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Research Paper|Volume 12, Issue 16|pp 16155—16171

RPP30, a transcriptional regulator, is a potential pathogenic factor in glioblastoma

Guanzhang Li1, You Zhai1, Hanjie Liu1, Zhiliang Wang1, Ruoyu Huang1, Haoyu Jiang2, Yuemei Feng1, Yuanhao Chang1, Fan Wu1, Fan Zeng1, Tao Jiang1,2,3,4,5, Wei Zhang2,4,5
  • 1Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
  • 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  • 3Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
  • 4China National Clinical Research Center for Neurological Diseases, Beijing, China
  • 5Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA)
Received: January 29, 2020Accepted: June 13, 2020Published: July 23, 2020
https://doi.org/10.18632/aging.103596

Copyright © 2020 Li 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: Old age has been demonstrated to be a risk factor for GBM, but the underlying biological mechanism is still unclear. We designed this study intending to determine a mechanistic explanation for the link between age and pathogenesis in GBM.

Results: The expression of RPP30, an independent prognostic factor in GBM, was negatively correlated with age in both tumor and non-tumor brain samples. However, the post-transcriptional modifications carried out by RPP30 were different in primary GBM and non-tumor brain samples. RPP30 affected protein expression of cancer pathways by performing RNA modifications. Further, we found that RPP30 was related to drug metabolism pathways important in GBM. The decreased expression of RPP30 in older patients might be a pathogenic factor for GBM.

Conclusion: This study revealed the role of RPP30 in gliomagenesis and provided the theoretical foundation for targeted therapy.

Methods: In total, 616 primary GBM samples and 41 non-tumor brain samples were enrolled in this study. Transcriptome data and clinical information were obtained from the CGGA, TCGA, and GSE53890 databases. Gene Set Variation Analysis and Gene Ontology analyses were the primary analytical methods used in this study. All statistical analyses were performed using R.