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. 2021 Apr 19;13(9):12431-12455.
doi: 10.18632/aging.202886. Epub 2021 Apr 19.

Identification of a mesenchymal-related signature associated with clinical prognosis in glioma

Zhengwei Zhang  1   2 , Jie Chen  1   2 , Xiuhao Huo  1   2 , Gang Zong  1   2 , Kebing Huang  1   2 , Meng Cheng  1   2 , Libo Sun  1   2 , Xiaoyu Yue  1   2 , Erbao Bian  1   2 , Bing Zhao  1   2
Affiliations

Identification of a mesenchymal-related signature associated with clinical prognosis in glioma

Zhengwei Zhang et al. Aging (Albany NY). .

Abstract

Malignant glioma with a mesenchymal (MES) signature is characterized by shorter survival time due to aggressive dissemination and resistance to chemoradiotherapy. Here, this study used the TCGA database as the training set and the CGGA database as the testing set. Consensus clustering was performed on the two data sets, and it was found that two groups had distinguished prognostic and molecular features. Cox analysis and Lasso regression analysis were used to construct MES signature-based risk score model of glioma. Our results show that MES signature-based risk score model can be used to assess the prognosis of glioma. Three methods (ROC curve analyses, univariate Cox regression analysis, multivariate Cox regression analysis) were used to investigate the prognostic role of texture parameters. The result showed that the MES-related gene signature was proved to be an independent prognostic factor for glioma. Furthermore, functional analysis of the gene related to the risk signature showed that the genes sets were closely related to the malignant process of tumors. Finally, FCGR2A and EHD2 were selected for functional verification. Silencing these two genes inhibited the proliferation, migration and invasion of gliomas and reduced the expression of mesenchymal marker genes. Collectively, MES-related risk signature seems to provide a novel target for predicting the prognosis and treatment of glioma.

Keywords: gene signature; glioma; mesenchymal; prognosis; proliferation.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare that there is no conflict of interests.

Figures

Figure 1
Figure 1
Acquisition and verification of MES-related genes. (A) Venn diagram indicating that 21 MES-related genes were selected from the Ivy GAP and TCGA databases. (B) The heat map shows that 15 of the MES-related genes were significantly different between the normal and tumor groups. **P <. 01; ***P < 0.001.
Figure 2
Figure 2
MES-related gene sets could classify the clinical and molecular features of gliomas. (A) Relative change in the area under the CDF curve for k = 2 to k = 9. (B) Consensus clustering CDF for k = 2 to k = 9. (C) Consensus clustering matrix of 650 samples from the TCGA dataset for k = 2. (D) Heat map of MES-related genes between cluster 1 and cluster 2 of the TCGA cohort. (E) Survival analysis of patients in cluster 1 and cluster 2 based on TCGA clinical data. (F) Survival analysis of patients in cluster 1 and cluster 2 based on CGGA clinical data. CDF, cumulative distribution function; ***P <0.001.
Figure 3
Figure 3
Identification of the 6-gene risk signature by Lasso regression analysis in the TCGA dataset. (A) The hazard ratio and P value of the 6 MES-related genes. (B) Coefficient values for each of the 6 selected genes. (C) Heat map showing the association of risk scores and clinicopathological features. ***P < 0.001.
Figure 4
Figure 4
Outcome prediction of the 6-gene signature in stratified patients of the TCGA cohort and CGGA cohort. (A) Kaplan-Meier overall survival analysis between the high- and low-risk groups in the TCGA cohort. (B) Kaplan-Meier overall survival analysis between the high- and low-risk groups in the CGGA cohort. (C, D) Univariate (C) and multivariate Cox regression (D) analyses of clinical features and the 6-gene-based risk score for OS in the TCGA dataset. (E) ROC curves indicating the sensitivity and specificity of predicting 1-, 3- and 5-y survival with the MES-related signature in the TCGA dataset. (F) ROC curves indicating the sensitivity and specificity of predicting 1-, 3- and 5-y survival with the MES-related signature in the CGGA dataset.
Figure 5
Figure 5
Functional analysis of the 6-gene signature. (A–D) GSEA revealed that the two cohorts were enriched for hallmarks of malignant tumors. (E) GO annotations based on the top 4400 genes positively associated with the 6-gene signature. (F) KEGG pathways associated with the risk score.
Figure 6
Figure 6
FCGR2A and EHD2 were selected from the six MES-related genes. (A) Differences in FCGR2A expression between the normal group and the glioma group from the TCGA and GTEx data sets. (B, C) Overall survival analysis (B) and disease-free survival analysis (C) of the relationship between FCGR2A expression level and survival time from the TCGA database. (D) Differences in EHD2 expression between the normal group and the glioma group from the TCGA and GTEX data sets. (E, F) Overall survival analysis (E) and disease-free survival analysis (F) of the relationship between EHD2 expression level and survival time from TCGA database. ***P<0.001.
Figure 7
Figure 7
Selection of cell lines and verification of the silencing effect. (A) Relative expression of FCGR2A in five cell lines. (B) Relative expression of EHD2 in five cell lines. (C) The LN18 cell line was transfected with three siRNA fragments separately and in combination (pro), and the relative silencing level of FCGR2A. (D) The U251 cell line was transfected with three siRNA fragments separately and in combination (pro), the relative silencing level of EHD2. KONG stands for untreated cell; Control stands for Negative control group. **P<0.01; ***P<0.001.
Figure 8
Figure 8
The effect of glioma cell clone, proliferation, migration and invasion ability after silencing FCGR2A or EHD2. (A, B) Cell proliferation was measured by the MTT assay for 24 hours up to 72 hours. (C, D) Representative imaging (C) or counting (D) of the colonies formed by LN18 cells after silencing with FCGR2A for 7 days. (E–G) Representative imaging (E) or counting (F, G) of migration assays after silencing FCGR2A and EHD2 in glioma cells. (H–J) Representative imaging (H) or counting (I, J) of invasion assays after silencing FCGR2A and EHD2 in glioma cells. *P<0.05; **P<0.01; ***P<0.001.
Figure 9
Figure 9
The relative expression of GBM mesenchymal markers and MES protein markers after FCGR2A or EHD2 silencing. (A, B) The relative mRNA expression of CHI3L1 and CD44 after silencing FCGR2A. (C, D) The relative mRNA expression of CHI3L1 and CD44 after silencing EHD2. (E) The western blot analysis of CD44, BMI1, VIM and ZEB1 protein markers after silencing FCGR2A. (F) The western blot analysis of CD44, BMI1, VIM and ZEB1 protein markers after silencing EHD2. Ns: no significance; *P<0.05; **P<0.01; ***P<0.001.
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