Overview of Competing Endogenous RNA (ceRNA)

What is Competitive Endogenous RNA?

Competitive Endogenous RNA (ceRNA) is an intricate regulatory phenomenon encompassing various RNA molecules. This intriguing concept postulates that these RNA molecules engage in a competitive struggle to bind microRNAs (miRNAs), which are diminutive non-coding RNA molecules with pivotal roles in gene regulation.

According to the ceRNA hypothesis, RNA molecules that possess comparable or identical miRNA recognition elements (MREs) engage in a contest for the limited pool of miRNAs. Upon binding to an MRE within an RNA molecule, a miRNA typically instigates the degradation or translational repression of the targeted RNA. However, if multiple RNA molecules harbor the same MRE, they can potentially act as a "sponge" or sequester the miRNA, impeding its interaction with its intended targets.

By sequestering miRNAs, ceRNAs exert an indirect regulatory influence on the expression of target genes that are typically repressed by those miRNAs. This regulatory mechanism orchestrates a multifaceted interplay among diverse RNA molecules, including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and other RNA variants. Consequently, the ceRNA network possesses the potential to modulate gene expression and cellular processes by regulating the availability of miRNAs.

Please refer to our article A Journey Through Circular RNA: Tracing the Evolution and Discovery of a Fascinating RNA World for more details.

History of ceRNA Mechanism

The ceRNA mechanism, also known as the competing endogenous RNA mechanism, is a hypothesis proposed by Professor Pier Paolo Pandolfi in 2011. The hypothesis suggests that different RNA molecules, including both coding and non-coding RNAs, can compete for shared microRNAs (miRNAs) and indirectly regulate each other's expression levels.

In 2011, Pier Paolo Pandolfi published an article titled "A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?" in the scientific journal Cell. Although the professor did not test the hypothesis experimentally in that article, it generated significant attention and was recognized as one of the most discussed articles in Cell at the time.

One way this reciprocal relationship occurs is through the interaction of miRNAs and microRNA response elements (MREs). MREs are specific sequences found in the 3' untranslated regions (UTRs) of mRNA molecules. These sequences provide binding sites for miRNAs. When a miRNA binds to an MRE in an mRNA molecule, it can regulate the expression of that mRNA.

The level of "communication" and co-regulation between RNA molecules can be influenced by the number of shared MREs. If two or more mRNA molecules have a greater number of shared MREs, it suggests a higher potential for communication and co-regulation between those molecules.

In addition to regulating the mRNA molecule itself, MREs found in the 3' UTRs of RNA molecules can also potentially regulate levels of miRNAs, which can then influence the expression of other RNAs. This suggests that MREs may function not only in cis (acting on the same RNA molecule) but also in trans (acting on other RNA molecules).

The Basis of the ceRNA Language.The Basis of the ceRNA Language. (Salmena et al., 2011)

Later in the same year, Pier Paolo Pandolfi and his colleagues published two articles in Cell that provided experimental evidence supporting the ceRNA mechanism. The first article, titled "Coding-Independent Regulation of the Tumor Suppressor PTEN by Competing Endogenous mRNAs," investigated the ceRNA hypothesis in the context of PTEN, a tumor suppressor gene. The researchers combined computational and experimental approaches to identify and validate endogenous protein-coding transcripts that regulate PTEN. They demonstrated that these transcripts can compete for common miRNAs and thereby influence PTEN expression, affecting signaling pathways involved in tumor development.

A multifaceted scheme involving integrated computational analysis and experimental validation.A multifaceted scheme involving integrated computational analysis and experimental validation. (Tay et al., 2011)

The second article, titled "In vivo identification of tumor-suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma," focused on melanoma. The researchers identified multiple ceRNAs for PTEN in a mouse model of melanoma and specifically characterized the ZEB2 transcript. They showed that ZEB2 acts as a ceRNA for PTEN in a miRNA-dependent, protein-coding-independent manner. The authors also analyzed human cancer databases, which confirmed the functional relationship between ZEB2 and PTEN and suggested that dysregulation of PTEN by ceRNA through miRNA competition contributes to melanoma development.

Mutagenesis screen identifies tumor suppressive PTEN cd RNAs. Mutagenesis screen identifies tumor suppressive PTEN cd RNAs. (Karreth et al., 2011)

Another article supported the existence of ceRNA mechanisms in muscle differentiation. The researchers identified a muscle-specific long noncoding RNA called linc-MD1, which acts as a ceRNA in mouse and human myogenic cells. They found that linc-MD1 regulates the timing of muscle differentiation by "sponging" miR-133 and miR-135, thereby modulating the expression of MAML1 and MEF2C, transcription factors involved in muscle-specific gene expression.

A muscle-specific long noncoding RNA, linc-MD1, which governs the time of muscle differentiation by acting as a ceRNA. A muscle-specific long noncoding RNA, linc-MD1, which governs the time of muscle differentiation by acting as a ceRNA. (Cesana et al., 2011)

These studies collectively provided experimental evidence supporting the ceRNA mechanism and its role in regulating gene expression through miRNA-mediated interactions between different RNA molecules. The ceRNA mechanism has since been a subject of extensive research, further expanding our understanding of post-transcriptional regulation and its implications in various biological processes and diseases.

Research Progress of ceRNA Mechanism

The competing endogenous RNA (ceRNA) mechanism, also known as RNA crosstalk, has gained significant attention in recent years as a regulatory network involving various types of RNA molecules. This mechanism suggests that non-coding RNAs can competitively bind to shared microRNAs (miRNAs), thereby regulating the expression of target genes.

