Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

Autor: Qian Jiang, Guo-Yuan Ling, Jun Yan, Ju-Yuan Tan, Ren-Bao Nong, Jian-Wen Li, Teng Deng, Li-Gen Mo, Qian-Rong Huang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Biochemistry and Biophysics Reports, Vol 37, Iss , Pp 101605- (2024)
Druh dokumentu: article
ISSN: 2405-5808
DOI: 10.1016/j.bbrep.2023.101605
Popis: Background: Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma. Methods: We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining. Results: Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses. Conclusion: The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.
Databáze: Directory of Open Access Journals