ArticleEuropean journal of medical research2025
A fibroblast-specific gene signature as a therapeutic target for glioblastoma developed based on the characteristics of tumor microenvironment.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis study identified fibroblast-specific genes to develop a RiskScore model to improve prognostic accuracy and guide personalized treatment in glioblastoma (GBM).
methodsWe analyzed fibroblast-specific signatures in the GSE273274 cohort using "Seurat" R package for scRNA-seq data processing. Fibroblast-related gene modules were identified via WGCNA, and functional enrichment was assessed with "clusterProfiler" package. A RiskScore model was established using univariate, Lasso Cox regression analysis, and "survival" package, validated by "timeROC" for receiver operator characteristic (ROC) curve. Finally, immune infiltration and drug sensitivity was evaluated applying "ESTIMATE," "TIMER," "MCPcounter," and "pRRophetic" packages. Experimental validation included qPCR for gene expression detection, and CCK-8, wound healing, and Transwell assays for functional measurement.
resultsThe scRNA-seq analysis identified nine cell types of cells, with fibroblasts elevated in the GBM group. Fibroblast signatures were linked to tumorigenesis, cytoskeleton remodeling, and regulation of neuronal development process that affected GBM invasion. A 6-gene RiskScore divided GBM patients into high- and low-risk groups in training and validation sets, with high-risk patients exhibiting poorer survival, elevated StromalScore, and negative correlations with the infiltration of neutrophils and B_cells. Moreover, high-risk patients demonstrated heightened sensitivity to Cisplatin, MG-132, AZ628, Dasatinib, CGP-60474, A-770041, TGX221, and Bortezomib. Finally, qPCR showed that the VWA1 was upregulated in GBM cells, while knock-down of VWA1 inhibited the cell proliferation, migration, and invasion activity.
conclusionWe constructed a RiskScore model for predicting the survival outcomes based on fibroblasts-related genes. These findings highlighted the role of fibroblasts in GBM development and offered six potential therapeutic targets (VWA1, DUSP6, LOXL1, IGFBP4, CYGB, and ZIC3) for GBM treatment. Additionally, immune infiltration analysis and drug sensitivity prediction further supported the model's utility in guiding personalized treatment of GBM.
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