REFERENCES

1. Zhang B, Xie L, Liu J, Liu A, He M. Construction and validation of a cuproptosis-related prognostic model for glioblastoma. Front Immunol. 2023;14:1082974.

2. Samioti SE, Benos LT, Sarris IE. Effect of fractal-shaped outer boundary of glioblastoma multiforme on drug delivery. Comput Methods Programs Biomed. 2019;178:191-9.

3. Polivka J Jr, Polivka J, Holubec L, et al. Advances in experimental targeted therapy and immunotherapy for patients with glioblastoma multiforme. Anticancer Res. 2017;37:21-33.

4. Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. Lancet. 2018;392:432-46.

5. Garcia CR, Slone SA, Dolecek TA, Huang B, Neltner JH, Villano JL. Primary central nervous system tumor treatment and survival in the United States, 2004-2015. J Neurooncol. 2019;144:179-91.

6. Zhao YY, Chen SH, Hao Z, Zhu HX, Xing ZL, Li MH. A nomogram for predicting individual prognosis of patients with low-grade glioma. World Neurosurg. 2019;130:e605-12.

7. Kiran M, Chatrath A, Tang X, Keenan DM, Dutta A. A prognostic signature for lower grade gliomas based on expression of long non-coding RNAs. Mol Neurobiol. 2019;56:4786-98.

8. Razzak M. Genetics: new molecular classification of gastric adenocarcinoma proposed by The Cancer Genome Atlas. Nat Rev Clin Oncol. 2014;11:499.

9. Deng S, Li Y, Yi G, et al. Overexpression of COX7A2 is associated with a good prognosis in patients with glioma. J Neurooncol. 2018;136:41-50.

10. Sonkin D, Thomas A, Teicher BA. Cancer treatments: past, present, and future. Cancer Genet. 2024;286-287:18-24.

11. Li SR, Bu LL, Cai L. Cuproptosis: lipoylated TCA cycle proteins-mediated novel cell death pathway. Signal Transduct Target Ther. 2022;7:158.

12. Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 2022;375:1254-61.

13. Wang W, Li S, Huang Y, Guo J, Sun L, Sun G. Comprehensive analysis of the potential biological significance of cuproptosis-related gene LIPT2 in pan-cancer prognosis and immunotherapy. Sci Rep. 2023;13:22910.

14. Kim BE, Nevitt T, Thiele DJ. Mechanisms for copper acquisition, distribution and regulation. Nat Chem Biol. 2008;4:176-85.

15. Rae TD, Schmidt PJ, Pufahl RA, Culotta VC, O’Halloran TV. Undetectable intracellular free copper: the requirement of a copper chaperone for superoxide dismutase. Science. 1999;284:805-8.

16. Denoyer D, Masaldan S, La Fontaine S, Cater MA. Targeting copper in cancer therapy: ‘copper that cancer’. Metallomics. 2015;7:1459-76.

17. Trammell R, Rajabimoghadam K, Garcia-Bosch I. Copper-promoted functionalization of organic molecules: from biologically relevant Cu/O₂ model systems to organometallic transformations. Chem Rev. 2019;119:2954-3031.

18. Tang D, Chen X, Kroemer G. Cuproptosis: a copper-triggered modality of mitochondrial cell death. Cell Res. 2022;32:417-8.

19. Wang C, Zhou Y. Cuproptosis-related gene subtypes predict prognosis in patients with head and neck squamous cell carcinoma. J Otolaryngol Head Neck Surg. 2023;52:58.

20. Shi B, Zhang W, Wang T, Cui Z. The therapeutic and prognostic role of cuproptosis-related genes in triple negative breast cancer. BMC Bioinformatics. 2023;24:223.

21. Wang S, Xing N, Meng X, Xiang L, Zhang Y. Comprehensive bioinformatics analysis to identify a novel cuproptosis-related prognostic signature and its ceRNA regulatory axis and candidate traditional Chinese medicine active ingredients in lung adenocarcinoma. Front Pharmacol. 2022;13:971867.

22. Long S, Wang Y, Chen Y, Fang T, Yao Y, Fu K. Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma. Biol Direct. 2022;17:28.

23. Liu JY, Liu LP, Li Z, Luo YW, Liang F. The role of cuproptosis-related gene in the classification and prognosis of melanoma. Front Immunol. 2022;13:986214.

24. Zhou Z, Zhou Y, Liu D, Yang Q, Tang M, Liu W. Prognostic and immune correlation evaluation of a novel cuproptosis-related genes signature in hepatocellular carcinoma. Front Pharmacol. 2022;13:1074123.

25. Liu H, Tang T. Pan-cancer genetic analysis of cuproptosis and copper metabolism-related gene set. Front Oncol. 2022;12:952290.

26. Liu H. Pan-cancer profiles of the cuproptosis gene set. Am J Cancer Res. 2022;12:4074-81.

27. Gene expression omnibus.Available from: https://www.ncbi.nlm.nih.gov/geo/ [Last accessed on 8 Aug 2025].

28. Frankish A, Diekhans M, Ferreira AM, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019;47:D766-73.

29. Goldman MJ, Craft B, Hastie M, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38:675-8.

30. Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31:213-9.

31. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747-56.

32. Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12:R41.

33. Gravendeel LA, Kouwenhoven MC, Gevaert O, et al. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 2009;69:9065-72.

34. Kruthika BS, Jain R, Arivazhagan A, et al. Transcriptome profiling reveals PDZ binding kinase as a novel biomarker in peritumoral brain zone of glioblastoma. J Neurooncol. 2019;141:315-25.

35. Franz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46:W60-4.

36. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284-7.

37. Zhang R, Chen C, Dong X, et al. Independent validation of early-stage non-small cell lung cancer prognostic scores incorporating epigenetic and transcriptional biomarkers with gene-gene interactions and main effects. Chest. 2020;158:808-19.

38. Saldana DF, Feng Y. SIS: an R package for sure independence screening in ultrahigh-dimensional statistical models. J Stat Soft. 2018;83:1-25.

39. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61:92-105.

40. Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77:e108-10.

41. Wang Q, Zhang J, Tu H, et al. Soluble immune checkpoint-related proteins as predictors of tumor recurrence, survival, and T cell phenotypes in clear cell renal cell carcinoma patients. J Immunother Cancer. 2019;7:334.

42. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

43. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1.

44. Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401-4.

45. Lánczky A, Nagy Á, Bottai G, et al. miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 2016;160:439-46.

46. Weller M, Wen PY, Chang SM, et al. Glioma. Nat Rev Dis Primers. 2024;10:33.

47. Cluntun AA, Lukey MJ, Cerione RA, Locasale JW. Glutamine metabolism in cancer: understanding the heterogeneity. Trends Cancer. 2017;3:169-80.

48. Restall IJ, Cseh O, Richards LM, Pugh TJ, Luchman HA, Weiss S. Brain tumor stem cell dependence on glutaminase reveals a metabolic vulnerability through the amino acid deprivation response pathway. Cancer Res. 2020;80:5478-90.

49. Huang Q, Lian C, Dong Y, et al. SNAP25 inhibits glioma progression by regulating synapse plasticity via GLS-mediated glutaminolysis. Front Oncol. 2021;11:698835.

50. Rosati A, Poliani PL, Todeschini A, et al. Glutamine synthetase expression as a valuable marker of epilepsy and longer survival in newly diagnosed glioblastoma multiforme. Neuro Oncol. 2013;15:618-25.

51. Li X, Sun T, Jiang C. Intelligent delivery systems in tumor metabolism regulation: exploring the path ahead. Adv Mater. 2024;36:e2309582.

52. Zhao T, Zhang Z, Li Y, et al. Brucella abortus modulates macrophage polarization and inflammatory response by targeting glutaminases through the NF-κB signaling pathway. Front Immunol. 2023;14:1180837.

53. Köhler N, Ruess DA, Kesselring R, Zeiser R. The role of immune checkpoint molecules for relapse after allogeneic hematopoietic cell transplantation. Front Immunol. 2021;12:634435.

54. Lee YH, Martin-Orozco N, Zheng P, et al. Inhibition of the B7-H3 immune checkpoint limits tumor growth by enhancing cytotoxic lymphocyte function. Cell Res. 2017;27:1034-45.

55. Picarda E, Ohaegbulam KC, Zang X. Molecular pathways: targeting B7-H3 (CD276) for HUMAN CANCER IMMUNOTHerapy. Clin Cancer Res. 2016;22:3425-31.

56. Dröge W, Eck HP, Betzler M, Näher H. Elevated plasma glutamate levels in colorectal carcinoma patients and in patients with acquired immunodeficiency syndrome (AIDS). Immunobiology. 1987;174:473-9.

57. Nabe S, Yamada T, Suzuki J, et al. Reinforce the antitumor activity of CD8⁺ T cells via glutamine restriction. Cancer Sci. 2018;109:3737-50.

58. Liu H, Li Y, Karsidag M, Tu T, Wang P. Technical and biological biases in bulk transcriptomic data mining for cancer research. J Cancer. 2025;16:34-43.