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… Click to show full 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.
               
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