Prognosis Analysis and Validation of m6A Signature and Tumor Immune Microenvironment in Glioma

Abstract

Glioma is one of the most typical intracranial tumors, comprising about 80% of all brain malignancies. Several key molecular signatures have emerged as prognostic biomarkers, which indicate room for improvement in the current approach to glioma classification. In order to construct a more veracious prediction model and identify the potential prognosis-biomarker, we explore the differential expressed m 6 A RNA methylation regulators in 665 gliomas from TCGA-GBM and TCGA-LGG. Consensus clustering was applied to the m6A RNA methylation regulators, and two glioma subgroups were identified with a poorer prognosis and a higher grade of WHO classification in cluster 1. The further chi-squared test indicated that the immune infiltration was significantly enriched in cluster 1, indicating a close relation between m 6 A regulators and immune infiltration. In order to explore the potential biomarkers, the weighted gene co-expression network analysis (WGCNA), along with Least absolute shrinkage and selection operator (LASSO), between high/low immune infiltration and m 6 A cluster 1/2 groups were utilized for the hub genes, and four genes ( TAGLN2, PDPN, TIMP1, EMP3) were identified as prognostic biomarkers. Besides, a prognostic model was constructed based on the four genes with a good prediction and applicability for the overall survival (OS) of glioma patients (the area under the curve of ROC achieved 0.80 (0.76–0.83) and 0.72 (0.68–0.76) in TCGA and Chinese Glioma Genome Atlas (CGGA), respectively). Moreover, we also found PDPN and TIMP1 were highly expressed in high-grade glioma from The Human Protein Atlas database and both of them were correlated with m6A and immune cell marker in glioma tissue samples. In conclusion, we construct a novel prognostic model which provides new insights into glioma prognosis. The PDPN and TIMP1 may serve as potential biomarkers for prognosis of glioma.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

“…Specifically, we found strong correlations of CD86 expression with immune infiltration of CD4+ cells, macrophage, neutrophil and dendritic cells. These results were consistent with previous studies (38,42) indicating higher levels of immune cell infiltration may contribute to worse prognosis of LGG. Additionally, CD86 levels demonstrated strong correlations with multiple immune checkpoint molecules, including VSIR, HAVCR2, and PDCD1LG2 (PD-L2).…”

Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis

“…Although there are several published studies using WGCNA on glioma, most of them has focused on the relationship between module and traits like age, gender, overall survival, IDH mutation, and so on. 21,[29][30][31] In this case, our study paid more attention to identify genes which are clustered with primary and recurrent types of four WHO grade I to IV. Similarly, Mukherjee's work has analyzed relationship between module and WHO grade II to IV.…”

Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

“…Wang et al established prognostic model for glioma, and the AUC values in predicting 1-year survival for glioma were TCGA: 0.623 and CGGA: 0.607; and 3-year survival: TCGA: 0.735 and CGGA: 0.803 [ 27 ]. Lin et al used four genes (TAGLN2, PDPN, TIMP1, EMP3) to build prognostic model for glioma and its AUC values were 0.80 in TCGA cohort and 0.72 in CGGA cohort [ 28 ].…”