Related Experiment Video

Updated: Jan 17, 2026

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

Published on: October 7, 2025

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MicroRNAs in oncology: a translational perspective in the era of AI

Ancuta Jurj1, Mihnea P Dragomir2,3,4, Ziyi Li5

  • 1Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Nature Reviews. Clinical Oncology
|January 15, 2026
PubMed

Insights

MicroRNAs (miRNAs) are crucial in cancer biology, acting as tumor suppressors or oncogenes. Advances in artificial intelligence (AI) are accelerating the clinical translation of miRNA biomarkers and therapeutics.

Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • MicroRNA (miRNA) biology has evolved significantly over 30 years, revealing their roles in normal physiology and diseases, especially cancer.
  • miRNA dysregulation is implicated in tumorigenesis, with specific miRNAs functioning as tumor suppressors or oncogenes.
  • miRNAs show promise as cancer biomarkers and therapeutic targets, with several progressing to clinical trials.

Purpose of the Study:

  • To review the critical roles of miRNAs in cancer biology.
  • To highlight the potential of miRNAs as diagnostic tools and therapeutic targets.
  • To discuss the impact of artificial intelligence (AI) and machine learning on miRNA-based cancer research and clinical translation.

Main Methods:

  • Review of existing literature on miRNA biology in cancer.
  • Analysis of the application of AI and machine learning in miRNA biomarker identification and therapeutic development.
  • Discussion of challenges and opportunities in the clinical translation of miRNA-based strategies.

Main Results:

  • miRNAs play multifaceted roles in cancer, influencing tumorigenesis.
  • AI and machine learning are revolutionizing biomarker discovery and clinical trial design in oncology.
  • Recent advances are overcoming previous hurdles for the clinical application of miRNA biomarkers and therapeutics.

Conclusions:

  • miRNAs are integral to cancer biology and hold significant potential for diagnostics and therapeutics.
  • AI-driven approaches are crucial for advancing miRNA-based cancer research and clinical translation.
  • The integration of AI with miRNA research offers new avenues for effective cancer management.

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