综合分析 scRNA-Seq 和 bulk RNA-Seq 揭示膀胱癌肿瘤免疫微环境的动态变化，并建立一个预后模型。

BACKGROUND: The prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA. METHODS: BLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated. RESULTS: scRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy. CONCLUSION: By integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

背景：膀胱癌（BLCA）的预后管理仍然是临床医生面临的巨大挑战。最近，批量 RNA-seq 测序数据已被用作许多癌症的预后标志物，但不能准确检测肿瘤细胞中的核心细胞和分子功能。在本研究中，将批量 RNA-seq 和单细胞 RNA 测序（scRNA-seq）数据相结合，构建 BLCA 的预后模型。

方法：从基因表达综合数据库（GEO）下载 BLCA scRNA-seq 数据。从 UCSC Xena 获取批量 RNA-seq 数据。使用 R 包“Seurat”处理 scRNA-seq 数据，采用统一流形逼近和投影（UMAP）进行降维和聚类识别。使用 FindAllMarkers 函数鉴定每个聚类的标记基因。使用 limma 包获得影响 BLCA 患者总生存（OS）的差异表达基因（DEGs）。采用加权基因相关网络分析（WGCNA）鉴定 BLCA 关键模块。通过单因素 Cox 和最小绝对值收缩和选择算子（LASSO）分析，将核心细胞的标记基因和 BLCA 关键模块的基因与 DEGs 进行交集，构建预后模型。还研究了高低风险组之间的临床病理特征、免疫微环境、免疫检查点和化疗药物敏感性的差异。

结果：分析 scRNA-seq 数据以鉴定 19 个细胞亚群和 7 个核心细胞类型。ssGSEA 显示，BLCA 肿瘤样本中所有 7 个核心细胞类型均显著下调。从 scRNA-seq 数据集获得 474 个标记基因，从 Bulk RNA-seq 数据集获得 1556 个 DEGs，从 WGCNA 鉴定的关键模块获得 2334 个基因。进行交集、单因素 Cox 和 LASSO 分析后，基于 3 个特征基因（MAP1B、PCOLCE2 和 ELN）的表达水平获得了一个预后模型。该模型通过内部训练集和两个外部验证集进行了验证。此外，高风险评分的患者更倾向于经历不良的 OS、更高的 III-IV 期、更大的 TMB、更高的免疫细胞浸润以及对免疫治疗反应不佳的可能性。

结论：通过整合 scRNA-seq 和批量 RNA-seq 数据，构建了一种新的预测 BLCA 患者生存的预后模型。风险评分是一个有前途的独立预后因素，与免疫微环境和临床病理特征密切相关。