Tumor microenvironment: new era in exosomal circRNA research - a bibliometric analysis
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Abstract
Aim: The domain of exosomal circRNAs has seen rapid growth over the past few years, yet a thorough synthesis of the scholarly contributions has been lacking. This study employs bibliometric analysis to uncover the focal points and trends in exosomal circRNA research.
Methods: We selected articles on exosomal circRNAs from the Web of Science Core Collection (WoSCC) and deployed VOSviewer for co-authorship and co-occurrence analysis. CiteSpace was tapped to calculate citation bursts for documents and keywords, generating a time-zone map. Additionally, the bibliometrix R package was employed to identify locally cited references and construct thematic maps and trend topics.
Results: The exosomal circRNA research field has produced 767 articles over the past decade. China and Fudan University lead in publication counts, ranking first among countries and institutions, respectively. The journal with the highest number of publications and citations is Molecular Cancer. Zhang Wei and Li Yan are the top contributors and the most co-cited authors, respectively. Analysis of the results suggests that research related to the tumor microenvironment (TAM) may be a recent research hotspot. Among the latest keywords, terms such as microenvironment, macrophage, mesenchymal stem cells, and ceRNA appear frequently. The keyword with the most recent citation burst is ceRNA. The latest trend topic is the macrophage, and mesenchymal stem cell is identified as an emerging theme.
Conclusion: This study elucidates the hotspots and trends in exosomal circRNA research through bibliometric analysis. Themes related to the TAM, especially directions involving macrophages, fibroblasts, and mesenchymal stem cells, will likely open a new chapter in exosomal circRNA research.
Keywords
INTRODUCTION
Since the 1980s, a breakthrough in cell biology has been the identification of exosomes, and research on them has been active in various fields in recent years. Exosomes, with a size range of 40-160 nm, are extracellular vesicles that come from endosomes and are generated through the budding process of multivesicular bodies (MVBs)[1]. Early theories regarded exosomes as insignificant cellular waste products. However, after years of research, they have been identified as functional carriers containing various molecules such as miRNAs, lncRNAs, circRNAs, proteins, and lipids[2,3]. Almost all cells can produce exosomes, which can enter the bloodstream and circulate across the body[4]. Exosomes in circulation transport their rich contents to target cells, influencing various physiological and pathological processes like intercellular communication, immune responses, cardiovascular diseases, and cancers[5-7]. Currently, research has increasingly focused on the function of exosomes in the tumor microenvironment (TME) and immune responses, as well as their potential for diagnosing and treating diseases[1,8,9].
In the past decade, circRNAs, a new class of non-coding RNAs, have attracted considerable attention. Due to their unique covalently closed-loop structure, circRNAs are resistant to RNase R, providing them with significant stability[10]. Although circRNAs were first discovered in RNA viruses as early as 1976, they were once mistakenly considered byproducts of erroneous splicing. This misconception was overturned in 2013 with the proposal of the miRNA sponges concept[11,12]. Over 20,000 circRNAs have now been identified, and these circRNAs are widely distributed across various tissues, exhibiting high cell or tissue specificity[13]. Certain circRNAs have been identified as being related to cancer progression or metastasis, including liver cancer, lung cancer, and breast cancer.[14-16]. They are also linked to non-cancerous diseases, including cardiovascular diseases, diabetes, and autoimmune diseases[17-19]. These circRNAs show potential as biomarkers.
In 2015, a study first revealed the close link between exosomes and circRNAs, discovering that exosomes contain abundant and highly stable circRNAs. Through exosomes, these circRNAs enter the bloodstream, and exosomal circRNAs found in serum can help distinguish colorectal cancer patients from healthy individuals[20]. Recent studies further suggest that exosomal circRNAs participate in the circulation of plasma, saliva, and urine, and may be related to disease prognosis[21]. Exosomal circRNAs derived from cancer cells play roles in tumorigenesis, proliferation, and metastasis, affecting the TME, with some circRNAs enhancing resistance to treatment[22]. Furthermore, the clinical potential of exosomal circRNAs has generated considerable interest, as they serve not only as potential biomarkers but also as tools for targeted drug delivery[23].
Research on exosomal circRNAs has been ongoing for several years, but still faces many challenges. Currently, there is no standardized method for extracting and analyzing exosomal circRNAs, making it difficult to replicate study results. Furthermore, circRNAs are present at low abundance, and share sequence overlaps with linear RNAs, making accurate functional assessment challenging[21,24]. Additionally, the understanding of basic mechanisms, such as how circRNAs are enriched in exosomes and how they are degraded, is still insufficient[25]. Clinically, the diagnostic sensitivity and specificity of most exosomal circRNAs have not yet reached clinical standards, and further screening is required for clinical trials[26]. These challenges suggest that research on exosomal circRNAs remains in the early stages. It is crucial to systematically integrate and analyze the current research outcomes and identify future research directions. However, there is currently a lack of systematic analysis of the substantial body of scientific work in this field. Bibliometrics, a mature method of qualitative and quantitative analysis based on literature, can uncover the development process, collaboration networks, research trends, and hotspots of a research field[27]. Its use has become widespread in the analysis of multiple disciplines, providing a deeper comprehension of the present state of research. It has been used to study areas such as cancer, central nervous system diseases, and ncRNAs[28-30]. As publications on exosomal circRNAs grow and the variety of research topics increases, the numerous dispersed research directions hinder researchers from grasping the full picture of the field. Bibliometric analysis will not only assist in organizing the knowledge system but also promote in-depth research and the clinical application of exosomal circRNAs.
This paper will employ bibliometric analysis tools such as CiteSpace, VOSviewer, and Bibliometrix (an R package) to conduct a comprehensive analysis of exosomal circRNAs, explore the hotspots and future trends in this field, and offer insightful guidance for further research.
METHODS
Data collection
Tens of thousands of high-quality journals and comprehensive citation records are included in the Web of Science (WoS) database. Compared to Scopus, WoS has a longer index history and more detailed citation analysis. Additionally, unlike PubMed, which focuses on life sciences and medical fields, WoS provides a broader, multidisciplinary perspective[31,32]. Furthermore, PubMed does not include references as part of metadata, which is not conducive to bibliometric analysis. Thus, among popular databases, WoS is considered the most suitable for bibliometric studies. This research is based on the Web of Science Core Collection (WoSCC). Literature was retrieved on December 11, 2024, and the screening and export were completed in one day. The search strategy used was: TS = (circRNA* OR “circular RNA*” OR “circular noncoding RNA” OR “circular ncRNA” OR “circular nonprotein-coding RNA” OR “circular nonprotein coding RNA”) AND TS = (exosome* OR EVs OR EV OR Extracellular vesicle*), resulting in 1,397 articles. Notably, to ensure a comprehensive search, “Extracellular vesicle” was included in the search query, and studies not related to exosomes were excluded during the manual screening phase. The general inclusion criteria were to avoid overlapping topics or low relevance. The detailed criteria are as follows: Inclusion criteria: Only English articles published as “article” type focusing on exosomal circRNAs were included. Exclusion criteria: Articles were excluded if they were classified “article” types (e.g., reviews or conference abstracts), did not have exosomal circRNAs as the main research focus (e.g., studies on extracellular vesicles or non-coding RNAs), or had been retracted. Ultimately, 767 articles were retained. After saving the full record and cited references, the data was exported in Plain Text File format. The research workflow is presented in Figure 1. All data are available in the WoSCC.
Figure 1. Research process. WoSCC: Web of Science Core Collection.
Data analysis
This study used VOSviewer (version 1.6.18), CiteSpace (version 6.4.R1), and the Bibliometrix R package to perform bibliometric analysis on the exported data. VOSviewer is a bibliometric and scientific visualization software tool with clustering algorithms that can effectively analyze knowledge units based on association strength or similarity[33]. VOSviewer was employed in this research to carry out most of the data analysis and generate several visual maps, which include networks of countries, institutions, author collaborations, co-cited literature, and keyword co-occurrence. In terms of parameter settings, the counting method was full counting, while the resolution and minimum cluster size were set at 1.0 and 1, respectively. The minimum number of occurrences was adjusted based on the readability of the map.
