New insights into the role of ribonuclease P protein subunit p30 from tumor to internal reference

Ribonuclease P protein subunit p30 (RPP30) is a highly conserved housekeeping gene that exists in many species and tissues throughout the three life kingdoms (archaea, bacteria, and eukaryotes). RPP30 is closely related to a few types of tumors in human diseases but has a very stable transcription level in most cases. Based on this feature, increasing number of studies have used RPP30 as an internal reference gene. Here, the structure and basic functions of RPP30 are summarized and the likely relationship between RPP30 and various diseases in plants and human is outlined. Finally, the current application of RPP30 as an internal reference gene and its advantages over traditional internal reference genes are reviewed. RPP30 characteristics suggest that it has a good prospect of being selected as an internal reference; more work is needed to develop this research avenue.

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

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.