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An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer
Federica Conte1, Giulia Fiscon1,2, Pasquale Sibilio1,3
1Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Rome, Italy.
Abstract:
Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.
Insights
Competing endogenous RNAs (ceRNAs) form complex networks that regulate gene expression post-transcriptionally. Understanding ceRNA interactions is crucial for insights into biological processes and diseases like cancer.
Area of Science:
- Molecular Biology
- Genetics
- Bioinformatics
Background:
- Competing endogenous RNAs (ceRNAs) represent a regulatory layer influencing gene expression by interacting with shared microRNAs (miRNAs).
- ceRNA cross talk involves complex interactions where RNAs compete for miRNAs, impacting physiological and pathological processes.
- These interactions can lead to significant biological effects despite the weak nature of individual interactions.
Purpose of the Study:
- To review current advancements in predicting ceRNA cross talk.
- To summarize databases and computational methods for identifying ceRNA networks.
- To provide a global view of gene regulation fine-tuning in biological processes and disease.
Main Methods:
- Review of databases containing computationally predicted and experimentally validated miRNA-target and ceRNA-ceRNA interactions.
- Categorization of computational methods into statistics-based and mathematical approaches.
- Statistics-based methods utilize multivariate analysis and miRNA expression levels; mathematical methods employ deterministic or stochastic models.
Main Results:
- Databases provide valuable resources for ceRNA interaction data.
- Diverse computational methods exist for discovering and modeling ceRNA networks.
- ceRNA network analysis offers insights into gene regulation and disease mechanisms.
Conclusions:
- ceRNA cross talk is a significant regulatory mechanism in biology and disease.
- Computational tools and databases are essential for studying complex ceRNA networks.
- Further research into ceRNA interactions can advance understanding of gene regulation and inform therapeutic strategies.
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