Functional Blockade of Small GTPase RAN Inhibits Glioblastoma Cell Viability

Abstract

Glioblastoma, the most common malignant tumor in the brain, lacks effective treatments and is currently incurable. To identify novel drug targets for this deadly cancer, the publicly available results of RNA interference screens from the Project Achilles database were analyzed. Ten candidate genes were identified as survival genes in 15 glioblastoma cell lines. RAN, member RAS oncogene family (RAN) was expressed in glioblastoma at the highest level among all candidates based upon cDNA microarray data. However, Kaplan-Meier survival analysis did not show any correlation between RAN mRNA levels and patient survival. Because RAN is a small GTPase that regulates nuclear transport controlled by karyopherin subunit beta 1 (KPNB1), RAN was further analyzed together with KPNB1. Indeed, GBM patients with high levels of RAN also had more KPNB1 and levels of KPNB1 alone did not relate to patient prognosis. Through a Cox multivariate analysis, GBM patients with high levels of RAN and KPNB1 showed significantly shorter life expectancy when temozolomide and promoter methylation of O⁶-methylguanine DNA methyltransferase were used as covariates. These results indicate that RAN and KPNB1 together are associated with drug resistance and GBM poor prognosis. Furthermore, the functional blockade of RAN and KPNB1 by importazole remarkably suppressed cell viability and activated apoptosis in GBM cells expressing high levels of RAN, while having a limited effect on astrocytes and GBM cells with undetectable RAN. Together, our results demonstrate that RAN activity is important for GBM survival and the functional blockade of RAN/KPNB1 is an appealing therapeutic approach.

Keywords: KPNB1; RAN; cell survival; glioblastoma; glioblastoma prognosis; glioblastoma treatment; importazole.

Figure 1

Analysis of RNAi screen results from Project Achilles. RNAi screen results were retrieved from Project Achilles. Candidate genes were selected based on the following criteria: (1) Genes are targeted by two or more different shRNAs; (2) The fold changes of shRNA loss (log2) are lower than −3; (3) The average fold changes of shRNA loss (log2) are lower than −4. Ten candidates were selected. The fold changes of shRNA loss (log2) in each GBM cell lines are shown in (A) and the average fold changes of shRNA loss are shown in (B). Red lines indicate cut-off numbers ^*P < 0.05.

Figure 2

Expression of candidate genes in GBM. (A) cDNA microarray data were retrieved from the BioGPS database. The intensities of probes detecting candidate mRNAs in GBM cell lines were divided by those in astrocytes, yielding fold changes (GBM/Astrocyte). Fold changes of NHP2L1, PSMD1, RAN, RPS7, and UBB were above 1 (red line), suggesting that levels of these candidate mRNAs were high in GBM cell lines. Among these candidates, RAN levels in GBM are the highest. Error bars represent standard deviations from six GBM cell lines. (B) RAN protein levels in GBM cell lines. RAN and GAPDH proteins were detected in 7 GBM cell lines as indicated using immunoblotting. Band intensities were quantified using Image J. Fold changes (RAN/GAPDH) were obtained by dividing intensities of RAN with those of GAPDH. (C) RAN protein levels in primary GBM cells.

Figure 3

Kaplan-Meier analysis of RAN expression and GBM patient survival. (A) Survival curve of GBM patients with different levels of RAN. Results were retrieved from the GlioVis database. Survival curves of classical (B), mesenchymal (C), and proneural (D) GBM patients with different levels of RAN were retrieved from the Glioblastoma Bio Discovery Portal. Log-rank P-values are shown.

Figure 4

Correlation between levels of RAN and KPNA2 or KPNB1 in GBM. (A) Levels of RAN mRNA in astrocytes and GBM cell lines. Data were retrieved from the BioGPS database. The intensities of probes that detect RAN mRNA are shown. (B) KPNA2 mRNA levels in astrocytes and GBM cell lines. (C) KPNB1 mRNA levels in astrocytes and GBM cell lines. (D) Correlation between mRNA levels of RAN and KPNA2 in GBM cell lines. (E) Correlation between mRNA levels of RAN and KPNB1. A linear regression model was used. R square (R²) is the coefficient of determination.

Figure 5

GBM patients with more RAN and KPNB1 exhibit MGMT-dependent TMZ resistance and have shorter life expectancies. Survival curves of GBM patients with different levels of KPNA2 (A) or patients with different levels of KPNB1 (B) were retrieved from the Human Protein Atlas. Log-rank P-values are shown. (C) Cox univariate and multivariate analyses of GBM patients with different levels of RAN and/or KPNB1. Data were retrieved from the TCGA database and re-analyzed using JMP software. Hazard ratios (HRs) that determine chances of death are shown. P-values indicate the statistical significance of HRs. TMZ treatment (TMZ) and promoter methylation status of MGMT (MGMT) were used as covariates. (D) MGMT promoter methylation in GBM patients expressing different levels of RAN. P < 0.05 indicates that GBM patients with high levels of RAN often have an unmethylated MGMT promoter.

Figure 6

Functional blockade of RAN by importazole induces growth inhibition and activates apoptosis in RAN-expressing GBM cells. (A) Viability of GBM cells expressing different levels of RAN and astrocytes when treated with importazole. Cells were incubated with DMSO (light blue bars) or 12.5 μM importazole (dark blue bars) for 3 days. Cell viability was determined using the MTS viability assay. Percentages of viability were obtained by dividing the MTS absorbances of importazole-treated cells with those of DMSO-treated cells. RAN+: RAN-positive; RAN–: RAN-negative. Statistical significance between DMSO and importazole in each cell line was determined using a student t test. #P > 0.05; ^**P < 0.01; ^***P < 0.001. (B) Viability of primary GBM cells when treated with importazole. Primary GBM cell lines VTC-103 (RAN+; red line) and VTC-037 (RAN–; blue line) were incubated with importazole at different concentrations ranging from 0 to 25 μM. P value that determines the statistical significance between responses of VTC-103 and VTC-037 to importazole at different doses was obtained using a two-way ANOVA analysis. (C) Viability of RAN+ SF-295 cells when treated with importazole at different time points. RAN+ SF-295 cells were treated with importazole at different doses ranging from 0 to 50 μM for 3 or 6 days. P-value that determines the statistical significance between different time points was obtained using a two-way ANOVA analysis. (D) Importazole-induced apoptosis in astrocytes and GBM cells expressing different levels of RAN. Cells were incubated with DMSO or 12.5 μM importazole for 3 days. Apoptosis was assessed using the caspase 3/7 activity assay. Fold changes of caspase 3/7 activity were obtained by dividing luminescence intensities of importazole-treated cells with those of DMSO-treated cells. P-value was obtained using the student t-test. Standard deviations (error bars) were derived from three independent experiments.

Figure 7

The model of action of RAN in glioblastoma. RAN and its partner KPNB1 regulate nuclear import to promote glioblastoma cell survival and to induce drug resistance in patients (Left). Importazole blocks interactions between RAN and KPNB1, thereby inhibiting nuclear import. The consequences of this blockade are induction of cell death and growth inhibition in glioblastoma (Right).