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  • Review Article
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MicroRNAs in oncology: a translational perspective in the era of AI

Nature Reviews Clinical Oncology (2026)Cite this article

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Abstract

Over the past three decades, knowledge of microRNA (miRNA) biology has advanced from the initial discovery of their regulatory functions to the finding of abnormal activity in leukaemias, and then to a comprehensive understanding of the roles of miRNAs in both normal physiology and most diseases, with cancer being extensively studied. miRNA dysregulation contributes to tumorigenesis, with certain miRNAs acting as either tumour suppressors or oncogenic factors in a context-dependent manner. A subset of miRNAs have shown promise as tumour biomarkers and therapeutic targets in preclinical studies, with several miRNA-based diagnostic tools and treatments progressing to clinical trials. Artificial intelligence (AI) and machine learning techniques began to be introduced into cancer research and oncology a decade ago and are now on the verge of revolutionizing biomarker identification and clinical trials. In this Review, we highlight important roles of miRNAs in cancer biology and their potential as diagnostic tools and therapeutic targets. In particular, we discuss emerging challenges and opportunities presented by AI-driven data analysis and combinatorial strategies, and how advances in these areas have addressed previous doubts on the clinical translation of miRNA-based biomarkers and therapeutics.

Key points

  • Despite extensive studies, microRNAs (miRNAs) are not used clinically as biomarkers, owing to insufficient diagnostic specificity and sensitivity.

  • Contemporary studies of miRNA biomarkers aim to improve diagnostic tools by using miRNA profiles, considering miRNA transport mechanisms, integrating miRNA and other omics data, and applying machine learning (ML) and other artificial intelligence (AI) methods to integrate the data.

  • AI and ML enable complex data analysis to identify multi-miRNA signatures for cancer subtyping, early detection and pan-cancer diagnostics using models such as random forests, deep learning and foundation models.

  • AI and ML tools are also advancing the development of miRNA-based therapeutics, although challenges relating to data quality and algorithm transparency highlight the need for standardized, collaborative and interpretable frameworks for reliable translational research.

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Fig. 1: Integrative landscape of miRNA biology and miRNA-based therapeutics in cancer.
Fig. 2: Landscape of AI and ML applications in oncology and miRNA-based cancer research and biomarker development.

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Acknowledgements

G.A.C. is the Charles B. Barker Chair. Work in G.A.C.’s laboratory is supported by NCI grant 1R01CA222007-01A1, NIDCR grant R01DE032018, DoD CDRMP Idea Award BC200208P1 (W81XWH-21-1-0030), Team DoD Grant in Gastric Cancer CA200990P2 (W81XWH-21-1-0715), DoD Idea Award PC230419 (HT9425-24-1-0052), the 2019 Faculty Achievement Award, CLL Global Research Foundation 2019 grant, CLL Global Research Foundation 2020 grant, CLL Global Research Foundation 2022 grant, CLL Global Research Foundation 2024 grant, The G. Harold & Leila Y. Mathers Foundation, two grants from Torrey Coast Foundation, an Institutional Research Grant 2024, a Development Grant associated with the Brain SPORE 2P50CA127001, an Institutional Bridge Funding grant 2023, and the Ben and Catherine Ivy Foundation grant. The work of Z.L. was partially supported by US NIGMS grant R35GM159819 and a Translational and Basic Science Research in Early Lesions (TBEL) Coordinating and Data Management Center (CDMC) grant U24CA274212. The work of M.P.D. is supported by the Berlin Institute of Health Clinician Scientist Program, a German Cancer Research Center (DKTK) Berlin Young Investigator Grant 2022, the Berliner Krebsgesellschaft DRFF202204, and an Else Kröner-Fresenius Foundation First and Second Applications Grant (2024_EKEA.77).

Author information

Author notes

  1. These authors contributed equally: Ancuta Jurj, Mihnea P. Dragomir.

Authors and Affiliations

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

    Ancuta Jurj & George A. Calin

  2. Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    Mihnea P. Dragomir

  3. German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Mihnea P. Dragomir

  4. Berlin Institute of Health, Berlin, Germany

    Mihnea P. Dragomir

  5. Department of Biostatistics, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ziyi Li

  6. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    George A. Calin

  7. Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    George A. Calin

Authors
  1. Ancuta Jurj
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  4. George A. Calin
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Contributions

All authors made a substantial contribution to all aspects of the preparation of this manuscript.

