltration in the low-risk group samples.CTLA4 and PD-L1 had a higher level of expression in the low-risk group.Conclusions:We used bioinformatics analysis technology for data mining and constructed a prognostic model related to pyroptosis genes.Patients in the low-risk group tended to have longer survival times,and patients in the low-risk group had higher levels of immune cell infiltration,immune pathway activity,and immune checkpoint expression than highrisk group.