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

Shaojian Lin; Houshi Xu; Anke Zhang; Yunjia Ni; Yuanzhi Xu; Tong Meng; Mingjie Wang; Meiqing Lou

doi:10.3389/fonc.2020.541401

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

Front Oncol. 2020 Oct 5:10:541401. doi: 10.3389/fonc.2020.541401. eCollection 2020.

Authors

Shaojian Lin^{1

2}, Houshi Xu¹, Anke Zhang¹, Yunjia Ni¹, Yuanzhi Xu¹, Tong Meng³, Mingjie Wang⁴, Meiqing Lou¹

Affiliations

¹ Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² School of Medicine, Tongji University, Shanghai, China.
³ Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Department of Digestive Diseases, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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 m⁶A 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 m⁶A 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 m⁶A 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.

Keywords: WGCNA; glioma; immune infiltration; m6A; prognostic model.