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Gene Editing and Aging: A Bibliometric Analysis of Global Trends and Frontier Themes (2015-2024)

Chen, L.; Li, H.; Zhu, Y.; Zheng, Z.; Wang, J.; Wang, H.; Huang, W.; Luo, Y.

2025-05-02 scientific communication and education
10.1101/2025.05.01.651618 bioRxiv
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ObjectiveThe accumulation of DNA damage and mutations is a key contributor to aging. Recent studies have shown that disrupting the Beclin 1-BCL2 autophagy regulatory complex through gene editing can extend lifespan in mice. The precise application of gene editing technologies offers a promising strategy for aging. This study conducted a bibliometric analysis to map the knowledge landscape of gene editing and aging. MethodsWe retrieved publications related to genome editing and aging from the Web of Science Core Collection, covering the period from 2015 to 2024. The data were analyzed using VOSviewer and R package Bibliometrix. These tools enabled us to identify the most productive researchers, journals, institutions, countries and visualized current trends, emerging research hotspots. ResultsA total of 982 publications on genome editing and aging were identified. The United States (n=285) and China (n=214) form a dual-core structure leading global output. Harvard University (n=116) emerged as the most prolific institution. Scientific Reports was the top-publishing journal, with 23 articles in 2024. ZHANG Y (n=12, citations=102, H-index=6) was identified as the most productive author. KIM Es 2017 publication in Nature Communications (TC=494, TC/year=54.9, NTC=9.33) has had a significant and ongoing impact. The analysis indicates that future directions will include CRISPR optimization and AI-assisted genomic analysis. ConclusionThis study presents the first comprehensive bibliometric analysis and visualization of the knowledge structure in gene editing and aging research up to 2024. It offers researchers a detailed overview of current developments, trends, and emerging frontiers in this rapidly evolving domain.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Genomics, Proteomics & Bioinformatics
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Top 0.5%
10.5%
2
PLOS ONE
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3
npj Aging
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9.5%
4
FASEB BioAdvances
15 papers in training set
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7.4%
5
FEBS Open Bio
29 papers in training set
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7.1%
6
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7
International Journal of Molecular Sciences
453 papers in training set
Top 2%
3.7%
50% of probability mass above
8
Aging Cell
144 papers in training set
Top 1%
3.2%
9
eneuro
389 papers in training set
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2.2%
10
PLOS Computational Biology
1633 papers in training set
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2.0%
11
PLOS Biology
408 papers in training set
Top 7%
2.0%
12
Frontiers in Aging Neuroscience
67 papers in training set
Top 2%
1.7%
13
Bioinformatics
1061 papers in training set
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1.5%
14
eLife
5422 papers in training set
Top 46%
1.4%
15
Artificial Intelligence in the Life Sciences
11 papers in training set
Top 0.1%
1.4%
16
Scientific Reports
3102 papers in training set
Top 69%
1.0%
17
F1000Research
79 papers in training set
Top 3%
1.0%
18
Computational and Structural Biotechnology Journal
216 papers in training set
Top 7%
1.0%
19
Cells
232 papers in training set
Top 4%
1.0%
20
GeroScience
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Top 1%
0.9%
21
GigaScience
172 papers in training set
Top 2%
0.9%
22
The FEBS Journal
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Top 0.6%
0.9%
23
Journal of Cellular Physiology
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0.8%
24
Genome Biology and Evolution
280 papers in training set
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0.8%
25
Heliyon
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0.8%
26
Nucleic Acids Research
1128 papers in training set
Top 17%
0.8%
27
Communications Biology
886 papers in training set
Top 22%
0.8%
28
PLOS Genetics
756 papers in training set
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0.8%
29
Nature Human Behaviour
85 papers in training set
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0.8%
30
Gene
41 papers in training set
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0.8%