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Integrated Multiomic Analysis Reveals disulfidptosis Subtypes in Glioblastoma

ABSTRACT: Glioblastoma (GBM) stands out as the most aggressive and deadly brain tumor, presenting an alarming scarcity of viable treatment alternatives. Recognizing the pronounced heterogeneity in the prognosis of GBM patients and subtyping them is crucial for precision treatment. disulfidptosis, recently identified as a form of cell death, is closely associated with tumor glucose metabolism abnormalities and disulfide stress, primarily occurring in tumors with elevated expression of SLC7A11. Previous studies have shown that gliomas exhibit high expression of SLC7A11, high metabolism, and glucose deficiency. Therefore, we conducted an in-depth investigation into the role of disulfidptosis in glioblastoma. Samples from glioblastoma patients who underwent surgical treatment at Tiantan Hospital from January 2022 to December 2023 were collected for transcriptome sequencing. Simultaneously, patient data from the CGGA and TCGA databases were gathered. Based on the disulfidptosis features, GBM patients were classified into two subtypes using consensus clustering, with patients in disulfidptosis related group cluster(DRGcluster) A exhibiting significantly improved overall survival (OS), whereas those in DRGcluster B demonstrated the opposite trend. Additionally, the two DRGcluster subtypes exhibited distinct patterns in the tumor immune microenvironment, including differences in immune cell infiltration or cytokine expression. Additional scrutiny of immune dysfunction, exclusion, and subclass mapping in tumor analysis revealed a heightened likelihood of positive responses to immunotherapy, notably anti-PD1 treatment, among patients categorized in DRGcluster A. The pRRophetic algorithm further uncovered noteworthy distinctions in IC50 values for prevalent chemotherapeutic and targeted treatments across diverse DRGclusters. Lastly, utilizing gene sets closely associated with glioblastoma occurrence from the Weighted correlation network analysis (WGCNA) model and significantly differential gene sets between DRGcluster A and B patients, we developed an 8-gene disulfidptosis high-low risk subtype predictor using the Least absolute shrinkage and selection operator (LASSO) machine learning algorithm. The predictor was validated for survival in an external independent GBM cohort from CGGA. This novel disulfidptosis-based classification holds promise as a prognostic predictor for GBM. Furthermore, the pRRophetic algorithm indicated significant differences in half maximal inhibitory concentration 50 (IC50) values for common chemotherapy and targeted therapy among patients in different risk groups, suggesting potential guidance for physicians in selecting patients with advantages in chemotherapy and targeted therapy.

ORGANISM(S): Homo sapiens

PROVIDER: GSE252709 | GEO | 2026/01/01

REPOSITORIES: GEO

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Project description:Gliomas are the most frequent type of primary tumor of the central nervous system in adults with significant morbidity and mortality. Despite the development of novel, complex, multidisciplinary, and targeted therapies, glioma therapy has not progressed much over the last decades. Therefore, there is an urgent need to develop novel patient-adjusted immunotherapies that actively stimulate antitumor T cells generating long-term memory and thus resulting in significant clinical benefits. This work aimed to investigate the efficacy and molecular mechanism of dendritic cell (DC) vaccines loaded with glioma cells undergoing immunogenic cell death (ICD) induced by photosens based photodynamic therapy (PS-PDT) and to identify reliable prognostic genes’ signatures for predicting patients’ overall survival (OS). The analysis of the transcriptional program of the ICD-based DC vaccine led to the identification of robust induction of Th17 signature when used as a vaccine, these DCs demonstrate retinoic acid receptor-related orphan receptor-γt dependent efficacy in orthotopic mouse model. Moreover, by comparative analysis of transcriptome program of the ICD-based DC vaccine with transcriptome data from the TCGA-low grade glioma (LGG) dataset the four-gene signature (CFH, GALNT3, SMC4, VAV3) associated with OS of glioma patients have been identified. This model has been validated on OS of CGGA-LGG, TCGA-glioblastoma multiforme (GBM) and CGGA-GBM datasets to determine whether it has a similar prognostic value. For this the sensitivity and specificity of the prognostic model for predicting the OS were evaluated by calculating the area under curve (AUC) of the time-dependent receiver operating characteristic (ROC) curve. The values of AUC for TCGA-LGG, CGGA-LGG, TCGA-GBM and CGGA-GBM were, respectively, 0.75, 0.73, 0.9 and 0.69 for predicting 5-year survival rates. These data open attractive prospects for improving glioma therapy by employing ICD and PS-PDT-based DC vaccines to induce Th17 immunity and to use this prognostic model to predict the OS of glioma patients.

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Integrated Multiomic Analysis Reveals disulfidptosis Subtypes in Glioblastoma

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