推进膀胱癌管理：预后模型的建立与个体化治疗。

BACKGROUND: Bladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses. RESULTS: Six molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups. CONCLUSIONS: The study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.

背景：膀胱癌（BLCA）由于其高发病率和死亡率，被认为是一个重大的公共卫生挑战。分子亚型对治疗结果的影响已得到充分认识，因此需要进一步探索其特征和应用。本研究旨在通过绘制膀胱癌的分子异质性图谱，并使用单细胞和批量 RNA 测序数据构建稳健的预后模型，来增强对膀胱癌的认识。此外，还通过风险评分研究了免疫特征和个性化治疗策略。

方法：使用 GSE135337 的单细胞 RNA 测序（scRNA-seq）数据和包括 GSE13507、GSE31684、GSE32894、GSE69795 和 TCGA-BLCA 在内的多个来源的批量 RNA-seq 数据，鉴定了分子亚型，特别是与预后不良相关的基底鳞状（Ba/Sq）亚型。使用 LASSO 和 Cox 回归分析构建了一个基于与 Ba/Sq 亚型相关基因的预后模型。该模型在内部和外部数据集上进行验证，以确保预测准确性。基于 TCGA-BLCA 数据的风险评分，分析高风险和低风险组，以研究它们的免疫相关分子特征和治疗反应。

结果：鉴定了六个分子亚型，Ba/Sq 亚型始终与预后不良相关。基于基底鳞状亚型相关基因（BSSRGs）的预后模型在不同的临床环境中表现出很强的预测性能，在 1、3 和 5 年的 AUC 值表明在训练、测试和整个数据集上具有稳健的预测能力。不同风险组的分析揭示了不同的免疫浸润和微环境。低风险组通常具有更高的肿瘤突变负荷（TMB）评分和更低的肿瘤免疫功能障碍和排除（TIDE）评分，表明两组之间的系统药物反应潜力不同。最后，还观察到风险组之间的潜在系统药物反应率存在显著差异。

结论：本研究提出并验证了一个基于 BSSRGs 的 BLCA 新预后模型，该模型在预后预测方面具有良好的效果。通过患者分层和免疫分析，风险评分突出了个性化治疗的潜力，旨在提高治疗效果。这种方法有望在管理 BLCA 方面取得重大进展，根据详细的分子和免疫学见解定制治疗方案。