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.