Pyroptosis-Related Gene Signatures Enable Robustly Diagnosis, Prognosis and Immune Responses Prediction in Uterine Corpus Endometrial Carcinoma.

ABSTRACT: Purpose: Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignancy with poor prognosis and high lethality rates. Pyroptosis, a pro-inflammatory programmed cell death pattern, significantly influences tumor growth, development, and metastasis. We intend to explore whether pyroptosis-related genes can be screened as targets for early detection and patient prognosis. Methods: We used nine common machine learning algorithms to build classifiers based on the pyroptosis-related genes, evaluated the classifiers' performance using metrics like the receiver operating characteristic curve (ROC), and verified the results using external datasets. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, we built a predictive model. ROC and univari

SUBMITTER: Chen X 

PROVIDER: S-EPMC12171010 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature