DataSheet_3_Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy, targeted therapy, and chemotherapy.pdf

IntroductionGlioblastoma (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.
MethodsTranscriptome 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.
ResultsPatients 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.
DiscussionDisulfidptosis-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.

引言
胶质母细胞瘤（Glioblastoma, GBM）因恶性程度高、治疗选择有限而面临严峻挑战。精准治疗需依据患者预后进行分型。二硫化物死亡（disulfidptosis）是一种新型细胞死亡机制，与异常糖代谢及二硫化物应激密切相关，尤其在高表达SLC7A11的肿瘤中。对二硫化物死亡的探索可为胶质母细胞瘤的精准诊断与治疗提供全新视角。

方法
本研究对2022年1月至2023年12月在天坛医院接受治疗的胶质母细胞瘤患者样本开展转录组测序。同时收集了CGGA及TCGA数据库的公开数据。基于二硫化物死亡特征的共识聚类（consensus clustering）将胶质母细胞瘤患者划分为两个亚型（DRGclusters）。随后分析了肿瘤免疫微环境、免疫治疗应答情况及药物敏感性。采用LASSO机器学习算法构建了基于8个基因的二硫化物死亡亚型预测模型，并在CGGA数据集上完成验证。

结果
DRGcluster A组患者的总生存期（overall survival, OS）显著优于DRGcluster B组。不同DRGcluster亚型在肿瘤免疫微环境及免疫治疗应答方面存在差异。该预测模型可有效将患者分为高风险组与低风险组。不同风险组患者的化疗及靶向治疗的半最大效应浓度（IC50）存在显著差异。

讨论
基于二硫化物死亡的分型有望成为胶质母细胞瘤的预后预测工具，可为解析肿瘤免疫微环境与治疗应答提供新见解。该预测模型有助于实现患者分层及个性化治疗方案选择，有望改善胶质母细胞瘤患者的临床结局。