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Updated: Jun 30, 2025
Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy,
Xue Yang1, Zehao Cai1, Ce Wang1
1Department of Neuro-oncology Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Introduction:
Glioblastoma (GBM) presents significant challenges due to its malignancy and limited treatment options. Precision treatment requires subtyping patients based on prognosis. Disulfidptosis, a novel cell death mechanism, is linked to aberrant glucose metabolism and disulfide stress, particularly in tumors expressing high levels of SLC7A11. The exploration of disulfidptosis may provide a new perspective for precise diagnosis and treatment of glioblastoma.
Methods:
Transcriptome sequencing was conducted on samples from GBM patients treated at Tiantan Hospital (January 2022 - December 2023). Data from CGGA and TCGA databases were collected. Consensus clustering based on disulfidptosis features categorized GBM patients into two subtypes (DRGclusters). Tumor immune microenvironment, response to immunotherapy, and drug sensitivity were analyzed. An 8-gene disulfidptosis-based subtype predictor was developed using LASSO machine learning algorithm and validated on CGGA dataset.
Results:
Patients in DRGcluster A exhibited improved overall survival (OS) compared to DRGcluster B. DRGcluster subtypes showed differences in tumor immune microenvironment and response to immunotherapy. The predictor effectively stratified patients into high and low-risk groups. Significant differences in IC50 values for chemotherapy and targeted therapy were observed between risk groups.
Discussion:
Disulfidptosis-based classification offers promise as a prognostic predictor for GBM. It provides insights into tumor immune microenvironment and response to therapy. The predictor aids in patient stratification and personalized treatment selection, potentially improving outcomes for GBM patients.
Insights
Disulfidptosis, a novel cell death pathway, helps classify glioblastoma (GBM) patients into subtypes. This classification improves prognosis prediction and guides personalized treatment strategies for better outcomes.
Area of Science:
- Oncology
- Cell Death Mechanisms
- Molecular Biology
Background:
- Glioblastoma (GBM) is a highly malignant brain tumor with limited therapeutic options.
- Precision medicine necessitates patient stratification based on prognostic factors.
- Disulfidptosis, a cell death pathway linked to glucose metabolism and disulfide stress (especially in SLC7A11-high tumors), presents a novel avenue for GBM research.
Purpose of the Study:
- To investigate the potential of disulfidptosis as a basis for glioblastoma (GBM) patient subtyping.
- To develop and validate a disulfidptosis-based predictor for GBM prognosis and treatment response.
- To explore the relationship between disulfidptosis subtypes and the tumor immune microenvironment.
Main Methods:
- Transcriptome sequencing of GBM patient samples.
- Consensus clustering using disulfidptosis-related features to define subtypes (DRGclusters).
- Development and validation of an 8-gene predictor using LASSO regression and external datasets (CGGA).
Main Results:
- Two distinct GBM subtypes (DRGcluster A and B) were identified, with DRGcluster A showing improved overall survival.
- Significant differences in tumor immune microenvironment and immunotherapy response were observed between subtypes.
- The 8-gene predictor accurately stratified patients into high and low-risk groups, correlating with differential sensitivity to chemotherapy and targeted therapies.
Conclusions:
- Disulfidptosis-based classification provides a promising prognostic tool for glioblastoma.
- This approach offers insights into the tumor immune microenvironment and potential therapeutic responses.
- The developed predictor facilitates patient stratification for personalized treatment selection in GBM management.
