Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.

Background and Purpose: Contrast-enhanced MRI findings of leptomeningeal enhancement (LME) indicate dysfunction of the blood–brain barrier and can be seen in various neoplastic, infectious, and inflammatory conditions. To the best of our knowledge, systematic evaluation of different patterns of LME remains limited. This case series aims to describe and compare MRI patterns of leptomeningeal enhancement across diverse etiologies. Materials and Methods: We retrospectively evaluated six patients with known or suspected leptomeningeal disease on contrast-enhanced MRI studies of the brain and/or spine with regard to pattern, distribution, localization, and thickness of LME. Results: The patterns of neoplastic causes (glioblastoma, lymphoma, and adenocarcinoma) were characterized by diffuse, thick, and nodular LME, as well as frequently affected cranial nerve and nerve-root involvement. The infections caused various patterns: in cases of tuberculous meningitis, LME was focal and basal in nature, accompanied by parenchymal lesions; whereas in cases of HIV-related meningitis, LME was diffuse and exhibited a pseudocisternogram pattern. The inflammation (suspected neurosarcoidosis) revealed smooth-to-nodular LME with spinal and suprasellar involvement. Conclusions: Although LME patterns are not specific, systematic evaluation of enhancement characteristics may provide useful diagnostic clues and assist in narrowing the differential diagnosis.