| Background:Tumor microenvironment(TME)is a special environment for the growth and development of tumor cells,including the surrounding blood vessels,immune cells,fibroblasts,bone marrow-derived inflammatory cells,various signaling molecules,and the extracellular matrix.TME not only provides fertile soil for tumor growth and development but also widely involves immune evasion as well as the resistance towards therapeutic response.Accumulating interest has been attracted from the biological function of TME to its effects on patient outcomes and treatment efficacy.However,the relationship between the TME-related gene expression profiles and the prognosis of bladder cancer(BLCA)remains unclear.Having a further understanding of TME in BLCA will provide recommendations for prognosis evaluation and treatment strategy of BLCA.Methods:The TME-related gene expression data of BLCA were collected from The Cancer Genomic Atlas(TCGA)database.Non-negative matrix factorization(NMF)algorithm was used to identify distinct molecular patterns based on differentially expressed and prognostic TME-related genes.LASSO(Least Absolute Shrinkage and Selection Operator)regression analysis and Cox regression analysis were used to identify TME-related gene markers related to the prognosis of BLCA,and to establish the TME-related risk score which was validated in the external cohort of BLCA,a microarray gene expression dataset(GSE13507).Kaplan-Meier survival curve and the receiver operating characteristic(ROC)curve were used to evaluate the predictive efficacy of the model.Next,the TME-related model was compared with the previously published prognostic model of BLCA.Additionally,the expression of immunoregulatory genes was extracted from the transcriptomics data of TCGA-BLCA,and the abundance of immune cells in the microenvironment of BLCA was quantified using the Cibersort deconvolution approach.The correlation among TME risk score,the relative infiltration level of immune cells,and the expression level of immunomodulatory genes were analyzed.Finally,the IPS(immunophenoscore)and TIDE(tumor immune dysfunction and exclusion)scores were used to predict the immunotherapy response in the TCGA-BLCA cohort.The relationship between the established risk score and immunotherapy response was analyzed.The accuracy of the risk score in predicting the efficacy of immunotherapy was verified in the IMvigor210 cohort which was consisted of metastatic urothelial carcinoma(mUC)patients treated by immunotherapy.Results:A total of 1018 differentially expressed TME-related genes were obtained,and ten TME-related genes(AHNAK,PFKFB4,P4HB,RAC3,EMP1,PRKY,OR2B6,OCIAD2,OAS1 and KCNJ15)were finally identified to establish the TME-related risk score.According to the median risk score of patients,the high-risk and low-risk groups were divided.The prognosis of the low-risk group was better,and the applicability of the results was confirmed in the external dataset GSE13507.The C-index and RMS curve indicated that the established risk score was able to predict the prognosis of BLCA patients with greater accuracy than previously published models.In addition,the risk score was strongly associated with multiple immune cells infiltration and immunoregulatory genes including T cell exhaustion markers.Finally,when the TME risk score was applied to the mUC patients treated with immune checkpoint inhibitor(ICI)targeting the PD-1/PD-L1 axis,similarly,the patients in the low-risk group had a better prognosis and a higher response rate to immunotherapy,which further confirmed the predictive power of the TME model for immunotherapy sensitivity.Conclusion:The risk score can function as an independent prognostic biomarker and a predictor for evaluating immunotherapy response in BLCA patients,which provides recommendations for improving patients’ response to immunotherapy and promoting personalized tumor immunotherapy in the future. |
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