CiteSpace enables multidimensional and dynamic analysis of disciplines over time. Based on this feature, we used CiteSpace to create temporal zone maps of keywords. We also identified citation bursts and co-citation bursts of literature and keywords. Given the short duration of emerging bursts, we set the minimum duration for keyword burst detection to 1 for greater sensitivity. Additionally, we created a dual-map overlay of journals to reveal relationships between publishing journals and co-cited journals. The configuration parameters were set to a period from January 2015 to December 2024, with yearly time slices to capture the annual dynamic changes in the research field. In CiteSpace, the g-index was used to measure the impact of authors or a group of articles, with the k-value adjusting the threshold to select highly impactful literature. Larger k-values result in larger networks. To balance the readability and comprehensiveness of the maps, we set the k-value to 25. The pathfinder method was chosen for pruning, which reduces redundant connections in the network, including pruning sliced networks and pruning the merged network[34]. The remaining parameters were kept at their default settings.
R (Version 4.3.2) is a programming language commonly used for statistical computation and graphical expression. In this research, we utilized the Bibliometrix R package for further bibliometric analysis of the collected data, complementing the functionality of VOSviewer and CiteSpace[35]. Based on its features, we identified locally cited references from the data. The Walktrap algorithm of Bibliometrix can effectively identify communities in large networks, making it suitable for processing complex co-word networks and revealing the thematic structure within academic literature[36]. Therefore, using Walktrap’s clustering algorithm, we generated thematic maps and trend topics for keywords plus to highlight emerging research hotspots.
Additionally, we merged keywords with similar meanings to eliminate duplicate records. Although different software may identify keywords differently, the merging criteria remained consistent: Different forms of the same term. Other expressions of the same term. Highly overlapping research fields.
RESULTS
The trend of publication outputs
After applying our designed search strategy and filtering, we included 767 articles in this analysis. Figure 2 illustrates the trend in the number of exosomal circRNA-related articles and their citations from 2015 to December 11, 2024. The first article on exosomal circRNAs was published in 2015, and the number of related publications consistently grew, with a particularly sharp rise occurring between 2019 and 2022. However, after reaching 177 publications in 2022, the number began to decline, and it is expected to return to 2020 levels in 2025. On the other hand, while the number of citations has continued to rise, the growth rate noticeably slowed after 2022.
Figure 2. The publication and citation trends of exosomal circRNA research.
Distribution of countries/regions and institutions
A total of 32 countries/regions have participated in exosomal circRNA research. The top 10 countries/regions based on publication output are shown in Table 1. China ranks first with 708 articles and 23,142 citations, far surpassing the United States, which ranks second with 43 articles and 3,760 citations. These two countries also have the highest H-index values, 81 and 23, respectively. In total, 747 institutions contributed to exosomal circRNA research [Table 2]. In terms of output, Fudan University (61, 3.62%), Sun Yat-sen University (42, 2.50%), and Nanjing Medical University (40, 2.37%) rank at the top. The top 10 institutions are all from China, which aligns with China’s leading position in the number of published articles. Figure 3A illustrates the collaboration network for these countries/regions and institutions. The collaboration strength between China (39) and the United States (36) is the highest, followed by Germany (14), with other countries showing lower collaboration strength. Figure 3B shows that Fudan University is at the center of the network, with the strongest connection strength (26), followed by Sun Yat-sen University (21) and Nanjing Medical University (21), which form the core of their respective clusters.
Figure 3. Collaboration in exosomal circRNA research, based on VOSviewer. (A) The co-authorship network among countries. The size of each node indicates the publication count, while the connecting lines between nodes indicate the strength of collaboration; (B) The co-authorship network among institutions.
The Top 10 countries with the most published articles
| Rank | Country | Documents | Citations | Citations/documents | Total link strength | H-index |
| 1 | China | 708 (84.47%) | 23,142 | 32.7147 | 39 | 81 |
| 2 | United States | 43 (5.13%) | 3,760 | 87.4419 | 36 | 23 |
| 3 | Germany | 13 (1.55%) | 769 | 59.1538 | 14 | 8 |
| 4 | Italy | 10 (1.20%) | 522 | 52.2 | 6 | 9 |
| 5 | Canada | 5 (0.60%) | 85 | 49.4 | 6 | 7 |
| 6 | Denmark | 5 (0.60%) | 247 | 17 | 5 | 4 |
| 7 | Iran | 5 (0.60%) | 23 | 4.6 | 2 | 6 |
| 8 | Australia | 4 (0.48%) | 54 | 38.75 | 3 | 4 |
| 9 | England | 4 (0.48%) | 155 | 13.5 | 4 | 6 |
| 10 | Netherlands | 4 (0.48%) | 27 | 26 | 6 | 3 |
The Top 10 institutions with the most published articles
| Rank | Institution | Documents | Citations | Citation/document | Total link strength |
| 1 | Fudan University | 61 (3.62%) | 3,704 | 60.7213 | 26 |
| 2 | Sun Yat-sen University | 42 (2.50%) | 3,060 | 72.8571 | 21 |
| 3 | Nanjing Medical University | 40 (2.37%) | 1,640 | 41 | 21 |
| 4 | Zhengzhou University | 34 (2.02%) | 1,364 | 40.1176 | 18 |
| 5 | Shanghai Jiaotong University | 26 (1.54%) | 641 | 24.6538 | 4 |
| 6 | Zhejiang University | 26 (1.54%) | 733 | 28.1923 | 2 |
| 7 | Nantong University | 25 (1.48%) | 776 | 31.04 | 13 |
| 8 | Southern Medical University | 23 (1.36%) | 1,102 | 47.913 | 8 |
| 9 | Central South University | 23 (1.36%) | 676 | 29.3913 | 14 |
| 10 | Huazhong University of Science and Technology | 22 (1.30%) | 1,363 | 61.9545 | 17 |
Analysis of journals and co-cited journals
The 767 publications on exosomal circRNAs were published across 332 journals. Using VOSviewer, we filtered and visualized 21 core journals with a publication threshold of 8 [Figure 4A]. Table 3 displays the top 10 journals according to publication volume. Ranking first with 21 publications, Molecular Cancer is the most influential journal in this area, boasting 2,023 citations. Other high-impact journals include Cell Death & Disease (17), Aging-US (15), and Frontiers in Oncology (15). The Impact Factor (IF) is an important measure of a journal’s influence, calculated by the average number of citations per article[37]. Among the top 10 journals, Molecular Cancer holds the highest IF2023 and the highest average citations per article, with values of 27.7 and 139, respectively. Journal of Experimental and Clinical Cancer Research (11.4, 106) and Cell Death and Disease (8.1, 60.1765) follow. These journals form the core platform for publishing research on exosomal circRNAs.
Figure 4. Journal distribution in exosomal circRNA research. (A) Journals publishing research on exosomal circRNAs, based on VOSviewer. The closer the label is to yellow, the newer the average publication year of the journal; (B) Distribution of exosomal circRNA research across various co-cited journals, based on VOSviewer; (C) The dual-map overlay of journals on exosomal circRNAs, based on CiteSpace. The citing journals on the left represent the frontier areas of exosomal circRNA research, while the cited journals on the right form the knowledge base of the field. Cited relationships are defined by the connecting lines, with the f-value indicating the citation counts.
