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<p>Recent studies have highlighted the impact of copper-induced cell death (cuproptosis) on cancer progression, prognosis, and treatment, but it remains unclear whether cuproptosis-related genes (CRGs) play any role in the glioma tumor microenvironment (TME). The CRGs expression patterns in TCGA glioma samples were evaluated based on genetic and transcriptional alterations identifying three different molecular groupings and showing that CRGs changes were linked to clinical characteristics, prognosis, and TME infiltration. Machine learning algorithms were then used to develop an overall survival score for cuproptosis-related prognostic genes (CRPGs), and its prognostic ability was validated for glioma patients. An elevated CRPGs score indicates a heightened mutation burden, increased glioma metabolism, compromised immunity, and strong correlation with both the cancer stem cells (CSC) index and medication sensitivity to chemotherapeutics. This extensive examination of CRGs in gliomas showed their possible significance in the tumor microenvironment as well as their prognostic value. This extremely precise CRPGs nomogram has furthered our understanding of cuproptosis in gliomas, which will allow new approaches to prognosis and immunotherapy development.</p>

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A novel cuproptosis-related prognostic gene signature is identified by machine learning and integrative analyses in gliomas

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Jiangchun Ma,
  • Weixian Liu,
  • Xiaoyong Shi,
  • Zhuxiao Tang,
  • Tao Xiong,
  • Hu Sun,
  • Yuan Hong

摘要

Recent studies have highlighted the impact of copper-induced cell death (cuproptosis) on cancer progression, prognosis, and treatment, but it remains unclear whether cuproptosis-related genes (CRGs) play any role in the glioma tumor microenvironment (TME). The CRGs expression patterns in TCGA glioma samples were evaluated based on genetic and transcriptional alterations identifying three different molecular groupings and showing that CRGs changes were linked to clinical characteristics, prognosis, and TME infiltration. Machine learning algorithms were then used to develop an overall survival score for cuproptosis-related prognostic genes (CRPGs), and its prognostic ability was validated for glioma patients. An elevated CRPGs score indicates a heightened mutation burden, increased glioma metabolism, compromised immunity, and strong correlation with both the cancer stem cells (CSC) index and medication sensitivity to chemotherapeutics. This extensive examination of CRGs in gliomas showed their possible significance in the tumor microenvironment as well as their prognostic value. This extremely precise CRPGs nomogram has furthered our understanding of cuproptosis in gliomas, which will allow new approaches to prognosis and immunotherapy development.