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. 2013 Aug;15(8):1008-16.
doi: 10.1093/neuonc/not042. Epub 2013 Apr 17.

ZIP4 is a novel molecular marker for glioma

Yi Lin  1 , Yong Chen, Yongzhi Wang, Jingxuan Yang, Vivian F Zhu, Yulun Liu, Xiaobo Cui, Leon Chen, Wei Yan, Tao Jiang, Georgene W Hergenroeder, Stephen A Fletcher, Jonathan M Levine, Dong H Kim, Nitin Tandon, Jay-Jiguang Zhu, Min Li
Affiliations

ZIP4 is a novel molecular marker for glioma

Yi Lin et al. Neuro Oncol. 2013 Aug.

Abstract

Background: Dysregulated zinc transport has been observed in many cancers. However, the status of zinc homeostasis and the expression profile of zinc transporters in brain and brain tumors have not been reported.

Methods: The gene profiles of 14 zinc importers (ZIPs) and 10 zinc exporters (ZnTs) in patients with glioma were studied by investigating the association between the zinc transporters and brain tumor characteristics (tumor grade and overall survival time). Three independent cohorts were analyzed to cross-validate the findings: the Chinese Glioma Genome Atlas (CGCA) cohort (n = 186), the US National Cancer Institute Repository for Molecular Brain Neoplasia Data (REMBRANDT) cohort (n = 335), and The University of Texas (UT) cohort (n = 34).

Results: The expression of ZIP3, 4, 8, 14, ZnT5, 6, and 7 were increased, and the expression of ZnT10 was decreased in grade IV gliomas, compared with grade II gliomas. Among all 24 zinc transporters, ZIP4 is most significantly associated with tumor grade and overall survival; this finding is consistent across 2 independent cohorts (CGCA and REMBRANDT) and is partially validated by the third cohort (UT). High ZIP4 expression was significantly associated with higher grade of gliomas and shorter overall survival (hazard ratio = 1.61, 95% confidence interval = 1.02-2.53, P = .040 in CGCA cohort; hazard ratio = 1.32, 95% confidence interval = 1.08-1.61, P = .007 in REMBRANDT cohort).

Conclusions: Dysregulated expression of zinc transporters is involved in the progression of gliomas. Our results suggest that ZIP4 may serve as a potential diagnostic and prognostic marker for gliomas.

Keywords: ZIP4; biomarker; brain tumor; prognosis; survival; zinc transporter.

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Figures

Fig. 1.
Fig. 1.
Heatmap diagram depicting the expression levels of zinc transporters in CGCA cohort. The colors represent the relative levels of gene expression with the brightest red indicating the highest level of expression and green depicting low levels or absence of expression. Data were standardized by subtracting the mean and dividing by the standard deviation for the probe set. Probe sets and samples were arranged and displayed using Cluster and Treeview software. Genes were clustered in 3 groups (upregulated in grade IV, downregulated in grade IV, and no significant difference); in each group, genes were ordered by statistical significance evaluated from Student's t-test. If multiple probes were used for a single candidate gene, the reference sequence of each probe was searched in GenBank, and the probes detecting exons were chosen for further analysis. The probe for ZnT4 is missing in the CGCA database, and the probe for ZIP11 is missing in the REMBRANDT database.
Fig. 2.
Fig. 2.
Kaplan-Meier analyses of overall survival in CGCA and REMBRANDT cohorts according to gene expression level. Discriminative power of ZIP4 in CGCA cohort (A–C) and REMBRANDT cohort (D–F) were assessed with Kaplan-Meier plotting method and log-rank test.
Fig. 3.
Fig. 3.
Quantitative evaluation of ZIP4 expression in UT cohort. Grouped dot plot was used to show the distribution of ZIP4 expression between grade II and grade IV gliomas. The median values for each group is superimposed on these dot plots. There were statistically significant differences between ZIP4 expression levels of grade II gliomas, compared with grade IV gliomas (P = .040, Student's t-test).
Fig. 4.
Fig. 4.
Correlations between ZIP4 with MMP-9 (A), VEGF-A (B), PDGF-A (C), IL-6 (D), IL-8 (E), and IGFBP-2 (F) levels in UT cohort. The transcription levels of these genes were assessed with real-time PCR and were correlated using linear regression model after log transformation.
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