Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response.

ABSTRACT:

Background

Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.

Methods

This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug inf

SUBMITTER: Zhang L 

PROVIDER: S-EPMC11092068 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature