Machine learning-based model identifies a novel cuproptosis-related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma.

ABSTRACT: Lung adenocarcinoma (LUAD) remains one of the most prevalent and lethal cancers globally, making it critical to understand the mechanisms driving its progression and improve prognosis. Moreover, cuproptosis and mitochondrial dysfunction may be involved in lung cancer. Therefore, the present study aimed to identify mitochondrial genes associated with cuproptosis to develop a prognostic model for patients with LUAD, with the potential to predict survival outcomes and responses to treatment. Bulk RNA-sequencing data was utilized from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO), and Pearson correlation analysis was employed to identify mitochondrial genes associated with cuproptosis. A prognostic model was constructed using univariate Cox regression combined with least absolu

SUBMITTER: Liu YH 

PROVIDER: S-EPMC12402859 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature