Systematic Identification of Novel Ferroptosis-Associated Multigene Models for Predicting Patient Prognosis Based on Endometrial Cancer.

Objective: Uterine corpus endometrial carcinoma (UCEC) is a frequent epithelial cancer in females. The rate of UCEC occurrence increases year by year and the age is getting younger and younger, which requires more active treatments to improve its prognosis. Ferroptosis is a kind of regulatory cell death that relies on iron and may be triggered by sorafenib, which has been elucidated in several cancers, but the mechanism of ferroptosis-related genes in UCEC has yet to be fully defined and will need more investigation. Methods: The mRNA expression profiles and accompanying clinical data of UCEC patients included in this research were obtained from a publicly available database. We subsequently classified the patients into experimental and training sets. Next, utilizing the least absolute shrinkage and selection operator (LASSO) Cox regression model, we established the multigene features of the TCGA experimental set and verified them in the validation set. Results: Per the findings of our investigation, the TCGA experimental set cohort had four differentially expressed genes (DEGs) that were linked to overall survival (OS). An analysis was conducted using univariate Cox regression (with all variables corrected for Conclusions: A unique model of genes linked to ferroptosis has the potential to be a treatment option for UCEC and can be utilized for the prognostic prediction of the disease.