Evidence map›Paper›PMID 41030277›Full record

ArticleBioengineering & translational medicine2025

Engineered lung cell targeting and SLC7A11 siRNA expressing bacterial extracellular vesicles impair the progression of none-small cell lung cancer.

Xiao-Dan Wan, Xue-Liang Zhou, Jin-Long Liu, Hua Xu

Abstract read
In one paragraph

Article in Bioengineering & translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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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.

Xiao-Dan WanDepartment of Electrocardiogram, The First Affiliated Hospital Nanchang University Nanchang Jiangxi China.
Xue-Liang ZhouDepartment of Cardiac Surgery, The First Affiliated Hospital Nanchang University Nanchang Jiangxi China.
Jin-Long LiuInstitute of Translational Medicine Shanghai University Shanghai China.
Hua XuDepartment of Thoracic Surgery, The First Affiliated Hospital Nanchang University Nanchang Jiangxi China.ORCID https://orcid.org/0009-0006-3109-5622

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-small cell lung cancer (NSCLC) presents significant therapeutic challenges, often characterized by aggressive proliferation and metastasis. This study investigates the role of SLC7A11, a ferroptosis-related gene, in NSCLC progression and the potential of engineered bacterial extracellular vesicles (BEVs) expressing SLC7A11-targeting siRNA as a therapeutic strategy. Using TCGA and GEO databases, we identified that SLC7A11 was significantly upregulated in NSCLC tissues. Functional assays demonstrated that SLC7A11 knockdown in NSCLC cell lines (NCI-H2122 and NCI-H647) via qPCR, Western blot, and immunofluorescence resulted in impaired proliferation, migration, and invasion abilities. In vivo xenograft models further revealed that SLC7A11 knockdown inhibited tumor growth and metastasis, corroborated by histological analyses. To enhance targeted delivery of SLC7A11 siRNA, we engineered BEVs with a lung cell targeting peptide, verifying their structure and function through transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). In vivo toxicity assessments indicated safety for these bioengineered vesicles. Importantly, treatment with BEVs-LCTP-siSLC7A11 not only impaired tumorigenesis but also activated ferroptosis pathways, as evidenced by altered expression levels of SLC7A11 and transferrin in tumor and metastatic tissues. Our findings suggest that targeting SLC7A11 through engineered BEVs presents a promising approach to inhibit NSCLC progression while activating ferroptosis, offering insights into novel therapeutic strategies against lung cancer.

Indexed as

engineered bacterial extracellular vesiclesferroptosisnone‐small cell lung cancerSLC7A11

Identifiers

PMID41030277
PMCPMC12478443

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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.