Evidence map›Paper›PMID 37765333›Full record

ReviewPharmaceutics2023

Exosome Cargos as Biomarkers for Diagnosis and Prognosis of Hepatocellular Carcinoma.

Yulai Zeng, Shuyu Hu, Yi Luo, Kang He

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Observational
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Review
  19. Review
  20. Review
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

4 authors.

Yulai ZengDepartment of Liver Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
Shuyu HuDepartment of Liver Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
Yi LuoDepartment of Liver Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
Kang HeDepartment of Liver Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.ORCID 0000-0003-4671-1337

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Due to the insidiousness of HCC onset and the lack of specific early-stage markers, the early diagnosis and treatment of HCC are still unsatisfactory, leading to a poor prognosis. Exosomes are a type of extracellular vesicle containing various components, which play an essential part in the development, progression, and metastasis of HCC. A large number of studies have demonstrated that exosomes could serve as novel biomarkers for the diagnosis of HCC. These diagnostic components mainly include proteins, microRNAs, long noncoding RNAs, and circular RNAs. The exosome biomarkers showed high sensitivity and high specificity in distinguishing HCC from health controls and other liver diseases, such as chronic HBV and liver cirrhosis. The expression of these biomarkers also exhibits correlations with various clinical factors such as tumor size, TMN stage, overall survival, and recurrence rate. In this review, we summarize the function of exosomes in the development of HCC and highlight their application as HCC biomarkers for diagnosis and prognosis prediction.

Indexed as

biomarkercircular RNAexosomehepatocellular carcinomalong noncoding RNAmicroRNA

Identifiers

PMID37765333
PMCPMC10537613

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.