Evaluation of pyroptosis-associated genes in endometrial cancer utilizing a 101-combination machine learning framework and multi-omics data.

ABSTRACT:

Background

Endometrial cancer (EC) is a common and increasingly prevalent gynecological malignancy. Pyroptosis, a pro-inflammatory form of programmed cell death, plays dual roles in cancer but remains poorly understood in the context of EC and its immune microenvironment.

Methods

We identified pyroptosis-associated genes (PAGs) and applied a 101-combination machine learning framework to construct and validate a robust prognostic model using TCGA bulk RNA-seq and single-cell transcriptomic data. Immune infiltration was assessed using CIBERSORT and Tumor Immune Dysfunction and Exclusion (TIDE), while CellChat was employed to investigate pyroptosis-related cell-cell communication. Drug sensitivity was predicted with OncoPredict.

Results

A seven-gene prognostic model demo

SUBMITTER: Huang LJ 

PROVIDER: S-EPMC12176823 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature