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Research Paper|Volume 13, Issue 12|pp 16198—16218

A risk signature of four aging-related genes has clinical prognostic value and is associated with a tumor immune microenvironment in glioma

Haitao Luo1,2, Chuming Tao1, Xiaoyan Long2, Kai Huang1,3, Xingen Zhu1,3
  • 1Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
  • 2East China Institute of Digital Medical Engineering, Shangrao, Jiangxi Province, China
  • 3Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi Province, China
Received: March 13, 2021Accepted: May 20, 2021Published: June 10, 2021
https://doi.org/10.18632/aging.203146

Copyright: © 2021 Luo 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

An accumulation of studies has indicated aging to be a significant hazard factor for the development of tumors. Cellular senescence is positively associated with aging progress and aging-related genes (AGs) can regulate cellular senescence and tumor malignancy. While the association between AGs and the prognosis of patients with glioma is still unclear. In our study, we initially selected four survival-associated AGs and performed consensus clustering for these AGs based on The Cancer Genome Atlas (TCGA) database. We then explored the potential biological effects of four selected AGs. A prognostic risk model was constructed according to four selected AGs (LEP, TERT, PON1, and SSTR3) in the TCGA dataset and Chinese Glioma Genome Atlas (CGGA) database. Then we indicated the risk score was an independent prognostic index, and was also positively correlated with immune scores, estimate score, immune cell infiltration level, programmed death ligand 1 (PD-L1) expression, and expression of proinflammatory factors in patients with glioma. Finally, we performed the RT-qPCR and immunohistochemistry assay to validate our bioinformatics results. Thus, this study indicated the risk model was concluded to possibly have potential function as an immune checkpoint inhibitor and to provide promising targets for developing individualized immunotherapies for patients with glioma.