Evidence map›Paper›PMID 40210865›Full record

ArticleNature communications2025

FRET imaging of glycoRNA on small extracellular vesicles enabling sensitive cancer diagnostics.

Tingju Ren, Yingzhi Zhang, Yuxiao Tong, Qi Zhang, Tianhao Wang, Yue Wang, Chunguang Yang, Zhangrun Xu

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Review
  17. Review
  18. Article
  19. GlycoRNA, a novel RNA modification.Discover oncology · 2025
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tingju RenResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China.ORCID http://orcid.org/0000-0002-8318-6167
Yingzhi ZhangNational Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
Yuxiao TongResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China.
Qi ZhangResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China.
Tianhao WangResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China.
Yue WangResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China.
Chunguang YangResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China.
Zhangrun XuResearch Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province, China. xuzr@mail.neu.edu.cn.ORCID http://orcid.org/0000-0003-3834-1145

Funding

National Natural Science Foundation of China (National Science Foundation of China) 22374016
6 · The paper itself

Abstract

Glycosylated RNAs (glycoRNAs), a recently discovered class of membrane-associated glyco-molecules, remain poorly understood in function and clinical value due to limited detection methods. Here, we show a dual recognition Förster resonance energy transfer (drFRET) strategy using nucleic acid probes to detect N-acetylneuraminic acid-modified RNAs, enabling sensitive, selective profiling of glycoRNAs on small extracellular vesicles (sEVs) from minimal biofluids (10 μl initial biofluid). Using drFRET, we identify 5 prevalent sEV glycoRNAs derived from 7 cancer cell lines. In a 100-patient cohort (6 cancer types and non-cancer controls), sEV glycoRNA profiles achieve 100% accuracy (95% confidence interval) in distinguishing cancers from non-cancer cases and 89% accuracy in classifying specific cancer types. Furthermore, drFRET reveal that sEV glycoRNAs specifically interact with Siglec proteins and P-selectin, which is critical for sEV cellular internalization. The drFRET strategy provides a versatile and sensitive platform for the imaging and functional analysis of sEV glycoRNAs, with promising implications for clinical applications.

Indexed as

Extracellular VesiclesFluorescence Resonance Energy TransferNeoplasmsCell Line, TumorGlycosylationHumans

Identifiers

PMID40210865
PMCPMC11985951

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.