Glioblastoma (GBM) is a devastating type of brain tumor, and current therapeutic treatments, including surgery, chemotherapy, and radiation, are palliative at best. The design of effective and targeted chemotherapeutic strategies for the treatment of GBM require a thorough analysis of specific signaling pathways to identify those serving as drivers of GBM progression and invasion. The Wnt/β-catenin and PI3K/Akt/mTOR (PAM) signaling pathways are key regulators of important biological functions that include cell proliferation, epithelial–mesenchymal transition (EMT), metabolism, and angiogenesis. Targeting specific regulatory components of the Wnt/β-catenin and PAM pathways has the potential to disrupt critical brain tumor cell functions to achieve critical advancements in alternative GBM treatment strategies to enhance the survival rate of GBM patients. In this review, we emphasize the importance of the Wnt/β-catenin and PAM pathways for GBM invasion into brain tissue and explore their potential as therapeutic targets.

Gliomas are the most frequent and deadly form of human primary brain tumors. Among them, the most common and aggressive type is the high-grade glioblastoma multiforme (GBM), which rapidly grows and renders patients a very poor prognosis. Meanwhile, cancer stem cells (CSCs) have been determined in gliomas and play vital roles in driving tumor growth due to their competency in self-renewal and proliferation. Studies of gliomas have recognized CSCs via specific markers. This review comprehensively examines the current knowledge of the most significant CSCs markers in gliomas in general and in glioblastoma in particular and specifically focuses on their outlook and importance in gliomas CSCs research. We suggest that CSCs should be the superior therapeutic approach by directly targeting the markers. In addition, we highlight the association of these markers with each other in relation to their cascading pathways, and interactions with functional miRNAs, providing the role of the networks axes in glioblastoma signaling pathways.

Cardiotoxicity is associated with doxorubicin (DOX), an effective anticancer drug. Apigenin has cardioprotective properties; it may be employed as a capping and reducing agent in synthesizing gold nanoparticles (AuNPs). This study examined the cardioprotective impact of AuNPs synthesized with apigenin (Api) in DOX-induced cardiotoxicity (DIC). Api-AuNPs were synthesized in a single pot without needing additional reagents for reducing gold ions or stabilizing the NPs. The cytotoxicity of Api-AuNPs on H9c2 heart cells was subsequently determined using the MTT assay. In the animal investigation, 40 male rats were randomly assigned to one of four groups: control, cardiotoxicity (DOX), DOX treated with apigenin (DOX + Api), or DOX treated with Api-AuNPs (DOX + Api-AuNPs). To examine heart function, echocardiography was conducted. Blood samples were obtained to evaluate injury indicators (Lactate dehydrogenase (LDH), creatine kinase MB (CK-MB), Cardiac Troponin I (cTn-I), Alanine transaminase (ALT), and Aspartate transaminase (AST)). The heart was removed under general anesthetic, weighed, and preserved in formalin solution. Six micrometer-thick cardiac tissue sections were stained with hematoxylin, eosin (H&E), and immunohistochemistry to identify cardiomyocyte apoptotic markers (Bax, Bcl-2, and caspase3). Api-AuNPs have an average size of 21.4 ± 11.6 nm and are stable in physiological environments. Api-AuNPs therapy substantially reduced body and heart weight loss compared to the DOX group. Injury indicators were reduced dramatically by Api-AuNPs treatment. Api-AuNPs inhibited myocardial apoptosis via modulating Bax, caspase3, and Bcl-2 and ameliorating tissue damage caused by DOX. Api-AuNPs' anti-apoptotic activities provide cardioprotection against DIC. It has the potential to reduce cardiotoxicity and boost myocardial performance.

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

Aggressive glioblastoma (GBM) has no known treatment as a primary brain tumor. Since the cancer is so heterogeneous, an immunosuppressive tumor microenvironment (TME) exists, and the blood–brain barrier (BBB) prevents chemotherapeutic chemicals from reaching the central nervous system (CNS), therapeutic success for GBM has been restricted. Drug delivery based on nanocarriers and nanotechnology has the potential to be a handy tool in the continuing effort to combat the challenges of treating GBM. There are various new therapies being tested to extend survival time. Maximizing therapeutic effectiveness necessitates using many treatment modalities at once. In the fight against GBM, combination treatments outperform individual ones. Combination therapies may be enhanced by using nanotechnology-based delivery techniques. Nano-chemotherapy, nano-chemotherapy–radiation, nano-chemotherapy–phototherapy, and nano-chemotherapy–immunotherapy for GBM are the focus of the current review to shed light on the current status of innovative designs.

Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible visual impairment in the elderly. The current management of nAMD is limited and involves regular intravitreal administration of anti-vascular endothelial growth factor (anti-VEGF). However, the effectiveness of these treatments is limited by overlapping and compensatory pathways leading to unresponsiveness to anti-VEGF treatments in a significant portion of nAMD patients. Therefore, a system view of pathways involved in pathophysiology of nAMD will have significant clinical value. The aim of this study was to identify proteins, miRNAs, long non-coding RNAs (lncRNAs), various metabolites, and single-nucleotide polymorphisms (SNPs) with a significant role in the pathogenesis of nAMD. To accomplish this goal, we conducted a multi-layer network analysis, which identified 30 key genes, six miRNAs, and four lncRNAs. We also found three key metabolites that are common with AMD, Alzheimer’s disease (AD) and schizophrenia. Moreover, we identified nine key SNPs and their related genes that are common among AMD, AD, schizophrenia, multiple sclerosis (MS), and Parkinson’s disease (PD). Thus, our findings suggest that there exists a connection between nAMD and the aforementioned neurodegenerative disorders. In addition, our study also demonstrates the effectiveness of using artificial intelligence, specifically the LSTM network, a fuzzy logic model, and genetic algorithms, to identify important metabolites in complex metabolic pathways to open new avenues for the design and/or repurposing of drugs for nAMD treatment.