Smoking-related epigenetic modifications are associated with the prognosis and chemotherapeutics of patients with bladder cancer

Abstract

Objective: Epidemiologic studies have linked smoking to various malignancies, including bladder cancer, but its underlying biological functions remain elusive. Currently, we aimed to identify the smoking-related epigenetic modifications and disclose their impacts on prognosis and therapies in bladder cancer.

Methods: DNA methylation, transcriptome, and clinical profiles were acquired from The Cancer Genome Atlas (TCGA) using "TCGAbiolinks" Differential expression analyses were performed with "limma" and visualized by the "pheatmap" package. Smoking-related interactions were displayed using Cytoscape. Least absolute shrinkage and selection operator (LASSO) algorithm was for generation of a smoking-related prognostic model. Kaplan-Meier analysis with log-rank test was for survival analysis, followed by a prognostic nomogram. The Gene Set Enrichment Analysis (GSEA) was used for functional analysis. The "oncoPredict" package was applied for drug sensitivity analysis.

Results: We recruited all types of bladder cancers and found that smoking was involved in poor prognosis, with the hazard ratio (HR) of 1.600 (95%CI: 1.028-2.491). A total of 1078 smoking-related DNA methylations (526 hypermethylation and 552 hypomethylation) were identified and 9 methylation-driven genes differentially expressed in bladder cancer. Also, 506lncRNAs (448 upregulated and 58 downregulated lncRNAs) and 102 miRNAs (74 upregulated and 28 downregulated miRNAs) were determined as smoking-related ncRNAs. We then calculated the smoking-related risk score and observed that cases of high risk were predicted with poor prognosis. We constructed a prognostic nomogram to predict the 1-, 3-, and 5-year overall survival rates. Several cancer-related pathways were enriched in the high-risk group, and patients with high-risk were more sensitive to Gemcitabine, Wnt-C59, JAK1_8709, KRAS (G12C) Inhibitor-12, and LY2109761. Whereas, those with low-risk were more sensitive to Cisplatin, AZ960, and Buparlisib.

Conclusions: Totally, we initially identified the smoking-related epigenetic modifications in bladder cancer and constructed a corresponding prognostic model, which was also linked to disparate sensitivities to chemotherapeutics. Our findings would provide novel insights into the carcinogenesis, prognosis, and therapies in bladder cancer.

Keywords: The Cancer Genome Atlas; bladder cancer; chemotherapies; epigenetic modification; prognosis; smoking behavior.

Figure 1.

Hazard ratio (HR) with 95% confidence interval (CI) for smoking-related death in bladder cancer. (a) Univariate Cox regression analysis; (b) Multivariate Cox regression analysis.

Figure 2.

Differential expression analyses in bladder cancer. (a) Heatmap of the tumor-related mRNAs; (b) Heatmap of the smoking-related DNA methylations; (c) Heatmap of the smoking-related lncRNAs; (d) Heatmap of the smoking-related miRNAs; (e) The smoking-related lncRNAs regulatory network; (f) The smoking-related miRNAs regulatory network.

Figure 3.

Generation of the smoking-related prognostic model and risk-score calculations. (a, b) The LASSO Cox regression analysis; (c) The survival time distribution of patients with different risk scores; (d) Heatmap of smoking-related signatures among patients with high-risk and low-risk groups.

Figure 4.

Hazard ratio (HR) with 95% confidence interval (CI) for smoking-related risk score in bladder cancer. (a) Univariate Cox regression analysis; (b) Multivariate Cox regression analysis; (c) Calibration curves of risk signature to predict the 1-, 3-, and 5-year overall survival rates; (d) A prognostic nomogram to predict the 1-, 3-, and 5-year overall survival rates.

Figure 5.

Gene set enrichment analysis (GSEA) and drug sensitivity exploration. (a, b) The GSEA analyses of smoking-related prognostic signatures; (c) Comparisons of drug sensitivity between high-risk and low-risk groups.