Development of a cuproptosis-related signature for prognosis prediction in lung adenocarcinoma based on WGCNA.

Background: Cuproptosis is a novel mitochondrial respiration-dependent cell death mechanism induced by copper that can kill cancer cells via copper carriers in cancer therapy. However, the clinical significance and prognostic value of cuproptosis in lung adenocarcinoma (LUAD) remains unclear.

Methods: We performed a comprehensive bioinformatics analysis of the cuproptosis gene set, including copy number aberration, single-nucleotide variation, clinical characteristics, survival analysis, etc. Cuproptosis-related gene set enrichment scores (cuproptosis Z-scores) were calculated in The Cancer Genome Atlas (TCGA)-LUAD cohort using single-sample gene set enrichment analysis (ssGSEA). Modules significantly associated with cuproptosis Z-scores were screened by weighted gene co-expression network analysis (WGCNA). The hub genes of the module were then further screened by survival analysis and least absolute shrinkage and selection operator (LASSO) analysis, in which TCGA-LUAD (497 samples) and GSE72094 (442 samples) were used as the training and validation cohorts, respectively. Finally, we analyzed the tumor characteristics, immune cell infiltration levels, and potential therapeutic agents.

Results: Missense mutation and copy number variant (CNV) events were general in the cuproptosis gene set. We identified 32 modules, of which the MEpurple (107 genes) and MEpink (131 genes) modules significantly positively and negatively correlated with cuproptosis Z-scores, respectively. We identified 35 hub genes significantly related to overall survival and constructed a prognostic model consisting of 7 cuproptosis-related genes in patients with LUAD. Compared with the low-risk group, patients in the high-risk group had a worse overall survival and gene mutation frequency, as well as significantly higher tumor purity. In addition, infiltration of immune cells was also significantly different between the 2 groups. Furthermore, the correlation between the risk scores and half-maximum inhibitory concentration (IC50) of antitumor drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) v. 2 database was explored, revealing differences in drug sensitivity across the 2 risk groups.

Conclusions: Our study provided a valid prognostic risk model for LUAD and improved understanding of its heterogeneity, which may aid in the development of personalized treatment strategies.