Development of a Multilayered Prognostic Model for Wilms' Tumor Based on Characteristic Lymphocyte Genes

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Wilms' tumor (WT) necessitates improved prognostic tools for accurate risk stratification.
• Integrating genetic and clinical data can enhance the predictive power of existing models.

Purpose of the Study:

• To develop a prognostic nomogram for Wilms' tumor (WT) by integrating genetic and clinical factors.
• To improve the accuracy and clinical utility of WT evaluation.

Main Methods:

• RNA sequencing (RNA-seq) and single-cell RNA (scRNA-seq) data were analyzed using bioinformatics tools.
• Differential gene expression analysis identified prognostic genes, refined by clinical factors (age, sex) into the Genetic Feature Prognostic Model for WT (GPM-WT).
• Survival analysis, Cox regression, and ROC curves evaluated the prognostic capabilities of the developed Lymphocyte Gene and Clinical Features Prognostic Nomogram for WT (LGCPN-WT).

Main Results:

• Six prognostic lymphocyte genes were identified for the GPM-WT.
• The LGCPN-WT model accurately distinguished high-risk from low-risk WT patients, with Area Under the Curve (AUC) values ranging from 0.751 to 0.785 for 2-5 year survival.
• High-risk patients showed increased immune cell infiltration and sensitivity to chemotherapy, targeted therapy, and immunotherapy.

Conclusions:

• The integrated LGCPN-WT model significantly enhances survival prediction accuracy and clinical utility for WT.
• This nomogram supports personalized treatment approaches for Wilms' tumor patients.