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Aggressive glioma exhibits a poor survival rate. Increased tumor aggression is linked to both tumor cells and tumor-associated macrophages (TAMs), which induce pro-aggression, invasion, and metastasis. Imperatively, for effective treatment, it is important to target both glioma cells and TAMs. Haloperidol, a neuropsychotic drug, avidly targets the sigma receptor (SR), which is expressed in higher levels in both the cell types. Herein, we present the development of a novel cationic lipid-conjugated reduced haloperidol (±RHPC8), which aims to mediate the SR-targeted antiglioma effect. Hypothetically, ±RHPC8 would act simultaneously as an SR-targeting ligand and anticancer agent. As the blood-brain barrier (BBB) obstructs direct targeting of in situ glioma, we used BBB-crossing glucose-based carbon nanospheres (CSPs) to deliver ±RHPC8 within the glioma tumor-bearing mouse brain. The resultant ±RHPC8-CSP nanoconjugate targeted SR-expressing glioma cells. In both orthotopic and subcutaneous mouse tumor models, ±RHPC8-CSP prolonged survival and regressed tumors compared to other treated groups. Notably, ±RHPC8-CSP was significantly taken up by SR-expressing TAMs thus resulting in macrophage polarization from M2 to M1, as exhibited by markedly reduced expression of immunosuppressive cytokines released by TAMs, including TGF-β, IL-10, and VEGF. In conclusion, the designed ±RHPC8-CSP nanoconjugate presented an effective nanodrug delivery system for brain cancer treatment.

Glioma is a tumor in the brain and spinal cord originating in the glial cells that surround the nerve cells. Among several microRNAs reported, miRNA-363 is associated with human glioma. Based on miRNA-363 levels, the development and progression of glioma can be monitored. The current study used an interdigitated electrode sensor to monitor microRNA-363 levels, which indeed reflects the severity of glioma. The interdigitated electrode was generated using a photolithography technique followed by surface chemical modification carried out to insert miRNA-363 complementary oligo as the probe complexed with gold nanoparticles. The proposed sensor works based on the dipole moment between two electrodes, and when molecular immobilization or interaction occurs, the response by the signal output changes. The changes in the target microRNA-363 sequence were standardized to identify glioma. The limit of detection of miRNA-363 was 10 fM with an R 2 value of 0.996 on the linear coefficient regression ranges between 1 fM and 100 pM. Furthermore, unrelated sequences failed to increase the response of the current with the complementary probe, indicating specific miRNA-363 detection on the interdigitated electrode. This study demonstrates the platform to be used for determining the presence of microRNA-363 in glioma and as the basis for other biomarker analyses., (© 2022 International Union of Biochemistry and Molecular Biology, Inc.)

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature., (© 2022. International Federation for Medical and Biological Engineering.)

Introduction: Cancer bioenergetics is an essential hallmark of neoplastic transformation. Warburg postulated that mitochondrial OXPHOS is impaired in cancer cells, leading to aerobic glycolysis as the primary metabolic pathway. However, mitochondrial function is altered but not entirely compromised in most malignancies, and that mitochondrial uncoupling is known to increase the carcinogenic potential and modifies treatment response by altering metabolic reprogramming. Our earlier study showed that transient DNP exposure increases glycolysis in human glioma cells (BMG-1). The current study investigated the persistent effect of DNP on the energy metabolism of BMG-1 cells and its influence on tumor progression in glioma xenografts., Methods: BMG-1 cells were treated with 2,4-dinitrophenol (DNP) in-vitro , to establish the OXPHOS-modified (OPM-BMG) cells. Further cellular metabolic characterization was carried out in both in-vitro cellular model and in-vivo tumor xenografts to dissect the role of metabolic adaptation in these cells and compared them with their parental phenotype., Results and Discussion: Chronic exposure to DNP in BMG-1 cells resulted in dual-state hyper-energy metabolism with elevated glycolysis ++ and OXPHOS ++ compared to parental BMG-1 cells with low glycolysis + and OXPHOS + . Tumor xenograft of OPM-BMG cells showed relatively increased tumor-forming potential and accelerated tumor growth in nude mice. Moreover, compared to BMG-1, OPM-BMG tumor-derived cells also showed enhanced migration and invasion potential. Although mitochondrial uncouplers are proposed as a valuable anti-cancer strategy; however, our findings reveal that prolonged exposure to uncouplers provides tumor growth advantage over the existing glioma phenotype that may lead to poor clinical outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rai, Singh, Pandey, Sah, Sah, Roy, Dwarakanath and Bhatt.)

