Reproducible transcriptional modules define glioblastoma ecosystems across independent cohorts.

Glioblastoma (GBM) comprises a complex ecosystem of malignant, immune, vascular and neural transcriptional states. However, it remains difficult to determine which gene expression programmes are reproducibly recovered across independent cohorts and profiling platforms, because programme-level analyses are sensitive to cohort composition, technical context and factorization rank. Here, we analyzed three public GBM datasets--GLASS and IVYGAP bulk RNA-seq cohorts and the HEILAND Visium spatial transcriptomics cohort--to examine whether deconvolution-derived programmes could be organized into shared cross-cohort modules. Integrating 279 programmes inferred by consensus non-negative matrix factorization identified eight transcriptional communities, including myeloid immune microenvironment, neuronal and synaptic, oligodendrocyte and myelin, developmental, tumour-associated mesenchymal or hypoxic, proliferative, and ciliated or ependymal-like modules, as well as one cohort-restricted community. Community activity showed coherent associations with independent annotations: the myeloid community correlated with ESTIMATE immune score and inversely with tumour purity; the oligodendrocyte and myelin community was reduced in recurrent tumours; and ciliated or ependymal-like and neuronal communities showed modest exploratory associations with overall survival. Spatial projection onto Visium data provided qualitative support for the histological coherence of several modules, while also highlighting the limits of spot-level interpretation. Together, these results provide a proof-of-concept that cross-cohort integration can recover recurrent transcriptional structure across heterogeneous GBM datasets and offer an interpretable framework for comparing gene expression programmes while preserving cohort-specific signal and uncertainty in biological assignment.