28S rRNA-Derived Fragments Represent an Independent Molecular Predictor of Short-Term Relapse in Prostate Cancer.

Prostate cancer (PCa) is a global health concern, being a leading cause of cancer-related mortality among males. Early detection and accurate prognosis are crucial for effective management. This study delves into the diagnostic and prognostic potential of 28S rRNA-derived fragments (rRFs) in PCa. Total RNA extracted from 89 PCa and 53 benign prostate hyperplasia (BPH) tissue specimens. After 3'-end polyadenylation, we performed reverse transcription to create first-strand cDNA. Using an in-house quantitative real-time PCR (qPCR) assay, we quantified 28S rRF levels. Post-treatment biochemical relapse served as the clinical endpoint event for survival analysis, which we validated internally through bootstrap analysis. Our results revealed downregulated 28S rRF levels in PCa compared to BPH patients. Additionally, we observed a significant positive correlation between 28S rRF levels and higher Gleason scores and tumor stages. Furthermore, PCa patients with elevated 28S rRF expression had a significantly higher risk of post-treatment disease relapse independently of clinicopathological data. In conclusion, our study demonstrates, for the first time, the prognostic value of 28S rRF in prostate adenocarcinoma. Elevated 28S rRF levels independently predict short-term PCa relapse and enhance risk stratification. This establishes 28S rRF as a potential novel molecular marker for PCa prognosis.

Identification and expression analysis of ten novel small non-coding RNAs (sncRNAs) in cancer cells using a high-throughput sequencing approach.

Non-coding RNAs are characterized as RNA molecules, which lack the capacity to encode protein structures and appear to include a level of internal signals. Moreover, they control various stages of gene expression, thus controlling the cell physiology and development. In this study, we implemented a high-throughput sequencing approach based on the primary semi-conductor technology and computational tools, in order to identity novel small non-coding RNAs. Fourteen human cancer cell lines were cultured, and RNA samples were enriched for small RNAs following semi-conductor next generation sequencing (NGS). Bioinformatics analysis of NGS data revealed the existence of several classes of ncRNAs using the miRDeep* and CPSS 2.0 software. To investigate the existence of the predicted non-coding RNA sequences in cDNA pools of cell lines, a developed qPCR-based assay was implemented. The structure of each novel small ncRNA was visualized, using the RNAfold algorithm. Our results support the existence of twenty (20) putative new small ncRNAs, ten (10) of which have had their expression experimentally validated and presented differential profiles in cancerous and normal cells. A deeper comprehension of the ncRNAs interactive network and its role in cancer can therefore be translated into a wide range of clinical applications. Despite this progress, further scientific research from different perspectives and in different fields is needed, so that the riddle of the human transcriptome can be solved.