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.

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.

Одномоментное тотальное удаление шванномы спинного мозга типа «песочные часы» с распространением в грудную клетку из заднего паравертебрального доступа без вскрытия плевральной полости (клиническое наблюдение)

Wealth of data indicates that microRNAs (miRNAs) are directly involved in carcinogenesis and that miRNA can, on their own, act as diagnostic and prognostic markers for various types of cancers, including gliomas. The aim of this study was to conduct a comparative analysis of expression profile for 10 microRNAs (miR-124, -125b, -16, -181b, -191, -21, -221, -223, -31, and -451) in surgical specimens of various hystotypes of glioimatissues vs adjacent normal tissues from the same patient (n = 77). The study identified specific microRNA expression profiles for different histotypes of tumors that are related to their degree of malignancy. We have outlined approaches to development of miRNA-based diagnostic and prognostic panel, which may be used to compensate for the lack of appropriate screening methods.

The numerous data show, that microRNA (miRNA) are direct participants of carcinogenesis. Also miRNA plays the role of a diagnostic and prognostic marker for different types of cancer, including gliomas. The aim of this research is to make the comparative analysis of 10 micro RNA (miR-124, -125b, -16, -181b, -191, -21, -221, -223, -31 and -451) expression profiles. The analysis was made for gliomas with different malignancy degree, then compared with the samples of the adjacent not changed tissues (n = 90). During the study the specific profiles of miRNA expression for various histotypes of tumors were revealed. It was determined, that miRNA acts as a predictor of patient survival in the cases with malignant supratentorial brain tumors. The diagnostic approaches based on miRNA expression profile were designed. It will help to determine the malignancy level and to predict the course of the disease.

Aim. To identify novel microRNA markers as survival predictors in patients with supratentorial gliomas.Methods. 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.Results. 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 > 48 years were identified as factors for worse prognosis. Received 14 May 2019. Revised 21 June 2019. Accepted 25 June 2019. Funding: This work was supported by the program of fundamental scientific research on the topic 0310-2019-0003. Conflict of interest: The authors declare no conflict of interest.