Glioblastoma (GBM) is often resistant to conventional and targeted therapeutics. ERBB4 is expressed throughout normal brain and is an oncogene in several pediatric brain cancers; therefore, we investigated ERBB4 as a prognostic marker and therapeutic target in GBM. Using RT-qPCR, we quantified mRNA encoding total ERBB4 and known ERBB4 variants in GBM and non-neoplastic normal brain (NNB) samples. Using immunohistochemistry, we characterized the localization of total and phosphorylated ERBB4 (p-ERBB4) and EGFR protein in archived GBM samples and assessed their association with patient survival. Furthermore, we evaluated the effect of ERBB4 phosphorylation on angiogenesis and tumorigenicity in GBM xenograft models. Total ERBB4 mRNA was significantly lower in GBM than NNB samples, with the JM-a and CYT-2 variants predominating. ERBB4 protein was ubiquitously expressed in GBM but was not associated with patient survival. However, high p-ERBB4 in 11% of archived GBM samples, independent of p-EGFR, was associated with shorter patient survival (12.0±3.2 months) than was no p-ERBB4 (22.5±9.5 months). Increased ERBB4 activation was also associated with increased proliferation, angiogenesis, tumorigenicity and reduced sensitivity to anti-EGFR treatment in xenograft models. Despite low ERBB4 mRNA in GBM, the functional effects of increased ERBB4 activation identify ERBB4 as a potential prognostic and therapeutic target.

Glioblastoma multiforme (GBM) is the most frequent and aggressive primary brain tumor. GBM is formed by a very heterogeneous astrocyte population, neurons, neovascularization and infiltrating myeloid cells (microglia and monocyte derived macrophages). The IQGAP1 scaffold protein interacts with components of the cytoskeleton, cell adhesion molecules, and several signaling molecules to regulate cell morphology and motility, cell cycle and other cellular functions. IQGAP1 overexpression and delocalization has been observed in several tumors, suggesting a role for this protein in cell proliferation, transformation and invasion. IQGAP1 has been identified as a marker of amplifying cancer cells in GBMs. To determine the involvement of IQGAP1 in the onco-biology of GBM, we performed immunohistochemical confocal microscopic analysis of the IQGAP1 protein in human GBM tissue samples using cell type-specific markers. IQGAP1 immunostaining and subcellular localization was heterogeneous; the protein was located in the plasma membrane and, at variable levels, in nucleus and/or cytosol. Moreover, IQGAP1 positive staining was found in podosome/invadopodia-like structures. IQGAP1⁺ staining was observed in neurons (Map2⁺ cells), in cancer stem cells (CSC; nestin⁺) and in several macrophages (CD31⁺ or Iba1⁺). Our results indicate that the IQGAP1 protein is involved in normal cell physiology as well as oncologic processes.

Background: Tumor microenvironment (TME) plays a crucial role in virtually every aspect of tumorigenesis of glioblastoma multiforme (GBM). The dysfunctional TME promotes drug resistance, disease recurrence and distant metastasis. Recent evidence indicates that exosomes released by stromal cells within TME may promote oncogenic phenotypes via transferring signaling molecules such as cytokines, proteins and microRNAs. Results: In this study, clinical GBM samples were collected and analyzed. We found that GBM-associated macrophages (GAMs) secreted exosomes which were enriched with oncomiR-21. Co-culture of GAMs (and GAM derived exosomes) and GBM cell lines resulted in the increased GBM cells’ resistance against temozolomide (TMZ) by upregulating pro-survival gene, PDCD4 and stemness markers Sox2, STAT3, Nestin and miR-21-5p and increased M2 cytokines, IL-6 and TGF-β1 secreted by GBM cells, promoting the M2 polarization of GAMs. Subsequently, pacritinib treatment suppressed GBM tumorigenesis and stemness; more importantly, pacritinib-treated GBM cells showed markedly reduced ability to secret M2 cytokines and reduced miR-21 enriched exosomes secreted by GAMs. Pacritinib-mediated effects were accompanied by a reduction of oncomiR miR-21-5p, by which tumor suppressor PDCD4 was targeted. We subsequently established a patient-derived xenograft models where mice bore patient GBM and GAMs. The treatment of pacritinib, and the combination of pacritinib/TMZ appeared to significantly reduce tumorigenesis of GBM/GAM PDX mice, overcome TMZ-resistance, and M2 polarization of GAMs. Conclusion: In summation, we showed that potential of pacritinib alone or in combination with TMZ for suppressing GBM tumorigenesis via modulating STAT3/miR-21/PDCD4 signaling. Further investigations are warranted for adopting pacritinib for the treatment of TMZ-resistant GBM in the clinical settings.

