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. 2024 Aug 12:15:1427380.
doi: 10.3389/fimmu.2024.1427380. eCollection 2024.

Current perspectives and trends of CD39-CD73-eAdo/A2aR research in tumor microenvironment: a bibliometric analysis

Tian Huang #  1   2   3 , Xiangqing Ren #  1   2   3 , Xiaolong Tang #  1   4 , Yuping Wang  2   3 , Rui Ji  2   3 , Qinghong Guo  2   3 , Qian Ma  5 , Ya Zheng  1   2   3 , Zenan Hu  2   3 , Yongning Zhou  2   3
Affiliations

Current perspectives and trends of CD39-CD73-eAdo/A2aR research in tumor microenvironment: a bibliometric analysis

Tian Huang et al. Front Immunol. .

Abstract

Background and objective: Extracellular adenosine (eAdo) bridges tumor metabolism and immune regulation. CD39-CD73-eAdo/A2aR axis regulates tumor microenvironment (TME) and immunotherapy response. In the era of immunotherapy, exploring the impact of the CD39-CD73-eAdo/A2aR axis on TME and developing targeted therapeutic drugs to enhance the efficacy of immunotherapy are the current research hotspots. This study summarizes and explores the research trends and hotspots of the adenosine axis in the field of TME to provide ideas for further in-depth research.

Methods: Literature information was obtained from the Web of Science core collection database. The VOS viewer and the bibliometric tool based on R were used to quantify and identify cooperation information and individual influence by analyzing the detailed information of the global annual publication volume, country/region and institution distribution, article authors and co-cited authors, and journal distribution of these articles. At the same time, the distribution of author keywords and the co-occurrence of author keywords, highly cited articles, and highly co-cited references of CD39-CD73-eAdo/A2aR in the field of TME were analyzed to determine research hotspots and trends.

Result: 1,721 articles published in the past ten years were included in this study. Through bibliometric analysis, we found that (1) 69 countries and regions explored the effect of the CD39-CD73-eAdo/A2aR on TME, and the research was generally on the rise. Researchers in the United States dominated research in this area, with the highest total citation rate. China had the most significant number of publications. (2) Harvard University has published the most articles in this field. (3) 12,065 authors contributed to the publication of papers in this field, of which 23 published at least eight papers. STAGG J had significant academic influence, with 24 published articles and 2,776 citations. Co-cited authors can be clustered into three categories. Stagg J, Allard B, Ohta A, and Antonioli, L occupied a central position in the network. (4) 579 scholarly journals have published articles in this field. The journal FRONTIERS IN IMMUNOLOGY published the most significant number of papers, with 97 articles and a total of 2,317 citations, and the number of publications increased year by year. (5) "The ectonucleotidases CD39 and CD73: Novel checkpoint inhibitor targets" was the most frequently local cited article (163 times). The "A2A adenosine receptor protects tumors from antitumor T cells" was the most co-cited reference (224 times). (6) Through the analysis of author keywords, we found that the relationship between adenosine and immunotherapy was a core concept for many researchers in this field. Breast cancer, melanoma, colorectal cancer, ovarian cancer, glioblastoma, pancreatic cancer, hepatocellular carcinoma, and lung cancer were the most frequent cancer types in adenosine-related tumor studies. Immunotherapy, immunosuppression, immune checkpoint, and immune checkpoint inhibitors were the hot keywords in the research, reflecting the importance of the adenosine metabolic pathway in tumor immunotherapy. The keywords such as Immunogenic cell death, T cells, Sting, regulatory T cells, innate immunity, and immune infiltration demonstrated the pathways by which adenosine affected the TME. The famous author keywords in recent years have been immunotherapy, immunogenic cell death, inflammation, lung cancer, and gastric cancer.

Conclusion: The effect of CD39-CD73-eAdo/A2aR on the infiltration and function of various immune cells in TME, tumor immunotherapy response, and patient prognosis has attracted the attention of researchers from many countries/regions. American scholars still dominate the research in this field, but Chinese scholars produce the most research results. The journal FRONTIERS IN IMMUNOLOGY has published the wealthiest research in the field. Stagg J was a highly influential researcher in this field. Further exploration of targeted inhibition of CD39-CD73-eAdo/A2aR alone or in combination with other immunotherapy, radiotherapy, and chemotherapy in treating various cancer types and developing effective clinical therapeutic drugs are continuous research hotspots in this field.

Keywords: A2aR; CD39; CD73; adenosine; tumor microenvironment.

