ReviewBrain and behavior2026
Natural Compounds Targeting Oncogenic Signaling Networks in Glioblastoma: Molecular Mechanisms, Translational Advances, and Clinical Perspectives.
Review in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Authors and funding
14 authors.
Funding
Abstract
purposeGlioblastoma (GBM), the most aggressive and common primary brain tumor in adults, has a poor prognosis, rapid growth, and resistance to treatment. Abnormal activation of signaling pathways like PI3K/AKT/mTOR, MAPK/ERK, JAK/STAT, Wnt/β-catenin, NF-κB, and Notch facilitates uncontrolled proliferation, angiogenesis, invasion, and immune evasion. Conventional treatments are insufficient in modifying complex networks, necessitating the urgent need for novel multitargeted treatments. Natural compounds are increasingly being considered as potential treatments for GBM due to their ability to control multiple oncogenic pathways simultaneously and their lower toxicity compared to synthetic medicines. This review provides an integrated and translational perspective on GBM, differing from past reviews that focused on the anticancer effects of individual phytochemicals or specific signaling pathways. We discussed the relationship between natural compounds and key oncogenic signaling networks, focusing on GBM pathogenesis, blood-brain barrier penetration, nanotechnology-assisted delivery, and strategies combining standard therapies. Furthermore, this review critically distinguished in vitro, in vivo, and clinical evidence, focusing on bioavailability challenges, ongoing clinical trials, and future opportunities in precision oncology.
methodTo ensure the inclusion of the most appropriate articles in this review, an in-depth search was carried out on prominent medical, biological, and chemical databases, including Scopus, PubMed, and Web of Science. The search strategy employed combinations of Medical Subject Headings and Boolean operators, focusing on terms related to glioblastoma, natural compounds, and various molecular mechanisms involved in drug delivery, clinical trials, and diagnosis. FINDING: These natural compounds demonstrated potential in preclinical GBM models for inducing apoptosis, suppressing angiogenesis, modulating oxidative stress, and enhancing chemosensitivity. Additionally, some natural compounds can cross the blood-brain barrier, thereby increasing their translational significance. The use of molecular diagnostics, including biomarkers, genetic profiling, and advanced imaging, has significantly improved early identification and patient stratification. Specific compounds with strong translational potential, such as curcumin, resveratrol, EGCG, quercetin, berberine, and luteolin, are highlighted along with their main molecular targets and therapeutic effects. The integration of natural compounds into precision-based therapy approaches has been made possible.
conclusionFuture research aims to combine molecular diagnostics with phytochemical therapies, validate clinical trial outcomes, and enhance bioavailability with nanotechnology delivery systems. The combination of natural compounds shows potential for creating safe, effective, and pathway-specific treatments for GBM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.