{"title":"神经胶质瘤亚型分型和精准治疗的综合多组学和机器学习框架。","authors":"Yi Ding, Zhaiyue Xu, Wenjing Hu, Peng Deng, Mian Ma, Jiandong Wu","doi":"10.1038/s41598-025-09742-0","DOIUrl":null,"url":null,"abstract":"<p><p>Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 glioblastomas and 419 IDH-mutant WHO-grade 2/3 diffuse gliomas) together with two validation cohorts (CGGA n = 970; GEO n = 110). Using the MOVICS framework, we derived three integrative molecular subtypes-CS1, CS2 and CS3. Ten machine-learning algorithms in MIME were benchmarked, and the Lasso + SuperPC combination yielded an eight-gene GloMICS (Glioma Multi-Omics Consensus Signature) prognostic score. The subtypes display discrete biology: CS1 (astrocyte-like) is characterized by glial lineage features, immune-regulatory signaling, and relatively favorable prognosis; CS2 (basal-like/mesenchymal) shows epithelial-mesenchymal transition, stromal activation, and high immune infiltration, including PD-L1 expression; CS3 (proneural-like/IDH-mut metabolic) exhibits metabolic reprogramming (OXPHOS, hypoxia) and an immunologically cold tumour microenvironment (TME). CS2 is associated with the worst overall survival, whereas CS1 confers the most favourable outcome. Dual checkpoint blockade or T-cell-rejuvenation strategies may benefit CS2 tumours, while metabolic inhibitors could prove effective in CS3. The eight-gene GloMICS score outperformed 95 published prognostic models (C-index 0.74-0.66 across TCGA, CGGA and GEO). TME deconvolution, immune checkpoint profiling and TIDE analysis indicate that high-risk GloMICS tumours harbour immunosuppressive fibroblast-rich niches and exhausted CD8⁺ T cells. Connectivity-map screening nominated dabrafenib, irinotecan and three additional CTRP/PRISM compounds as candidate agents for the high-risk group. Our study establishes robust glioma subtypes and a transferable prognostic signature, offering a blueprint for biomarker-guided therapy. Future work should include single-cell and immunohistochemical validation of subtype hallmarks and prospective trials stratified by GloMICS score.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"24874"},"PeriodicalIF":3.9000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246227/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.\",\"authors\":\"Yi Ding, Zhaiyue Xu, Wenjing Hu, Peng Deng, Mian Ma, Jiandong Wu\",\"doi\":\"10.1038/s41598-025-09742-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. 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CS2 is associated with the worst overall survival, whereas CS1 confers the most favourable outcome. Dual checkpoint blockade or T-cell-rejuvenation strategies may benefit CS2 tumours, while metabolic inhibitors could prove effective in CS3. The eight-gene GloMICS score outperformed 95 published prognostic models (C-index 0.74-0.66 across TCGA, CGGA and GEO). TME deconvolution, immune checkpoint profiling and TIDE analysis indicate that high-risk GloMICS tumours harbour immunosuppressive fibroblast-rich niches and exhausted CD8⁺ T cells. Connectivity-map screening nominated dabrafenib, irinotecan and three additional CTRP/PRISM compounds as candidate agents for the high-risk group. Our study establishes robust glioma subtypes and a transferable prognostic signature, offering a blueprint for biomarker-guided therapy. 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引用次数: 0
摘要
胶质瘤是一种高度异质性和侵袭性的脑肿瘤,需要对其分子和免疫学景观进行综合理解。我们收集了575例TCGA弥漫性胶质瘤患者的多组学数据(156例idh野生型who级4级胶质母细胞瘤和419例idh突变型who级2/3级弥漫性胶质瘤)以及两个验证队列(CGGA n = 970;GEO n = 110)。利用MOVICS框架,我们得到了三个整合分子亚型cs1、CS2和CS3。对MIME中的10种机器学习算法进行了基准测试,Lasso + SuperPC组合产生了8个基因的GloMICS(胶质瘤多组学共识签名)预后评分。这些亚型表现出离散的生物学特性:CS1(星形细胞样)具有神经胶质谱系特征、免疫调节信号和相对良好的预后;CS2(基底样/间充质)表现为上皮-间充质转化、间质活化和高免疫浸润,包括PD-L1表达;CS3 (proneural-like/IDH-mut metabolic)表现出代谢重编程(OXPHOS、缺氧)和免疫冷肿瘤微环境(TME)。CS2与最差的总生存期相关,而CS1则带来最有利的结果。双检查点阻断或t细胞年轻化策略可能有益于CS2肿瘤,而代谢抑制剂可能对CS3有效。8基因GloMICS评分优于95个已发表的预后模型(TCGA、CGGA和GEO的c指数为0.74-0.66)。TME反卷积、免疫检查点分析和TIDE分析表明,高风险的GloMICS肿瘤含有富含免疫抑制成纤维细胞的壁龛和耗尽的CD8 + T细胞。连接图筛选提名达非尼、伊立替康和另外三种CTRP/PRISM化合物作为高风险组的候选药物。我们的研究建立了强大的胶质瘤亚型和可转移的预后特征,为生物标志物引导的治疗提供了蓝图。未来的工作应该包括对亚型标记的单细胞和免疫组织化学验证,以及根据GloMICS评分分层的前瞻性试验。
Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.
Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 glioblastomas and 419 IDH-mutant WHO-grade 2/3 diffuse gliomas) together with two validation cohorts (CGGA n = 970; GEO n = 110). Using the MOVICS framework, we derived three integrative molecular subtypes-CS1, CS2 and CS3. Ten machine-learning algorithms in MIME were benchmarked, and the Lasso + SuperPC combination yielded an eight-gene GloMICS (Glioma Multi-Omics Consensus Signature) prognostic score. The subtypes display discrete biology: CS1 (astrocyte-like) is characterized by glial lineage features, immune-regulatory signaling, and relatively favorable prognosis; CS2 (basal-like/mesenchymal) shows epithelial-mesenchymal transition, stromal activation, and high immune infiltration, including PD-L1 expression; CS3 (proneural-like/IDH-mut metabolic) exhibits metabolic reprogramming (OXPHOS, hypoxia) and an immunologically cold tumour microenvironment (TME). CS2 is associated with the worst overall survival, whereas CS1 confers the most favourable outcome. Dual checkpoint blockade or T-cell-rejuvenation strategies may benefit CS2 tumours, while metabolic inhibitors could prove effective in CS3. The eight-gene GloMICS score outperformed 95 published prognostic models (C-index 0.74-0.66 across TCGA, CGGA and GEO). TME deconvolution, immune checkpoint profiling and TIDE analysis indicate that high-risk GloMICS tumours harbour immunosuppressive fibroblast-rich niches and exhausted CD8⁺ T cells. Connectivity-map screening nominated dabrafenib, irinotecan and three additional CTRP/PRISM compounds as candidate agents for the high-risk group. Our study establishes robust glioma subtypes and a transferable prognostic signature, offering a blueprint for biomarker-guided therapy. Future work should include single-cell and immunohistochemical validation of subtype hallmarks and prospective trials stratified by GloMICS score.
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