Gliomas, one of the most severe malignant tumors of the central nervous system, have a high mortality rate and an increased risk of recurrence. Therefore, early glioma diagnosis and the control of treatment have great significance. The blood plasma samples of glioma patients, patients with skull craniectomy defects, and healthy donors were studied using terahertz time-domain spectroscopy (THz-TDS). An analysis of experimental THz data was performed by machine learning (ML). The ML pipeline included (i) THz spectra smoothing using the Savitzky–Golay filter, (ii) dimension reduction with principal component analysis and t-distribution stochastic neighborhood embedding methods; (iii) data separability analyzed using Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The ML models’ performance was evaluated by a k-fold cross validation technique using ROC-AUC, sensitivity, and specificity metrics. It was shown that tree-based ensemble methods work more accurately than SVM. RF and XGBoost provided a better differentiation of the group of patients with glioma from healthy donors and patients with skull craniectomy defects. THz-TDS combined with ML was shown to make it possible to separate the blood plasma of patients before and after tumor removal surgery (AUC = 0.92). Thus, the applicability of THz-TDS and ML for the diagnosis of glioma and treatment monitoring has been shown.

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The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.

Introduction: The study of brain tumors has shown that microRNAs can act as both oncogenes and tumor suppressors and, consequently, can be used as biomarkers for the diagnosis and prognosis of such tumors. Thus, big interest arises in the role of microRNA and its part in oncogenesis in the human brain to find key molecules that can act as tumor markers for diagnostic and prognostic purposes, as well as potential therapeutic agents.Study aim: The sim of this study was to assess histological, molecular, and genetic metrics in patients with supratentorial gliomas, and indicate diagnostic and prognostic abilities of microRNA usage as biomarkers of the grade of malignancy of the tumor.Materials and methods: Clinical and genetic studies were performed in 107 operated patients with supratentorial gliomas of different malignancies. The expression levels of 10 microRNAs (-16, -21¸ -31, -124, -125b, -181b, -191, -221, -223, and -451) were analyzed using real-time polymerase chain reaction (PCR). The results were analyzed statistically using Statistica 12.0 (Statistica, Hamburg, Germany) and GraphPad Prism 9 software (GraphPad Software Inc., Boston, Massachusetts, United States).Results: Based on a comprehensive statistical analysis involving the database of the clinical results of treatment of all 107 patients (combined treatment methods, quality of life, and survival) and microRNA expression levels, specific profiles of microRNA expression typical of different histotypes of gliomas of different malignancy were identified, the prognostic significance of the studied microRNAs as potential predictors of survival in patients with brain gliomas was determined, and microRNAs with the highest prognostic value were identified among them.

<p><strong>Aim.</strong> To identify novel microRNA markers as survival predictors in patients with supratentorial gliomas.<br /><strong>Methods.</strong> This study involved the analysis of tumour and normal brain tissue biopsy samples obtained from patients undergoing combination treatment for supratentorial gliomas of different World Health Organization (WHO) grades. Real-time polymerase chain reaction was used to determine the expression profiles of ten microRNAs, following comparison with clinical treatment results: tumour morphology, WHO grade, patient age, Karnofsky scale, treatment type, postsurgical survival rate and histological diagnosis. The mean age of surgically treated patients [62 (57.9%) males and 45 (42.1%) females] was 48.8 ± 14 years. There were 17 (16%), 30 (28%) and 60 (56%) patients with grade II, III and IV (glioblastoma) gliomas, respectively. Statistical analysis was performed using Statistica version 10.0 and GraphPad Prism version 5.<br /><strong>Results.</strong> Four microRNAs (miRNA-31, miRNA-21, miRNA-223 and miRNA-221) were strongly correlated with worse survival, when over-expressed, indicating their potential utility as survival predictors in glioma patients. Overexpression of these microRNAs in glioma tissue, lack of adjuvant therapy such as chemotherapy or radiotherapy and age &gt; 48 years were identified as factors for worse prognosis.</p><p><strong>Funding:</strong> This work was supported by the program of fundamental scientific research on the topic 0310-2019-0003.</p><p><strong>Conflict of interest:</strong> The authors declare no conflict of interest.</p>