Risk Score Model of Aging-Related Genes for Bladder Cancer and Its Application in Clinical Prognosis

Abstract

Purpose: Bladder cancer (BLCA) ranks as the tenth most common malignancy worldwide, with rising incidence and mortality rates. Owing to its molecular and clinical heterogeneity, BLCA is associated with high rates of recurrence and metastasis after surgery, contributing to a poor 5-year survival rate. There is a pressing need for highly sensitive and specific molecular biomarkers to enable early identification of high-risk patients, guide clinical management, and improve patient outcomes. This study aimed to develop a prognostic model on the basis of aging-related genes (ARGs) to evaluate survival outcomes and immunotherapy responsiveness in patients with BLCA, and to further explore its relevance to the tumor immune microenvironment and drug sensitivity.

Materials and methods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were used to construct a 12-gene ARG-based prognostic signature through LASSO and Cox regression analyses. Patients were stratified into high-risk and low-risk groups according to the median risk score. Kaplan-Meier survival curves, receiver operating characteristic analyses, and nomograms were used to assess the predictive value of the model. Univariate and multivariate Cox regression analyses were conducted to determine its prognostic independence.

Results: Twelve ARGs were identified. Patients in the low-risk group exhibited significantly better overall survival (P < .0001). In the TCGA cohort, the model yielded AUC values ranging from 0.772 to 0.794 across 1-5 years. Cox regression confirmed the ARG score as an independent prognostic indicator. External validation using the GSE32894 data set supported its clinical reliability. The ARG signature was also associated with immune cell infiltration and predicted chemosensitivity.

Conclusion: The ARG-based risk score independently predicts clinical prognosis in BLCA and correlates with immune microenvironment characteristics, offering potential value in guiding personalized treatment strategies.

FIG 1.

ARG prediction model construction and performance testing. (A) Collection of senescence-associated genes via univariate Cox analysis. (B) Construction of a prognostic prediction model for bladder cancer via multivariate Cox analysis. (C) Distribution of risk scores and survival outcomes for the high-risk and low-risk groups in the training group pool. (D) Heatmap of the distribution of senescence-related gene expression in the high-risk and low-risk groups of the training cohort. (E) Time ROC curves showing the ROC curves for the training cohort for 1-5 years as well as the AUC data. (F) Survival curves for the high-risk and low-risk groups in the training cohort. ARG, aging-related gene; HR, hazard ratio; ROC, receiver operating characteristic.

FIG 2.

The stability of the constructed model was verified centrally in the TCGA_BLCA and GSE32894 data sets. (A) Distribution of risk scores and survival outcomes for the high-risk and low-risk groups in the TCGA_BLCA data set. (B) Heatmap of senescence-related gene expression in the TCGA_BLCA data set for the high-risk and low-risk groups. (C) Time ROC curves showing the ROC curves and AUC values for years 1-5 of the TCGA_BLCA data set. (D) Survival curves for the high-risk and low-risk groups in the TCGA_BLCA data set. (E) Distribution of risk scores and survival outcomes for the high-risk and low-risk groups in the GSE32894 data set. (F) Heatmap of senescence-related gene expression in the high-risk and low-risk groups of the GSE32894 data set. (G) Time ROC curves showing the ROC curves and AUC values for years 1-5 of the GSE32894 data set. (H) Survival curves for the high-risk and low-risk groups in the GSE32894 data set. BLCA, bladder cancer; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

FIG 3.

Determining the risk score as an independent prognostic factor for OS in patients with BLCA. (A) The results of univariate Cox analyses of significant factors in the TCGA_BLCA data set. (B) Incorporation of significant factors from the univariate Cox analysis in the TCGA_BLCA data set into the results of the multivariate Cox analysis. (C) Cox multivariate analysis of the GSE32984 data set. (D) Incorporation of significant factors from the univariate Cox analysis of the GSE32984 data set into the multivariate Cox analysis results. BLCA, bladder cancer; OS, overall survival; TCGA, The Cancer Genome Atlas; VIF, variance inflation factor.

FIG 4.

Construction and validation of a clinical prediction nomogram on the basis of the TCGA_BLCA data set. (A) Clinical prediction nomogram containing independent prognostic factors identified by multivariate Cox regression analysis. The total number of points on the bottom scale represents the probabilities of 1-, 3-, and 5-year OS. (B) Calibration plots of 1-, 3-, and 5-year OS predictions on the basis of the GSE32894 data set nomogram. (C) Time ROC curves showing the ROC curves and AUC values for years 1, 3, 5 of the GSE32894 data set. BLCA, bladder cancer; LR, logistic regression; OS, overall survival; TCGA, The Cancer Genome Atlas.

FIG 5.

Analysis of the correlation of the risk score with immune cell infiltration. (A) Bubble plots showing the correlation between the risk score in the TCGA_BLCA data set and the immune cell infiltration score calculated via TIMER. (B) Bubble plots showing the correlation between the risk score in the TCGA_BLCA data set and the immune cell infiltration score calculated by CIBERSORT. (C) Scatterplot showing the correlation of risk scores with macrophages on the basis of TIMER calculations. (D) Scatterplot showing the correlation between the risk score and M2 macrophage stage on the basis of CIBERSORT calculations. (E) Scatterplot showing the correlation between the risk score and the M0 macrophage stage on the basis of CIBERSORT calculations. (F) Bubble plots showing the correlation between the risk score in the GSE32894 data set and the immune cell infiltration score calculated by CIBERSORT. (G) Bubble plots showing the correlation between the risk score in the GSE32894 data set and the immune cell infiltration score calculated via TIMER. (H) Scatterplot showing the correlation of risk scores with monocytes in the GSE32894 data set on the basis of CIBERSORT calculations. (I) Scatterplot showing risk score versus macrophage correlation in the GSE32894 data set on the basis of CIBERSORT calculations. BLCA, bladder cancer; TCGA, The Cancer Genome Atlas.

FIG 6.

Risk scores are associated with tumor progression. (A, B) Volcano plots showing the results of the correlation analysis between risk scores and oncogene expression in the TCGA_BLCA and GSE32894 data sets. (C) Heatmap showing 39 cancer-related genes highly correlated with risk scores in both data sets. (D, E) Bubble plots showing the results of enrichment analyses of the biological processes and KEGG pathways of DEGs in the TCGA_BLCA and GSE32894 data sets. (F, G) GSEA plot showing the associations of risk scores with focal adhesion and the TGF beta signaling pathway. BLCA, bladder cancer; DEGs, differentially expressed genes; GSEA, gene set enrichment analysis; TCGA, The Cancer Genome Atlas; TGF, transforming growth factor.

FIG 7.

Risk scores correlate with antitumor drug sensitivity and cellular senescence. (A, B) Volcano plots showing the results of the correlation analysis between risk scores and antitumor drug susceptibility for the TCGA_BLCA and GSE32894 data sets. (C) Heatmap showing small molecule drugs with high correlations with risk scores in the TCGA_BLCA and GSE32894 data sets. (D, E) Volcano plots showing the results of correlation analysis between risk scores in TCGA_BLCA and GSE32894 and the expression of aging-related genes in the Cell Age database. (F) Heatmap showing genes with |r| > 0.3 at the intersection of the two data sets. BLCA, bladder cancer; TCGA, The Cancer Genome Atlas.