Integrative transcriptomics and network analysis reveals core genes driving meningioma pathogenesis and clinical outcomes

Meningiomas are the most common primary CNS tumors, often managed conservatively due to their benign histology. Molecular phenotyping is considered the current frontier in meningioma classification to predict progression and guide clinical management of adjuvant therapies. While several biomarkers have been studied, a definitive molecular prognostic panel has yet to be established—a gap this study seeks to address. Using patient-resected meningioma tumor tissues paired with normal meninges, transcriptomic analysis identified a subset of significantly differentially-expressed-genes(DEG) linked to cellular proliferation and immune suppression in the tumor microenvironment. Graph-theory analysis revealed a rich-club organization in the gene networks, with highly connected core nodes. Core gene expression levels significantly correlated with tumor WHO grading, achieving an ROC-AUC of 0.8 for distinguishing low-grade(grade-1) from high-grade(grades-2&3) tumors(P < 0.001). For clinical outcomes, core gene expression achieved an AUC of 0.96 for predicting tumor recurrence(P < 0.001) and 0.74 for patient survival(P = 0.018). This study employs an analytical pipeline integrating transcriptomic analysis of meningiomas with a graph-theory approach to identify core regulators within pathological tumor networks. This refined method facilitated the discovery of phenotype-encoding biomarkers, which accurately predicted histological grade and clinical outcomes. The identified core markers provide insights into meningioma-specific pathophysiological pathways and offer potential targets for therapeutic intervention. Overall design: RNA-seq profiling of human resected meningiomas and compared to normal meninges.