Juglone is a natural pigment, which has cytotoxic effect against various human tumor cells. However, its cytotoxicity to glioma cells, especially to tumor stem-like cells (TSCs) has not been demonstrated. TSCs of glioma were enriched from U87 and two primary cells (SHG62, and SHG66) using serum-free medium supplemented with growth factors, including bFGF, EGF and B27. After treatment of juglone with gradient concentrations (0, 10, 20, and 40 μM), the viability and apoptosis of TSCs were evaluated by WST-8 assay and flow cytometry. Reactive oxygen species (ROS) was labeled by the cell-permeable fluorescent probe and detected with flow cytometry. ROS scavenger (NAC) and p38-MAPK inhibitor (SB203580) were applied to resist the cytotoxic effect. Caspase 9 cleavage and p38 phosphorylation (P-p38) were quantified by western blot. Juglone as well as temozolomide (TMZ) were administrated in intracranial xenografts and MR scan was performed every week to evaluate the anti-tumor effect in vivo. Juglone could obviously inhibit the proliferation of TSCs in glioma by decreasing cell viability (P < 0.01) and inducing apoptosis (P < 0.01), which was accompanied by increased caspase 9 cleavage in a dose-dependent manner (P < 0.01). In the meantime, juglone could generate ROS significantly and increase p38 phosphorylation (P < 0.01). In addition, pretreatment with ROS scavenger or p38-MAPK inhibitor could reverse juglone-induced cytotoxicity (P < 0.01). More importantly, juglone could also suppress tumor growth in vivo and improve the survival of U87-bearing mice compared with control (P < 0.05), although TMZ seemed to have better effect. Juglone could inhibit the growth of TSCs in gliomas through the activation of ROS-p38-MAPK pathway in vitro, and the anti-glioma effect was validated in vivo, which offers a potential therapeutic agent to gliomas.

Background: The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative. Methods: In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein–protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. Results: The gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). Conclusions: Our study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.