The top 10 journals with the most published articles
| Rank | Journal | Document | Citation | Total link strength | IF2023 | JCR |
| 1 | Molecular Cancer | 21 | 2,919 | 119 | 27.7 | Q1 |
| 2 | Cell Death & Disease | 17 | 1,023 | 45 | 8.1 | Q1 |
| 3 | Aging-US | 15 | 597 | 33 | 3.9 | Q2 |
| 4 | Frontiers in Oncology | 15 | 230 | 50 | 3.5 | Q2 |
| 5 | Cancer Cell International | 14 | 375 | 27 | 5.3 | Q1 |
| 6 | Frontiers in Cell and Developmental Biology | 14 | 379 | 16 | 4.6 | Q1 |
| 7 | Frontiers in Genetics | 14 | 455 | 37 | 2.8 | Q2 |
| 8 | Journal of Experimental & Clinical Cancer Research | 13 | 1,378 | 75 | 11.4 | Q1 |
| 9 | Bioengineered | 11 | 207 | 16 | 4.2 | Q2 |
| 10 | Anti-Cancer Drugs | 10 | 105 | 15 | 1.8 | Q3 |
A co-citation relationship is formed when two papers are cited together in the same article, which helps assess the impact of the cited articles or journals in a specific research area[38]. Using VOSviewer, we identified the co-citation network of 39 journals with a citation threshold of 167 [Figure 4B]. Detailed information is included in Table 4. Molecular Cancer leads with 1,426 citations, followed by Nature (705), Cell (627), and Nucleic Acids Research (573) in citation frequency. In the co-citation network, Molecular Cancer and Nature are the core nodes of their respective clusters. Of the top 10 co-cited journals, five have an IF2023 greater than 10, namely Molecular Cancer (27.7), Nature (50.5), Cell (45.6), Nucleic Acids Research (16.7), and Nature Communications (14.7).
The top 10 co-cited journals
| Rank | Journal | Citations | Total link strength | IF2023 |
| 1 | Mol Cancer | 1,426 | 24,604 | 27.7 |
| 2 | Nature | 705 | 15,066 | 50.5 |
| 3 | Cell | 627 | 15,860 | 45.6 |
| 4 | Nucleic Acids Res | 573 | 12,683 | 16.7 |
| 5 | Cell Death Dis | 502 | 10,670 | 8.1 |
| 6 | Nat Commun | 480 | 10,747 | 14.7 |
| 7 | Cancer Lett | 475 | 10,560 | 9.1 |
| 8 | Int J Mol Sci | 419 | 8,917 | 4.9 |
| 9 | PLOS One | 400 | 8,962 | 2.9 |
| 10 | Sci Rep-UK | 399 | 8,129 | 3.8 |
Further, we used CiteSpace for dual-map overlay analysis of journals, which reveals the association between journals and co-cited journals and shows the citation paths in the literature [Figure 4C]. Two main citation paths were identified: research published in MOLECULAR/BIOLOGY/GENETICS journals was mainly cited by MOLECULAR/BIOLOGY/IMMUNOLOGY (f = 5,738) and MEDICINE/MEDICAL/CLINICAL (f = 2,680) journals.
Authors and co-cited authors
A total of 5,200 authors and 16,894 co-cited authors have published articles related to exosomal circRNAs. We visualized the collaboration network of these authors using VOSviewer [Figure 5], and the top 10 authors contributing the most are shown in Tables 5 and 6. Zhang Wei (12), Wang Yan (9), Li Yan (8), and others have published the most articles. Chen Bing (82), Zhang Wei (54), and Qian Hui (41) are the top 3 authors with the highest H-index. Figure 5A displays the collaboration network among 38 authors, each having contributed at least 5 articles. These 38 authors are divided into 5 clusters, each forming a local collaborative network, with relatively few collaborations between different author groups. Among the co-cited authors, Li Y (267), Hansen TB (221), and Kristensen LS (221) are the most frequently cited [Figure 5B]. The H-index is used to evaluate a researcher’s academic contribution and influence, indicating that a scholar has at least H papers cited H times, which considers both the quantity and quality of their work[39]. Tables 5 and 6 show that Chen Bing and Kalluri R have the highest H-index among authors and co-cited authors, indicating their exceptional research quality.
Figure 5. Author collaboration network in exosomal circRNA research, based on VOSviewer. (A) The co-authorship network among authors; (B) The network among co-cited authors.
The top 10 authors with the most published articles
| Rank | Authors | Document | Citation | H-Index | Institution |
| 1 | Zhang, Wei | 12 | 376 | 54 | Zunyi Medical University (China) |
| 2 | Wang, Yan | 9 | 407 | 11 | Zunyi Medical University (China) |
| 3 | Li, Yan | 8 | 925 | 13 | Fudan University (China) |
| 4 | Chen, Bing | 7 | 675 | 82 | Fudan University (China) |
| 5 | Zhang, Ning | 7 | 235 | 8 | Central South University (China) |
| 6 | Liu, Qi | 6 | 336 | 28 | Peking University (China) |
| 7 | Qian, Hui | 6 | 77 | 41 | Jiangsu University (China) |
| 8 | Zhang, Lei | 6 | 83 | 32 | Zhejiang University (China) |
| 9 | Zhang, Qian | 6 | 214 | 33 | Zunyi Medical University (China) |
| 10 | Chen, Chen | 5 | 376 | 15 | Zunyi Medical University (China) |
The top 10 co-authors with the most published articles
| Rank | Authors | Citations | Total link strength | H-Index | Institution |
| 1 | Li, Y | 267 | 1,382 | 13 | Fudan University (China) |
| 2 | Hansen, TB | 221 | 1,243 | 31 | Aarhus University (Denmark) |
| 3 | Kristensen, LS | 221 | 1,011 | 31 | Aarhus University (Denmark) |
| 4 | Memczak, S | 182 | 1,071 | 10 | Max DelbrückCenter (Germany) |
| 5 | Zhang, Y | 138 | 752 | 39 | Nantong University (China) |
| 6 | Kalluri, R | 134 | 615 | 124 | Baylor College of Medicine (USA) |
| 7 | Chen, LL | 120 | 581 | 80 | ShanghaiTech University (China) |
| 8 | Jeck, WR | 120 | 768 | 21 | University of North Carolina (USA) |
| 9 | Li, J | 118 | 635 | 77 | Third Military Medical University (China) |
| 10 | Wang, YX | 107 | 493 | 8 | Zhengzhou University (China) |
Co-cited references and references citation burst
The foundation of a discipline’s knowledge is formed by co-cited references[40]. By analyzing co-cited references, we reveal the research results on which the study of exosomal circRNAs is based. Table 7 presents the 10 most cited co-references, five of which have accumulated over 100 citations: Li Yan (222), Hansen Thomas B (193), Memczak Sebastian (159), Kristensen Lasse S. (151), and Wang Yangxia (102). This result aligns with the co-cited author analysis, indicating that these articles are the cornerstone of the discipline. Notably, the most cited paper, titled “Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis”, was the first to reveal, through RNA sequencing, that exosomes in human blood circulation contain abundant and stable circRNAs. These circRNAs are widely and stably present in cancer cell-derived exosomes. Moreover, exosomal circRNAs in serum can effectively differentiate colorectal cancer patients from healthy controls[41]. This paper is the origin of research on exosomal circRNAs and has become the factual foundation for subsequent studies.
The top 10 co-cited references based on the number of citations
| Rank | Citations | Title | First author | Document type | Year |
| 1 | 222 | Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis | Li, Yan | Letter | 2015 |
| 2 | 193 | Natural RNA circles function as efficient microRNA sponges | Hansen, Thomas B | Article | 2013 |
| 3 | 159 | Circular RNAs are a large class of animal RNAs with regulatory potency | Memczak, Sebastian | Article | 2013 |
| 4 | 151 | The biogenesis, biology and characterization of circular RNAs | Kristensen, Lasse S | Review | 2019 |
| 5 | 102 | Exosomal circRNAs: biogenesis, effect and application in human diseases | Wang, Yangxia | Review | 2019 |
| 6 | 92 | The biology, function, and biomedical applications of exosomes | Kalluri, Raghu | Review | 2020 |
| 7 | 86 | Global cancer statistics | Jemal, Ahmedin | Article | 2011 |
| 8 | 78 | Circular RNAs are abundant, conserved, and associated with ALU repeats | Jeck, William R | Article | 2013 |
| 9 | 68 | Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs | Zheng, Qiupeng | Article | 2016 |
| 10 | 54 | circRNA Biogenesis Competes with Pre-mRNA Splicing | Ashwal-Fluss, Reut | Article | 2014 |
Furthermore, by analyzing reference bursts, we identified the top 25 articles with the most significant citation bursts. Citation bursts refer to a phenomenon where the citation frequency of a particular article sharply increases over a certain period, which can help predict research frontiers[40]. Figure 6 displays the top 25 references with the strongest citation bursts. The strongest burst is for Li Y’s Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Recent bursts occurred from 2022 onwards, including Kalluri R’s The biology, function, and biomedical applications of exosomes, and Panda AC’s Circular RNAs act as miRNA sponges. Additionally, we used the bibliometrix package to obtain the top 10 locally cited papers. “Locally cited” refers to the results produced within a discipline being cited by the same discipline. The details are provided in [Supplementary Table 1].
