Identification of immunogenic cell death-related subtypes used for predicting survival and immunotherapy of endometrial carcinoma through a bioinformatics analysis

Abstract

Immunogenic cell death (ICD) is a unique phenomenon that can trigger comprehensive, adaptive immune responses through damage-associated molecular patterns, offering a promising avenue for tumor immunotherapy. However, the role of ICD-related genes and their correlation with endometrial carcinoma (EC), the most prevalent gynecologic malignancy, remains unclear. This study examined genetic, transcriptional, and clinical data of EC obtained from the Cancer Genome Atlas database. Unsupervised clustering analysis was utilized to identify distinct ICD clusters based on the expression of ICD-related genes. Regarding the different clusters, their survival analysis, assessment of the immune microenvironment, immune cell infiltration, immune checkpoint analysis, and tumor mutation burden analysis were performed. Furthermore, an ICD risk signature was established using univariate Cox regression and least absolute shrinkage and selection operator analysis. The Chi-square test was employed to investigate the relationship between the ICD score and clinical features. Multiple computational analytical tools were used to assess immune annotation, somatic mutations, tumor mutation burden, and response to immunotherapy and chemotherapy drugs in different ICD score groups. Two ICD clusters were identified, indicating that the ICD-high cluster was associated with improved prognosis, abundant immune cell infiltration, and enrichment of pathways related to immunologic activation. Moreover, the ICD risk signature showed predictive value for the immune microenvironment, immunotherapy response, chemotherapy susceptibility, and prognosis in EC. Our findings offer novel insights into personalized treatment strategies for EC patients.