# Non-Coding RNA Biomarkers in Prostate Cancer: Evidence Mapping and In Silico Characterization

## TL;DR

This study maps non-coding RNAs linked to prostate cancer and explores their regulatory roles using computational methods.

## Contribution

The paper provides a systematic evidence map and in-depth in silico characterization of ncRNA biomarkers in prostate cancer.

## Key findings

- 94 miRNAs, 8 lncRNAs, and other ncRNAs were identified as differentially expressed in prostate cancer.
- In silico analysis revealed 13,493 miRNA–mRNA interactions and key regulatory proteins like QKI and CDK6.
- Enrichment analysis linked these ncRNAs to metabolic processes and cancer-related pathways.

## Abstract

Non-coding RNAs (ncRNAs) have emerged as promising biomarkers for prostate cancer (PCa), yet evidence remains dispersed across heterogeneous studies and their regulatory context is seldom analyzed in an integrated manner. This study systematically maps ncRNAs reported as diagnostic biomarkers for PCa and characterizes their molecular interactions through in silico analyses. A comprehensive evidence-mapping strategy across major bibliographic databases identified 693 studies, of which 58 met eligibility criteria. Differentially expressed ncRNAs were extracted and classified by RNA type. Subsequently, miRNA–target prediction, miRNA–protein interaction network construction, and functional enrichment analyses were performed to explore the regulatory landscape of miRNA-associated proteins. Results: The final dataset included 4500 participants (2871 PCa cases and 2093 controls) and reported 94 differentially expressed miRNAs, eight lncRNAs, and several circRNAs, snoRNAs, snRNAs, and piRNAs. In silico analyses predicted 13,493 miRNA–mRNA interactions converging on 4916 unique target genes, with an additional 2481 prostate tissue-specific targets. The miRNA–protein network comprised 845 nodes and 2335 edges, revealing highly connected miRNAs (e.g., hsa-miR-16-5p, hsa-miR-20a-5p) and protein hubs (QKI, YOD1, TBL1XR1; prostate-specific CDK6, ACVR2B). Enrichment analysis showed strong overrepresentation of metabolic process-related GO terms and cancer-associated KEGG pathways. Conclusions: These findings refine the list of promising ncRNA biomarkers and highlight candidates for future clinical validation.