Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insightsand targets across predominant cancer landscapes

Sep 21, 2026

Competing endogenous RNAs drive key post-transcriptional cross-talk mechanisms, where ceRNA dynamics can vary with the abundance of free transcripts across tissues and conditions. Yet comprehensive analyses linking ceRNA interactions in normal tissues, ceRNAs gained or lost in cancer, and their miRNA-mediated relationships remain limited. This study maps tumor-specific ceRNA fluctuations in LUAD, PRAD, and BRCA versus healthy lung, prostate, and breast tissues, identifying cancer-specific ceRNAs and a core ceRNA–miRNA network for RNA-targeted strategies.

LanguageEnglish

CategoryResearch & Report

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Pages16

Reading Time40 min

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Introduction

Ari Yuka andYilmaz BioData Mining (2024) 17:11 [https://doi.org/10.1186/s13040-024-00362-4](https://doi.org/10.1186/s13040-024-00362-4) BioData Mining RESEARCH Open Access Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insightsand targets across predominant cancer landscapes Selcen Ari Yuka1,2*† and Alper Yilmaz1† | †Selcen Ari Yuka and AlperYilmaz contributed equally to this work. | | --- | | *Correspondence: [selcenay@yildiz.edu.tr](selcenay@yildiz.edu.tr) | 1 Department of Bioengineering, Yildiz Technical University, Istanbul 34220, Turkey 2 Health Biotechnology Joint Research and Application Center of Excellence, Yildiz Technical University, Istanbul 34220, Turkey Abstract Competing endogenous RNAs play key roles in cellular molecular mechanisms through cross-talk in post-transcriptional interactions. Studies on ceRNA cross-talk, which is particularly dependent on the abundance of free transcripts, generally involve large-and small-scale studies involving the integration of transcriptomic data from tissues and correlation analyses. This abundance-dependent nature of ceRNA interactions suggests that tissue-and condition-specific ceRNA dynamics may fluctuate. However, there are no comprehensive studies investigating the ceRNA interactions in normal tissue, ceRNAs that are lost and/or appear in cancerous tissues or their interactions. In this study, we comprehensively analyzed the tumor-specific ceRNA fluctuations observed in the three highest-incidence cancers, LUAD, PRAD, and BRCA, compared to healthy lung, prostate, and breast tissues, respectively. Our observations pertaining to tumor-specific competing endogenous RNA (ceRNA) interactions revealed that, in the cases of lung adenocarcinoma (LUAD), prostate adenocarcinoma (PRAD), and breast invasive carcinoma (BRCA), 3,204, 1,233, and 406 ceRNAs, respectively, engage in post-transcriptional intercommunication within tumor tissues, in contrast to their absence in corresponding healthy samples. We also found that 90 ceRNAs are shared by the three cancer types and that these ceRNAs participate in ceRNA interactions in tumor tissues compared to those in normal tissues. Among the 90 ceRNAs that directly interact with miRNAs, we uncovered a core network of 165 miRNAs and 63 ceRNAs that should be considered in RNA-targeted and RNA-mediated approaches in future studies and could be used in these three aggressive cancer types. More specifically, in this core interaction network, ceRNAs such as GALNT7, KLF9, and DAB2 and miRNAs like miR-106a/b-5p, miR-20a-5p, and miR-519d-3p may have potential as common targets in the three critical cancers. In contrast to conventional methods that construct ceRNA networks using differentially expressed genes compared to normal tissues, our proposed approach identifies ceRNA players by considering their context within the ceRNA:miRNA interactions. Our results have the potential to reveal distinct and common ceRNA interactions in cancer types and to pinpoint critical RNAs, thereby paving the way for RNA-based strategies in the battle against cancer. © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.org/licen

FAQs

What is the central focus of the study?

The study decodes dynamic miRNA:ceRNA interactions to reveal therapeutic insights and targets across predominant cancer landscapes, comparing three major cancers with corresponding healthy tissues.

How does the analysis compare cancer tissues with normal tissues?

It identifies ceRNA interactions that appear within tumor tissues but are absent in the corresponding healthy samples, highlighting tumor-specific ceRNA fluctuations.

What core molecular network does the study uncover?

Among ceRNAs that directly interact with miRNAs, the study identifies a core network comprising 165 miRNAs and 63 ceRNAs, suggesting candidate components for future RNA-targeted and RNA-mediated approaches.

Sep 21, 2026

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