RESUMO
Aim: The aim of this study was to investigate the prognostic relevance of disulfidptosis-related genes in glioblastoma using bioinformatic analysis in The Cancer Genome Atlas Program-Glioblastoma (TCGA-GBM) database and develop a gene signature model for predicting patient prognosis. Methods: We conducted a bioinformatic analysis using the TCGA-GBM database and employed weighted co-expression network analysis to identify disulfidptosis-related genes. Subsequently, we developed a predictive gene signature model based on these genes to stratify glioblastoma patients into high and low-risk groups. Results: Patients categorized into the high-risk group based on the disulfidptosis-related gene signature exhibited a significantly reduced survival rate in comparison to those in the low-risk group. Functional analysis also revealed notable differences in the immune status between the two risk groups. Conclusion: In conclusion, a new disulfidptosis-related gene signature can be utilised to predict prognosis in GBM.
This research aimed to explore the importance of certain genes related to disulfidptosis in glioblastoma, a type of brain cancer. By analyzing a large database of cancer information, we identified these genes and created a model to predict how well patients with glioblastoma might do. The results showed that patients in the high-risk group, as determined by the disulfidptosis-related gene model, had a worse chance of survival compared with those in the low-risk group. This suggests that these genes could help doctors predict how glioblastoma patients will fare, which is important for their treatment and care.