Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential.

Abstract

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

• Authors: Zhigang Qin, Bin Yang, Xingyi Jin, Hang Zhao, Naijie Liu
• Journal: Frontiers in oncology (Front Oncol) Vol. 14 Pg. 1359778 ( 2024) ISSN: 2234-943X [Print] Switzerland
• PMID: 38606090 (Publication Type: Journal Article)
• Copyright: Copyright © 2024 Qin, Yang, Jin, Zhao and Liu.