ReviewNature reviews. Clinical oncology2026
MicroRNAs in oncology: a translational perspective in the era of AI.
Review in Nature reviews. Clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Natural product therapy in diabetic kidney disease: emerging multiomics-mediated signalling pathway and molecular target.Chinese medicine · 2026Review
- miRBind2 enables sequence-only prediction of miRNA binding and transcript repression.Bioinformatics (Oxford, England) · 2026Article
- Development of a Novel AAV-Mediated microRNA Gene Therapy for Spatial Suppression of BACE1 to Improve Cognitive Function in Alzheimer's Disease Model Mice.Biomolecules · 2026Article
- Hesperidin and Hesperetin: Epigenetic-Stemness Crosstalk, Antitumor Mechanisms, Preclinical Data and Translation Barriers.Biomolecules · 2026Review
- Mitochondrial and Epigenetic Drivers of Skeletal Muscle Dysfunction in Chronic Obstructive Pulmonary Disease.Antioxidants (Basel, Switzerland) · 2026Review
- Aquaporin-4 and MicroRNA Expression in Meningiomas: A Tissue-Level Exploratory Analysis.Biomedicines · 2026Article
- Multiple roles of MicroRNAs in melanoma: biomarkers for diagnosis, prognosis, and treatment prediction.Frontiers in immunology · 2026Review
- MicroRNA-based integrated diagnosis and therapy for GBM: current status and advances.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
41540122What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.