Evidence map›Paper›PMID 42503304›Full record

ReviewAdvanced healthcare materials2026

Extracellular Vesicles in Cancer: Biomarkers, Mechanisms, and Emerging Diagnostic Technologies.

Cheng Wang, Juan Peng, Sichong Xie, Chao Kang, Ya-Juan Liu

Abstract readReview
In one paragraph

Review in Advanced healthcare materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Cheng WangPrecise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou, China.
Juan PengReproductive and Genetic Medicine Department, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, Hunan, China.ORCID https://orcid.org/0000-0001-7110-0744
Sichong XiePrecise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou, China.
Chao KangSchool of Chemistry and Chemical Engineering, Guizhou University, Guiyang, China.ORCID https://orcid.org/0000-0002-5156-4506
Ya-Juan LiuGuangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.ORCID https://orcid.org/0000-0002-6897-8561

Funding

Foundation for Young Talents in Higher Education of Guangdong 2024KQNCX098Guangdong Basic and Applied Basic Research Foundation General Project 2026A1515011578Guangzhou Basic and Applied Basic Research Foundation SL2024A04J00739National Natural Science Foundation of China 22464006This research is funded by Hunan Provincial Natural Science Foundation 2025JJ60671
6 · The paper itself

Abstract

Extracellular vesicles (EVs) are nanoscale, membrane-bound particles that transport diverse biomolecules-including proteins, nucleic acids, lipids, and metabolites-between cells, thereby orchestrating key processes in cancer progression. Tumor-derived EVs modulate angiogenesis, epithelial-to-mesenchymal transition, extracellular matrix remodeling, fibroblast activation, and immune evasion, shaping the tumor microenvironment, and driving metastasis as well as therapy resistance. With their stability in biofluids and cargo reflective of cellular origin, EVs have emerged as powerful non-invasive biomarkers for early detection, disease monitoring, and prognosis across multiple cancer types. Recent advances in enrichment, characterization, and molecular profiling technologies-ranging from ultracentrifugation and microfluidics to proteomics, RNA sequencing, and surface-enhanced Raman spectroscopy, have greatly expanded the diagnostic potential of EVs. Integration with machine learning further enhances sensitivity, specificity, and tumor classification, while multiomic and multiplex platforms enable high-throughput and clinically relevant applications. This review highlights the multifaceted roles of EVs in cancer biology, catalogs emerging biomarkers, and compares state-of-the-art detection technologies, offering a comprehensive reference for advancing EV-based diagnostics and precision oncology.

Indexed as

Biomarkers, TumorExtracellular VesiclesNeoplasmsAnimalsHumansTumor MicroenvironmentBiomarkers, Tumorbiomarkerscancer diagnosisextracellular vesiclesliquid biopsy

Identifiers

PMID42503304
PMCPMC13507622

What Socratic holds

Textmetadata
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 Socratic graph.