| Objective:The innate immunity database contains the immune-related genes identified in current studies,while the TCGA database and GEO database cover the relevant genomic datasets of most cancers and contain complete clinical data.Therefore,this paper combines several databases to mine and validate the prognostic models of immunerelated genes in bladder cancer to provide a basis for the prognosis and immunotherapy of bladder cancer patients.Methods:The transcriptomic data and clinical information of bladder cancer and normal tissues adjacent to cancer were downloaded from TCGA and GEO databases,and immune gene data from Imm Port and Innate DB databases were also downloaded to screen differentially expressed immune genes;weighted gene co-expression network analysis was used to identify and screen immune gene modules associated with bladder cancer,and one-way Cox regression analysis,Lasso regression and multi-factor Cox regression analysis were performed to construct prognostic assessment models.The training and validation groups of TCGA dataset and GEO group(GSE32894)were divided into high and low risk groups by calculating the median risk score,and K-M survival analysis was performed to assess the prognostic model efficacy with ROC curves,column line plots and calibration curves.Meanwhile,several immunogenetic prognostic models were collected on the Pub Med website,and their C-index values and survival analysis P-values were calculated for comparative validation.Then,univariate and multifactorial analyses of patient clinical characteristics and risk scores were performed to screen for independent prognostic factors that could be influential in patients with bladder cancer.Finally,the risk model was subjected to GSEA enrichment analysis as well as immune correlation analysis.Results:Based on the WGCNA package in R language,a total of 232 differentially expressed immune genes were screened and included in Cox univariate analysis,which showed that 40 differentially expressed immune genes were associated with bladder tumor prognosis(P < 0.01),and then LASSO and COX regression were performed to obtain a prognostic assessment model for bladder cancer containing 6 differentially expressed immune genes;by calculating the median value of risk scores to classify patients in the training,validation and geo groups into high-and low-risk groups,and KM survival curves showed that the high-risk survival rate was lower than that of the lowrisk group in all three groups and P < 0.05;the areas under the ROC curves at 1,3 and 5years were 0.674,0.693,0.716,0.714,0.660,0.611 and 0.733,0.683,0.613,respectively.Columnar plots were drawn to predict patient survival at 1,3,and 5 years,and calibration curves were able to test that the model had good predictive accuracy.Independent prognostic analysis showed that the risk model could be used as an independent prognostic factor for a patient with P < 0.01.The plotting of survival curves and C-index bar graphs for multiple models revealed that our constructed model performed well in all models.Immune escape and immune checkpoint analysis revealed that patients in the high-risk group had a high potential for immune escape,were poorly treated with immunotherapy,and were highly correlated with checkpoint genes such as CD276 and NRP1.The immune correlation analysis showed that immune cells such as cancerassociated fibroblasts and macrophages were positively correlated with the risk score,and immune cells such as CD4+ T cells and regulatory T cells were negatively correlated with the risk score.Finally,the GSEA enrichment analysis revealed that cytokine receptor interactions,chemokine signaling pathways,etc.were mainly concentrated in the highrisk group.Retinol,cytochrome p450,xenobiotics metabolic processes,etc.were mainly concentrated in the low-score risk group.Conclusions:1.Prognostic models for six immune genes were successfully constructed and validated based on TCGA and GEO databases with good predictive accuracy.2.The results of the risk model GSEA and immune correlation analysis showed an association with the progression of bladder tumors.3.AHNAK and PDGFD may have more potential to be biomarkers for bladder cancer immunotherapy among the model genes. |
