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Review
. 2024 May 17;10(10):e31478.
doi: 10.1016/j.heliyon.2024.e31478. eCollection 2024 May 30.

Bibliometric analysis of the research hotspots and trends of circular RNAs

Reyila Tuerdi  1 , Hui Zhang  2 , Wenxin Wang  1 , Minghui Shen  3 , Xingmin Wei  1
Affiliations
Review

Bibliometric analysis of the research hotspots and trends of circular RNAs

Reyila Tuerdi et al. Heliyon. .

Abstract

Background and objective: Circular RNAs (circRNAs) have garnered considerable attention in the study of various human diseases due to their ubiquitous expression and potential biological functions. This study conducts a bibliometric and visualization-based analysis of circRNA-related research in diseases, aiming to reveal the current status, hotspots and emerging trends within the field.

Methods: Literature published between 2013 and 2022 and indexed in the Web of Science core databases was retrieved. Visualizations of publication volume, countries, authors, institutions, journals, references, and keywords were performed. Microsoft Excel (2021) was used to analyze and graph publication volume and growth trends. Additionally, CiteSpace (version 6.1.R6) and VOSviewer (version 1.6.18) were employed to visualize the bibliographic information.

Results: Between 2013 and 2022, a total of 4195 relevant articles on circRNA in the context of diseases were identified. These articles covered 56 countries, 2528 institutions, 19,842 authors and 698 journals, citing 85,541 references. The annual publication volume showed an exponential growth trend, with rapid development post-2017. China, the United States and Germany emerged as the top three contributors, demonstrating high publication volume and total citations. Notably, Nanjing Medical University exhibited the highest publication volume, boasting 291 articles. Burton B. Yang and Li Yang consistently ranked among the top 10 authors in terms of publication volume and citations, emerging as core contributors in this research field. The journal Bioengineered ranked first in terms of published articles (160), with an impact factor of 6.832, while Molecular Cancer garnered the highest impact factor (41.4), solidifying its position as a top journal in this field. Furthermore, high-frequency keywords included "expression" "proliferation" "biomarker" "microRNA" "cancer", signifying the prevailing research hotspots and principal themes of this field over the past decade. As of 2022, "biomarker", "prostate cancer","drug resistance","papillary thyroid carcinoma", etc. continued as keywords during the outbreak period. At present, the value of circRNA application is mainly reflected in the two aspects of biomarkers and therapeutic targets, and the prediction of accurate diagnosis and precise treatment based on big data analysis, especially in cancer, will become a hot spot of research in the future.

Conclusion: The trajectory of circRNA research from its biological origins to its applications in diseases has been delineated from 2013 to 2022. However, the transition to disease-specific applications and exploration of biological functions warrants further attention in future research endeavors.

Keywords: Bibliometric analysis; CiteSpace; VOSviewer; Visualization; circRNA.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Distribution of the number of publications related to circRNA research in disease areas.
Fig. 2
Fig. 2
Country distribution of circRNA and disease research.(A)Co-occurrence network map, each node represents a country, the nodes are displayed in the form of a yearly wheel, the width of the wheel represents the number of papers published by the country in that year, and the size of the node represents the frequency of occurrence.(B) Cooperation network map, the connection between nodes represents cooperation, and the thickness of the line represents the strength of cooperation.
Fig. 3
Fig. 3
The dual-map overlay of journals. Publications on this field primarily appeared from two regions of the citing map: Molecular/Biology/Immunology in orange and Medicine/Medical/Clinical in green. The orange-colored curve indicated that the publications belonging to Molecular/Biology/Immunology journals primarily cited those in Molecular/Biology/Genetics journals and Health/Nursing/Medicine. The green-colored curve revealed that the publications from Medicine/Medical/Clinical journals mainly cited thuse in Molecular/Biology/Genetics journals.
Fig. 4
Fig. 4
Author distribution of circRNA and disease research.(A)Co-occurrence of authors network map, nodes represent different authors, node size represents the number of author posts, the thickness of the connecting line between nodes represents the strength of cooperation. (B)Cooperation network map, the color represents different clusters, and the same color represents similar research directions.
Fig. 5
Fig. 5
Funding support landscape for research on circRNA in diseases.
Fig. 6
Fig. 6
Co-citation analysis of references used in publications on circRNA-related research in diseases, The color represents different clusters, and the same color represents similar research directions.
Fig. 7
Fig. 7
Keyword distribution of circRNA and disease research.(A)Co-occurrence of keywords, each node represents a keyword, and the size of the node represents the frequency of their occurrence, and the line between nodes represents the intensity of co-occurrence.(B) Keyword clustering map, CiteSpace uses Log-likelihood rate(LLR) to cluster closely related keywords. Different patterns represent a cluster. Tag # was assigned to the cluster, and the smaller the number is, the more keywords are in the cluster.
Fig. 8
Fig. 8
Timeline view of keyword clustering, in the timeline view, the keywords on the same horizontal line belong to the right cluster. The colors of lines and keywords in the view correspond to the colors of the time bar in the lower left corner.
Fig. 9
Fig. 9
Top 35 keywords with the strongest citation bursts. From left to right, the first column represents the year in which this keyword first appeared. The second column represents the bursts' strength of the keyword. The third column represents the year in which this keyword begins and ends the burst. The fourth column in red represents the period during which the keyword is burst.
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