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Research Paper|Volume 15, Issue 17|pp 9086—9104

Integrated clustering signature of genomic heterogeneity, stemness and tumor microenvironment predicts glioma prognosis and immunotherapy response

Yangyang Wu1, Meng Mao1,2,3, Lin-Jian Wang1,3,4
  • 1Advanced Medical Research Center of Zhengzhou University, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
  • 2Department of Anesthesiology and Perioperative Medicine, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
  • 3Research of Trauma Center, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
  • 4Department of Neurosurgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
Received: May 16, 2023Accepted: August 21, 2023Published: September 11, 2023
https://doi.org/10.18632/aging.205018

Copyright: © 2023 Wu 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

Background: Glioma is the most frequent primary tumor of the central nervous system. The high heterogeneity of glioma tumors enables them to adapt to challenging environments, leading to resistance to treatment. Therefore, to detect the driving factors and improve the prognosis of glioma, it is essential to have a comprehensive understanding of the genomic heterogeneity, stemness, and immune microenvironment of glioma.

Methods: We classified gliomas into various subtypes based on stemness, genomic heterogeneity, and immune microenvironment consensus clustering analysis. We identified risk hub genes linked to heterogeneous characteristics using WGCNA, LASSO, and multivariate Cox regression analysis and utilized them to create an effective risk model.

Results: We thoroughly investigated the genomic heterogeneity, stemness, and immune microenvironment of glioma and identified the risk hub genes RAB42, SH2D4A, and GDF15 based on the TCGA dataset. We developed a risk model utilizing these genes that can reliably predict the prognosis of glioma patients. The risk signature showed a positive correlation with T cell exhaustion and increased infiltration of immunosuppressive cells, and a negative correlation with the response to immunotherapy. Moreover, we discovered that SH2D4A, one of the risk hub genes, could stimulate the migration and proliferation of glioma cells.

Conclusions: This study identified risk hub genes and established a risk model by analyzing the genomic heterogeneity, stemness, and immune microenvironment of glioma. Our findings will facilitate the diagnosis and prediction of glioma prognosis and may lead to potential treatment strategies for glioma.