Immunotherapeutic Significance of a Prognostic Alternative Splicing Signature in Bladder Cancer

Abstract

Objectives: Bladder cancer is the fourth most common malignancy in men in the United States. Aberrant alternative splicing (AS) events are involved in the carcinogenesis, but the association between AS and bladder cancer remains unclear. This study aimed to construct an AS-based prognostic signature and elucidate the role of the tumor immune microenvironment (TIME) and the response to immunotherapy and chemotherapy in bladder cancer. Methods: Univariate Cox regression analysis was performed to detect prognosis-related AS events. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to build prognostic signatures. Kaplan-Meier survival analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curves were conducted to validate the prognostic signatures. Then, the Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and tumor immune estimation resource (TIMER) databases were searched and the single-sample gene set enrichment analysis (ssGSEA) algorithm and CIBERSORT method were performed to uncover the context of TIME in bladder cancer. The Tumor Immune Dysfunction and Exclusion (TIDE) web tool and pRRophetic algorithm were used to predict the response to immunotherapy and chemotherapy. Finally, we constructed a correlation network between splicing factors (SFs) and survival-related AS events. Results: A total of 4684 AS events were significantly associated with overall survival in patients with bladder cancer. Eight prognostic signatures of bladder cancer were established, and a clinical survival prediction model was built. In addition, the consolidated prognostic signature was closely related to immune infiltration and the response to immunotherapy and chemotherapy. Furthermore, the correlation identified EIF3A, DDX21, SDE2, TNPO1, and RNF40 as hub SFs, and function analysis found ubiquitin-mediated proteolysis is correlated most significantly with survival-associated AS events. Conclusion: Our findings highlight the prognostic value of AS for patients with bladder cancer and reveal pivotal players of AS events in the context of TIME and the response to immunotherapy and chemotherapy, which may be important for patient management and treatment.

Keywords: Bladder cancer; alternative splicing; drug response; immunotherapeutic; prognostic; tumor immune microenvironment.

Figure 1.

UpSet plots of alternative splicing (AS) events in bladder cancer. The horizontal axis represents the number of genes in each AS type. The vertical axis represents the number of genes for one or several splicing types. (a) The gene interactions among the 7 types of AS events in The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) cohort. (b) The gene interactions among the 7 types of survival relevant AS events.

Figure 2.

The survival-relevant alternative splicing (AS) events. (a) The blue dots represent insignificant AS events, whereas the red dots represent prognosis-related AS events. The most significant survival-relevant AA, AD, AP, AT, ES, ME, and RI in The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) cohort (b-i). The vertical axis represents the ID of the specific AS event. Events with greater significance are represented by larger circles and are colored in red.

Figure 3.

Identification of the integrated alternative splicing (AS)-based prognostic signature. (a) LASSO coefficient profiles of the whole AS events. The horizontal axis represents the Log Lambda. The vertical axis represents the coefficients. (b) Ten-fold cross-validation for tuning parameter selection in the lasso regression. The lowest point of the ordinate is the minimum point of the cross-validation error. (c) Heatmap of PSI values of AS events for building the final prognostic signature. (d) Distribution of the signature risk score. (e) The survival status and survival duration of patients with bladder cancer. (f) Kaplan–Meier plot presenting survival in the high-risk and low-risk groups. (g) Received operating characteristic (ROC) analysis for the final prognostic signature based on all 7 types of AS events. (h) Univariate Cox regression results. (i) Multivariate Cox regression results.

Figure 4.

Identification of the composite prognostic nomogram. (a) Composite nomogram prediction of 1-, 2-, and 3-year overall survival (OS). (b) One-year nomogram calibration curves. (c) Two-year nomogram calibration curves. (d) Three-year nomogram calibration curves.

Figure 5.

Relationship between infiltrating immune cells and the final alternative splicing (AS) prognostic signature. (a) Correlation between this signature and CD8 + T cells. (b) Correlation between this signature and dendritic cells. (c) Correlation between this signature and macrophages. (d) Correlation between this signature and neutrophils. (e) Violin plot for the stromal score between the low-risk and high-risk groups. (f) Violin plot for the immune score between the low-risk and high-risk groups. (g) Violin plot for the ESTIMATE score between the low-risk and high-risk groups. (h) Heatmap of enrichment of 29 immune signatures for the final prognostic signature. (i) Distinction of enrichment of immune-related signatures for the low-risk and high-risk groups. (j) Difference of infiltrating immune cell subpopulations and levels for the low-risk and high-risk groups.

Figure 6.

Immune checkpoint and prediction of the response to immunotherapy and chemotherapy. (a) Comparison of immune checkpoint blockade-related genes expression levels between the low-risk and high-risk groups. (b) Violin plots of TIDE scores for patients with bladder cancer in the high-risk and low-risk groups. (c) Sensitivity analysis of patients taking cisplatin at high and low risk. (d) Sensitivity analysis of patients taking docetaxel at high and low risk. (e) Sensitivity analysis of patients taking methotrexate at high and low risk.

Figure 7.

Biological function analysis. (a) Terms identified by Gene Ontology (GO) analysis. (b) Enrichment pathways identified by the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Figure 8.

Correlation network between splicing factors (SFs) and survival-related alternative splicing (AS) events generated using Cytoscape. The yellow round bubbles represent SFs, SFs with greater significance are represented by larger circles. Red/green quadrate represents adverse/favorable AS events. Red/green lines represent positive/negative correlations (r > 0.6 or r < − 0.6) between substances.