Paper type and quantity
The research flow chart of our study is shown in Figure 1. A total of 504 records that met our inclusion criteria were identified in this study. The records were classified as 9 types (Fig. 2A). Articles (64.22%) accounted for the largest proportion of all records, followed by review articles (32.41%). Therefore, we will focus on these two types of papers in the following analysis.
Fig. 2B displays the number of papers and the exponential trend line over time, which can reflect the relationship between the publication year and the number of publication records to a certain extent. The data show that the first paper among all selected papers was published in 1999, and the number of papers in related fields was relatively low (no more than 10) until 2015. Since 2016, however, the number of published papers has just risen rapidly. A total of 274 papers were published in the past two years, accounting for 73.27% of all the selected papers.
Moreover, we also determined the first five research fields with the most published papers in Table 1. Among them, the first area was the field of cell biology (27.63%), followed by biochemical molecular biology (20.48%), research experimental medicine (18.49%), oncology (18.29%), and genetics heredity (9.74%). The quantity of papers in other related fields, such as Biotechnology Applied Microbiology and Pharmacology Pharmacy, is also continuing to increase.
Author, organization, country and funding agency
A total of 3027 authors were included in this study. Table 2 shows the top ten and cocited authors according to the numbers of papers and citations. China was the main distribution with the most researchers in the related field, followed by the USA, Germany, and Italy. Among authors in China, and were the main contributors.
In terms of organizations, a total of 637 research institutions were included in this study. The top 3 organizations with the most contributions are displayed in Fig. 2C, which are all from China. The top one is Nanjing Medical University, with 22 related papers, followed by Shanghai Jiao Tong University (21 papers) and Sun Yat-sen University (20 papers). Furthermore, close collaborative connections between various institutions were also identified. The Chinese Academy of Science is ranked first, having 16 links with other institutions, followed by Shanghai Jiao Tong University (12 links) and Sun Yat-sen University (11 links). The data also showed that the collaborations among Chinese universities were very close. Frequent exchanges occurred between Peking University, Tongji University, Central South University and China Medical University. These bibliometric network maps can provide reliable information about influential institutions, which can help researchers or students be more convinced when making a related decision.
All the papers were from 40 countries. Regarding the research strength of the countries, China (332 papers) was the most productive country, accounting for 63.85%, followed by the USA (86 papers, 16.54%), Germany (24 papers, 4.62%) and Italy (20 papers, 3.85%). Table 3 shows that after adjustment by population, Israel was first with 0.86 papers per million people. After adjustment by GDP, Iran ranked first, with 41.76 papers per trillion GDP, followed by China, with 22.55 papers per trillion GDP. Fig. 2D shows the 22 most productive countries in terms of the quantity of papers and the collaboration situation. The color of the node represents a different cluster, and the size of the node represents a different number of papers, with larger nodes meaning more papers. The collaborations among various countries were very close. The connections between Western European countries and the relationship between China and other countries were relatively obvious.
The top 10 funding agencies are shown in Table 4, which are ranked by the number of papers. Half of the top 10 funding agencies are based on China. The National Natural Science Foundation of China endorsed 206 papers in this field (ranked first, 40.95%), followed by the National Institutes of Health (45 papers, 8.95%) and the United States Department Of Health and Human Services (45 papers, 8.95%).
Publication distribution of journals and cocited journals
Currently, research on the stemness of circRNAs has been published in 269 journals, and the top 10 journals were selected according to the number of published papers. The publishing countries, the number of citations and the impact factor (IF) in 2020 are listed in Table 5. The journal with the most published papers was Molecular Cancer, which published 11 related papers, with 504 citations. Four of the top 10 journals are British journals, ranked 1 and 2. The United States had three journals, followed by Switzerland (2 journals) and Greece (1 journal). The table also shows that the ranking of the number of papers has some disagreement with the ranking of the number of citations. Based on the number of citations and the IF, the authority of the journals Journal of Experimental Clinical Cancer Research and Molecular Therapy Nucleic Acids is relatively high, although they were not ranked in the top three. Furthermore, “citation/N” represents the average citations per paper. Molecular Cancer ranked first, with 11 papers and 504 citations, meaning that each paper was cited an average of 48.51 times.
Table 5 also shows the names, total citations, publishing countries, and IF of the top 10 cocited journals. Nature from the UK ranked number one with 1476 citations and an impact factor of 49.962, followed by Cell from the USA (1330 citations, 41.58 IF). Among these ten journals, 6 were based in the US, and 4 were based in the UK.
Co-occurrence keywords and burst keyword analysis
All the keywords extracted from 505 papers were identified and analyzed. Fig. 3A shows the density visualization based on the keywords. The color of each point in a map depends on the density of items at that point. The warm red color represents hot and important areas, and the cool blue color represents cool areas. According to the order of occurrence of the keywords, the main hot keywords are circular RNA, expression, stem cells, proliferation, identification, cancer, differentiation, microRNA, etc. (similar keywords removed).
Fig. 3B shows the overlay visualization result, with keywords from inception (1999) to 2020. According to the legend in the lower right corner, the yellow node indicates the new and emerging point, while the purple node indicates the old point before 2015. It is obvious that the majority of the keywords have emerged in the last 3 years, which reflects that the stemness of circRNA is an emerging field. Furthermore, Fig. 3C shows the network visualization with 4 clusters, which were classified by different node and wire colors. The size of the nodes in the figure represents the importance of the keywords.
Cluster 1 focuses on the autophagy, chemoresistance and ceRNA network of circular RNA. Part of this cluster also involves the relationship between stem cells and circRNAs and their role as tumor suppressors for certain cancers. From Fig. 5a, Cluster 1 is a mainly emerging research field. Cluster 2 was also new in time, and the keywords with the highest frequency were expression, cancer, proliferation, metastasis and biomarkers. Clusters 1 and 2 both reveal the importance of circRNAs in cancer, which is currently the hottest field. In contrast, Cluster 3 is a relatively early and mature research field that focuses on the identification, transcription and biogenesis of circular RNAs and embryonic stem cells. Cluster 4 revolves around mesenchymal stem cells, osteogenic differentiation and exosomes, which is also a new direction in research.
Fig. 3D can be used to identify the hot topics. The red line in the figure represents the time period with the strongest citation bursts. The first keyword is embryonic stem cells, which appeared in 2016. Later, translation, expression, differentiation and migration became hot keywords. All the top keywords had the strongest citation after 2016, demonstrating that this is an emerging field.
To make the result more convincing, the analysis of keyword co-occurrence in CiteSpace was also conducted. The visualization result is shown in Fig. 4A. Each node in the figure represents one keyword, and the node size indicates the frequency of the keywords; the larger the circle is, the higher the frequency. Careful comparison shows that the result is highly similar to the one we obtained in VOSviewer. Fig. 4B clearly shows the top 8 clusters from all the keywords: circular RNA, neural stem cell, miRNAs, lncRNAs, etc. Furthermore, Fig. 4C and Fig. 4D can make it more intuitive to see the research status of each cluster at different time nodes.