Malignant glioma is an aggressive brain cancer that responds poorly to chemotherapy. However, the molecular mechanism underlying the development of chemoresistance in glioma is not well-understood. In this study, we show that long non-coding RNA AC023115.3 is induced by cisplatin in human glioblastoma cells and that elevated AC023115.3 promotes cisplatin-induced apoptosis by inhibiting autophagy. Further mechanistic studies revealed that AC023115.3 acts as a competing endogenous RNA for miR-26a and attenuates the inhibitory effect of miR-26a on GSK3β, a proline-directed serine-threonine kinase that promotes the degradation of Mcl1, leading to an increase in GSK3β and a decrease in autophagy. Additionally, we discovered that AC023115.3 improves chemosensitivity of glioma cells to cisplatin by regulating the miR-26a-GSK3β-Mcl1 pathway. Thus, these data indicate that the AC023115.3-miR-26a-GSK3β signalling axis plays an important role in reducing the chemoresistance of glioma.

Long non-coding RNAs (lncRNAs) function as oncogenes or tumor suppressors, and are involved in mediating tumorigenesis and resistance to chemotherapy by altering the expression of genes at various levels. Accumulating evidence suggests that the maternally expressed gene 3 (MEG3) lncRNA serves an important role in a number of cancers. However, its functional role in mediating cisplatin‑induced apoptosis of glioma cells is unknown. To investigate the role of MEG3, the mRNA levels of MEG3 under cisplatin treatment were investigated by reverse transcription‑quantitative polymerase chain reaction, and the cell viability and apoptosis were examined by MTT assay, and flow cytometry analysis and western blotting, respectively. The results demonstrated that MEG3 expression levels were increased in U87 cells following cisplatin treatment. Elevated MEG3 by lentiviral transfection enhanced the chemosensitivity of U87 cells to cisplatin, whereas knockdown of MEG3 expression by small interfering RNA transfection increased the resistance of U87 cells to cisplatin. Subsequent mechanistic studies revealed that MEG3 eliminated autophagy induced by cisplatin. Decreased MEG3‑induced autophagy improved the chemosensitivity of U87 cells to cisplatin. The results present a novel therapeutic strategy for the treatment of patients with glioblastoma multiforme.

The proneural (PN) and mesenchymal (MES) subtypes of glioblastoma multiforme (GBM) are robust and generally consistent with classification schemes. GBMs in the MES subclass are predominantly primary tumors that, compared to PN tumors, exhibit a worse prognosis; thus, understanding the mechanism of MES differentiation may be of great benefit for the treatment of GBM. Nuclear factor kappa B (NF-κB) signaling is critically important in GBM, and activation of NF-κB could induce MES transdifferentiation in GBM, which warrants additional research. NUDT21 is a newly discovered tumor-associated gene according to our current research. The exact roles of NUDT21 in cancer incidence have not been elucidated. Here, we report that NUDT21 expression was upregulated in human glioma tissues and that NUDT21 promoted glioma cell proliferation, likely through the NF-κB signaling pathway. Gene set enrichment analysis, western blotting, and quantitative real-time reverse transcription polymerase chain reaction confirmed that NF-κB inhibitor zeta (NFKBIZ) was a downstream target affected by NUDT21 and that the MES identity genes in glioblastoma cells, CHI3L1 and FN1, were also differentially regulated. Our results suggest that NUDT21 is an upstream regulator of the NF-κB pathway and a potential molecular target for the MES subtype of GBM.

