Background: Wilms tumor (WT), the most common pediatric malignant renal tumor, shows high recurrence in high-risk subtypes due to chemoresistance. Tumor microenvironment (TME) remodeling, particularly M2-type tumor-associated macrophages (TAMs), contributes to chemoresistance, but underlying mechanisms remain unclear. This study explored TME-related chemoresistance mechanisms in WT and developed targeted therapeutic strategies. Methods: Clinical WT samples were analyzed for M2-type TAMs infiltration and SNRPC expression. Bioinformatics analysis of TARGET-WT data identified M2-associated genes. In vitro experiments (cell transfection, qRT-PCR, Western blot, co-culture, ChIP and dual-luciferase reporter assays) explored SNRPC’s role in regulating M2-type TAMs. Animal models (orthotopic tumor and lung metastasis) verified in vivo effects. A hybrid exosome nanosystem (DOX/siSNRPC@hEVs) was constructed and evaluated for efficacy and safety. Statistical analyses included t-test, ANOVA, and survival analysis. Results: M2-type TAMs (CD68⁺CD163⁺) infiltration was higher in chemoresistant WT and associated with poor prognosis. SNRPC was overexpressed in chemoresistant WT, correlated with M2-type TAMs, and promoted tumor malignancy and M2-type TAMs polarization. Mechanistically, SNRPC activated NF-κB signaling, inducing CXCL17 upregulation to recruit M2-type TAMs, with partial CXCL17 release via migrasomes. DOX/siSNRPC@hEVs showed high targeting, reduced toxicity, inhibited tumor growth/metastasis, and reversed chemoresistance by reducing M2-type TAMs. Conclusions: The SNRPC-NF-κB-CXCL17-M2 TAMs axis drives WT chemoresistance. DOX/siSNRPC@hEVs effectively targets this axis, providing a novel strategy for high-risk WT.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic models to personalize treatment strategies for IPF patients. Using transcriptome sequencing and clinical data from 176 IPF patients, we developed a Random Survival Forest (RSF) model through machine learning and bioinformatics techniques. The model demonstrated superior predictive accuracy and clinical utility, as shown by the concordance index (C-index), the area under the operating characteristic curve (AUC), Brief scores, and decision curve analysis (DCA) curves. Additionally, a novel prognostic staging system was introduced to stratify IPF patients into distinct risk groups, enabling individualized predictions. The model’s performance was validated using a bleomycin-induced pulmonary fibrosis mouse model. In conclusion, this study offers a new prognostic staging system and predictive tool for IPF, providing valuable insights for treatment and management.