Integrating single-cell and bulk transcriptomes with machine learning reveals a CAF signature for immunotherapy response in dMMR endometrial cancer

By systematically integrating 101 machine learning models derived from 10 algorithms, we constructed a Wnt-CAF-driven risk signature. Combined with clinicopathological features in a nomogram, this model effectively predicted prognosis and potential immunotherapy response in dMMR EC. These findings deepen the understanding of the EC microenvironment and provide valuable guidance for personalized treatment strategies and prognostic assessment in clinical practice.