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Research Paper|Volume 13, Issue 9|pp 12431—12455

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

Zhengwei Zhang1,2, Jie Chen1,2, Xiuhao Huo1,2, Gang Zong1,2, Kebing Huang1,2, Meng Cheng1,2, Libo Sun1,2, Xiaoyu Yue1,2, Erbao Bian1,2, Bing Zhao1,2
  • 1Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China
  • 2Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei 230601, China
* Equal contribution
Received: November 30, 2020Accepted: March 4, 2021Published: April 19, 2021
https://doi.org/10.18632/aging.202886

Copyright: © 2021 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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.