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. 2024 Oct 28:14:1484074.
doi: 10.3389/fonc.2024.1484074. eCollection 2024.

Organoids research progress in gynecological cancers: a bibliometric analysis

Baiyun He #  1 , Huihao Ma #  2 , Hongbo Yu  1 , Dongmei Li  1 , Li Zhang  1 , Junjie Wang  1
Affiliations

Organoids research progress in gynecological cancers: a bibliometric analysis

Baiyun He et al. Front Oncol. .

Abstract

Background: Gynecological cancers (GC) pose a severe threat to the health and safety of women's lives, and organoids, as in-vitro research models, have demonstrated significant advantages in simulating tissue characteristics and drug screening. In recent years, there has been a rapid increase in research outcomes related to organoids in GC. However, there has been no bibliometric study concerning.

Methods: Publications related to GC and organoids from 2010-2023 were retrieved from the Web of Science Core Collection (WoSCC). We conducted a bibliometric analysis and visualization using CiteSpace, VOSviewer, and the Bibliometrix R Package. This analysis included the spatiotemporal distribution, author, sources, references, and keywords.

Results: A total of 333 publications were included. The number of annual publications indicated an explosive phase of development since 2019. The USA was the most important country in terms of cooperation, publication output, citation and centrality. University of California system ranked first in productivity among institutions, and HIPPO Y is the most relevant author in the research field. CANCERS published the most documents, and NATURE is the most cited sources. Analysis of Keywords and References, it is possible to establish the trend, and find the hotspots in the research field.

Conclusion: This bibliometric analysis delineated global landscapes and progress trends in GC organoids research. This study emphasized that organoids can effectively replicate the original tissue or tumors, providing a good in-vitro model for research on tumor-related mechanisms and showing significant advantages in drug screening and efficacy clinical prediction. Additionally, as preclinical models, they provide compelling evidence for personalized therapy and prediction of patient drug responses.

Keywords: bibliometric analysis; cervical cancer (CC); endometrial cancer (EC); gynecological cancers (GC); organoids; ovarian cancer (OC).

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of the study identification and inclusion process.
Figure 2
Figure 2
Global trends and country/region research contributions to organoids in GC. (A) Annual and Cumulative number of publications related to organoids in GC. (B) The top 10 Corresponding Author’s Countries (C) The top 10 Cited Countries/Region. (D) The international cooperation analysis (cooperation frequency > 2). The line between two countries/regions indicates cooperative relationship. (E) The network of countries/regions from CiteSpace. The size of the nodes represents the total number of publications for a country/region, with larger nodes indicating a greater number of articles, and the nodes have purple outer rings in the network representing high betweenness centrality which is the importance of location in a network of nodes.
Figure 3
Figure 3
The distribution of authors and institutions in GC and organoids research. (A, B) The top 10 most relevant authors and authors’ production over time. (C) The top 10 institutions with counts and centrality. (D) The network of institutions from CiteSpace. (E) The Top 5 institutions with the strongest citation Bursts from CiteSpace.
Figure 4
Figure 4
The distribution of authors and institutions in GC and organoids research. (A) The Top 10 most relevant sources. (B) Network visualization of sources analysis from VOSviewer. (C) The top 10 most local cited sources. (D) Network visualization of cited sources analysis from VOSviewer. (E) Dual-map overlay of journals from CiteSpace.
Figure 5
Figure 5
The network of keywords plus co-occurrence. (A) Overlay map of keyword according to clusters from VOSviewer. (B) The timestamp visualization of keywords from VOSviewer. (C) the trend topics of KeywordPlus from Bibliometrix R Package.
Figure 6
Figure 6
The references and documents analysis. (A) The co-citation network of references according to clusters from VOSviewer. (B) Density map of cocited analysis of references from VOSviewer. (C) Visualization map of the top 22 references with the strongest citation bursts from CiteSpace. (D) the Historiograph of the GC and organoids research from Bibliometrix R Package.
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