A novel cuproptosis-associated LncRNA model predicting prognostic and immunotherapy response for glioma.

UNLABELLED

Recent studies have identified cuproptosis as a novel form of regulated cell death (RCD), and long non-coding RNAs (lncRNAs) have been implicated in glioma progression and prognosis. However, the role of cuproptosis-associated lncRNAs in gliomas has not been systematically assessed. In this study, data from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were used, and cuproptosis-related genes were obtained from previous research. Cuproptosis-associated lncRNAs were identified through co-expression network analysis, Cox regression, and Least Absolute Shrinkage and Selection Operator (LASSO). A total of 10 cuproptosis-associated lncRNAs were selected to construct a prognostic prediction model. The high-risk group was associated with poor overall survival (OS) and progression-free survival (PFS). Multivariate Cox regression, Receiver Operating Characteristic (ROC) curve analysis, C-index, and nomogram demonstrated the accuracy of the 10-lncRNA signature in predicting outcomes in glioma patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA) enrichment analyses revealed a strong association between the signature and immune response pathways. Immune cell infiltration and Single-Sample Gene Set Enrichment Analysis (ssGSEA) further confirmed that the signature is closely linked to immune responses in glioma patients. Further investigation revealed significant differences in tumor immune dysfunction and rejection (TIDE) scores and half-maximal inhibitory concentration (IC50) values for many drugs between low- and high-risk subgroups. This risk signature may serve as a prognostic tool and offer valuable insights into treatment strategies for glioma patients. Additionally, the expression levels of the 10 signature genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR).

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12672-025-02912-6.