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. 2016 Apr 12;7(15):20140-51.
doi: 10.18632/oncotarget.7917.

Survival Kinase Genes Present Prognostic Significance in Glioblastoma

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Free PMC article

Survival Kinase Genes Present Prognostic Significance in Glioblastoma

Robin T Varghese et al. Oncotarget. .
Free PMC article

Abstract

Cancer biomarkers with a strong predictive power for diagnosis/prognosis and a potential to be therapeutic targets have not yet been fully established. Here we employed a loss-of-function screen in glioblastoma (GBM), an infiltrative brain tumor with a dismal prognosis, and identified 20 survival kinase genes (SKGs). Survival analyses using The Cancer Genome Atlas (TCGA) datasets revealed that the expression of CDCP1, CDKL5, CSNK1E, IRAK3, LATS2, PRKAA1, STK3, TBRG4, and ULK4 stratified GBM prognosis with or without temozolomide (TMZ) treatment as a covariate. For the first time, we found that GBM patients with a high level of NEK9 and PIK3CB had a greater chance of having recurrent tumors. The expression of CDCP1, IGF2R, IRAK3, LATS2, PIK3CB, ULK4, or VRK1 in primary GBM tumors was associated with recurrence-related prognosis. Notably, the level of PIK3CB in recurrent tumors was much higher than that in newly diagnosed ones. Congruent with these results, genes in the PI3K/AKT pathway showed a significantly strong correlation with recurrence rate, further highlighting the pivotal role of PIK3CB in the disease progression. Importantly, 17 SKGs together presented a novel GBM prognostic signature. SKGs identified herein are associated with recurrence rate and present prognostic significance in GBM, thereby becoming attractive therapeutic targets.

Keywords: PIK3CB; glioblastoma; prognosis; survival kinase genes; tumor recurrence.

Conflict of interest statement

CONFLICTS OF INTEREST

None.

Figures

Figure 1
Figure 1. A loss-of-function screen identifies SKGs in U87MG cells
(A) A diagram illustrating the loss-of-function screen. In principle, a short hairpin (sh) RNA of a potential SKG that is depleted overtime is under-represented in P7, compared to P0. (B) Candidate SKGs with at least 2-fold reduction of shRNA sequence copies in P7 compared to those in P0. (C) Viability assay. 63 individual shRNAs of SKGs were introduced into U87MG cells. The cell viability was measured using the MTS cell viability assay. Candidate SKG shRNAs were normalized to non-silencing (NS) shRNA. The cutoff line is 60%. (D) Knockdown efficiency. The knockdown of SKGs by their shRNAs was assessed using the quantitative RT-PCR. The cut-off line is 2-fold reduction.
Figure 2
Figure 2. SKGs are enriched in GBM
(A) mRNA levels of SKGs in U87MG. Gene expression data was retrieved from the BioGPS database. Fold changes of SKG mRNAs in U87MG cells compared to those in astrocytes were shown. The cut-off line was 1.5-fold increase. SKGs with a high level of mRNA in U87MG cells were labeled as grey bars. (B) mRNA levels of SKGs in glioblastoma tissues. Gene expression data was retrieved from the Oncomine database. The mRNA levels of SKGs in glioblastoma were compared to those in normal brain tissues. Results from two studies (Bredel brain #2 and TCGA brain) were shown. *P < 0.05. SKGs with a statistically significant increase of mRNA in glioblastoma in either study were labeled as grey bars. (C) Protein levels of SKGs in glioma. Protein expression data was retrieved from the Human Protein Atlas database. The percentages of glioma cases with a higher level of SKGs compared to the normal brain were shown. SKGs with more protein in glioma were labeled as grey bars. PRPSAP1 and PIK3CB showed a negative result in all these analyses.
Figure 3
Figure 3. Expression of SKGs correlates with GBM prognosis
The Kaplan Meier analysis was performed using the TCGA GBM datasets. The survival curves of MGMT (A), CDCP1 (B), CDKL5 (C), LATS2 (D), PRKAA1 (E), STK3 (F), and ULK4 (G) with Log-Rank P values were shown. The Log-Rank P values of other SKGs that showed no statistical significance were listed in (H). The media survival time of prognostic SKGs was shown in (I).
Figure 4
Figure 4. Expression of SKGs correlates with the incidence rate and prognosis of recurrent GBMs
(A) Recurrence rate. GBM recurrence rates in patients with a high level (white bars) or a low level of SKGs (grey bars) were shown. The statistical difference between two groups was determined by the Fisher's exact test. (B) KM survival analysis. The relationship of SKGs with the prognosis of GBMs patients with recurrent tumors was analyzed. The KM Log-Rank P values were shown. The Log-Rank P values less than 0.05 were highlighted in grey. VRK1 showed an effect on patient survival opposite to that exhibited by other SKGs. Median survival time of high- or low-level groups together with the lower or upper 95% CI (confidence interval) was listed. (C) Expression of PIK3CB in newly diagnosed and recurrent GBM tumors. RNA-seq data were retrieved from the TCGA database. The mean values of PIK3CB transcripts were shown. (D) GBM recurrence and PI3K/AKT pathway. Kinases involved in or downstream of PI3K/AKT pathway were analyzed. *P < 0.05; **P < 0.01.
Figure 5
Figure 5. A group of SKGs presents a novel prognostic signature for GBM
(A) GBM patient clustering using the GBM Bio Discovery Portal. (B) KM survival analysis. (C) Log-Rank P values of KM survival analysis of different subgroups or combinations were listed.

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