PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■膀胱癌（BLCA）由于其高发病率和死亡率而被认为是重大的公共卫生挑战。分子亚型对治疗结果的影响是公认的，需要进一步探索其表征和应用。这项研究旨在通过绘制分子异质性并使用单细胞和批量RNA测序数据开发强大的预后模型来增强对BLCA的理解。此外,通过风险评分调查免疫学特征和个性化治疗策略。
■来自GSE135337的单细胞RNA测序（scRNA-seq）数据和来自多个来源的大量RNA-seq数据，包括GSE13507、GSE31684、GSE32894、GSE69795和TCGA-BLCA，被利用。分子亚型,特别是与预后不良相关的基底鳞状（Ba/Sq）亚型，已确定。使用LASSO和Cox回归分析构建了预后模型，该模型侧重于与Ba/Sq亚型相关的基因。该模型在内部和外部数据集上进行了验证,以确保预测准确性.根据TCGA-BLCA数据得出的风险评分，对高危组和低危组进行了分析，以检查其免疫相关分子谱和治疗反应。
■确定了六种分子亚型，Ba/Sq亚型始终与不良预后相关。预后模型，基于基底鳞状亚型相关基因（BSSRGs），被证明在不同的临床设置中具有强大的预测性能，AUC值在1年、3年和5年表明在训练中具有强大的可预测性，测试,和整个数据集。对不同风险组的分析显示出不同的免疫浸润和微环境。一般较高的肿瘤突变负荷（TMB）评分和较低的肿瘤免疫功能障碍和排除（TIDE）评分低风险组表现，提示组间全身药物反应的可能性不同。最后,在风险组之间,潜在的全身药物反应率也存在显著差异.
■该研究引入并验证了基于BSSRGs的BLCA的新预后模型，这在预后预测中被证明是有效的。个性化治疗的潜力，通过患者分层和免疫分析进行优化，我们的风险评分突出了，旨在提高治疗效果。这种方法被承诺在管理BLCA方面提供重大进步，根据详细的分子和免疫学见解定制治疗。
■来自GSE135337的单细胞RNA测序（scRNA-seq）数据和来自多个来源的大量RNA-seq数据，包括GSE13507、GSE31684、GSE32894、GSE69795和TCGA-BLCA，被利用。分子亚型,特别是与预后不良相关的基底鳞状（Ba/Sq）亚型，已确定。使用LASSO和Cox回归分析构建了预后模型，该模型侧重于与Ba/Sq亚型相关的基因。该模型在内部和外部数据集上进行了验证,以确保预测准确性.根据TCGA-BLCA数据得出的风险评分，对高危组和低危组进行了分析，以检查其免疫相关分子谱和治疗反应。
■确定了六种分子亚型，Ba/Sq亚型始终与不良预后相关。预后模型，基于基底鳞状亚型相关基因（BSSRGs），被证明在不同的临床设置中具有强大的预测性能，AUC值在1年、3年和5年表明在训练中具有强大的可预测性，测试,和整个数据集。对不同风险组的分析显示出不同的免疫浸润和微环境。一般较高的肿瘤突变负荷（TMB）评分和较低的肿瘤免疫功能障碍和排除（TIDE）评分低风险组表现，提示组间全身药物反应的可能性不同。最后,在风险组之间,潜在的全身药物反应率也存在显著差异.
■该研究引入并验证了基于BSSRGs的BLCA的新预后模型，这在预后预测中被证明是有效的。个性化治疗的潜力，通过患者分层和免疫分析进行优化，我们的风险评分突出了，旨在提高治疗效果。这种方法被承诺在管理BLCA方面提供重大进步，根据详细的分子和免疫学见解定制治疗。
