Machine learning constructs a ferroptosis related signature for predicting prognosis and drug sensitivity in lung cancer.

ABSTRACT:

Purpose

Ferroptosis is a novel form of iron-dependent programmed cell death that is associated with the progression of various tumors and cancer treatment responses. However, its role in the clinical treatment of lung cancer, particularly in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), remains poorly understood. This study aims to explore the prognostic value of ferroptosis-related genes in lung cancer and establish a reliable predictive model.

Methods

We collected the GSE4573, TCGA-LUAD, and TCGA-LUSC datasets, comprising a total of 1,271 samples and a ferroptosis gene set of 717 genes. Weighted gene coexpression network analysis (WGCNA) was used to identify ferroptosis-related markers, followed by the application of 101 machine learning algorithms co

SUBMITTER: Li Z 

PROVIDER: S-EPMC12698758 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature