深入了解膀胱癌（BLCA）的单细胞RNA测序（scRNA-seq）结果提供了单个癌细胞的转录组学分析，这可能揭示参与BLCA致癌的分子机制。scRNA数据来自GSE169379数据集。我们使用InferCNV软件以正常上皮细胞为参考来确定拷贝数变异（CNV），并使用Monocle3软件对上皮细胞亚群进行伪时序分析。使用Dorothea软件进行转录因子分析。使用Liana软件进行细胞间通讯分析。应用Cox分析和LASSO回归建立预后模型。我们研究了BLCA癌症四种不同细胞类型（即免疫细胞、内皮细胞、上皮细胞和成纤维细胞）中肿瘤的异质性。我们评估了 BLCA 中不同免疫细胞的转录因子活性，并发现 CD8 T 细胞中 TCF7 和 TBX21 显着富集。此外，我们还发现了癌症相关成纤维细胞 (CAF) 的两种不同亚型，即 iCAF 和 myoCAF，它们表现出不同的通讯模式。通过亚簇和细胞轨迹分析，我们确定了上皮细胞中正常细胞向恶性细胞转化的不同状态。 TF 分析进一步揭示了肿瘤细胞中 MYC 和 SOX2 的高度激活。最后，我们确定了用于开发预后模型的五个模型基因（SLCO3A1、ANXA1、TENM3、EHBP1、LSAMP），该模型在对七个不同队列的患者进行分层方面表现出很高的有效性。我们开发了一种预后模型，在对 BLCA 患者进行分层。

Gaining a deeper insight into the single-cell RNA sequencing (scRNA-seq) results of bladder cancer (BLCA) provides a transcriptomic profiling of individual cancer cells, which may disclose the molecular mechanisms involved in BLCA carcinogenesis.scRNA data were obtained from GSE169379 dataset. We used the InferCNV software to determine the copy number variant (CNV) with normal epithelial cells serving as the reference, and performed the pseudo-timing analysis on subsets of epithelial cell using Monocle3 software. Transcription factor analysis was conducted using the Dorothea software. Intercellular communication analysis was performed using the Liana software. Cox analysis and LASSO regression were applied to establish a prognostic model.We investigated the heterogeneity of tumors in four distinct cell types of BLCA cancer, namely immune cells, endothelial cells, epithelial cells, and fibroblasts. We evaluated the transcription factor activity of different immune cells in BLCA and identified significant enrichment of TCF7 and TBX21 in CD8+ T cells. Additionally, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs), namely iCAFs and myoCAFs, which exhibited distinct communication patterns. Using sub-cluster and cell trajectory analyses, we identified different states of normal-to-malignant cell transformation in epithelial cells. TF analysis further revealed high activation of MYC and SOX2 in tumor cells. Finally, we identified five model genes (SLCO3A1, ANXA1, TENM3, EHBP1, LSAMP) for the development of a prognostic model, which demonstrated high effectiveness in stratifying patients across seven different cohorts.We have developed a prognostic model that has demonstrated significant efficacy in stratifying patients with BLCA.