Biological role and diagnostic utility of ribosomal protein L23a pseudogene 53 in cutaneous melanoma

Abstract

Background: Skin melanoma is one of the deadliest types of skin cancer and develops from melanocytes. The genetic aberrations in protein-coding genes are well characterized, but little is known about changes in non-coding RNAs (ncRNAs) such as pseudogenes. Ribosomal protein pseudogenes (RPPs) have been described as the largest group of pseudogenes which are dispersed in the human genome. Materials and methids:We looked deeply at the role of one of them, ribosomal protein L23a pseudogene 53 (RPL23AP53), and its potential diagnostic use. The expression level of RPL23AP53 was profiled in melanoma cell lines using real time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and analyzed based on the Cancer Genome Atlas (TCGA) data depending on BRAF status and clinicopathological parameters. Cellular phenotype, which was associated with RPL23AP53 levels, was described based on the REACTOME pathway browser, Gene Set Enrichment Analysis (GSEA) analysis as well as Immune and ESTIMATE Scores. Results:We indicted in vitro changes in RPL23AP53 level depending on a cell line, and based on in silico analysis of TCGA samples demonstrated significant differences in RPL23AP53 expression between primary and metastatic melanoma, as well as correlation between RPL23AP53 and overall survival. No differences depending on BRAF status were observed. RPL23AP53 is associated with several signaling pathways and cellular processes. Conclusions:This study showed that patients with higher expression of RPL23AP53 displayed changed infiltration of lymphocytes, macrophages, and neutrophils compared to groups with lower expression of RPL23AP53. RPL23AP53 pseudogene is differently expressed in melanoma compared with normal tissue and its expression is associated with cellular proliferation. Thus, it may be considered as an indicator of patients' survival and a marker for the immune profile assessment.

SRY-Related Transcription Factors in Head and Neck Squamous Cell Carcinomas: In Silico Based Analysis

“…PFI and OS for data from GEO datasets were calculated using the log-rank (Mantel-Cox) test and the Gehan-Breslow-Wilcoxon test. In all analyses, p < 0.05 was used to determine statistical significance, as described previously [30][31][32][33][34].…”

“…Data from ESTIMATE contains information about the presence of stroma in tumor tissues (stromal score), representation inflation of immune cells in tumor tissues (immune score) and tumor purity (estimate score). We separated each gene expression group in two, low and high relative to average in all samples, as described previously [30][31][32][33][34].…”

The RNA world: from experimental laboratory to "in silico" approach. Part 1: User friendly RNA expression databases portals

“…Increasingly, more types of RNA and their functions are being discovered and tested for suitability in medicine as diagnostic or therapeutic measures. They are derived from different sources of biomaterials [15][16][17][18][19]. The uses of RNA-seq data in research are presented in Figure 2.…”