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The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches., (© 2024. The Author(s).)

Objectives: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease., Methods: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve., Results: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively)., Conclusions: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions., Clinical Relevance Statement: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans., Key Points: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Three-dimensional (3D) contrast-enhanced T 1 -weighted flow-sensitive black-blood (CE-T 1 WI FSBB) is a newly developed black blood sequence by adding motion probing gradient pulses to gradient echo (GRE) sequences, which has important value for the preoperative assessment of tumor brain blood supply vessels and intratumoral microbleeds., Purpose: To compare 3D CE-T 1 WI FSBB and 3D contrast-enhanced fast spin echo (FSE) sequence for T 1 WI for preoperative assessment of blood vessels and microbleeds in brain tumors and to investigate the correlation between visible vessels and microbleeds., Study Type: Prospective., Subjects: One hundred and seventy-five patients with brain tumors, 65 were male, 110 were female. Including histologically confirmed 73 meningiomas, 23 schwannomas, 20 gliomas, 7 hemangioblastomas, 5 metastases, 2 lymphomas, 2 hemangiopericytomas, 2 germ cell tumors, 1 craniopharyngioma, and 1 cholesteatoma., Field Strength/sequence: A 3-T, CE-T 1 WI FSBB, GRE; 3-T, CE-T 1 WI, FSE., Assessment: Three neuroradiologists counted the number of intratumoral vessels on CE-T 1 WI and CE-T 1 WI FSBB images separately, and they counted the number of intratumoral microbleeds on CE-T 1 WI FSBB images. Brain tumors were classified into grade I, grade II, and grade IV according to the World Health Organization (WHO) grading. Differences in the ability of CE-T 1 WI FSBB and CE-T 1 WI to display intratumoral vessels were compared. The mean counts of three observers were used to study the correlation between vessels and microbleeds., Statistical Tests: Two-way random intraclass correlation coeficient (ICC) was used for inter-reader agreement regarding intratumoral vessel and microbleed counts, and the linear regression analysis (with F-test) was used to study the correlation between intratumoral vessels and microbleeds based on CE-T 1 WI FSBB (α = 0.05)., Results: Inter-reader agreements for intratumoral vessel count on CE-T 1 WI (ICC = 0.93) and CE-T 1 WI FSBB (ICC = 0.92), and the agreement for intratumoral microbleed count on CE-T 1 WI FSBB (ICC = 0.99) were excellent. There were statistically significant differences in intratumoral vessel counts between CE-T 1 WI and CE-T 1 WI FSBB using Mann-Whitney U -test: image readers could identify more intratumoral vessels on CE-T 1 WI FSBB images, particularly for meningiomas, schwannomas, gliomas, and WHO grade I tumors. The number of intratumoral vessels had a significant positive effect on the number of intratumoral microbleeds (microbleeds = 5.024 + 1.665 × vessels; F = 11.51)., Data Conclusion: More intratumoral vessels could potentially be identified using a 3D CE-T 1 WI FSBB sequence compared to a CE-T 1 WI sequence, and the number of intratumoral vessels showed a positive linear relationship with the number of intratumoral microbleeds, which might suggest that brain tumors with rich blood supply were more prone to intratumoral microbleeds., Evidence Level: 2 TECHNICAL EFFICACY: Stage 3., (© 2022 International Society for Magnetic Resonance in Medicine.)

