Evidence map›Paper›PMID 40542144›Full record

ReviewCancer gene therapy2025

AAV for ovarian cancer gene therapy.

Hee Chan Yoo, Sangkil Lee, Joong Yull Park, Eun-Ju Lee

Abstract readReview
In one paragraph

Review in Cancer gene therapy, 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

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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Advances in Engineered Virus-Like Particles for Genome Editing and Therapy.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2026
    Review
  4. 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.

Hee Chan YooCollege of Pharmacy, Chung-Ang University, Seoul, Republic of Korea. heechan@cau.ac.kr.ORCID http://orcid.org/0000-0003-0554-3592
Sangkil LeeCollege of Pharmacy, Chung-Ang University, Seoul, Republic of Korea.
Joong Yull ParkOrganoid Medical Center, Chung-Ang University, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0002-0164-8701
Eun-Ju LeeOrganoid Medical Center, Chung-Ang University, Seoul, Republic of Korea.

Funding

Chung-Ang University (CAU) 20230339National Research Foundation of Korea (NRF) RS-2024-00412879
6 · The paper itself

Abstract

Recent advancements in ovarian cancer treatment, particularly with PARP inhibitors, have markedly enhanced the recurrence-free interval, shifting the treatment paradigm and increasing treatment success in patients with BRCA mutations or HRD (homologous recombination deficiency). However, a significant proportion of cases experience relapse, resulting in poorer long-term survival rates when compared to other female cancers, such as breast cancer. This review explores the potential of adeno-associated virus (AAV) vectors for gene therapy in ovarian cancer and examines rational gene therapy strategies by categorizing them based on target cells and target genes to determine the most effective approach for ovarian cancer treatment. Specifically, it examines strategies such as anti-angiogenesis and immune modulation, highlighting the strategy of gene supplementation to hinder ovarian cancer progression. Innovations in AAV capsid design now allow for targeted delivery, focusing on ovarian cancer stem cells (CSCs) identified by specific markers. Additionally, leveraging DNA sequencing technologies enhances the identification and incorporation of therapeutic genes into AAV vectors, promising new avenues for ovarian cancer gene therapy.

Indexed as

DependovirusGenetic TherapyGenetic VectorsOvarian NeoplasmsAnimalsFemaleHumans

Identifiers

PMID40542144
PMCPMC12353829

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.