Development and validation of a prognostic model based on a single-cell RNA-seq in Wilms tumor in children

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.