A Cuproptosis Activation Scoring model predicts neoplasm-immunity interactions and personalized treatments in glioma

Abstract

Gliomas are malignant tumors in the central nervous system. Cuproptosis is a newly discovered cell death mechanism targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that cuproptosis participates in tumor progression, but its role in gliomas is still elusive. Here, we systematically explored the bulk-tumor and single-cell transcriptome data to reveal its role in gliomas. The cuproptosis activity score (CuAS) was constructed based on cuproptosis-related genes, and machine learning techniques validated the score stability. High CuAS gliomas were more likely to have a poor prognosis and an aggressive mesenchymal (MES) subtype. Subsequently, the SCENIC algorithm predicted 20 CuAS-related transcription factors (TFs) in gliomas. Function enrichment and microenvironment analyses found that CuAS was associated with tumor immune infiltration. Accordingly, intercellular communications between neoplasm and immunity were explored by the R package "Cellchat". Five signaling pathways and 8 ligand-receptor pairs including ICAM1, ITGAX, ITGB2, ANXA1-FRR1, and the like, were identified to suggest how cuproptosis activity connected neoplastic and immune cells. Critically, 13 potential drugs targeting high CuAs gliomas were predicted according to the CTRP and PRISM databases, including oligomycin A, dihydroartemisinin, and others. Taken together, cuproptosis is involved in glioma aggressiveness, neoplasm-immune interactions, and may be used to assist in drug selection.

Keywords: Cell-cell communication; Cuproptosis; Drug prediction; Glioma; Immunocyte; Mesenchymal.