A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer.

ABSTRACT: Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent of the immunotherapy era has profoundly improved patient outcomes, not all patients benefit from immunotherapy; some patients experience hyperprogression while on immunotherapy. Hence, there is a pressing need to further delineate the distinct immune response profiles in patients with endometrial cancer to enhance prognosis prediction and facilitate the prediction of immunotherapeutic responses. The ssGSEA method was used to evaluate the activities of the immune response pathways in patients with endometrial cancer. Unsupervised clustering was employed to identify the different immune response patterns. WGCNA was employed to identify the genes

SUBMITTER: Wang X 

PROVIDER: S-EPMC11680691 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature