LAUSR

Background: Glioblastoma multiforme (GBM) is the most common tumor of the central nervous system with poor prognosis. Cuproptosis is a novel programmed cell death pathway targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that it participates in tumor progression, but its role in GBM is still elusive. In this study, we aimed to develop a cuproptosis gene-signature risk score using bioinformatics analysis and machine learning. Methods: We acquired transcriptomic and clinical information of GBM patients from The Cancer Genome Atlas (TCGA). A total of 2283 differentially expressed genes (DEGs) were obtained from the GEPIA2 database. 26 cuproptosis-related genes (CRGs) were retrieved from literature. A correlation analysis between the 26 CRGs and the DEGs were conducted to retrieve the cuproptosis-related DEGs. Then, a univariate cox analysis was conducted to obtain the prognostic-related DEGs for overall survival (OS). The least absolute shrinkage and selection operator (LASSO) were conducted for regularization and the gene risk score was constructed using the multivariate cox coefficients. Results: A total of 731 downregulated DEGs were correlated with CRGs, while 68 upregulated DEGs were correlated with CRGs and were further screened for prognostic value using the univariate cox analysis. A total of 70 prognostic related CRGs were identified and were further screened using the LASSO cox analysis. After multivariate cox analysis, a total of seven genes were significantly associated with survival (p-value<0.01). A risk-score gene signature was constructed from the cox coefficients multiplied by the expression of the following genes: -0.0012*DPP10+0.0021*EGR4+0.0015*ITPKA+ 0.0003* PTPRN+ 0.0007* STEAP2+ 0.0006* TENM2+- 0.0017* ZNF540. Conclusion: Univariate and multivariate Cox regression analyses showed the CRGs-based prognostic signature independently functioned as a risk factor for OS in GBM patients. Furthermore, our results gave a promising understanding of cuproptosis in GBM, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients. Citation Format: Yaman B. Ahmed, Ayah N. Al-Bzour, Ghayda'a N. Al-Majali, Zaid M. Khalefa, Saja M. Alzghoul. Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2046.