A Novel Disulfidptosis-related Gene Signature Predicts Overall Survival of Glioblastoma Patients

Overview

Journal Future Sci OA

Specialty Biotechnology

Date 2024 May 31

PMID 38817361

Authors

Yuxia Zhang

Bing Liu

Yuelian Zhou

Affiliations

Soon will be listed here.

Abstract

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. 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. 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. In conclusion, a new disulfidptosis-related gene signature can be utilised to predict prognosis in GBM.

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