Construction and evaluation of a prognostic risk model of aging-related genes in bladder cancer

Abstract

Background: Bladder cancer (BLCA) is the most common malignant tumor of the urinary system. With the aging of the population, the incidence of bladder cancer is increasing year by year. Thus, the current research aimed to determine the role of aging-related biomarkers in bladder cancer.Methods: Based on TCGA, we downloaded gene expression data of 410 BLCA samples and 19 control samples. Differentially expressed genes (DEGs) between BLCA and normal samples were intersected with human aging-related genes (ARGs). TCGA cohort was divided into train set and test set according to the proportion of 7:3. The univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic aging-related signatures, followed by the construction of the risk score model and nomogram for predicting the survival of BLCA patients. Kaplan-Meier (K-M) analysis and receiver operating characteristic (ROC) analysis were conducted to assess its prognostic power. The CIBERSORT algorithm was used to explore the tumor immune microenvironment characteristics.Results: We identified 6194 DEGs closely related to BLCA. A total of 83 differentially expressed aging-related genes (DEARGs) were found in BLCA. Subsequently, seven genes (IGF1, NGF, GCLM, PYCR1, EFEMP1, APOC3, IFNB1) were determined and used to build a prognostic risk model. In the training and test dataset, survival analysis demonstrated that the overall survival of high-risk patients was worse compared with the patients in the low-risk group (P <0.05). The AUC of the ROC curve reached 0.66, 0.675, and 0.684 at 1, 2, and 3 years in the training dataset. Also, AUC of 1, 2, and 3 years were 0.625, 0.675, and 0.534 in the test dataset. T4, N3 or female BLCA patients had a significantly increased risk score. However, M stage had no significant difference in the risk score. We further found seven immune cells and many immune checkpoints with significant differences between the low- and high-risk groups.Conclusion: The seven aging biomarkers can be used for prognostic prediction in bladder cancer. Additionally, the immune microenvironment and immune checkpoints can provide a potential target for individualized therapy.