Corresponding authors

Correspondence to Ziyi Li or George A. Calin.

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Competing interests

G.A.C. is one of the scientific founders of Ithax Therapeutics. The remaining authors declare no competing interests.

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Nature Reviews Clinical Oncology thanks H.-D. Huang, N. Seki and the other, anonymous, reviewer for their contribution to the peer review of this work.

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Glossary

Adaptor dimer

Sequencing phenomenon whereby two adaptors bind to each other or build short adaptor-only fragments with consequences such as wasted sequencing reads and/or reduced depth per-cell depth, especially problematic when cDNA amounts are low.

Adaptor ligation bias

Certain RNA sequences are favoured during adaptor ligation, which can distort gene expression quantification and result in cell type-specific artefacts.

Artificial intelligence

A broad field focused on building computational systems that mimic human intelligence, including reasoning, learning and decision-making.

Black box

A term used to describe artificial intelligence and machine learning models with internal decision-making processes that are difficult to interpret, with a lack of transparency regarding the biological rationale underlying their predictions that might reduce trust in the outputs, thus posing challenges for clinical translation.

Decision trees

A supervised machine learning method that uses a tree-like structure to make predictions. At each branching point (‘node’), the algorithm applies a decision rule based on one feature, splitting the data into subgroups. This process continues until terminal nodes (‘leaves’), which assign a final prediction, are reached. Decision trees are highly interpretable and form the basis for many ensemble approaches such as random forests and boosting algorithms.

Ensemble learning

A machine learning strategy in which multiple predictive models (for example, decision trees, neural networks or statistical classifiers) are combined to improve overall performance.

Feature spaces

Multidimensional spaces in which each dimension represents a measurable attribute or feature of the data. Each sample can be visualized as a point in such a space, and the geometric relationships between points often reflect underlying biological or clinical differences. Machine learning models operate within these feature spaces to detect patterns, classify samples, or make predictions.

Hyperplane

A hyperplane is a mathematical boundary that separates data points into different groups within a multidimensional feature space. In machine learning, classifiers such as support vector machines use hyperplanes to distinguish between classes by finding the boundary that best maximizes the margin between them.

Kernel tricks

A computational technique used in algorithms such as support vector machines to analyse data that are not linearly separable. The kernel trick maps the original data into a higher-dimensional feature space whereby a simple linear boundary (hyperplane) can separate the classes without explicitly computing the high-dimensional transformation. This approach enables efficient modelling of complex, non-linear relationships.

Locked nucleic acid

(LNA). A chemically modified nucleic acid analogue in which the ribose sugar is conformationally constrained by a 2′-O,4′-C methylene bridge. This ‘locked’ C3′ endo conformation increases the thermal stability and affinity of LNA-containing oligonucleotides for complementary RNA, enhances mismatch discrimination, and provides strong resistance to nuclease degradation. LNAs are widely used in antisense and microRNA-based therapeutics to improve potency, specificity and in vivo stability.

Machine learning

A key form of artificial intelligence in which data are used to train computational models to identify patterns and make predictions or decisions without explicit programming.

miRNA signatures

Defined sets of microRNAs (miRNAs), the combined expression pattern of which distinguishes biological states such as cancer from non-malignant tissue and tumour subtypes as well as disease prognosis or responsiveness to therapy. Unlike single miRNA species, miRNA signatures capture the network-level regulation of gene expression and often provide greater sensitivity, specificity and robustness in clinical applications.

miRNome

The complete set of microRNAs (miRNAs) expressed in a specific cell type, tissue or organism under defined conditions. Analysis of the miRNome enables comprehensive profiling of miRNA expression patterns, identification of disease-associated miRNA signatures and the study of regulatory networks involving miRNAs.

Seed matching

Putative microRNA (miRNA) targets are identified by complementarity within the seed region (nucleotides 2–7/8 of the miRNA). Although seed pairing is the primary determinant of target specificity, it often yields false-positive results because short complementary motifs occur by chance, and functional targeting also requires favourable mRNA structure, site accessibility, sequence context and co-expression of the miRNA and its target.

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Jurj, A., Dragomir, M.P., Li, Z. et al. MicroRNAs in oncology: a translational perspective in the era of AI. Nat Rev Clin Oncol (2026). https://doi.org/10.1038/s41571-025-01114-x

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