Home > In process > Sphingolipid-associated signature unveils TIMP1-driven temozolomide resistance and guides stratified therapy in glioblastoma. > print

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024 7 _ |a 10.3389/fimmu.2026.1753274
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037 _ _ |a DZNE-2026-00364
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Lyu, Feng
|0 P:(DE-2719)9003386
|b 0
|e First author
|u dzne
245 _ _ |a Sphingolipid-associated signature unveils TIMP1-driven temozolomide resistance and guides stratified therapy in glioblastoma.
260 _ _ |a Lausanne
|c 2026
|b Frontiers Media
336 7 _ |a article
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520 _ _ |a Glioblastoma (GBM) remains the most prevalent and aggressive primary central nervous system (CNS) malignancy; however, the clinical efficacy of the preferred chemotherapeutic agent, Temozolomide (TMZ), is severely compromised by innate and acquired resistance. Sphingolipid metabolism acts as a pivotal regulator of GBM cell fate, and the imbalance of the 'sphingolipid rheostat' is intimately linked to TMZ resistance. This provides potential targets for developing novel prognostic models to inform stratified treatment risk strategies, while offering a promising entry point for TMZ chemosensitization and stratified drug combinations.We integrated single-cell and bulk transcriptomics from TCGA and GEO. Through a multi-dimensional framework combining Weighted Gene Co-expression Network Analysis (WGCNA), differential expression profiling, Cox regression, and machine learning, we identified candidate genes associated with the molecular landscape coupled with sphingolipid dysregulation and TMZ sensitivity in GBM to construct a reliable prognostic model. We verified mRNA expression of model genes and protein expression of TIMP1 in clinical specimens via RT-qPCR and tissue microarrays (TMA), respectively. Furthermore, we functionally characterized the core target, TIMP1, via lentiviral knockdown in U87 cells, employing Transwell, CCK-8, and IC50 assays to evaluate its impact on malignancy and, crucially, its capacity to modulate TMZ chemosensitization.Single-cell analysis stratified GBM samples into distinct metabolic subclasses, revealing significant metabolic heterogeneity. Integrating TCGA and GEO profiles with WGCNA-based multi-dimensional intersection, we identified 95 candidate genes, refined via Cox regression and machine learning into a potent six-gene model (MXRA8, TIMP1, TREM1, S100A4, RMI2, IRF7) reflecting critical axes of extracellular matrix (ECM) remodeling, inflammation, and DNA repair. We delineated the model's role in shaping an immune-excluded tumor microenvironment (TME) characterized by stromal remodeling, T-cell exhaustion and functional impairment of natural killer (NK) cell subsets, while uncovering specific therapeutic vulnerabilities for distinct risk subgroups. Experimental validation confirmed widespread upregulation of core targets in clinical specimens. Functionally, TIMP1 knockdown significantly suppressed proliferation and invasion. Most importantly, silencing TIMP1 effectively restored sensitivity to TMZ (chemosensitization).This study establishes and validates a robust GBM prognostic model integrating the sphingolipid-associated molecular landscape with chemotherapy resistance. It provides a comprehensive perspective on the interplay among sphingolipid dysregulation, immune evasion, TMZ resistance, and the critical functional role of TIMP1. Beyond enabling precise patient stratification, this model highlights specific therapeutic vulnerabilities, offering a translational framework for developing combinatorial strategies to target the sphingolipid regulatory network and overcome GBM chemoresistance.
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650 _ 7 |a TIMP1
|2 Other
650 _ 7 |a glioblastoma
|2 Other
650 _ 7 |a pharmacogenomics
|2 Other
650 _ 7 |a prognostic model
|2 Other
650 _ 7 |a sphingolipid metabolism
|2 Other
650 _ 7 |a temozolomide resistance
|2 Other
650 _ 7 |a tumor microenvironment
|2 Other
650 _ 7 |a Sphingolipids
|2 NLM Chemicals
650 _ 7 |a Temozolomide
|0 YF1K15M17Y
|2 NLM Chemicals
650 _ 7 |a Tissue Inhibitor of Metalloproteinase-1
|2 NLM Chemicals
650 _ 7 |a TIMP1 protein, human
|2 NLM Chemicals
650 _ 7 |a Antineoplastic Agents, Alkylating
|2 NLM Chemicals
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Glioblastoma: drug therapy
|2 MeSH
650 _ 2 |a Glioblastoma: genetics
|2 MeSH
650 _ 2 |a Glioblastoma: metabolism
|2 MeSH
650 _ 2 |a Glioblastoma: pathology
|2 MeSH
650 _ 2 |a Sphingolipids: metabolism
|2 MeSH
650 _ 2 |a Temozolomide: pharmacology
|2 MeSH
650 _ 2 |a Temozolomide: therapeutic use
|2 MeSH
650 _ 2 |a Drug Resistance, Neoplasm: genetics
|2 MeSH
650 _ 2 |a Tissue Inhibitor of Metalloproteinase-1: genetics
|2 MeSH
650 _ 2 |a Tissue Inhibitor of Metalloproteinase-1: metabolism
|2 MeSH
650 _ 2 |a Brain Neoplasms: drug therapy
|2 MeSH
650 _ 2 |a Brain Neoplasms: genetics
|2 MeSH
650 _ 2 |a Brain Neoplasms: metabolism
|2 MeSH
650 _ 2 |a Gene Expression Regulation, Neoplastic
|2 MeSH
650 _ 2 |a Antineoplastic Agents, Alkylating: pharmacology
|2 MeSH
650 _ 2 |a Antineoplastic Agents, Alkylating: therapeutic use
|2 MeSH
650 _ 2 |a Cell Line, Tumor
|2 MeSH
650 _ 2 |a Prognosis
|2 MeSH
650 _ 2 |a Gene Expression Profiling
|2 MeSH
650 _ 2 |a Transcriptome
|2 MeSH
700 1 _ |a Wu, Jingjing
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700 1 _ |a Qi, Ji
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700 1 _ |a Wang, Gege
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700 1 _ |a Xie, Liqing
|0 P:(DE-2719)9002974
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700 1 _ |a Wang, Zhicong
|0 P:(DE-2719)9002782
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773 _ _ |a 10.3389/fimmu.2026.1753274
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