| ObjectiveThis study sought to construct a prognosis model associated with immunogenic cell death and immune checkpoint to predict the outcome of ccRCC patients and to predict immunotherapy response.MethodsThe transcriptome data and clinical information of patients with clear cell renal cell carcinoma were obtained from The Cancer Genome Atlas(TCGA)database,and differential expression genes(DEGs)were analyzed using the Limma method.34 immunogenic cell death genes(ICDGs)47 immune checkpoint genes(ICGs)were collected from published literature.The enrichment of immune cell death and immune checkpoint phenotypes in each patient was quantified using the single-sample gene set enrichment analysis(ssGSEA)algorithm,resulting in a score for each patient.These scores were used as functional phenotypes in weighted gene co-expression network analysis(WGCNA)to identify modules and genes that were most correlated with the immune cell death and immune checkpoint phenotypes.The intersection of immunogenic cell death and immune checkpoint genes with genes of the most correlated module was selected.Subsequently,ccRCC patients were randomly assigned to a training set(Train group)and a validation set(Test group)in a 7:3 ratio.Prognosis-associated intersection genes were identified through univariate Cox analysis of the intersection genes in the Train group.The obtained prognosis-associated genes were further subjected to LASSO regression for variable selection,followed by multivariate Cox regression analysis to construct the prognostic model.The performance of the model was assessed using ROC curves and survival analysis.The model was subsequently validated using the Test group and external datasets,Subsequently,the clinical prognostic significance of the model was evaluated,and a Nomogram was constructed by combining independent clinical prognostic factors.In addition,Gene Set Enrichment Analysis(GSEA),tumor Immune Microenvironment(TIME),tumor mutational burden(TMB),tumor immune infiltration,drug sensitivity and response on immune therapy were performed between high-and low-risk groups.Results1.A total of 5296 differentially expressed genes(DEGs)were identified through differential analysis,including 2564 downregulated genes and 2732 upregulated genes.2.The intersection of immunogenic cell death and immune checkpoint genes with genes of the most correlated module contain 30 genes,in which 12 genes was related to the prognosis of patients with ccRCC patients.Univariate Cox analysis of these 12 genes in the Train group identified 7 genes associated with prognosis.These 7 genes were then subjected to LASSO-COX regression analysis,and a 3-gene predictive model(TNFSF14,CD44,TNFRSF18)was ultimately established.Samples were divided into high-risk and low-risk groups based on their risk scores,and the Kaplan-Meier survival curves confirmed the prognostic difference between the two groups(P<0.001).The area under the ROC curve(AUC)was greater than 0.65,indicating good performance of the model in distinguishing high-risk and low-risk groups.Multivariate analysis showed that Risk score,age,tumor grade,and stage are independent factors for prognosis of patients with ccRCC.GSEA showed that the high-Risk Score group had a higher activities in pathways of DNA-sensing,cytoking-cytoking creceptor and so on.The high-risk group exhibited a greater immune cell infiltration and increased immune-related functional activity.In addition,the high-risk group was more likely to experience tumor escape in immune therapy.ConclusionsIn this study,we established a prognostic model of renal clear cell carcinoma comprising 3 genes based on immune cell death and immune checkpoint-related genes,and the risk score of the model was identified as an independent prognostic predictor for ccRCC,providing a reference for patient prognosis prediction.It is speculated that patients with high risk score are more likely to have tumor immune escape during immune therapy.However,the underlying mechanism is still lacking and needs further investigation. |
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