A pan-cancer cuproptosis signature predicting immunotherapy response and prognosis

Abstract

Background: Cuproptosis may represent a potential biomarker for predicting prognosis and immunotherapy response, but the available evidence is insufficient. Methods: The multiple single-cell RNA sequencing (scRNA-seq) datasets were analyzed to investigate the specific occurrence of cuproptosis in distinct cell populations. Utilizing 28 scRNA-seq datasets, TCGA pan-cancer cohort, and 10 immunotherapy cohorts, we developed a cuproptosis signature (Cup.Sig). This signature was used to construct prediction models for immunotherapy response and identify potential prognostic biomarkers for pan-cancer using 11 different machine learning algorithms. Results: Malignant cells demonstrate the higher cuproptosis scores in comparison to other cell types across diverse cancer types. The Cup.Sig exhibits significant associations with cancer hallmarks and immune cell response in multiple cancer types. Leveraging the Cup.Sig, the robust pan-cancer immunotherapy prediction model and prognostic biomarker have been established and validated using diverse datasets from various platforms. Conclusions: We developed a pan-cancer cuproptosis signature for predicting survival and immunotherapy response.

Keywords

• Cuproptosis
• Prognosis
• Immunotherapy
• Machine learning
• Pan-cancer