Background: Glioblastoma (GBM), a malignant grade IV tumor, is the most malignant brain tumor due to its hyper-proliferative and apoptosis-evading characteristics. The signal transducer and activators of transcription (STAT) family genes, including STAT3 and STAT5A, have been indicated to play important roles in GBM progression. Increasing number of reports suggest that Garcinol, a polyisoprenylated benzophenone and major bioactive component of Garcinia indica contains potent anti-cancer activities. Material & Methods: The present study investigated the anti-GBM effects of garcinol, focusing on the STAT3/STAT5A activation, using a combination of bioinformatics, in vitro, and ex vivo assays. Results: Our bioinformatics analysis of TCGA - GBM cohort (n=173) showed that STAT3 and STAT5A are preferentially elevated in primary and recurrent GBM, compared to non-tumor brain tissues, and is significantly correlated with reduced overall survival. In support, our immunohistochemical staining of a GBM cohort (n=30) showed an estimated 5.3-fold (p<0.001) elevation in STAT3 and STAT5A protein expression in primary and recurrent GBM versus the non-tumor group. In vitro, garcinol treatment significantly suppressed the proliferative, invasive, and migratory potential of U87MG or GBM8401 cells, dose-dependently. In addition, garcinol anticancer effect significantly attenuated the GBM stem cell-like phenotypes, as reflected by diminished ability of U87MG or GBM8401 to form colonies and tumorspheres and suppressed expression of OCT4 and SOX2. Furthermore, analysis on GBM transcriptome revealed an inverse correlation between the level of STAT3/5A and hsa-miR-181d. Garcinol-mediated anti-GBM effects were associated with an increased hsa-miR-181d/STAT3 and hsa-miR-181d/5A ratio. Conclusion: We present evidence of anti-GBM efficacy of garcinol mediated by enhancing the hsa-miR-181d/STAT3 and hsa-miR-181d/5A ratios in GBM cells. Our findings suggest a potential new therapeutic agent for combating aggressive GBM.

Glioblastoma is the most common and malignant primary brain tumor. Despite a century of research efforts, the survival of patients has not significantly improved. Currently, diagnosis is based on neuroimaging techniques followed by histopathological and molecular analysis of resected or biopsied tissue. A recent paradigm shift in diagnostics ranks the molecular analysis of tissue samples as the new gold standard over classical histopathology, thus correlating better with the biological behavior of glioblastoma and clinical prediction, especially when a tumor lacks the typical hallmarks for glioblastoma. Liquid biopsy aims to detect and quantify tumor-derived content, such as nucleic acids (DNA/RNA), circulating tumor cells (CTC), or extracellular vesicles (EV) in biofluids, mainly blood, cerebrospinal fluid (CSF), or urine. Liquid biopsy has the potential to overcome the limitations of both neuroimaging and tissue-based methods to identify early recurrence and to differentiate tumor progression from pseudoprogression, without the risks of repeated surgical biopsies. This review highlights the origins and time-frame of liquid biopsy in glioblastoma and points to recent developments, limitations and challenges of adding liquid biopsy to support the clinical management of glioblastoma patients.

The vacuolar proton translocating ATPase (V-ATPase) is a transmembrane multi-protein complex fundamental in maintaining a normal intracellular pH. In the tumoral contest, its role is crucial since the metabolism is mainly based on anaerobic glycolytic reactions. Moreover, the neoplastic cells use the V-ATPase to extrude chemotherapy drugs to the extra-cellular compartment, as a drug-resistance mechanism. In glioblastoma (GBM), the most malignant and incurable primary brain tumor, the expression of this pump is upregulated, making it a new possible therapeutic target. In this work, the bafilomycin A1 induced-inhibition of V-ATPase in patients-derived glioma stem cell (GSC) lines was evaluated together with temozolomide, the first-line line therapy against GBM. In contrast with previous published data, the proposed treatment did not overcome the resistance to the standard therapy. In addition, our data showed that nanomolar dosages of bafilomycin A1 lead to the blockage of the autophagy process and cellular necrosis, making the drug unusable in models that are more complex. Nevertheless, the increased expression of V-ATPase following bafilomycin A1 suggests a critical role of the proton pump in GBM stem component, encouraging the search for novel strategies to limit its activity in order to get around the resistance to the conventional therapy.

Glioblastoma multiforme (GBM) is a highly heterogeneous brain tumor with limited treatment options and a poor prognosis. Cancer stem cells (CSCs) have emerged as a critical factor in GBM resistance and management, contributing to tumor growth, heterogeneity, and immunosuppression. The transcription factor FOXM1 has been identified as a key player in the progression, spread, and therapy resistance of various cancers, including GBM. In this study, researchers conducted structure-based in silico screening to identify natural compounds that could target the DNA-binding domain (DBD) of the FOXM1 protein. Through molecular docking analyses, identified Silybin B as a potential inhibitor of FOXM1, exhibiting strong interaction with the protein. MD simulations were performed to validate the binding stability of the FOXM1-Silybin B complex. The study provides valuable insights into the potential of Silybin B as a FOXM1 inhibitor and its ability to induce senescence in GBM stem cells. These findings contribute to the development of structure-based design strategies for FOXM1 inhibitors and innovative therapeutic approaches for the treatment of Glioblastoma.

The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.

Information geometry (IG) is a fascinating combination of differential geometry and statistics where a Riemannian manifold's structure is applied to a statistical model. It may find numerous fascinating uses in the domains of theoretical neurology, machine learning, complexity, and (quantum) information theory, among others. (IG) aims to provide a differential-geometric perspective on statistical geodesic models' structure. In this case, IG, KD, and J-divergence (JD) are used to define the manifold of the Generalised Brownian Motion (GBM). Consequently, the geodesic equations (GEs) are devised, and GB information matrix exponential (IME) is presented. Moreover, for first time ever, the necessary and sufficient mathematical requirement that characterizes the developability of Generalized Brownian Motion(GBM) manifold is devised. Also, a novel sufficient and necessary conditions which characterizes the regions where the surface describing GBM is minimal is determined. Also, it is shown that GBM has a non-zero 0-Gaussian and Ricci curvatures. Consequently, this advances the establishment of a unified GBM- Relativistic Info-Geometric based analysis.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

Keywords: Artificial Neural Network (ANN), Support Vector Machine (SVM), Support Vector Regression (SVR), Lightweight Gradient Boosting Machines (Light GBM), Machine Learning, Solar Irradiance (SI), Solar forecasting

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.