Department of Urology, Hunan Children's Hospital, Changsha, China.
J Investig Med. 2023 Mar;71(3):173-182. doi: 10.1177/10815589221143739. Epub 2023 Jan 31.
To analyze the heterogeneity between different cell types in pediatric Wilms tumor (WT) tissue, and identify the differentially expressed genes (DEGs) of malignant tumor cells, thereby establishing a prognostic model. The single-cell sequencing data of pediatric WT tissues were downloaded from the public database. Data filtration and normalization, principal component analysis, and T-distributed stochastic neighbor embedding cluster analysis were performed using the Seurat package of R language. Cells were divided into different clusters, malignant tumor cells were extracted, and DEGs were obtained. Then, the pseudo-time trajectory analysis was performed. Prognostic biomarkers were determined by univariate and multivariate COX regression analyses and LASSO regression analysis. Kaplan-Meier survival analysis and receiver operator characteristic curve analysis were performed. Combined with the prognostic biomarkers and clinical characteristics, a nomogram was generated to predict WT prognosis. The prognostic power was validated in the external datasets. Cells in the WT tissue were divided into 10 clusters. Three prognostic biomarkers that affected the survival time of patients were screened from 215 DEGs in malignant tumor cells, and a nomogram was constructed using the three genes and clinical characteristics. The area under the curve (AUC) values of 3- and 5-year disease-free survival were 0.756 and 0.734, respectively. In the external validation dataset, the AUC value of this nomogram model was 0.826. Based on the single-cell RNA-seq, we recognized cell clusters in the WT tissue of children, identified prognostic biomarkers in malignant tumor cells, and established a comprehensive prognostic model. Our findings might provide new ideas and methods for the diagnosis and treatment of WT.
为分析小儿肾母细胞瘤（WT）组织中不同细胞类型的异质性，鉴定恶性肿瘤细胞差异表达基因（DEGs），从而建立预后模型。从公共数据库中下载小儿 WT 组织的单细胞测序数据。使用 R 语言的 Seurat 包进行数据过滤和归一化、主成分分析和 T 分布随机邻域嵌入聚类分析。将细胞分为不同的簇，提取恶性肿瘤细胞，获得 DEGs。然后进行伪时间轨迹分析。通过单因素和多因素 COX 回归分析和 LASSO 回归分析确定预后生物标志物。进行 Kaplan-Meier 生存分析和接收者操作特征曲线分析。结合预后生物标志物和临床特征，生成预测 WT 预后的列线图。在外部数据集进行验证。WT 组织中的细胞被分为 10 个簇。从恶性肿瘤细胞中的 215 个 DEGs 中筛选出 3 个影响患者生存时间的预后生物标志物，并使用这 3 个基因和临床特征构建列线图。3 年和 5 年无病生存率的曲线下面积（AUC）值分别为 0.756 和 0.734。在外部验证数据集中，该列线图模型的 AUC 值为 0.826。基于单细胞 RNA-seq，我们识别了小儿 WT 组织中的细胞簇，鉴定了恶性肿瘤细胞中的预后生物标志物，并建立了综合预后模型。我们的研究结果可能为 WT 的诊断和治疗提供新的思路和方法。
