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Updated: Jun 1, 2026
MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
Published on: October 7, 2025
Finding cancer-associated miRNAs: methods and tools
Anastasis Oulas1, Nestoras Karathanasis, Annita Louloupi
1Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.
Abstract:
Changes in the structure and/or the expression of protein coding genes were thought to be the major cause of cancer for many decades. The recent discovery of non-coding RNA (ncRNA) transcripts (i.e., microRNAs) suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) have been under investigation due to their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Due to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the features of miRNA biogenesis, have resulted in the prediction of novel miRNA genes. Computational approaches provide clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and cheaper. In combination with large scale, high throughput methods, such as deep sequencing, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This review focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution towards unraveling the role of miRNAs in cancer.
Insights
Computational methods accelerate the discovery of microRNAs (miRNAs), small RNA molecules implicated in cancer. These tools aid in identifying novel miRNA genes and their roles in tumor development, complementing experimental approaches.
Area of Science:
- Molecular Biology
- Genetics
- Bioinformatics
Background:
- Cancer research historically focused on protein-coding genes.
- The discovery of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), reveals greater complexity in cancer biology.
- miRNAs are investigated for their roles as tumor suppressors or oncogenes in carcinogenesis.
Purpose of the Study:
- To review computational methods for identifying miRNA genes.
- To provide an overview of the methodologies used by these tools.
- To highlight the contribution of computational approaches to understanding miRNA roles in cancer.
Main Methods:
- Review of existing computational tools for miRNA gene identification.
- Analysis of methodologies focusing on miRNA biogenesis features.
- Integration with high-throughput sequencing data.
Main Results:
- Computational procedures effectively predict novel miRNA genes.
- These methods identify key features characterizing miRNA regulatory units.
- Computational approaches expedite and reduce the cost of experimental verification.
- Combined with deep sequencing, they help discover molecular signatures of miRNA deregulation in human tumors.
Conclusions:
- Computational methods are essential complements to experimental miRNA identification.
- These tools enhance the speed and efficiency of discovering miRNA genes.
- Computational approaches significantly contribute to unraveling the complex role of miRNAs in cancer development.
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