Research Paper Advance Articles
RPP30, a transcriptional regulator, is a potential pathogenic factor in glioblastoma
- 1 Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- 2 Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- 3 Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
- 4 China National Clinical Research Center for Neurological Diseases, Beijing, China
- 5 Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA)
Received: January 29, 2020 Accepted: June 13, 2020 Published: July 23, 2020https://doi.org/10.18632/aging.103596
How to Cite
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.
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.