Evidence map›Paper›PMID 39006969›Full record

ArticleFrontiers in molecular biosciences2024

Diagnostic model for hepatocellular carcinoma using small extracellular vesicle-propagated miRNA signatures.

Xinyi Luo, Lin Jiao, Qin Guo, Yi Chen, Nian Wang, Yang Wen, JiaJia Song, Hao Chen, Juan Zhou, Xingbo Song

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Xinyi Luo *Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Lin Jiao *Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Qin GuoDepartment of Laboratory Medicine, the First People's Hospital of Ziyang, Ziyang, China.
Yi ChenDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Nian WangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Yang WenDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
JiaJia SongDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Hao ChenDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Juan ZhouDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Xingbo SongDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Small extracellular vesicles (sEVs) are bilayer lipid membrane vesicles containing RNA that exhibit promising diagnostic and prognostic potential as cancer biomarkers. Aims: To establish a miRNA panel from peripheral blood for use as a noninvasive biomarker for the diagnosis of HCC. Methods: sEVs obtained from plasma were profiled using high-throughput sequencing. The identified differential miRNA expression patterns were subsequently validated using quantitative real-time polymerase chain reaction analysis. Results: The random forest method identified ten distinct miRNAs distinguishing HCC plasma from non-HCC plasma. During validation, miR-140-3p ( Conclusion: These aberrantly expressed miRNAs delivered by sEVs potentially contribute to HCC pathology and may serve as diagnostic biomarkers for HCC.

Indexed as

biomarkercancerhepatocellular carcinomamicroRNAsmall extracellular vesicle

Identifiers

PMID39006969
PMCPMC11239443

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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.