Multi-omic analysis reveals prognostic and immune characteristics of cuproptosis for lung adenocarcinoma

Abstract Background: Cuproptosis is a newly defined form of regulated cell death triggered by excess copper and is distinct from other known types of cell death. Currently, reliable prognostic signatures based on cuproptosis-related regulators are underdeveloped. Herein, we aimed to investigate the cuproptosis landscape in lung adenocarcinoma (LUAD). Materials and Methods: We downloaded gene expression data and corresponding clinical information from The Cancer Genome Atlas and Gene Expression Omnibus. Cuproptosis subtypes were identified using unsupervised clustering based on the expression of 10 cuproptosis-related regulatory genes, including seven positively (FDX1, LIAS, LIPT1, DLAT, DLD, PDHA1, and PDHB) and three negatively (CDKN2A, GLS, and MTF1) correlated genes. CDKN2A expression was detected using immunohistochemistry tissue microarrays. Results: Unsupervised clustering revealed two LUAD cuproptosis-related subtypes (A and B). Patients with subtype B had a higher survival rate and were significantly enriched in innate immune cells compared to those with subtype A. We built a cuproptosis-related risk model (CRM) to calculate a risk score for each patient; the score was positively correlated with patient prognosis. Somatic mutation landscape analysis revealed a significant negative relationship between tumor mutational burden (TMB) and the CRM score. Patients with both a low CRM score and TMB had the worst prognosis. Immune landscape analysis revealed that patients with a high CRM score had a higher tumor immune dysfunction and exclusion score, suggesting a poorer immunotherapy response rate. Conclusion: Single-cell CRM score quantification revealed its correlation with the enrichment of different immune cell types in LUAD.