A bibliometric analysis of non-coding RNA and mesenchymal stem cell research

doi:10.12307/2025.006

Abstract

Abstract: BACKGROUND: Non-coding RNA, as a class of RNA molecules that do not encode proteins, plays a crucial role in cellular regulation. Simultaneously, mesenchymal stem cells possess multipotent differentiation capabilities, making them vital for tissue repair and regeneration. In recent years, researchers have extensively explored the role of non-coding RNA in regulating the functions of mesenchymal stem cells.
OBJECTIVE: To systematically understand the trends and key areas of research related to non-coding RNA and mesenchymal stem cells through bibliometric analysis.
METHODS: Literature data on non-coding RNA and mesenchymal stem cell-related research were retrieved from the Science Citation Index Expanded in the Web of Science Core Collection from 1990 to the present. The literature data were analyzed using bibliometric tools such as VOSviewer and CiteSpace, focusing on publication output, clustering, citation frequency, burstiness, and intermediary centrality with respect to years, countries or regions, research institutions, cited references, and keywords. This analysis aimed to reveal the knowledge base and emerging hotspots in the research field.
RESULTS AND CONCLUSION: (1) This study included a total of 5 348 articles for analysis, showing a significant growth trend in relevant literature from 1997 to 2021. Although there was a slight decrease in 2022, the publication output remained at a high level. China dominated the research in this field, with Shanghai Jiao Tong University being the most active contributor. (2) Analysis of cited references revealed that highly bursty, intermediary, and cited articles were mainly related to topics such as microRNA, extracellular vesicles, and bone metabolism. These articles constitute an important knowledge base for research on non-coding RNA and mesenchymal stem cells. (3) Burstiness analysis of keywords identified persistent bursts in keywords such as exosomes, circular RNA, extracellular vesicles, and injury. (4) Cluster evolution analysis of keywords with continuous growth in publication output included clusters related to tumor microenvironment, osteoarthritis, oxidative stress, and extracellular vesicles. These keywords reflect the current and future research hotspots in the field. (5) The findings of this study not only illustrate the research trends in the field of non-coding RNA and mesenchymal stem cells but also provide potential directions and insights for researchers.

Key words: non-coding RNA, mesenchymal stem cell, bibliometric analysis, visual analysis, hotspot prospect, knowledge base, CiteSpace, action mechanism, extracellular vesicle, translational application

CLC Number:

Zhou Jiaxin, Tao Jiang. A bibliometric analysis of non-coding RNA and mesenchymal stem cell research[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(1): 87-94.

Figures/Tables 5

2.1 发文趋势分析结果利用Web of Science核心集数据库检索关于非编码RNA与间充质干细胞研究相关的文献，文献数量共7 558篇，限定语言为英语且文章类型为“Article”后为5 848篇，使用CiteSpace6.2.R4软件进行查重，去除重复的500篇文献后，最后为5 348篇文献。最早的文献可追溯至1997年，1997-2023年的年发文量见图1A；2003-2011年的发文量呈指数增长趋势(R2=0.997 2)；2012-2021年，文献发文量继续增长，表现为线性增长趋势(R2=0.974 9)，2021年的年发文量最大，达729篇，2022年回落，2023年发文量未统计全年。按国家/地区统计累计发文量，共包括79个国家/地区，其中中国的累计发文量最大，占50.2%，为3 191篇，见图1B。 2.2 研究机构合作关系网络及贡献据统计，共202个研究机构纳入分析。由VOSviewer 1.6.20软件生成机构合作网络图，见图2，每个节点为研究机构，节点大小与机构发文量呈正比，两节点连线表示机构间存在合作关系。其中，合作强度排名前3的机构依次为上海交通大学、中国医学科学院和南京医科大学，主要为中国机构间的合作，国际机构间的合作较少。按研究机构统计累计发文量，其中发文量排名前3的机构分别是上海交通大学(198篇)、中山大学(152篇)和南京医科大学(139篇)。另外，计算了各个研究机构的中介中心性，见表1。中介中心性衡量了节点在网络中位置的重要性。中介中心性指的是一个节点(例如一个国家或一篇文章)在其他节点之间的最短路径上出现的次数[13]。中介中心性高( > 0.1)的节点被认为是关键枢纽，在网络图中被标记为紫色外圈。按中心性排名前3位的机构分别为俄亥俄大学系统(0.14)、韩国首尔大学(0.13)和加利福尼亚大学(0.12)。"

2.3 共被引用网络及突增性分析结果共被引用分析是一种用于评估文献之间相互关系的科学计量学方法。当2篇或多篇学术文献同时被后续文献引用，可视为共被引文献[14]。在共被引用网络图中，每一个节点代表一篇学术文献，节点的大小与该文献的被引用频率呈正比，节点的颜色表示文献的出版时间，两个节点之间的连线反映它们的共被引关系。共被引网络图中呈现聚类模块规模排名前14的聚类模块，并以不同色块标志，见图3。采用模块度(modularity)和加权平均轮廓系数(Weighted mean silhouette)用来衡量网络被分割成不同模块或群集的质量和有效性[15]，这两个指标分别大于0.3和0.5，提示聚类效果理想[16]。文章中，共被引聚类中的模块度为0.859 3，意味着各个子领域可以被清晰地划定。加权平均轮廓系数为0.950 4，表明文章的聚类结果在聚类紧密度和分离度方面具有高度的质量和效果。聚类模块以“#0-#13”序号命名，序号越小表示该模块越大包含的文献量越多。其中，规模最大的聚类模块是骨关节炎(#0 osteoarthritis)，包含114篇文献，平均出版时间是2019年。突增是指在某个时间段内，一篇文献被引频次或一个关键词出现频次快速增加，这通常表明该文献在该时间段内具有较高的学术影响力或者在研究领域中的重要性突然增加。突增有两个属性，分别是突增的强度和突增状态的持续时间。表2展示的是2年内被引突增最强的5篇文献[17-21]，其中突增强度最强的是学者ESKILDSEN等[17]在2011年于《Proceedings of the National Academy of Sciences of the United States of America》发表的论文，揭示了miR-138抑制间充质干细胞成骨分化的机制。其中，有3篇文献划分为外泌体(#3 exosomes)聚类[17-19]，它们的主要内容都与微小RNA调控骨代谢相关。另外，国际细胞外囊泡学会[20]和KALLURI等[21]发表的2篇文章划分为成骨分化(#1 osteogenic differentiation)聚类，它们综述了细胞外囊泡的特性、功能、应用和研究规范等。这些文献说明骨代谢相关研究与外泌体研究密切相关。 "

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