Purpose Glioblastoma (GBM) is the most aggressive primary brain tumor with poor outcomes despite surgery, radiotherapy (RT), and temozolomide (TMZ). Monitoring relies on imaging, which is confounded by pseudoprogression and provides limited molecular insight. Circulating biomarkers may offer a minimally invasive approach to track disease and treatment effects. Methods We performed targeted longitudinal proteomic profiling of plasma from post-surgery GBM patients ( n = 11) at baseline (pre-radiotherapy (RT)), after RT with concomitant TMZ, and after adjuvant TMZ, alongside healthy controls ( n = 15), using the NULISAseq CNS panel covering 131 proteins related to neurodegeneration, neuroinflammation, and vascular health. Differential expressions were assessed using volcano plots, hierarchical clustering, paired statistical comparisons and longitudinal trends analysis. Results At baseline, GBM plasma showed elevated markers of neuroaxonal and glial injury, extracellular matrix regulators, and inflammatory signaling compared to controls. Several established neurodegeneration and Alzheimer’s disease- related biomarkers were also higher in GBM plasma versus controls. Following radiotherapy and concomitant TMZ, cytokines including IL10, IL5, and IL33 increased, indicating systemic immune activation, while BDNF showed a modest decline. Adjuvant TMZ treatment attenuated radiotherapy-induced cytokine elevations and reduced neuronal injury markers, suggesting therapy-driven reduction of inflammatory and neurodegenerative signals. Longitudinal analysis confirmed dynamic shifts in specific proteins across baseline, radiotherapy, and TMZ phases. Conclusion High-sensitivity plasma proteomics suggested therapy-induced changes in CNS-specific and immune markers in GBM, highlighting candidate biomarkers that can be further developed for real-time monitoring of treatment response. These findings suggest candidate circulating markers that may complement imaging for monitoring disease biology and therapeutic response, and warrant validation in larger cohorts.
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Kumar et al. (2026) studied this question.
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