Background: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex., Objective: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy., Methods: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method., Results: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured., Conclusion: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Long noncoding RNAs play influential roles in the progression of many types of human malignancies. The present study aimed to explore the prognostic value of long noncoding RNA FTX (FTX) on patients with glioma., Methods: FTX expression in glioma specimens and matched adjacent non-neoplasm specimens was examined by a quantitative real-time polymerase chain reaction assay. Furthermore, assays of the relationships between FTX expression and clinicopathologic characteristics of patients with glioma were also performed. Kaplan-Meier methods were applied for the assays of the overall survival (OS) and progression-free survival (PFS) of patients and Cox regression assays were used to analyze the clinical value of FTX used as a possible biomarker., Results: FTX levels were significantly up-regulated in glioma specimens compared to the paired non-neoplasm specimens (p < 0.01). Furthermore, high FTX expression in neoplasm tissues was dramatically associated with World Health Organization grade (p = 0.001) and Karnofsky Performance Score (p = 0.009). Kaplan-Meier assays with 187 patients revealed that patients with high level of FTX expression displayed poorer OS (p = 0.002) and PFS (p = 0.000). Subsequently, multivariable Cox regression analysis identified FTX expression as an independent prognostic factor of unfavorable survivals in glioma (OS: p = 0.001; PFS: p = 0.002)., Conclusions: These findings indicated that FTX may be a novel predictor for prognostic assessment of glioma patients. However, studies conducted with larger numbers of patients are essential to confirm our findings., (© 2020 The Authors. The Journal of Gene Medicine published by John Wiley & Sons Ltd.)

Inter-individual heterogeneity of tumors leads to non-effectiveness of unique therapy plans. This issue has caused a growing interest in the field of personalized medicine and its application in tumor growth evaluation. Accordingly, in this paper, a framework of personalized medicine is presented for growth prediction of brain glioma tumors. A convection-diffusion-reaction model is used as the patient-specific tumor growth model which is associated with multimodal magnetic resonance images (MRIs). Two parameters of intracellular area fraction (ICAF) and metabolic rate have been used to incorporate the physiological data obtained from medical images into the model. The framework is tested on the data of two cases of glioma tumors to document the approach; parameter estimation is made using particle swarm optimization (PSO) and genetic algorithm (GA) and the model is evaluated by comparing the predicted tumors with the observed tumors in terms of root mean square error of the ICAF maps (IRMSE), relative area difference (RAD) and Dice's coefficient (DC). Results show the differences of IRMSE, RAD and DC in 4.1 ∓ 1.15%, 0.099 ∓ 0.041 and 85.5 ∓ 7.5%, respectively. Survival times are estimated by assuming the tumor radius of 35 mm as the fatal burden. Results confirm that less-diffusive tumors lead to higher survival times. The represented framework makes it possible to personally predict the growth behavior of glioma tumors only based on patients' routine MRIs and provides a basis for modeling the personalized therapy and walking in the path of personalized medicine.

Glioblastoma is the most common form of brain cancer in adults that produces severe damage to the brain leading to a very poor survival prognosis. The standard of care for glioblastoma is usually surgery, as well as radiotherapy followed by systemic temozolomide chemotherapy, resulting in a median survival time of about 12 to 15 months. Despite these therapeutic efforts, the tumor returns in the vast majority of patients. When relapsing, statistics suggest an imminent death dependent on the size of the tumor, the Karnofsky Performance Status, and the tumor localization. Following the standard of care, the administration of Bevacizumab, inhibiting the growth of the tumor vasculature, is an approved medicinal treatment option approved in the United States, but not in the European Union, as well as the recently approved alternating electric fields (AEFs) generator NovoTTF/Optune. However, it is clear that regardless of the current treatment regimens, glioma patients continue to have dismal prognosis and novel treatments are urgently needed. Here, we describe different approaches of recently developed therapeutic glioma brain cancer vaccines, which stimulate the patient's immune system to recognize tumor-associated antigens (TAA) on cancer cells, aiming to instruct the immune system to eventually attack and destroy the brain tumor cells, with minimal bystander damage to normal brain cells. These distinct immunotherapies may target particular glioma TAAs which are molecularly defined, but they may also target broad patient-derived tumor antigen preparations intentionally evoking a very broad polyclonal antitumor immune stimulation.

Purpose: The identification of a brain tumor imaged with PET or SPECT is usually performed with visual inspection of an expert medical clinician. However an automated diagnostic of such images hasn't been established or applied. In this study, we explored the possibility of establishing an automated statistical analysis for the diagnosis of glioma by means of IPA-SPECT data., Methods: On the basis of a dataset of 100 patients that have undergone MRI and IPA-SPECT acquisition, in this work, we identify an automated workflow. Three different approaches were explored: I. statistical non-parametric mapping analysis (SnPM), II. statistical non-parametric analysis with an increased number of permutations due to sign-flipping function (PALM) and III. statistical parametric analysis (SPM). The automated methods were compared with the visual inspection., Results: The study proved PALM and SPM approaches to have a high diagnostic power. Compared to the parametric methods, the non-parametric method is the mathematically correct approach for the problem in question. If we take the high resolution structural MRI information into account, the diagnostic power of PALM was not significantly inferior to the visual inspection (P = 0.5150), showing an area under the ROC curve (AUC) smaller only by less than 3%., Conclusions: The automated diagnostic method based on statistical inference, here applied to diagnose glioma tumors in IPA-SPECT data, seems to be a promising tool that can support the visual investigation in nuclear medicine. Moreover in the foreseeable future, the presented methodology has a big potential in various application like localization of active tumor tissues in surgical resection or stereotactic radiosurgery., (© 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)