Glioblastoma multiforme (GBM) was shown to harbor therapy-resistant cancer stem cells that were major causes of recurrence. PDGFR (platelet-derived growth factor receptor) and c-Kit (stem cell factor receptor) signaling play important roles in initiation and maintenance of malignant glioma. This study demonstrated that long-term culture with imatinib mesylate, the tyrosine kinase inhibitor against PDGFR and c-Kit resulted in reduced cancer stem cell ability in glioblastoma cells through cell differentiation. Derived from RG glioblastoma cells co-cultured with imatinib for 3 months, RG-IM cells showed distinct properties of cell cycle distribution and morphology in addition to significantly decreased ability to form aggregates and colonies in vitro and tumorigenicity in vivo. Increased expression of GFAP (astrocyte marker) and class III β-tubulin isotype (Tuj1, neuron marker) were detected with morphology like neurons or astrocytes in RG-IM cells. Furthermore, decreased expression of stem cell markers, i.e., CD133, Oct-3/4, nestin, and Bmi1, and increased terminal neural cell markers, GFAP, Tuj1, etc., were identified in RG-IM at the mRNA level. All these markers were changed in RG cells when PDGFRB and c-Kit expression were double knocked down by siRNA. Cell differentiation agent, all-trans retinoic acid (ATRA) caused similar effect as that with imatinib in RG cells, while adding PDGF-B and SCF in RG-IM resulted in cell dedifferentiation to some extent. Moreover, differentiation in RG cells treated by imatinib or ATRA was mainly driven by MAPK signaling pathways. In summary, continuous inhibition on PDGFR and c-Kit signaling disturbed glioma stem cells biology in subsets of GBM cells and may have potentials in clinical applications.

The aim of the present study was to analyse the antiproliferative effects and mechanisms of action of protein kinase inhibitors (PKIs) in human glioblastoma multiforme (GBM) cells with different epidermal growth factor receptor (EGFR) and phosphatase and tensin homologue (PTEN) status. The GBM cell models were established by transfection of plasmids carrying wild-type EGFR, mutated EGFRvIII or PTEN and clonal selection in U87MG cells. Phosphatidylinositol 3-kinase (PI3-K)/AKT pathway-focused gene profiles were examined by real-time polymerase chain reaction-based assays, protein expression was evaluated by western blotting and the antiproliferative effects of PKI treatment were determined by the 3-(4,5-dimethyl-2 thiazoyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay in GBM cells. The cell model with intact PTEN and low EGFR levels was the most sensitive to treatment with the EGFR inhibitor erlotinib, whereas the model with EGFRvIII was the most resistant to treatment with the mitogen-activated protein kinase kinase inhibitor U0126. The dual PI3-K and mammalian target of rapamycin (mTOR) inhibitor PI103 had the most potent antiproliferative effects against all GBM cells tested. Following simultaneous stimulation of AKT and extracellular signal-regulated kinase, rapamycin concentrations> 0.5 nmol/L failed to exhibit a further growth inhibitory effect. Concurrent inhibition of mTOR and ribosomal protein s6 activity may underlie the inhibition of GBM proliferation by PKI. In conclusion, overexpression of EGFR or EGFRvIII, accompanied by a loss of PTEN, contributed to the activation of multiple intracellular signalling pathways in GBM cells. Rigorous examination of biomarkers in tumour tissues before and after treatment may be necessary to determine the efficacy of PKI therapy in patients with GBM.

<div>Abstract<p>Recently, microRNAs (miRNA), small noncoding RNAs, have taken center stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA target–dysregulated network (MTDN) to prioritize novel disease miRNAs. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, 3 miRNA target regulations are verified to hold in prostate cancer cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis. <i>Mol Cancer Ther; 10(10); 1857–66. ©2011 AACR</i>.</p></div>

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