Purpose: Neuroplasticity can partially compensate for the neurological deficits caused by brain tumors. However, the structural plasticity of the brain caused by brain tumors is not fully understood. This study aimed to assess the structural plasticity of the contralesional hemisphere in patients with frontal low-grade gliomas (LGGs)., Methods: A total of 25 patients with left frontal LGGs (LFLGGs), 19 patients with right frontal LGGs (RFLGGs), and 25 healthy controls (HCs) were enrolled in this study. High-resolution structural T1-weighted imaging and fluid attenuation inversion recovery were performed on all participants. Voxel-based morphometry (VBM) analysis was used to detect differences in the brain structural plasticity between patients with unilateral LGGs and HCs., Results: VBM analysis revealed that compared with HCs, the gray matter volume (GMV) of the contralesional putamen and amygdala was significantly smaller and larger in the patients with RFLGGs and LFLGGs, respectively, while the GMVs of the contralesional cuneus and superior temporal gyrus (STG) were significantly larger in the patients with LFLGGs. The surviving clusters of the right hemisphere included 1357 voxels in the amygdala, 1680 voxels in the cuneus, 384 voxels in the STG, and 410 voxels in the putamen. The surviving clusters of the left hemisphere were 522 voxels in the amygdala and 320 voxels in the putamen., Conclusion: The unilateral frontal LGGs are accompanied by structural plasticity in the contralesional cortex and vary with tumor laterality. Contralesional structural reorganization may be one of the physiological basis for functional reorganization or compensation in the frontal LGGs., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Circular RNAs (circRNAs) have been shown to be essential for the emergence and growth of different cancers. However, further research is required to validate the function of circRNA in glioblastoma (GBM)., Methods: CircNDC80 expression in both normal brain tissues (NBTs) and glioma tissues was determined using real-time PCR. The impact of circNDC80 on GBM cell proliferation, migration, and invasion was then confirmed by CCK-8, colony formation, EdU incorporation, Transwell, and wound healing assays. To determine how circNDC80 affects the capacity of glioma stem cells (GSCs) to maintain their stemness and self-renewal, a CellTiter-Glo assay, clonogenic assay and extreme limiting dilution assay were utilized. To ascertain the impact of circNDC80 in vivo, intracranial xenograft models were established., Results: When compared to NBT, glioblastoma tissue had a higher level of circNDC80 expression. In functional assays, circNDC80 promoted glioblastoma cell proliferation, migration, and invasion, while sustaining the stemness and fostering the self-renewal of glioma stem cells. In addition, a dual luciferase reporter assay and circRIP were used to verify that circNDC80 simultaneously affects the expression of ECE1 mRNA by sponging miR-139-5p, and a rescue experiment was used to verify the above results further., Conclusions: According to our research, circNDC80 is an oncogenic factor that promotes glioblastoma through the miR-139-5p/ECE1 pathway. This implies that circNDC80 may be employed as a novel therapeutic target and a possible predictive biomarker., (© 2023. The Author(s).)

Objective: Preoperative diagnosis of diffuse midline glioma, H3 K27-altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), as well as midline primary CNS lymphoma (PCNSL) in adults, is challenging but crucial. The aim of this study was to develop a model for predicting these three entities using machine learning (ML) algorithms., Methods: Thirty-three patients with DMG-A, 35 with DMG-W, and 35 with midline PCNSL were retrospectively enrolled in the study. Radiomics features were extracted from contrast-enhanced T1-weighted MR images. Two radiologists evaluated the conventional MRI features of the tumors, such as shape. Patient age, tumor volume, and conventional MRI features were considered clinical features. The data set was randomly stratified into 70% training and 30% testing cohorts. Predictive models based on the clinical features, radiomics features, and integration of clinical and radiomics features were established through ML. The performances of the models were evaluated by calculating the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Subsequently, 10 patients with DMG-A, 10 with DMG-W, and 12 with PCNSL were enrolled from another institution to validate the established models., Results: The predictive models based on clinical features, radiomics features, and the integration of clinical and radiomics features through the support vector machine algorithm had the optimal accuracies in the training, testing, and validation cohorts, and the accuracies in the testing cohort were 0.871, 0.892, and 0.903, respectively. Age, 2 radiomics features, and 3 conventional MRI features were the 6 most significant features in the established integrated model., Conclusions: The integrated prediction model established by ML provides high discriminatory accuracy for predicting DMG-A, DMG-W, and midline PCNSL in adults.

Glioblastoma (GBM) is the most common primary intracranial tumor in the central nervous system, and resistance to temozolomide is an important reason for the failure of GBM treatment. We screened out that Solute Carrier Family 2 Member 10 (SLC2A10) is significantly highly expressed in GBM with a poor prognosis, which is also enriched in the NF-E2 p45-related factor 2 (NRF2) signalling pathway. The NRF2 signalling pathway is an important defence mechanism against ferroptosis. SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes. The micropeptide encoded by LINC02381 may be a potential treatment strategy for GBM, but the underlying mechanism of its function is not precise yet. We put forward the hypothesis: "The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM." This study innovatively used machine learning for micropeptide to provide personalized diagnosis and treatment plans for precise treatment of GBM, thereby promoting the development of translational medicine. The study aimed to help find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments., (© 2022. The Author(s).)