Figure 6. The top 25 references with the strongest citation bursts, based on CiteSpace. Citation burst reflects a sharp increase in citation frequency. The red bars indicate the start and end time intervals of the burst.
Keywords and hotspots
Keywords are highly concise summaries of the themes of the literature. By analyzing keywords, we can understand the hotspots in the research field. We collected 2,510 keywords using VOSviewer, and Table 8 presents information on the top 30 keywords, among which “circRNA”, “exosomes”, “expression”, “biomarker”, “cancer”, “miRNA”, and “extracellular vesicle” appeared more than 100 times, which are likely the most mentioned topics in exosomal circRNA research. We visualized 76 keywords with co-occurrence frequency greater than 9, and in Figure 7A, keywords closer to yellow represent newer terms on average. Table 9 shows these recent keywords, categorized by clusters, including “macrophage” and “microenvironment.” The software considers keywords within the same cluster to have a higher similarity or relevance. Additionally, we also analyzed keyword citation bursts [Figure 7B], with the latest keywords being “ceRNA” and “tumor-associated macrophages.”
Figure 7. Research hotspots and trends in exosomal circRNAs. (A) Network of keywords with the most recent average publication year, based on VOSviewer; (B) The top 12 keywords with the strongest citation bursts, based on CiteSpace; (C) Time-zone viewer map related to exosomal circRNAs, based on CiteSpace; (D) Thematic map related to exosomal circRNAs, based on R; (E) Trend topics related to exosomal circRNAs, based on R.
The top 30 keywords with the highest occurrence frequency
| Rank | Keywords | Count | Rank | Keywords | Count |
| 1 | circRNA | 482 | 16 | promotes | 57 |
| 2 | Exosomes | 462 | 17 | Colorectal cancer | 49 |
| 3 | Expression | 175 | 18 | Migration | 49 |
| 4 | Biomarker | 149 | 19 | HCC | 48 |
| 5 | Cancer | 131 | 20 | Gastric cancer | 41 |
| 6 | miRNA | 110 | 21 | ceRNA | 39 |
| 7 | Extracellular vesicle | 107 | 22 | Angiogenesis | 38 |
| 8 | Cell | 93 | 23 | Mechanism | 38 |
| 9 | Metastasis | 91 | 24 | Diagnosis | 36 |
| 10 | RNA | 90 | 25 | mRNA | 35 |
| 11 | Proliferation | 87 | 26 | Apoptosis | 33 |
| 12 | Progression | 84 | 27 | Breast cancer | 33 |
| 13 | Growth | 65 | 28 | Identification | 33 |
| 14 | Biogenesis | 64 | 29 | Protein | 32 |
| 15 | Invasion | 60 | 30 | Roles | 31 |
The keywords with the most recent average publication year
| Cluster | Keywords | Avg. pub. year | Count | Cluster | Keywords | Avg. pub. year | Count |
| 1 | Macrophage | 2022.8889 | 27 | 3 | ceRNA | 2022.2821 | 39 |
| 1 | Microenvironment | 2022.6364 | 11 | 3 | Bioinformatics | 2022.08 | 25 |
| 1 | Cisplatin | 2022.1818 | 22 | 3 | Communication | 2022 | 10 |
| 1 | Epidemiology | 2022.1667 | 18 | 4 | Prediction | 2022.5 | 10 |
| 2 | Repair | 2022.5455 | 11 | 4 | Prostate cancer | 2021.8235 | 17 |
| 2 | Injury | 2022.5333 | 15 | 4 | Liquid biopsy | 2021.8 | 15 |
| 2 | Mesenchymal stem cells | 2022.5 | 12 | 4 | Noncoding RNA | 2021.7778 | 27 |
| 2 | Mechanism | 2022.3684 | 38 | 5 | Lung cancer | 2022.5 | 18 |
| 3 | Disease | 2022.4375 | 16 | 5 | Nsclc | 2022.037 | 27 |
The distribution of keywords over time is depicted in the time-zone map, contributing to the understanding of how research hotspots evolve [Figure 7C]. In the time-zone map, keywords like “circRNA” and “exosome” reflecting the subject matter of the field appeared first. As time progressed, research began to focus on various cancers and directions such as “proliferation”, “metastasis”, “angiogenesis”, and “resistance.” After 2021, notable keywords such as “tumor microenvironment”, “cancer-associated fibroblasts”, “mesenchymal stem cells”, and “tumor-associated macrophages” emerged. These changes indicate that researchers’ interests are gradually shifting toward the TME.
To further explore the dynamic changes in research hotspots, we utilized the bibliometrix package to generate thematic maps and trend topics. As shown in Figure 7D, topics with higher centrality indices are considered core to the research field, while those with higher density indices represent more mature themes. The themes in the motor themes quadrant, such as autophagy and apoptosis, have developed well and are significant to the field of study. Topics in the basic themes quadrant, such as “circRNAs”, “cell”, and “metastasis”, represent foundational concepts in the research area. Although their development is still incomplete, they are crucial. The themes in the emerging or declining themes quadrant, such as “noncoding RNA”, “mesenchymal stem cells”, and “injury”, may either be newly emerging or nearing obsolescence. The themes in the niche themes quadrant, like “cisplatin” and “oxidative stress”, are more specialized and have limited relevance to the broader research field. In the trend topics [Figure 7E], it is evident that “injury” and “macrophages” are the most recent research topics along the timeline.
DISCUSSION
General information
This study involved a bibliometric analysis of 767 articles published between 2015 and December 11, 2024, focusing on exosomal circRNAs. The research on exosomal circRNAs began in 2015 when Li et al. first confirmed the widespread and stable presence of circRNAs in circulating exosomes, laying the foundation for subsequent studies[41]. As depicted in Figure 2, the publication count increased rapidly starting in 2018, possibly due to the establishment of the exoRBase database, which allowed researchers to more conveniently and comprehensively access circRNAs data[42]. Additionally, most landmark research results were published around 2020 [Supplementary Table 1], which contributed to a greater number of scholars becoming involved in this field. However, starting in 2022, the number of publications per year seems to be declining. Although this does not necessarily indicate that the research boom in exosomal circRNAs is waning, the absence of new groundbreaking findings may slow the pace of development in this field over the next few years.
In the field of exosomal circRNAs, China has published the most, representing 84.47% of all publications. Previous bibliometric studies have shown that Chinese scholars exhibit a stronger interest in ncRNAs compared to scholars from other countries[43]. Among the milestone achievements identified by bibliometrix [Supplementary Table 1], a large portion of contributions from China were funded by the National Natural Science Foundation of China (NSFC), suggesting that national policy encouragement may have played a certain role in driving this progress. However, the average citation count per article for China is only 32.7147, ranking 8th among all countries. Interestingly, China’s H-index (81) remains significantly higher than that of other countries. This suggests that China has a substantial amount of high-quality research in this field. Still, the large number of publications also includes many lower-impact studies, which brings down the average citation count. Chinese researchers should continue to maintain their advantage in the number of publications while also focusing on improving research quality, which will have a more positive impact on exosomal circRNAs studies. The majority of active institutions are located in China, with Fudan University, Sun Yat-sen University, and Nanjing Medical University being the most prominent. In terms of collaboration, China and the United States have a strong collaborative relationship, but cooperation with other countries is limited, with some countries even having no collaboration at all. While there are collaboration networks between different institutions, most are confined to domestic exchanges. We recommend that countries and institutions eliminate research barriers, strengthen academic exchanges, and promote the development of exosomal circRNA research.
