Integrative bulk and single-cell analyses implicate ASL in MIF-associated myeloid programs in glioblastoma

Arginine metabolism is implicated in glioblastoma (GBM) progression and immune regulation, but prognostic biomarkers with defined cellular context remain insufficiently characterized. Eighteen arginine metabolism-related genes were analyzed in TCGA GBM. LASSO Cox regression with tenfold cross-validation and subsequent univariate/multivariate Cox models were used to identify prognostic genes. Patients were stratified by optimal cutoffs for Kaplan-Meier analysis, with validation in CGGA and GEO datasets. Predictive performance was evaluated by ROC and decision curve analysis (DCA). scRNA-seq data from five GBM samples were processed using Seurat, arginine-metabolism activity was quantified at single-cell resolution. Myeloid cells were subsetted for subclustering, with state dynamics examined by pseudotime, RNA velocity, and CellChat. ASL, FAH, and NAGS were prioritized as prognostic candidates, with ASL characterized in the TCGA cohort as a prognostic indicator explicitly associated with high-risk molecular subtypes such as IDH-wildtype and 1p/19q non-codeletion.. High expression of ASL predicted shorter overall survival in TCGA and was further validated in CGGA and GEO datasets. ASL showed the best clinical prognosis discrimination and net benefit in DCA. In scRNA-seq, 38,263 high-quality cells were used for downstream analyses. ASL expression and arginine-metabolism activity were enriched in monocyte/macrophage populations. Trajectory analyses placed MKI67⁺ monocytes upstream and showed ASL decreasing along pseudotime. RNA velocity supported directional transitions. CellChat highlighted macrophage migration inhibitory factor (MIF)-, MHC-II-, and SPP1-associated signaling within myeloid networks, consistent with Spearman correlation patterns between ASL and MIF-axis genes. ASL is identified as an arginine metabolism-associated prognostic signal that is particularly tied to high-risk molecular subgroups of GBM, and exhibits strong correlations with myeloid-centered immune programs and MIF-related communication features in the tumor microenvironment.