As the most common aggressive malignant tumor in the central nervous system, glioma is still an insurmountable disease in the neural system. The mechanism of carcinogenesis in glioma remains largely unclear. In the present study, we identified a dysregulated long non-coding RNA (lncRNA) solute carrier family 8 member A1 antisense RNA 1 (SLC8A1-AS1) associated with glioma based on The Cancer Genome Atlas (TCGA) data. A validation experiment was conducted to confirm a high expression level of lncRNA SLC8A1-AS1 in glioma tissues. Down-regulation of lncRNA SLC8A1-AS1 suppressed the proliferation, colony formation, migration, and invasion of glioma cells in vitro and in vivo. Moreover, lncRNA SLC8A1-AS1 silencing decreased the activity of the Wnt/β-catenin pathway and suppressed the epithelial to mesenchymal transition (EMT) in glioma cells. These findings collectively provide novel insights into the function and mechanism of lncRNA SLC8A1-AS1 in the pathogenesis of glioma and highlight its potential as a therapeutic target for glioma intervention., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Glioma is a common type of malignant brain tumour with high mortality and relapse rate. However, the molecular mechanisms of glioma development have not been clarified. Differentially expressed genes in normal brain tissues and glioma tissues, low-grade and high-grade gliomas were screened out with GEO database analysis. We found that KLHDC8A (Kelch domain-containing 8A) expression level was significantly increased in high-grade glioma tissues and that high KLHDC8A expression was closely related with poor prognosis. Function assays indicated that KLHDC8A knockdown inhibited proliferation, migration and invasion, blocked the cell cycle and promoted apoptosis in glioma cells. Mechanistically, KLHDC8A regulated various functions in glioma by directly mediating Bcl2, BAX, p21, CDK2, MMP2 transcription and ERK and P38 MAPK activation. KLHDC8A overexpression enhances glioma tumorgenesis such as cell proliferation, migration and invasion. The ERK and P38 MAPK which activated by KLHDC8A overexpression could be reversed by U0126 and SB203580, respectively. Meanwhile, stimulation of lactate which produced by glycolysis is responsible for induction of KLHDC8A expression. Collectively, we demonstrated that KLHDC8A plays an important role in tumorgenesis of glioma, suggesting that it is a promising prognostic marker and a potential therapy target for the treatment of glioma., (© 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Ezrin is overexpressed in a variety of neoplastic cells and involved in the later stages of tumor progression and metastasis. Ezrin expression can be regulated at both the transcriptional and post-transcriptional levels. We used a combination of bioinformatics and experimental techniques to demonstrate that the miR-204 is a direct negative regulator of ezrin. Overexpression of miR-204 mimics decreased the activity of a luciferase reporter containing the ezrin 3' UTR and led to repression of ezrin protein. In contrast, ectopic expression of miR-204 inhibitor elevated ezrin expression. We also show that miR-204 is down-regulated in a panel of glioma tissues and in high invasive glioma cell lines we examined. Moreover, miR-204 mimics significantly reduced glioma cell migration and invasion, while miR-204 inhibitor generated the opposite results. Finally, overexpression of miR-204 and knockdown of ezrin reduced glioma cell invasion, and these effects could be rescued by re-expression of ezrin. These findings reveal that miR-204 could be partly due to its inhibitory effects on glioma cell migration and invasion through regulating ezrin expression.

Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, grades, and molecular characteristics of gliomas can lead to different behavioral deficits and prognosis, which are closely related to patients' quality of life and associated with neuroplasticity. Some advanced magnetic resonance imaging (MRI) technologies can explore the neuroplasticity of structural, topological, biochemical metabolism, and related mechanisms, which may contribute to the improvement of prognosis and function in glioma patients. In this review, we summarized the studies conducted on structural and topological plasticity of glioma patients through different MRI technologies and discussed future research directions. Previous studies have found that glioma itself and related functional impairments can lead to structural and topological plasticity using multimodal MRI. However, neuroplasticity caused by highly heterogeneous gliomas is not fully understood, and should be further explored through multimodal MRI. In addition, the individualized prediction of functional prognosis of glioma patients from the functional level based on machine learning (ML) is promising. These approaches and the introduction of ML can further shed light on the neuroplasticity and related mechanism of the brain, which will be helpful for management of glioma patients. [ABSTRACT FROM AUTHOR]