The laminin subunit alpha 2 (LAMA2) gene encodes an alpha 2 chain, which constitutes one of the subunits of laminin 2 (merosin) and laminin 4 (s-merosin). In the current study, we investigated the relationship between LAMA2 promoter methylation status and the invasiveness of clinically nonfunctioning pituitary adenomas (PitNETs). Specimens from patients with nonfunctioning PitNET were classified into three groups according to preoperative computed tomography (CT)/magnetic resonance imaging findings: a normal group (n = 6), non-invasive group (n = 11) and invasive group (n = 6). LAMA2 expression was assessed using quantitative real-time polymerase chain reaction (RT-qPCR) and western blotting, and the methylation status of the LAMA2 promoter region was observed using sodium bisulfite sequencing. Furthermore, 5-aza-2-deoxycytidine was used to explore the relationship between decreased LAMA expression and methylation in PitNET cells. According to the RT-qPCR and western blotting results, LAMA2 expression was downregulated in invasive PitNET, while the methylation of the LAMA2 promoter was increased. Methylation of the LAMA2 promoter decreased the expression of LAMA2. Thus, changes in LAMA2 expression due to promoter methylation were inversely correlated with the invasiveness of PitNET and the protein functions as a tumor suppressor. In addition, overexpression and demethylation of LAMA2 suppressed the invasion of PitNET cells, partially by exerting effects on the PTEN-PI3K/AKT signaling pathway and matrix metalloproteinase-9 (MMP-9). Furthermore, a xenograft model was also generated, and LAMA2 overexpression significantly suppressed tumor growth in vivo. Thus, LAMA2 expression and methylation patterns might be used as biomarkers to predict the prognosis of patients with PitNET.

Isoalantolactone (IATL), a sesquiterpene lactone compound, possesses many pharmacological and biological activities, but its role in glioblastoma (GBM) treatment is still unknown. The aim of the current study was to investigate the antiglioma effects of IATL and to explore the underlying molecular mechanisms. In the current study, the biological functions of IATL were examined by MTT, cell migration, colony formation, and cell apoptosis assays. Confocal immunofluorescence techniques, chromatin immunoprecipitation, and pull‐down assays were used to explore the precise underlying molecular mechanisms. To examine IATL activity and the molecular mechanisms by which it inhibits glioma growth in vivo, we used a xenograft tumor mouse model. Furthermore, Western blotting was used to confirm the changes in protein expression after IATL treatment. According to the results, IATL inhibited IKKβ phosphorylation, thus inhibiting both the binding of NF‐κB to the cyclooxygenase 2 (COX‐2) promoter and the recruitment of p300 and eventually inhibiting COX‐2 expression. In addition, IATL induced glioma cell apoptosis by promoting the conversion of F‐actin to G‐actin, which in turn activates the cytochrome c (Cyt c) and caspase‐dependent apoptotic pathways. In the animal experiments, IATL reduced the size and weight of glioma tumors in xenograft mice and inhibited the expression of COX‐2 and phosphorylated NF‐κB p65 in the transplanted tumors. In conclusion, the current study indicated that IATL inhibited the expression of COX‐2 through the NF‐κB signaling pathway and induced the apoptosis of glioma cells by increasing actin transformation. These results suggested that IATL could be greatly effective in GBM treatment.

The FE 2 computational homogenization method is a predictive multi-scale method without the need for constitutive assumptions and/or potential function postulates at the macro engineering scale. Instead, the effective micro-structural responses are extracted directly from a representative volume element (RVE) underlying each macro point. However, the FE 2 method is still computationally too expensive for most practical uses, since the micro-macro FE coupling is done at each loading step/iteration for the entire domain. To this end, the machine learning method has been utilized in the literature for the offline training of a surrogate model to predict the RVE homogenized response for general loading conditions. In this contribution, the neural network (NN) is incorporated into the macro finite element framework in a non-intrusive manner. This is termed as the FE-NN framework, in analogy to the FE 2 method. In general, online simulations in the FE-NN method is very efficient, with predictions matching closely to those obtained from reference direct numerical simulations (DNS). A bottleneck with the FE-NN framework, however, is the high computational cost associated with the data generation for offline NN model setup. In this paper, focusing on the FE-NN multi-scale framework for non-linear elastic deformation of heterogeneous materials, a sequential training strategy with knowledge transfer is proposed, to enable an efficient offline microscopic NN model setup.For a given target RVE, we first consider a simplified source RVE, where data can be generated rapidly, for the NN pre-training of surrogate model. The pre-trained network parameters are next downloaded to initialize the target NN surrogate model, followed by a fine-tuning training process, using only a small dataset generated by the computationally expensive high-fidelity RVE. The efficiency of the proposed sequential learning method over the conventional NN training, as well as, its excellent predictive capability for multi-scale analyses, are demonstrated for a multi-phase composite material. The proposed FE-NN-KT approach can be implemented easily without complicated pre-processing procedures, since the This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.