Non-Invasive Detection and Monitoring of Glioblastoma Subtypes via Dual-Marker Plasma Proteomics

Abstract Background Glioblastoma (GBM) is the most common and lethal primary brain tumour in adults, characterized by rapid progression and profound molecular heterogeneity. Current diagnostic and monitoring strategies rely on neuroimaging and invasive tissue sampling, which are limited in their ability to capture dynamic disease states and subtype-specific biology. There is an unmet need for minimally invasive biomarkers that can enable reliable detection and longitudinal surveillance. In this study, we investigated whether plasma proteomic profiling could reflect tumour-intrinsic features and systemic responses, thereby supporting non-invasive classification and monitoring of GBM subtypes. Methods We performed integrative proteomic profiling of matched GBM tumour and plasma samples using tandem mass tag-labelled mass spectrometry and machine learning. Pathway and weighted gene co-expression network analyses were applied to identify systemic alterations. A dual-marker classifier was developed and validated using longitudinal aptamer-based profiling. Results Tumour proteomes exhibited extensive heterogeneity, while plasma profiles showed marked inter-patient homogeneity at both diagnosis and recurrence. Systemic changes were observed in inflammation, coagulation, and complement signalling pathways. A dual-marker plasma classifier comprising coagulation factor IX (F9) and cartilage oligomeric matrix protein (COMP) distinguished GBM from healthy controls with high accuracy (area under the curve AUC = 0.96), maintaining performance in recurrent disease (AUC = 0.97). Longitudinal analysis revealed divergent trajectories: F9 levels declined post-treatment, while COMP increased, consistent with therapeutic response and disease progression. Conclusions Our findings support the development of a dual-marker, proteomics-based plasma assay for non-invasive GBM detection and real-time monitoring. This approach has the potential to complement imaging and inform therapeutic decision-making.