A considerable number of journals are willing to publish research on exosomal circRNAs. Among the top ten journals by publication count, half are from the JCR Q1 zone. However, only 10 out of the 332 journals have more than 10 articles on this topic. On the one hand, the research on exosomal circRNAs is relatively limited; on the other hand, high-impact journals are more inclined to accept high-quality research. Most of the 767 articles on exosomal circRNAs have been published in journals with relatively low IF, which indirectly suggests that the quality of the current research on exosomal circRNAs still has room for improvement. Molecular Cancer is the most active journal, publishing the most studies and being cited the most by research on exosomal circRNAs. These studies primarily focus on the molecular mechanisms of exosomal circRNAs in various cancers. Considering both the acceptance rate and IF2023, we recommend that researchers submit their work to these journals [Table 3]. In Figure 4C, the dual-map overlay of journals illustrates the current distribution of journal topics. At present, research on exosomal circRNAs has begun to shift toward clinical applications, with an increasing focus on immune-related issues.
According to the H-index, Zhang Wei is ranked as the top author in terms of productivity and influence. Following closely is Wang Yan, who is from the same research team that previously discovered the association between M2 macrophage-derived exosomal circRNAs and myocardial fibrosis after a heart attack[44]. The team’s latest study indicates that the system for delivering exosome-encapsulated circRNA mSCAR preferentially targets mitochondria within macrophages, promoting macrophages to polarize toward the M2 phenotype[45]. These findings suggest a connection between exosomal circRNAs and M2 macrophages. The most cited co-cited author is Li Yan, whose team first confirmed the widespread and stable presence of circRNAs in exosomes[41]. Hansen TB, Kristensen LS, and Memczak S are pioneers in the field of circRNAs. They were the first to summarize the biological functions of circRNAs and to propose the groundbreaking hypothesis of circRNAs acting as miRNA sponges, which researchers should revisit and learn from[12,20,46]. Additionally, Kalluri R. is the most cited author with the highest H-index in the field of exosomal circRNAs, exerting a significant influence. His team has recently focused on studies related to the TME[47,48]. Scholars studying the relationship between exosomal circRNAs and the TME should consider his work.
The hotspots and frontiers
A time-zone map can intuitively show the changes in hotspots over different years. Figure 7C shows that the number of co-occurring keywords each year corresponds with the annual number of publications. From 2015 to 2017, the number of publications per year was fewer than 10, resulting in a lower number of co-occurring keywords, which mostly reflected disciplinary research themes such as “circular RNA”, “cancer”, and “exosome.” This also explains why these were identified as basic themes, representing fundamental concepts in the research field. Most of the research in this phase was centered around the expression of circRNAs in body fluids in different pathological and physiological states[49,50]. Starting in 2018, the number of keywords significantly increased. Emerging keywords included various cancers such as “colorectal cancer”, “gastric cancer”, and “breast cancer”, as well as terms like “pathway”, “activation”, and “angiogenesis.” Researchers paid particular attention to tumor biology, discovering the functions of exosomal circRNAs in different cancers[51,52]. In addition, keywords such as “biomarker”, “resistant”, “diagnosis”, and “therapy” suggest that the clinical value of exosomal circRNAs has gradually gained more attention. For instance, hsa_circ_0000419 was found to potentially serve as a novel gastric cancer screening marker, and exosomal circATG4B was shown to induce chemotherapy resistance in colorectal cancer[53,54]. After 2021, due to a decrease in publications and limited accumulation of data, the number of emerging keywords and co-occurrence frequency declined in the past two years. This phase saw the appearance of similar keywords like “tumor-associated macrophages”, “tumor microenvironment”, and “cancer-associated macrophages”, possibly influenced by the growing academic interest in TME concepts[55]. These trends in research changes were also reflected in the trend topics [Figure 7E], where the basic themes like “reveals” and “miRNA” gradually transitioned to TME-related terms like “macrophage” and “mesenchymal stem cells.”
As early as 2015, researchers began focusing on the direction of cancer research. As many biological functions of exosomal circRNAs in cancer remain unexplored, research after 2018 primarily concentrated on uncovering the molecular mechanisms through which exosomal circRNAs affect cancer[56,57]. However, most studies limited their exploration of molecular mechanisms to single-cell-level verifications and heavily relied on in vitro models, overlooking the complex microenvironment in tumors. Moreover, current limitations in exosome separation technologies, including extraction and analysis standardization, have hindered the development of exosomal circRNA research[58]. The lack of basic research further prevents the large-scale clinical translation of findings related to exosomal circRNAs. While researchers have recognized the clinical value of exosomal circRNAs, the discussion often remains speculative, particularly regarding their roles as biomarkers. This explains why “biomarker” is one of the most frequent and earliest keywords. Exosomal circRNAs’ role in inducing chemotherapy resistance is another frequently discussed topic. The bibliometric analysis identified chemotherapy as a well-developed theme and the latest trending topic
To explore research trends, we cross-verified the results of references and keyword analysis. Among the latest keywords identified by VOSviewer were “microenvironment”, “macrophage”, and “mesenchymal stem cells” [Table 9], especially “microenvironment” and “macrophage”, which appear to be highly correlated and belong to the same cluster. From the time-zone map starting in 2021, “microenvironment”, “tumor-associated macrophages”, “mesenchymal stem cells”, and “fibroblasts” have been among the most significant keywords each year [Figure 7C]. In addition, the Bibliometrix package recognized “mesenchymal stem cells” as an emerging theme [Figure 7D] and identified “macrophage” as the latest trend topic [Figure 7E]. We reviewed the top 10 locally cited papers collected by the bibliometrix package
The TME consists of tumor cells, immune cells, tumor-associated endothelial cells, cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), and other components[55]. It provides a favorable environment for tumor cell proliferation, promoting metastasis and infiltration. It is also tightly connected to immune escape in tumors and drug resistance[61]. In the TME, exosomes act as key mediators of communication between cells. By transporting abundant cargo molecules, they facilitate communication between different cells[62,63]. Among the cargo molecules encapsulated in exosomes, exosomal circRNAs significantly influence various aspects of tumor progression, such as proliferation, angiogenesis, and drug resistance[64].
Tumor-associated macrophages (TAMs)
In the time-zone map, the top keyword in 2023 was “tumor-associated macrophages”, followed closely by macrophage polarization [Figure 7C]. TAMs are macrophages that infiltrate the tumor tissue or microenvironment, recruited and polarized by the TME, with the M2 phenotype TAMs being considered to promote tumor growth[65,66]. Tumor cell-derived exosomal circRNAs regulate M2 polarization. These circRNAs, delivered to macrophages, act as miRNA sponges, activating specific signaling pathways like the JAK/STAT pathway to induce M2 polarization[67-69]. Subsequently, M2 TAMs can release exosomal circRNAs that affect tumor cells. For example, in endometrial cancer, exosomal hsa_circ_0001610 derived from M2 macrophages reduces the sensitivity of endometrial cancer cells to radiotherapy[70]. A recent study found that using the HIF-1 alpha inhibitor PX-478 can suppress cancer cells from secreting exosomal circ-0100519, ultimately inhibiting M2 polarization[71]. Therefore, targeting specific points in the M2 polarization process to treat cancer is an important future research direction. Additionally, some exosomal circRNAs can inhibit M2 polarization, such as exosomal hsa_circ_0017252, which slows gastric cancer progression by inhibiting M2 polarization[72]. Packaging these therapeutic circRNAs into exosomes for delivery to the TAMs is a promising therapeutic strategy, but preventing off-target effects and optimizing the purification of exosomes and circRNAs remain current challenges[73].
Mesenchymal stem cells (MSCs)
VOSviewer identified MSCs as an important keyword around 2022 and clustered them with keywords such as injury and repair [Table 9]. This is because MSCs are stem cells with multi-directional differentiation potential, capable of differentiating into various mesenchymal tissues for injury repair, and thus, many studies focus on their repair functions[74]. Exosomes derived from MSCs exert similar effects as MSCs themselves, with ncRNAs being important effector molecules. Based on this characteristic, some studies suggest that exosome-mediated circRNA delivery from MSCs can repair liver fibrosis, thus preventing liver cancer[75]. Compared to MSC transplantation, using MSC-derived exosomes for repair results in fewer side effects and has better clinical potential. However, its therapeutic effect and safety still lack further validation in animal models, and exosome extraction and preservation remain ongoing challenges[76]. Additionally, MSCs outside the tumor have homing abilities, meaning they can be recruited to tumor tissue and differentiate into tumor-associated mesenchymal stem cells (TA-MSCs), affecting tumor occurrence, invasion, and metastasis through exosomal circRNAs[77-79]. Although the mechanism by which MSCs are recruited and become part of the TME has been linked to exosomes, whether exosomal circRNAs participate in this recruitment process still requires more verification[80,81]. Further exploration of the molecular mechanisms will help find new therapeutic strategies.
