Utilises Machine Learning Techniques to Deeply Analyse the Role of Lysosome-Dependent Cell Death in Endometrial Cancer and Its Interactions With the Tumour Microenvironment.

By integrating gene expression data, clinical features and multimodal data, we constructed a machine learning model capable of accurately predicting the prognosis of endometrial cancer patients. The study found that key genes related to lysosome-dependent cell death exhibit significant expression pattern heterogeneity in endometrial cancer and are closely associated with immune cell infiltration and metabolic characteristics within the tumour microenvironment. Patients in the high-risk group tend to have lower immune scores and a higher prevalence of immunosuppressive cell types, such as regulatory T cells and M2 macrophages, which may be linked to poorer prognosis and resistance to immunotherapy. Additionally, we discovered that the expression of lysosome-dependent cell death-related genes correlates with patients' sensitivity to chemotherapeutic drugs, providing new perspectives for personalised treatment of endometrial cancer. Through this study, we characterised the prognostic relevance of lysosome-dependent cell death-related genes in endometrial cancer, and identified biomarkers with potential utility for risk assessment and therapeutic stratification.