Xiaochun Xia,1,* Xiaoying Huang,2,* Longxiang Wu,3 Pengqin Xu,1 Peng Li4 

1Department of Radiation Oncology, Affiliated Tumor Hospital of Nantong University, Nantong Tumor Hospital, Nantong University, Nantong, Jiangsu, 226361, People’s Republic of China; 2Laboratory of Frontiers Science Center for Precision Oncology, Faculty of Health Sciences, University of Macau, Taipa, Macau, 999078, People’s Republic of China; 3Department of Gastrointestinal Surgery, Affiliated Tumor Hospital of Nantong University, Nantong Tumor Hospital, Nantong, Jiangsu, 226361, People’s Republic of China; 4Department of Radiation Oncology, Huaian Hospital of Huaian City, Huaian Cancer Hospital, Huaian, Jiangsu, 223200, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Peng Li, Department of Radiation Oncology, Huaian Hospital of Huaian City, Huaian Cancer Hospital, 19 Shanyang Avenue, Huaian, Jiangsu, 223200, People’s Republic of China, Email hazlyyflk@163.com Pengqin Xu, Department of Radiation Oncology, Affiliated Tumor Hospital of Nantong University, Nantong Tumor Hospital, Nantong University, Nantong, Jiangsu, 226361, People’s Republic of China, Email xuwenmei2011@163.com

Background: Cuproptosis, a metal-ion-dependent form of regulated cell death induced by copper overload, is emerging as a potential mechanism in high-grade glioma (HGG). Despite its significance, the role of cuproptosis in predicting the prognostic and therapeutic response in HGG remains poorly understood.
Methods: We performed unsupervised clustering to stratify patients with HGG in the Chinese Glioma Genome Atlas (CGGA) according to the expression of 14 cuproptosis-related genes (CRGs) and validated in The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to explore the biological processes and pathways involved within distinct groups. We constructed the CupScore model to predict the responsiveness to immune checkpoint inhibitors (ICIs) therapy and chemotherapy in patients with HGG. Additionally, in vivo and in vitro experiments were performed to investigate the potential biological function of CDKN2A in HGG.
Results: We identified two cuproptosis-related molecular subgroups with significantly different survival probabilities. Patients with HGG in cluster 1 were characterized as immune-desert phenotype with higher CupScore and lower expression of MHC complex, interferons, chemokines, interleukins, and immune checkpoints. In contrast, cluster 2 showed an immune-inflamed signature. We screened PI-103 as the most promising candidate for patients with higher CupScore and confirmed its experimental evidence and clinical trial status. Patients with lower CupScore showed higher response rates to anti-PD-L1 and anti-PD1 combined with anti-CTLA4 ICI therapy. Furthermore, in vivo and in vitro experiments revealed that CDKN2A enhanced the malignant phenotype of HGG.
Conclusion: Cuproptosis has the ability to reprogram the tumor microenvironment (TME) in HGG, leading to the stratification of patients into two distinct molecular subgroups. The CupScore model emerged as a robust metric for predicting the prognostic and therapeutic benefits, as well as may therefore facilitate personalized treatment strategies for patients with HGG.

Keywords: cuproptosis, high-grade glioma, molecular subtype, prognosis prediction, therapeutic response