# Identification of prognostic RNA editing profiles for clear cell renal carcinoma

## TL;DR

This study identifies RNA editing profiles that can predict outcomes for patients with clear cell renal cancer, offering potential new biomarkers.

## Contribution

The study introduces a novel RNA editing-based risk score as an independent prognostic factor for clear cell renal cell carcinoma.

## Key findings

- An RNA editing-based risk score was developed to distinguish high and low-risk ccRCC patients.
- Seven RNA editing sites were identified as potential biomarkers for ccRCC prognosis.
- qPCR confirmed significant expression changes in several genes across different cell lines.

## Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments.

Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2.

An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated.

RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.