Single-cell analysis reveals that NRG1/3-ERBB4 signaling affects metabolic reprogramming and immune escape in Wilms tumor.

BACKGROUND

Wilms tumor presents a heterogeneous tumor microenvironment. This study aimed to characterize the tumor microenvironment and identify prognostic genes to improve therapeutic strategies.

METHODS

We integrated single-cell RNA sequencing and transcriptomic data from paired tumor and normal tissues. Bioinformatics analyses included assessments of cellular heterogeneity, trajectories, and cell-cell communication. Prognostic genes were identified with differential expression, Cox regression, and machine-learning analyses. Furthermore, functional characterization, immune infiltration patterns, and therapeutic targets were systematically investigated. Potential therapeutic compounds were predicted using drug databases and a graph-based deep learning framework to predict compound-protein interactions.

RESULTS

Single-cell RNA sequencing revealed 17 cell clusters, with tumor-specific epithelial cells and renal progenitor cells. Pseudotime trajectory analysis revealed dynamic differentiation, highlighting NRG1/3-ERBB4 signaling. Intersection of the transcriptomic and single-cell data identified 405 key genes. A prognostic model incorporating eight prognostic genes (PRLR, SLC16A7, SGIP1, PPARGC1A, CDHR5, GRB7, FKBP10, and UGT2B7) stratified patients into high- and low-risk groups (p < 0.0001), with area under the curve values > 0.6 for 1-, 2-, and 3-year survival prediction. High-risk patients had elevated regulatory T cell infiltration and immune checkpoint genes (TNFRSF9 and KIR3DL3). Chitosan was identified as a multitarget agent that interacts with the eight prognostic proteins.

CONCLUSIONS

This study defined tumor cellular architecture and identified eight prognostic genes with potential clinical value.