A novel disulfidptosis-related gene signature predicts overall survival of glioblastoma patients

Abstract

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.

Plain language summary

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.

Summary points

• The study aimed to investigate the prognostic relevance of disulfidptosis-related genes in glioblastoma using bioinformatic analysis in the TCGA-GBM database.

• Weighted co-expression network analysis was employed to identify disulfidptosis-related genes, and a gene signature model was developed to predict patient prognosis.

• Patients classified into the high-risk group based on the disulfidptosis-related gene signature experienced significantly reduced survival rates compared with those in the low-risk group.

• Functional analysis indicated noteworthy differences in the immune status between the two risk groups.

• The findings suggest that the newly developed disulfidptosis-related gene signature can be used to predict prognosis in glioblastoma.

Keywords: :

• bioinformatic analysis
• disulfidptosis
• glioblastoma
• prognosis

Author contributions

Y Zhang, B Liu and Y Zhou contributed to the entire project, from the design project to the collection and collation of data, to the writing of the paper. B Liu and Y Zhang mainly participated in the revision of the manuscript. Y Zhang and B Liu helped retrieve and organize the data. Y Zhou was responsible for supervising and providing financial support.

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Acknowledgments

This study was supported by Shandong Dongying People's Hospital.