Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs

Abstract

Objectives: Bladder cancer (BLCA) is a tumor that affects men more than women. The biological function and prognostic value of androgen-responsive genes (ARGs) in BLCA are currently unknown. To address this, we established an androgen signature to determine the prognosis of BLCA.

Methods: Sequencing data for BLCA from the TCGA and GEO datasets were used for research. The tumor microenvironment (TME) was measured using Cibersort and ssGSEA. Prognosis-related genes were identified and a risk score model was constructed using univariate Cox regression, LASSO regression, and multivariate Cox regression. Drug sensitivity analysis was performed using Genomics of drug sensitivity in cancer (GDSC). Real-time quantitative PCR was performed to assess the expression of representative genes in clinical samples.

Results: ARGs (especially the CDK6, FADS1, PGM3, SCD, PTK2B, and TPD52) might regulate the progression of BLCA. The different expression patterns of ARGs may lead to different immune cell infiltration. The risk model indicates that patients with higher risk scores have a poorer prognosis, more stromal infiltration, and an enrichment of biological functions. Single-cell RNA analysis, bulk RNA data, and PCR analysis support the reliability of this risk model, and a nomogram was also established for clinical use. Drug prediction analysis showed that high-risk patients had a better response to fludarabine, AZD8186, and carmustine.

Conclusion: ARGs played an important role in the progression, immune infiltration, and prognosis of BLCA. The ARGs model has high accuracy in predicting the prognosis of BLCA patients and provides more effective medication guidelines.

Keywords: Androgen-responsive genes; Bladder cancer; Drug sensitivity; Immune cell infiltration; Men; Prognosis; Tumor microenvironment.

Fig. 1

Screening and genetic variation of androgen-responsive genes (ARGs) in bladder cancer (BLCA). (A) The network diagram illustrates the interaction between 32 ARGs in BLCA. The size of the circles indicates the p-value of each gene for survival prognosis. The red dots indicate risk factors, while the green dots indicate favorable factors. The thickness of the lines represents the correlation between genes, with red and blue lines representing positive and negative correlations respectively. (B) Molecular subtype clustering of bladder cancer patients based on ARGs. (C) Principal component analysis of clusters A and B. (D) Assessment of overall survival differences between cluster A and cluster B. Cluster A is shown in blue and cluster B in yellow. (E) Differences in the expression of 32 androgen-responsive genes between cluster A and cluster B. (F) Top frequency of CNV variants from the 32 ARGs

Fig. 2

Enrichment analysis and immune infiltration analysis of 33 androgen-responsive genes (ARGs). (A) Sample annotation of 33 ARGs by stage, grade, sex, and cluster. (B) KEGG analysis was performed on androgen response genes of two gene clusters. (C) sGSEA analysis performed on clusters A and B for immune cell infiltration

Fig. 3

Construction and validation of risk scores for 33 androgen-responsive genes (ARGs). (A) A Least Absolute Shrinkage and Selection Operator (LASSO) analysis was conducted on ARGs associated with prognosis. (B) Time-dependent receiver operating characteristics (ROC) were calculated for the training, test, and validation sets. (C) Kaplan-Meier curves were plotted for the training set (p < 0.001, log-rank test), test set (p < 0.001, log-rank test), and validation set (p < 0.001, log-rank test). (D) Heatmap showing the expression levels of the six ARGs (E) Sankey diagram showing the relationship between risk factors and clinical prognosis of clusters A and B. (F) The survival status percents difference of high-risk group and low-risk group

Fig. 4

Gene enrichment analysis of androgen-responsive genes (ARGs) in high and low-risk groups. (A) KEGG enrichment analysis of ARGs was performed on high and low-risk groups. (B) GO enrichment analysis of ARGs was performed on high and low-risk groups

Fig. 5

A nomogram based on BLCA risk score and clinical features. A nomogram based on BLCA risk score and clinical features. (A) A nomogram is generated based on an individual’s sex, age, risk score, pathological grade, and clinical stage. (B) Calibration curve showing the agreement between predicted survival at 1, 3, and 5 years and actual survival using the bias-adjusted prognostic nomogram. (C) The baseline cumulative hazards identified for each event number. (D) The decision curve analysis (DCA) of the train set and the test set containing the nomogram

Fig. 6

Analysis of the immune microenvironment in BLCA. (A) Analysis of immune infiltration using the Estimate algorithm. (B) The tumor purity conducted by ESTIMATE algorithm. (C) Determine the immune cell ratio using the Cibersort method. (D) Investigate the correlation between the risk score and regulatory T cells. (E) Examine the correlation between the risk score and CD8 T cells. (F) Explore the correlation between the risk score and CD4 resident memory T cells. (G) Examine the correlation between the Risk Score and M2 macrophages. (H) Correlation between risk score and M0 macrophages. (I) Correlation between risk score and monocytes. (J) Correlation between risk score and naive B cells. (K) Correlation between risk score and activated dendritic cells. (L) Correlation between risk score and plasma cells

Fig. 7

Expression levels of signature genes between normal and BLCA tissues were experimentally verified by qRT-PCR

Fig. 8

Single-cell data validation analysis of six ARGs. (A) Five cell clusters (urothelial cells, endothelial cells, fibroblasts, myeloid/macrophages, T cells) were identified on the UMAP map. (B) Violin plot of six ARGs in each cluster. (C) UMAP plot of six ARGs in each cluster

Fig. 9

Drug sensitivity analysis. (A) Sensitivity of trametinib between high-risk and low-risk groups. (B) Sensitivity of temozolomide between high and low-risk groups. (C) Sensitivity of VX-11e between high-risk and low-risk groups. (D) Sensitivity of fludarabine between high and low-risk groups. (E) Sensitivity of AZD8186 between high and low-risk groups. (F) Sensitivity of carmustine between high and low-risk groups