Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Simple Summary The conventional central nervous system (CNS) tumor diagnostic methods, especially the invasive intracranial surgical tissue sample collecting, imposes a heavy burden on both patients and healthcare providers. We aimed to explore the potential role of serum-derived small extracellular vesicles (sEVs) in diagnosing CNS tumors through Raman spectroscopic analyses. A relevant number of clinical samples (138) were obtained from four patient groups, namely glioblastoma multiforme, brain metastasis of non-small-cell lung cancer, meningioma, and lumbar disc herniation as controls. After the isolation and Raman measurements of sEV-sized particles, the Principal Component Analysis-Support Vector Machine algorithm was performed on the Raman spectra for pairwise classifications. The groups compared were distinguishable with 80-95% sensitivity and 80-90% specificity. Our results support that Raman spectroscopic analysis of sEV-sized particles is a promising liquid-biopsy-based method that could be further developed in order to be applicable in the diagnosis of CNS tumors. Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.