CAFs
In the TME, CAFs secrete more than 50% of the ECM in solid tumors, producing various factors that promote tumor proliferation and vascularization and are linked to resistance and immune suppression[82]. Exosomal circRNAs heavily influence this process[83,84]. Since CAFs are the largest producers of solid tumor ECM and secreted factors, they significantly affect tumor progression, and tumor drug resistance is largely influenced by them[85]. For example, exosomal circZFR derived from CAFs can inhibit the STAT3/NF-kappa B pathway in liver cancer cells, promoting cisplatin resistance[86]. However, benefiting from CAFs’ comprehensive influence on tumor progression, their secreted exosomes are considered excellent carriers for therapeutic molecules[85]. In the latest study, researchers used tumor vascular-targeting peptides to modify CAF-derived exosomes, delivering circ_0004872 to osteosarcoma cells, and reversing drug resistance by encoding peptides[87]. This research shows that CAF-derived exosomal circRNAs have cancer therapeutic potential, and the peptide-coding ability of circRNAs is also worth investigating. Although CAFs’ therapeutic potential has been widely confirmed, research specifically focusing on CAF-derived exosomal circRNAs remains limited. On the one hand, the overall number of studies on CAF-secreted exosomal circRNAs is still small. On the other hand, due to their heterogeneity, different CAF subpopulations exert distinct effects on tumors[88]. Therefore, further exploration is needed to elucidate the molecular mechanisms through which CAF-derived exosomal circRNAs induce drug resistance. Additionally, single-cell sequencing could be used to identify differences in both the levels and functions of exosomal circRNAs produced by different CAF subgroups, which may help identify more clinically relevant therapeutic targets.
In summary, we believe that the TME represents a promising new direction for ongoing research on exosomal circRNAs. This concept offers researchers a novel pathway to understanding tumors, enabling a more comprehensive grasp of the complex landscape of tumors and the construction of a more refined molecular mechanism framework. However, at present, studies on molecular mechanisms are mostly limited to unidirectional signaling pathways (e.g., circRNAs-miRNAs-mRNAs), without delving into the complex competitive regulatory networks within the recipient cells. More research tends to isolate the members of the TME to explore the connection between tumor cells and their surroundings. Yet, the TME is essentially a complex ecosystem with heterogeneity, where interwoven effects exist between its various components, and exosomal circRNAs serve as important mediators of communication. For example, CAFs can secrete exosomal circ_0084043 to induce the proliferation of endothelial cells within the TME[89]. Therefore, despite a foundational body of research, the heterogeneity of the TME and intercellular cross-talk have yet to be fully elucidated. In the keyword analysis results of this study, some terms may suggest new research approaches. Among them, the emerging keyword “ceRNA” stands out, while “machine learning” is a newly introduced keyword in 2022 [Figures 7B and C]. A previous study explored the regulatory network involving exosomal circRNAs from a ceRNA perspective, suggesting that ceRNA regulatory networks may play a significant role in the TME[90]. The rise of the ceRNA concept provides a fresh perspective on understanding the function of exosomal circRNAs in the TME, especially in understanding and constructing the complex interactions between various cell types within the TME. With advancements in bioinformatics, machine learning has become an emerging tool in exosomal circRNA research. Researchers have successfully identified circRNAs that could serve as pan-cancer biomarkers using machine learning[91]. These studies indicate that machine learning may prove to be a powerful tool for uncovering the complex regulatory networks within the TME. Furthermore, single-cell RNA sequencing technology, which offers certain advantages in revealing TME heterogeneity and intercellular cross-talk, has gained increasing attention in TME research[92]. Therefore, we suggest that researchers explore the use of these new technologies, along with concepts such as ceRNA and machine learning, to investigate how different cell types in the TME interact through exosomal circRNAs.
This study offers a comprehensive analysis of the research progress on exosomal circRNAs over the past decade. Previous bibliometric research on exosomes or circRNAs exists, but this is the first analysis that specifically targets exosomal circRNAs. Additionally, prior studies have primarily focused on bibliometric analyses of exosomes or circRNAs in the context of specific diseases. In contrast, this study emphasizes the overall discipline of exosomal circRNAs, exploring the structure of this field and its future development[93,94]. Of course, this study also has certain limitations. In terms of data collection, WoS does not cover all research outcomes, which may result in some omissions. However, due to digital object identifier (DOI) discrepancies across different databases, it is currently challenging to conduct multi-database joint analyses to reduce such omissions[95]. Only including English-language literature may also overlook important studies in other languages, leading to language bias. Furthermore, citation inflation could affect the objective assessment of a paper’s impact due to factors like self-citation and negative citations. Older publications may have higher citation counts, yet high citation frequency does not necessarily equate to high quality. Citation analysis struggles to differentiate between motivations for citing, potentially overestimating the academic contributions of certain studies. Database indexing delays are another limitation that cannot be ignored. There is usually a certain delay between a paper’s publication and its final indexing, so this study may not have included the most recent findings, leading to an incomplete evaluation of the latest research trends.
As research advances, cancer is increasingly recognized as a complex ecosystem, and the concept of the TME has gained growing attention. In fact, research on cancer in the field of exosomal circRNAs has developed rapidly in recent years[21]. As a result, the trend among researchers in the field has shifted toward focusing on the TME. The concept of the TME links tumor cells and surrounding tissues into a complex and unified system, with exosomal circRNAs serving as one of the critical mediators facilitating communication within this system. The intricate network formed by exosomal circRNAs originating from both tumor and non-tumor cells profoundly impacts the dynamics of the TME. Our research suggests that the TME may be a new direction for exosomal circRNA research. It emphasizes that researchers need to incorporate emerging technologies and concepts into their studies, offering corresponding recommendations. The rapid development of exosomal circRNA research has spanned only a few years. However, insufficient basic research has hindered its clinical translation, and this field remains in its early stages. The rise of TME research has marked the dawn of a new era in exosomal circRNA research, but more effort is still required from us.
DECLARATIONS
Acknowledgments
The authors thank the Guangxi Zhuang Autonomous Region Clinical Medicine Research Center for Molecular Pathology and Intelligent Pathology Precision Diagnosis for providing technical support.
Authors’ contributions
Made substantial contributions to the conception and design of the study and performed data analysis and interpretation: Wang HQ, Zhang L
Performed data acquisition and processing, as well as contributing to the writing of parts of the article: Li MJ, He RQ, Qin DY, Li B, Li JD
Polished the manuscript: Wu KJ, Li SD, He H, Feng ZB, Chen G
Availability of data and materials
In this study, the datasets used and analyzed were obtained from the Web of Science Core Collection (WoSCC). The raw data supporting the findings of this study are available in this article and its Supplementary Materials. Further data are available from the corresponding authors upon reasonable request.
Financial support and sponsorship
This study was funded by the Innovation Project of Guangxi Graduate Education (JGY2023068, YCBZ2023110), Guangxi Higher Education Undergraduate Teaching Reform Project (2022JGA146), and Guangxi Medical University “Four New” Project (SX202403).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2025.
Supplementary Materials
REFERENCES
1. Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Science. 2020;367:eaau6977.
2. van Niel G, D’Angelo G, Raposo G. Shedding light on the cell biology of extracellular vesicles. Nat Rev Mol Cell Biol. 2018;19:213-28.
3. Braicu C, Tomuleasa C, Monroig P, Cucuianu A, Berindan-Neagoe I, Calin GA. Exosomes as divine messengers: are they the Hermes of modern molecular oncology? Cell Death Differ. 2015;22:34-45.
4. Boriachek K, Islam MN, Möller A, et al. Biological functions and current advances in isolation and detection strategies for exosome nanovesicles. Small. 2018;14:1702153.
