Evidence map›Paper›PMID 32025612›Full record

ArticleHepatology communications2020

Targeting Liver Cancer Stem Cells Using Engineered Biological Nanoparticles for the Treatment of Hepatocellular Cancer.

Kaori Ishiguro, Irene K Yan, Laura Lewis-Tuffin, Tushar Patel

Open access · goldAbstract read
In one paragraph

Article in Hepatology communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
3.4field-weighted citation impact, top 6% of its field
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

49 citing papers in PubMed, 72 citations in OpenAlex.

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  12. Using aptamers for targeted delivery of RNA therapies.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
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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 at 2 institutions in 1 country.

Kaori IshiguroDepartment of Transplantation Mayo Clinic Jacksonville FL.
Irene K YanDepartment of Transplantation Mayo Clinic Jacksonville FL.
Laura Lewis-TuffinDepartment of Cancer Biology Mayo Clinic Jacksonville FL.
Tushar PatelDepartment of Transplantation Mayo Clinic Jacksonville FL.ORCID https://orcid.org/0000-0001-7021-9532
WinnMed · USMayo Clinic in Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

By exploiting their biological functions, the use of biological nanoparticles such as extracellular vesicles can provide an efficient and effective approach for hepatic delivery of RNA-based therapeutics for the treatment of liver cancers such as hepatocellular cancer (HCC). Targeting liver cancer stem cells (LCSC) within HCC provide an untapped opportunity to improve outcomes by enhancing therapeutic responses. Cells with tumor-initiating capabilities such as LCSC can be identified by expression of markers such as epithelial cell adhesion molecule (EpCAM) on their cell surface. EpCAM is a target of Wnt/β-catenin signaling, a fundamental pathway in stem-cell growth. Moreover, mutations in the β-catenin gene are frequently observed in HCC and can be associated with constitutive activation of the Wnt/β-catenin pathway. However, targeting these pathways for the treatment of HCC has been challenging. Using RNA nanotechnology, we developed engineered biological nanoparticles capable of specific and effective delivery of RNA therapeutics targeting β-catenin to LCSC. Extracellular vesicles isolated from milk were loaded with small interfering RNA to β-catenin and decorated with RNA scaffolds to incorporate RNA aptamers capable of binding to EpCAM. Cellular uptake of these EpCAM-targeting therapeutic milk-derived nanovesicles

Identifiers

PMID32025612
PMCPMC6996342
OpenAlexW2996839227

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.