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.

Mitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.