流行病学研究表明吸烟与多种恶性肿瘤，包括膀胱癌的发生有关，但其潜在的生物功能仍然不明确。目前，我们致力于确定吸烟相关的表观遗传修饰，揭示其对膀胱癌预后和治疗的影响。使用"TCGAbiolinks"从癌症基因组图谱（TCGA）中获取DNA甲基化、转录组和临床数据。使用"limma"进行差异表达分析，并使用"pheatmap"包进行可视化。使用Cytoscape显示与吸烟有关的相互作用。使用最小绝对值收缩和选择算法（LASSO）生成与吸烟相关的预后模型。使用Kaplan-Meier分析和log-rank检验进行生存分析，随后进行预后诊断图的制作。使用基因集富集分析（GSEA）进行功能分析。应用“oncoPredict”软件包进行药物敏感度分析。我们招募了所有类型的膀胱癌，并发现吸烟参与了预后不佳，其风险比为1.600（95％CI：1.028-2.491）。共确定了1078个与吸烟相关的DNA甲基化位点（526个高甲基化和552个低甲基化），以及9个膀胱癌中差异表达的甲基化驱动基因。此外，确定了506个与吸烟相关的lncRNA（448个上调和58个下调）和102个与吸烟相关的miRNA（74个上调和28个下调）。然后，我们计算出与吸烟相关的风险评分，并观察到高风险病例预后不佳。我们构建了一个预后诊断图以预测1、3和5年的总生存率。高风险组中富含多种与癌症相关的途径，高风险患者对Gemcitabine、Wnt-C59、JAK1_8709、KRAS（G12C）Inhibitor-12和LY2109761更敏感。相反，低风险组对顺铂、AZ960和Buparlisib更敏感。总之，我们初步确定了膀胱癌中吸烟相关的表观遗传修饰，并建立了相应的预后模型，这也与不同化疗药物的敏感性相关。我们的发现将为膀胱癌的癌症发生、预后和治疗提供新的见解。

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.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.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.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.