5. Kurywchak P, Tavormina J, Kalluri R. The emerging roles of exosomes in the modulation of immune responses in cancer. Genome Med. 2018;10:23.
6. Zamani P, Fereydouni N, Butler AE, Navashenaq JG, Sahebkar A. The therapeutic and diagnostic role of exosomes in cardiovascular diseases. Trends Cardiovasc Med. 2019;29:313-23.
7. Rajagopal C, Harikumar KB. The origin and functions of exosomes in cancer. Front Oncol. 2018;8:66.
8. Paskeh MDA, Entezari M, Mirzaei S, et al. Emerging role of exosomes in cancer progression and tumor microenvironment remodeling. J Hematol Oncol. 2022;15:83.
9. Boukouris S, Mathivanan S. Exosomes in bodily fluids are a highly stable resource of disease biomarkers. Proteomics Clin Appl. 2015;9:358-67.
10. Jeck WR, Sharpless NE. Detecting and characterizing circular RNAs. Nat Biotechnol. 2014;32:453-61.
11. Sanger HL, Klotz G, Riesner D, Gross HJ, Kleinschmidt AK. Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures. Proc Natl Acad Sci U S A. 1976;73:3852-6.
12. Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495:384-8.
13. Meng S, Zhou H, Feng Z, et al. CircRNA: functions and properties of a novel potential biomarker for cancer. Mol Cancer. 2017;16:94.
14. Huang XY, Huang ZL, Huang J, et al. Exosomal circRNA-100338 promotes hepatocellular carcinoma metastasis via enhancing invasiveness and angiogenesis. J Exp Clin Cancer Res. 2020;39:20.
15. Wang J, Zhao X, Wang Y, et al. circRNA-002178 act as a ceRNA to promote PDL1/PD1 expression in lung adenocarcinoma. Cell Death Dis. 2020;11:32.
16. Huang X, Song C, Zhang J, Zhu L, Tang H. Circular RNAs in breast cancer diagnosis, treatment and prognosis. Oncol Res. 2023;32:241-9.
17. Altesha MA, Ni T, Khan A, Liu K, Zheng X. Circular RNA in cardiovascular disease. J Cell Physiol. 2019;234:5588-600.
18. Xu YX, Pu SD, Li X, et al. Exosomal ncRNAs: novel therapeutic target and biomarker for diabetic complications. Pharmacol Res. 2022;178:106135.
19. Wang J, Yan S, Yang J, Lu H, Xu D, Wang Z. Non-coding RNAs in rheumatoid arthritis: from bench to bedside. Front Immunol. 2019;10:3129.
20. Kristensen LS, Andersen MS, Stagsted LVW, Ebbesen KK, Hansen TB, Kjems J. The biogenesis, biology and characterization of circular RNAs. Nat Rev Genet. 2019;20:675-91.
21. Wang Y, Liu J, Ma J, et al. Exosomal circRNAs: biogenesis, effect and application in human diseases. Mol Cancer. 2019;18:116.
22. Zhang F, Jiang J, Qian H, Yan Y, Xu W. Exosomal circRNA: emerging insights into cancer progression and clinical application potential. J Hematol Oncol. 2023;16:67.
23. Liu X, Zhang Y, Zhou S, Dain L, Mei L, Zhu G. Circular RNA: an emerging frontier in RNA therapeutic targets, RNA therapeutics, and mRNA vaccines. J Control Release. 2022;348:84-94.
26. Yi Y, Wu M, Zeng H, et al. Tumor-derived exosomal non-coding RNAs: the emerging mechanisms and potential clinical applications in breast cancer. Front Oncol. 2021;11:738945.
27. Zhang L, Zheng H, Jiang ST, et al. Worldwide research trends on tumor burden and immunotherapy: a bibliometric analysis. Int J Surg. 2024;110:1699-710.
28. Shen Z, Wu H, Chen Z, et al. The global research of artificial intelligence on prostate cancer: a 22-year bibliometric analysis. Front Oncol. 2022;12:843735.
29. Gong K, Chen Y, Liu W, Wang Z. Global research trends of Apolipoprotein E in central nervous system: a scientometric analysis. Int Immunopharmacol. 2021;98:107919.
30. Zhu X, Liu K, Tang X, Chen G. A bibliometric analysis of non-coding RNA studies in acute pancreatitis. Medicine. 2024;103:e37486.
31. Mongeon P, Paul-hus A. The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics. 2016;106:213-28.
32. AlRyalat SAS, Malkawi LW, Momani SM. Comparing bibliometric analysis using PubMed, Scopus, and Web of science databases. J Vis Exp. 2019;152:e58494.
33. Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523-38.
34. Chen C. Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci U S A. 2004;101:5303-10.
35. Wang S, Zhou H, Zheng L, et al. Global trends in research of macrophages associated with acute lung injury over past 10 years: a bibliometric analysis. Front Immunol. 2021;12:669539.
36. Pons P, Latapy M. Computing communities in large networks using random walks. In: Yolum p, Güngör T, Gürgen F, Özturan C, Editors. Computer and Information Sciences - ISCIS 2005. Springer, Berlin, Heidelberg; 2005. pp. 284-93.
37. Roldan-Valadez E, Salazar-Ruiz SY, Ibarra-Contreras R, Rios C. Current concepts on bibliometrics: a brief review about impact factor, Eigenfactor score, CiteScore, SCImago Journal Rank, Source-Normalised Impact per Paper, H-index, and alternative metrics. Ir J Med Sci. 2019;188:939-51.
39. Bertoli-Barsotti L, Lando T. A theoretical model of the relationship between the h-index and other simple citation indicators. Scientometrics. 2017;111:1415-48.
40. Chen C. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci. 2006;57:359-77.
41. Li Y, Zheng Q, Bao C, et al. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res. 2015;25:981-4.
42. Li S, Li Y, Chen B, et al. exoRBase: a database of circRNA, lncRNA and mRNA in human blood exosomes. Nucleic Acids Res. 2018;46:D106-12.
43. Bo X, Li Q, Chen S, et al. Evidence and perspectives on miRNA, circRNA, and lncRNA in myocardial ischemia-reperfusion injury: a bibliometric study. J Cardiothorac Surg. 2025;20:66.
44. Wang Y, Li C, Zhao R, et al. CircUbe3a from M2 macrophage-derived small extracellular vesicles mediates myocardial fibrosis after acute myocardial infarction. Theranostics. 2021;11:6315-33.
45. Fan L, Yao L, Li Z, et al. Exosome-based mitochondrial delivery of circRNA mSCAR alleviates sepsis by orchestrating macrophage activation. Adv Sci. 2023;10:e2205692.
46. Memczak S, Jens M, Elefsinioti A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495:333-8.
47. McAndrews KM, Mahadevan KK, Kalluri R. Mouse models to evaluate the functional role of the tumor microenvironment in cancer progression and therapy responses. Cold Spring Harb Perspect Med. 2024;14:a041411.
49. Zhang SJ, Chen X, Li CP, et al. Identification and characterization of circular RNAs as a new class of putative biomarkers in diabetes retinopathy. Invest Ophthalmol Vis Sci. 2017;58:6500-9.
50. Bahn JH, Zhang Q, Li F, et al. The landscape of microRNA, Piwi-interacting RNA, and circular RNA in human saliva. Clin Chem. 2015;61:221-30.
51. Hou JC, Xu Z, Zhong SL, et al. Circular RNA circASS1 is downregulated in breast cancer cells MDA-MB-231 and suppressed invasion and migration. Epigenomics. 2019;11:199-213.
52. Lyu ZZ, Li M, Yang MY, Han MH, Yang Z. Exosome-mediated transfer of circRNA563 promoting hepatocellular carcinoma by targeting the microRNA148a-3p/metal-regulatory transcription factor-1 pathway. World J Gastroenterol. 2023;29:6060-75.
53. Tao X, Shao Y, Lu R, et al. Clinical significance of hsa_circ_0000419 in gastric cancer screening and prognosis estimation. Pathol Res Pract. 2020;216:152763.
54. Pan Z, Zheng J, Zhang J, et al. A novel protein encoded by exosomal circATG4B induces oxaliplatin resistance in colorectal cancer by promoting autophagy. Adv Sci. 2022;9:e2204513.