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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
The mechanism diagram of CD39-CD73-eAdo/A2aR axis in tumor microenvironment.
Figure 2
Figure 2
Annual publications from 2015 to 2024.
Figure 3
Figure 3
Paper publication status in various countries. (A) Number of publications from the top 20 countries. (B) Patterns of international collaboration in paper publication: Single Country Publications (SCP); Multi-country Collaboration Publications (MCP). (C) Average article citations of each country.
Figure 4
Figure 4
The visualization of cooperation between countries by VOSviewer. (A) International cooperation network diagram. The same-colored logo indicates the cluster group of countries, the thickness of the line reflects the closeness of cooperation, and the node’s size corresponds to the number of papers published by each country. (B) Heat map of cooperation analysis between countries. Each label represents a country, and the color on the heat map changes gradually from cool to warm, indicating low to high cooperation.
Figure 5
Figure 5
Annual publication growth trend chart of institutions.
Figure 6
Figure 6
The visualization of cooperation among authors by VOSviewer. (A) Diagram of the collaborative network among authors. The same color logo indicates the author cluster group, the thickness of the line reflects the closeness of cooperation, and the node’s size corresponds to the number of papers published by the author. (B) Heat map analysis of collaboration among authors. Each label represents an author, and the colors on the heat map are graded from cool to warm, indicating a low to a high degree of cooperation.
Figure 7
Figure 7
Co-citation analysis of cited authors by VOSviewer. (A) Citation analysis of the cited authors. Each node represents an independent author, and the node’s size reflects the number of co-cited papers contributed by the noted author. A larger node means that the author is more co-cited with other authors. Lines indicate co-citation relationships between authors, and the same color represents the same author clustering group. (B) Heat map of citation analysis of authors. Each label represents a citation author, the color intensity of the label represents how often the author is cited, and the color gradient on the heat map from cool to warm marks the frequency of co-citation from low to high.
Figure 8
Figure 8
Journal publication trend and distribution map. (A) Trend chart of annual publication volume of journals. (B) Distribution map of Bradford’s Law for journals.
Figure 9
Figure 9
Coupling analysis of research results by VOSviewer. (A) Coupling analysis of relevant research results. Each node represents an independent research result, and the size of the node reflects the number of citations contributed by the research result in the bibliographic coupling analysis. The larger the node, the higher the coupling degree between the research result and other research results. The line indicates that there is a coupling relationship between the research results, and the same color represents the same research results cluster groups. (B) Heat map of coupling analysis of related research results. Each label represents an independent research result, and the color on the heat map changes gradually from cold to warm colors, marking the degree of coupling of an independent research result from low to high.
Figure 10
Figure 10
Co-citation analysis of references by VOSviewer. (A) Reference co-citation analysis. Each node represents a reference, and the node’s size reflects the number of co-citations of the reference. A larger node means that the reference has a higher co-citation degree with other references. Lines indicate co-citation relationships between references, and the same colors represent the same co-citation reference cluster groups. (B) Heat map of reference co-citation analysis. Each label represents a reference, the color intensity of the label represents the frequency of reference co-citation, and the color on the heat map changes gradually from cool to warm colors, marking the frequency of co-citation from low to high.
Figure 11
Figure 11
Author keyword analysis. (A) Word clouds show author keywords used by authors. The size of an author keyword is directly proportional to its frequency of occurrence; the larger the word, the more frequently it appears in the relevant literature. (B) The rectangular dendrogram shows the frequency of keywords with authors. Each different colored rectangle represents an author keyword, and its area indicates how often that author keyword appears in related papers. (C) Topic distribution map showing the degree of development and relevance of different research topics in this field. The chart’s vertical axis represents the degree of development of the topic (degree of development), and the horizontal axis represents the centrality or relevance of the topic (degree of relevance).
Figure 12
Figure 12
Author keywords co-occurrence analysis by VOSviewer. (A) Author keywords co-occurrence analysis. Each node represents an author keyword, and the node size reflects the occurrence frequency of the author keyword. The larger the node, the more frequent the occurrence frequency of the author keyword. The line indicates the co-occurrence relationship between the author keywords, and the same color represents the same author keywords cluster group. (B) The time trend chart of author keywords. The color changes gradually from green to yellow, representing the time range from 2020 to 2022. (C) Heatmap of author keywords co-occurrence analysis, each label represents an author keyword, and the color intensity of the label represents the occurrence frequency of the author keyword. The color on the heatmap gradually changes from cold color to warm color, indicating that the occurrence frequency of the author keywords changes from low to high.
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Natural Science Foundation of Gansu Province (22JR5RA902); the Education Technology Innovation Project of Gansu Province, China (2022B-001); The Scientific Research Project of the First Hospital of Lanzhou University, China (ldyyyn2021-3).

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