Since the discovery of the ceRNA hypothesis, researchers have made considerable progress in understanding its role in different biological processes and disease conditions.

  • Experimental Validation: Numerous studies have provided experimental evidence supporting the ceRNA mechanism. These studies have demonstrated specific examples of mRNA, pseudogene, lncRNA, and circRNA acting as ceRNAs to regulate gene expression through miRNA sponging.
  • CeRNA Networks: Researchers have identified and characterized complex ceRNA networks in various organisms, including microbes, viruses, animals, and plants. These networks involve the interplay between multiple RNA types, suggesting a sophisticated regulatory system.
  • Disease Association: The ceRNA mechanism has been implicated in numerous diseases, including cancer, neurological disorders, cardiovascular diseases, and immune-related disorders. Dysregulation of ceRNA networks can contribute to disease progression by affecting key signaling pathways.
  • Computational Approaches: Computational methods and bioinformatics tools have been developed to predict and analyze ceRNA interactions. These approaches integrate diverse omics data, such as RNA-seq, miRNA-seq, and CLIP-seq, to identify ceRNA interactions and construct regulatory networks.
  • RNA Modifications: Recent studies have started exploring the influence of RNA modifications, such as N6-methyladenosine (m6A), on the ceRNA mechanism. RNA modifications can affect RNA stability, localization, and interactions, potentially modulating ceRNA crosstalk.
  • Therapeutic Implications: The ceRNA mechanism has emerged as a potential target for therapeutic interventions. Modulating ceRNA networks through synthetic oligonucleotides, small molecules, or gene editing technologies holds promise for treating diseases associated with ceRNA dysregulation.

While significant progress has been made in understanding the ceRNA mechanism, there are still several challenges to overcome. These include improving the accuracy of computational prediction methods, unraveling the intricate regulatory relationships within ceRNA networks, and developing targeted therapeutic strategies based on ceRNA interactions.

Tips to Make Your ceRNA Research Experiments a Success

Experimental Design

  • Clearly define the research question and hypothesis related to the ceRNA mechanism.
  • Determine the appropriate experimental model (e.g., cell lines, animal models, patient samples) and experimental conditions (e.g., disease vs. control, different treatment groups) based on the research question.

RNA Differential Analysis

  • Perform high-throughput RNA sequencing (RNA-seq) or microarray analysis to obtain transcriptomic data from the experimental samples.
  • Use statistical methods to identify differentially expressed RNAs, such as mRNAs, miRNAs, lncRNAs, and circRNAs, between the experimental groups.
  • Apply rigorous statistical corrections for multiple testing and set appropriate significance thresholds.

Gene Enrichment and Functional Analysis

  • Conduct gene ontology (GO) analysis, pathway analysis, and functional enrichment analysis to identify biological processes, molecular functions, and pathways enriched with the differentially expressed genes.
  • Utilize bioinformatics tools and databases, such as DAVID, Gene Ontology Consortium, or Reactome, to perform these analyses.
  • Explore the functional implications and potential biological roles of the identified ceRNAs and their associated pathways.

miRNA-Target Interaction Prediction

  • Utilize bioinformatics algorithms and databases, such as TargetScan, miRanda, or miRDB, to predict the potential target genes of the identified miRNAs.
  • Consider both computational prediction algorithms and experimentally validated interactions to increase the accuracy of predictions.
  • Prioritize the predicted target genes based on the strength of computational evidence and potential biological relevance to the ceRNA mechanism being studied.

Experimental Validation of ceRNA Interactions

  • Perform functional assays, such as luciferase reporter assays, RNA immunoprecipitation (RIP) sequencing, or RNA pull-down experiments, to experimentally validate the ceRNA interactions predicted computationally.
  • Design appropriate controls and negative controls to ensure the specificity of the interactions.
  • Use techniques like qRT-PCR or western blotting to validate the regulatory effect of miRNAs on target gene expression.

Co-Expression Analysis and Network Construction

  • Analyze the expression patterns of miRNAs, target genes, and other RNA species across the experimental samples.
  • Apply correlation analysis methods (e.g., Pearson correlation, mutual information) to identify co-expressed RNAs.
  • Construct ceRNA networks or regulatory networks using bioinformatics tools like Cytoscape to visualize and interpret the regulatory relationships among the different RNA molecules.

Functional Validation and Mechanistic Studies

  • Perform functional assays, such as gain-of-function or loss-of-function experiments, to validate the functional impact of ceRNA interactions on cellular processes or disease phenotypes.
  • Utilize techniques like RNA interference (RNAi), overexpression, or CRISPR-Cas9-mediated genome editing to manipulate the expression of specific ceRNA components.
  • Investigate downstream signaling pathways, molecular mechanisms, and biological consequences of the ceRNA interactions through molecular biology, biochemistry, or cell biology techniques.

Integration with Clinical Data and Validation

  • Utilize clinical databases, patient cohorts, or independent datasets to validate the relevance and clinical significance of the identified ceRNA interactions.
  • Perform survival analysis, correlation analysis with clinical parameters, or stratification analysis to assess the potential diagnostic, prognostic, or therapeutic implications of the ceRNA regulatory networks.
  • Consider experimental validation in patient samples or in vivo models to confirm the translational relevance of the ceRNA mechanism.


  1. Salmena, Leonardo, et al. "A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?." cell 146.3 (2011): 353-358.
  2. Tay, Yvonne, et al. "Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs." Cell 147.2 (2011): 344-357.
  3. Karreth, Florian A., et al. "In vivo identification of tumor-suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma." Cell 147.2 (2011): 382-395.
  4. Cesana, Marcella, et al. "A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA." Cell 147.2 (2011): 358-369.
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