55. Tiwari A, Trivedi R, Lin SY. Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci. 2022;29:83.
56. Wang D, Yang S, Lyu M, Xu L, Zhong S, Yu D. Circular RNA HSDL2 promotes breast cancer progression via miR-7978 ZNF704 axis and regulating hippo signaling pathway. Breast Cancer Res. 2024;26:105.
57. Chen J, Wang H, Xu J, et al. CircZFR promotes colorectal cancer progression via stabilizing BCLAF1 and regulating the miR-3127-5p/RTKN2 axis. Sci China Life Sci. 2024;67:1881-98.
58. Dilsiz N. A comprehensive review on recent advances in exosome isolation and characterization: toward clinical applications. Transl Oncol. 2024;50:102121.
59. Guo X, Gao C, Yang DH, Li S. Exosomal circular RNAs: a chief culprit in cancer chemotherapy resistance. Drug Resist Updat. 2023;67:100937.
60. Zhang X, Wang S, Wang H, et al. Circular RNA circNRIP1 acts as a microRNA-149-5p sponge to promote gastric cancer progression via the AKT1/mTOR pathway. Mol Cancer. 2019;18:20.
61. de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41:374-403.
62. Han QF, Li WJ, Hu KS, et al. Exosome biogenesis: machinery, regulation, and therapeutic implications in cancer. Mol Cancer. 2022;21:207.
63. Cunha E Rocha K, Ying W, Olefsky JM. Exosome-mediated impact on systemic metabolism. Annu Rev Physiol. 2024;86:225-53.
64. Li J, Zhang G, Liu CG, et al. The potential role of exosomal circRNAs in the tumor microenvironment: insights into cancer diagnosis and therapy. Theranostics. 2022;12:87-104.
65. Yunna C, Mengru H, Lei W, Weidong C. Macrophage M1/M2 polarization. Eur J Pharmacol. 2020;877:173090.
66. Boutilier AJ, Elsawa SF. Macrophage polarization states in the tumor microenvironment. Int J Mol Sci. 2021;22:6995.
67. Hu X, Chen J, Wang L, Ivashkiv LB. Crosstalk among Jak-STAT, Toll-like receptor, and ITAM-dependent pathways in macrophage activation. J Leukoc Biol. 2007;82:237-43.
68. Huang X, Wang J, Guan J, et al. Exosomal circsafb2 reshaping tumor environment to promote renal cell carcinoma progression by mediating M2 macrophage polarization. Front Oncol. 2022;12:808888.
69. Du A, Yang Q, Sun X, Zhao Q. Exosomal circRNA-001264 promotes AML immunosuppression through induction of M2-like macrophages and PD-L1 overexpression. Int Immunopharmacol. 2023;124:110868.
70. Gu X, Shi Y, Dong M, Jiang L, Yang J, Liu Z. Exosomal transfer of tumor-associated macrophage-derived hsa_circ_0001610 reduces radiosensitivity in endometrial cancer. Cell Death Dis. 2021;12:818.
71. Zhuang M, Zhang X, Ji J, et al. Exosomal circ-0100519 promotes breast cancer progression via inducing M2 macrophage polarisation by USP7/NRF2 axis. Clin Transl Med. 2024;14:e1763.
72. Song J, Xu X, He S, et al. Exosomal hsa_circ_0017252 attenuates the development of gastric cancer via inhibiting macrophage M2 polarization. Hum Cell. 2022;35:1499-511.
73. Bryja A, Zadka Ł, Farzaneh M, et al. Small extracellular vesicles - a host for advanced bioengineering and “Trojan Horse” of non-coding RNAs. Life Sci. 2023;332:122126.
74. Pittenger MF, Mackay AM, Beck SC, et al. Multilineage potential of adult human mesenchymal stem cells. Science. 1999;284:143-7.
75. Li QY, Gong T, Huang YK, et al. Role of noncoding RNAs in liver fibrosis. World J Gastroenterol. 2023;29:1446-59.
76. Wang C, Zhou H, Wu R, et al. Mesenchymal stem cell-derived exosomes and non-coding RNAs: regulatory and therapeutic role in liver diseases. Biomed Pharmacother. 2023;157:114040.
77. Shi Y, Du L, Lin L, Wang Y. Tumour-associated mesenchymal stem/stromal cells: emerging therapeutic targets. Nat Rev Drug Discov. 2017;16:35-52.
78. Nicodemou A, Bernátová S, Čeháková M, Danišovič Ľ. Emerging roles of mesenchymal stem/stromal-cell-derived extracellular vesicles in cancer therapy. Pharmaceutics. 2023;15:1453.
79. Liu J, Ren L, Li S, et al. The biology, function, and applications of exosomes in cancer. Acta Pharm Sin B. 2021;11:2783-97.
80. Ba L, Xue C, Li X, et al. Gastric cancer cell-derived exosomes can regulate the biological functions of mesenchymal stem cells by inducing the expression of circular RNA circ_0004303. Stem Cells Dev. 2021;30:830-42.
81. Li W, Yang J, Zheng P, Li H, Zhao S. The origins and generation of cancer-associated mesenchymal stromal cells: an innovative therapeutic target for solid tumors. Front Oncol. 2021;11:723707.
82. Mao X, Xu J, Wang W, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021;20:131.
83. Jiang Q, Lei Z, Wang Z, et al. Tumor-associated fibroblast-derived exosomal circDennd1b promotes pituitary adenoma progression by modulating the miR-145-5p/ONECUT2 axis and activating the MAPK pathway. Cancers. 2023;15:3375.
84. Shi H, Huang S, Qin M, et al. Exosomal circ_0088300 derived from cancer-associated fibroblasts acts as a miR-1305 sponge and promotes gastric carcinoma cell tumorigenesis. Front Cell Dev Biol. 2021;9:676319.
85. Chen Y, McAndrews KM, Kalluri R. Clinical and therapeutic relevance of cancer-associated fibroblasts. Nat Rev Clin Oncol. 2021;18:792-804.
86. Zhou Y, Tang W, Zhuo H, et al. Cancer-associated fibroblast exosomes promote chemoresistance to cisplatin in hepatocellular carcinoma through circZFR targeting signal transducers and activators of transcription (STAT3)/ nuclear factor -kappa B (NF-κB) pathway. Bioengineered. 2022;13:4786-97.
87. Du J, Meng X, Yang M, et al. NGR-modified CAF-derived exos targeting tumor vasculature to induce ferroptosis and overcome chemoresistance in osteosarcoma. Adv Sci. 2025;12:e2410918.
88. Biffi G, Oni TE, Spielman B, et al. IL1-induced JAK/STAT signaling is antagonized by TGFβ to shape CAF heterogeneity in pancreatic ductal adenocarcinoma. Cancer Discov. 2019;9:282-301.
89. Payervand N, Pakravan K, Razmara E, et al. Exosomal circ_0084043 derived from colorectal cancer-associated fibroblasts promotes in vitro endothelial cell angiogenesis by regulating the miR-140-3p/HIF-1α/VEGF signaling axis. Heliyon. 2024;10:e31584.
90. Zhu W, Zhang H, Tang L, et al. Identification of a plasma exosomal lncRNA- and circRNA-based ceRNA regulatory network in patients with lung adenocarcinoma. Clin Respir J. 2024;18:e70026.
91. Wang X, Dong Y, Wu Z, Wang G, Shi Y, Zheng Y. Machine learning-based comparative analysis of pan-cancer and pan-normal tissues identifies pan-cancer tissue-enriched circRNAs related to cancer mutations as potential exosomal biomarkers. Front Oncol. 2021;11:703461.
92. Ma C, Yang C, Peng A, et al. Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment. Mol Cancer. 2023;22:170.
93. Pei Y, Guo Y, Wang W, et al. Extracellular vesicles as a new frontier of diagnostic biomarkers in osteosarcoma diseases: a bibliometric and visualized study. Front Oncol. 2024;14:1359807.
94. Zhou Z, Chen C, Han B, et al. Circular RNA in cholangiocarcinoma: a systematic review and bibliometric analysis. Pathol Res Pract. 2023;249:154755.
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