Identification of predictive markers in the cerebrospinal fluid of patients with glioblastoma - N. E. Arnotskaya; T. I. Kushnir; I. A. Kudryavtsev; A. A. Mitrofanov; A. Kh. Bekyashev; V. E. Shevchenko (Успехи молекулярной онкологии, 2023)
Introduction. Glioblastoma  (GB) is not yet curable despite recent advances in the treatment of other malignant solid tumors. The management of GB is based solely on histopathological features, imaging of the tumor and its genomic analysis (somatic mutations in the isocitrate dehydrogenase genes, methylation status of the O6-methylguanine-DNA methyltransferase gene promoter). To adapt the treatment to the most recent tumor evolution, molecular information should be received regularly throughout the course of therapy. However, tumor tissue is often not available for diagnosis as the disease progresses. In this regard, the development of less invasive methods, such as analysis of the proteome of biological fluids of patients, is of particular interest. Cerebrospinal fluid (CSF) is an important source disease biomarkers to monitor the presence and progression of the disease.Aim. To identify proteomic predictive biomarkers in the CSF of patients with GB.Materials and methods. During the study, samples of patients’ CSF samples, high-resolution proteomic mass spectrometry, modern biochemical methods and bioinformatic technologies were used.Results. For the first time, the analysis of proteomes of CSF samples of patients with GB obtained before and 7 days after the removal of the primary tumor was carried out. Potential biomarkers of GB have been identified. After their validation using open databases, 11 proteomic predictive markers of GB (S100A9,  S100A8, PLA2G15, PPIB, LTBP2, VIM, LAMB1, STC1, NRP1, COL6A1, HSPA5)  were selected and their role in the molecular mechanisms of gliomagenesis was assessed. Conclusion. The proposed panel of proteomic predictive CSF biomarkers in GB patients can be further used in the development of test systems for assessing the effectiveness of therapy and early detection of disease relapses.