Evidence map›Paper›PMID 40547876›Full record

ReviewJournal of ophthalmology2025

Systematic Identification of Candidate Genes for Inherited Retinal Disease Gene Therapy Integrating Worldwide IRD Cohort and Single-Cell Analysis.

Ching-Yun Wang, Lawrence Chen, Ting-Yi Lin, Shun-Ping Huang

Abstract readReview
In one paragraph

Review in Journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ching-Yun WangDepartment of Medical Education, Taichung Veterans General Hospital, Taichung, Taiwan.ORCID https://orcid.org/0000-0002-1367-4683
Lawrence ChenBiological Sciences Division, University of Chicago Pritzker School of Medicine, Chicago, Illinois, USA.
Ting-Yi LinDoctoral Degree Program of Translational Medicine, Academia Sinica, Taipei, Taiwan.
Shun-Ping HuangDepartment of Ophthalmology, Taichung Tzu Chi Hospital, Taichung, Taiwan.ORCID https://orcid.org/0000-0002-6060-3095

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inherited retinal dystrophies (IRDs) constitute a heterogeneous group of primarily monogenic orphan diseases caused by mutations in over 300 genes, collectively affecting millions of individuals worldwide with visual impairment. Despite significant advances, the development of gene replacement therapy for IRDs has predominantly focused on single-gene approaches, lacking a unified strategy driven by factors such as global prevalence, disease burden, and feasibility of therapeutic delivery. In this review, we propose a comprehensive protocol integrating multifaceted methodologies to refine the identification of suitable gene therapy candidates. We assessed gene prevalence, transcript size compatibility with vector packaging, and cell-type-specific expression, enabling the prioritization of promising therapeutic targets. Our approach focuses on assessing enzyme-coding genes as prime, more suitable candidates for therapeutic intervention due to their relatively similar replacement mechanism. Our findings provide a framework for identifying additional genes that may benefit from similar translational pipelines. This approach revealed a spectrum of potential candidates, including several underexplored genes with high therapeutic potential. Our findings underscore the necessity of adopting a strategic, data-driven framework to prioritize clinically impactful and scalable gene therapy targets, paving the way for broader and more effective therapeutic applications in the field of IRDs.

Identifiers

PMID40547876
PMCPMC12178778

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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.