ArticleiScience2025
Landscape of extracellular small RNA and identification of biomarkers in multiple human cancers.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Recent Advances in Material Basis for Co-treatment of Cardiovascular and Gastrointestinal Disorders.Chinese journal of integrative medicine · 2026Review
- Endogenous and Exogenous Small RNA Signatures as Novel Tools for Postmortem Interval Determination.Biomolecules · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extracellular RNAs (exRNAs) in biofluids, sourced from diverse tissues, exhibit various biological functions and diagnostic potential. Small non-coding RNAs, such as rsRNAs and tsRNAs, are abundant in tissues and likely secreted into biofluids, contributing to exRNA profiles. To comprehensively evaluate exRNAs, we employed traditional and enzymatic treatment RNA sequencing to systematically profile exRNAs across six human biofluids including serum, ascites, urine, milk, seminal plasma and saliva as well as sera from mice, rats, rabbits, and bovines. rsRNAs were identified as the most abundant exRNA species in human biofluids, with rsRNAs and tsRNAs showing high expression and species-specific profiles across animals. In serum samples from 51 healthy individuals and 69 cancer patients, exRNA-based machine learning model achieved 94.1% sensitivity and 100% specificity in cancer detection and accurately classified tumor origin. Altogether, this study reveals distinct rsRNA-abundant exRNA landscape across multiple biofluids and support the potential of exRNA signatures in pan-cancer diagnostics.
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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.