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. 2021 Oct 13:12:743738.
doi: 10.3389/fgene.2021.743738. eCollection 2021.

Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

Chao Sun  1 , Xin Zheng  1 , Yingxin Sun  1 , Ju Yu  1 , Minfeng Sheng  1 , Suji Yan  1 , Qing Zhu  1 , Qing Lan  1
Affiliations

Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

Chao Sun et al. Front Genet. .

Abstract

N6-methyladenosine (m6A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. It is involved in various types of cancer. However, its role in gliomas is not well known. This study aimed to determine the prognostic value of the m6A RNA methylation regulator in gliomas and investigate the underlying mechanisms of the aberrant expression of m6A-related genes.mRNA expression profiles and clinical information of 448 glioma samples were obtained from The Cancer Genome Atlas and cBioportal. The expression of m6A-related genes in normal controls and low-grade glioma and glioblastoma was obtained from Gene Expression Profiling Interactive Analysis. Further, m6A-related gene expression and its relationship with prognosis were obtained through The Chinese Glioma Genome Atlas (CGGA). Multivariate Cox regression analyses were performed, and a nomogram was built with potential risk factors based on a multivariate Cox analysis to predict survival probability. Online tools such as Gene Set Enrichment Analysis, STRING, Cytoscape, and Molecular Complex Detection were applied for bioinformatics analysis and to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. The multivariate Cox regression analysis found that higher expression levels of YTHDC2 and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3, also called IMP3) were independent negative and positive prognostic factors for overall survival (OS), respectively. Data from the CGGA database showed that IGF2BP3 expression increased when the tumor grade increased. Receiver operating characteristic (ROC) curve was used to evaluate the predictive specificity and sensitivity. The area under the ROC curve indicated that the OS prediction was 0.92 (1-year) and 0.917 (3-years), indicating that m6A-related genes could predict patient survival. In addition, IGF2BP3 was closely related to the shorter survival period of patients. Copy number variation and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of IGF2BP3 in gliomas. Significantly altered genes were identified, and the protein-protein interaction network was constructed. Based on the data presented, our study identified several m6A-related genes, especially IGF2BP3, that could be potential prognostic biomarkers of gliomas. The study unveiled the potential regulatory mechanism of IGF2BP3 in gliomas.

Keywords: IGF2BP3; M6A RNA methylation; glioma bioinformatics analysis; post-transcriptional modification; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Expression and genetic alterations in the m6A-related genes in gliomas. (A) Expression profiles of the m6A-related genes in the TCGA-LGG and TCGA-GBM datasets. (B) Genetic alteration profiles of the m6A-related genes. The data were from the cBioportal for Cancer Genomics. N, Normal; T, tumor.
FIGURE 2
FIGURE 2
Expression profiles of the m6A-related genes in gliomas. The mRNA expression of m6A-related genes was analyzed using TCGA-LGG and TCGA-GBM datasets through GEIPA. T, Tumor tissues; N, normal tissues. p < 0.05, versus noncancerous brain tissues.
FIGURE 3
FIGURE 3
Association between m6A regulator and prognosis of glioma samples. The forest plot showed the result of multivariate Cox regression analysis for the association between m6A regulator’s expression, clinical features, and Kaplan–Meier estimated overall survival probability of glioma samples. Values within brackets represent the 95% confidence interval of the hazard ratio.
FIGURE 4
FIGURE 4
Validation of IGF2BP3 in Chinese patients with LGG and GBM derived from the CGGA dataset. (A) Association between IGF2BP3 expression and tumor grade. The p value was generated from analysis of variance). (B) Association between IGF2BP3 and IDH. The p value was generated from the t test. (C) Overall survival was analyzed in terms of high (red) or low (blue) expression of IGF2BP3 in the CGGA dataset. The p value was generated from the log-rank test.
FIGURE 5
FIGURE 5
Glioma survival nomograms. For using the nomograms, the value for an individual patient is located on each variable axis, and a line is drawn upward to determine the number of points received for each variable value. The sum of these numbers is located on the Total Points axis, and a line is drawn downward to the survival axes to determine the likelihood of 1- or 3-years survival.
FIGURE 6
FIGURE 6
m6A-related gene model in prognosis. (A) ROC curves. AUC = 0.92 for 1-year (blue) and AUC = 0.917 for 3-years survival (red). (B) Overall survival was analyzed in high (red) or low (blue) expression of IGF2BP3 in the TCGA glioma dataset. The p value was generated from the log-rank test.
FIGURE 7
FIGURE 7
Mutation, CNV, and methylation analysis of IGF2BP3 in gliomas. (A) Heatmap showing the correlations between IGF2BP3 mRNA and somatic mutations, CNV, and methylation in gliomas via UCSCXena. (B) Correlation between IGF2BP3 mRNA and somatic mutation in gliomas via the cBioportal for Cancer Genomics.
FIGURE 8
FIGURE 8
Differentially expressed genes (DEGs) related to IGF2BP3 in gliomas. (A) Heatmap of the DEGs. Red scattered dots indicate highly expressed DEGs with the adjusted p value < 0.05, and green ones indicated low expressed DEGs with the adjusted p value < 0.05. (B) Volcano plot of the DEGs. (C) Top five significant cellular processes and pathways in GSEA analysis.
FIGURE 9
FIGURE 9
Based on the STRING database and Cytoscape software, protein–protein interaction (PPI) networks of the differentially expressed genes (DEGs) were constructed, followed by modular analyses. (A) PPI networks of DEGs generated using the STRING database. (B, C) Significant gene module in the PPI networks.
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