Metrology and visualization analysis of literatures related to tumor immunotherapy based on CiteSpace

To explore the development of global tumor immunotherapy in the past 20 years, and analyze the research status, hotspots and trends to provide theoretical support for subsequent research. CiteSpace 5.6.R2 software were used to analyze 290 articles related to tumor immunotherapy research from the Web of Science core data set from 2000 to 2019. CiteSpace was used to draw related visual maps and tables of coauthors, cooperating countries, cooperating institutions, literature and journal co‐citation, keyword co‐occurrence, and cluster analysis. The total amount of papers published in the field of tumor immunotherapy research has gradually increased. After receiving the Nobel Prize in Physiology or Medicine in 2018, the number of papers published in the field of research in tumor research reached 20. We found that the United States has the largest number of papers in all countries and China ranks 6. “Proceedings of the National Academy of Sciences of the United States of America,” “Journal of Immunology,” “Cancer Research,” “New England Journal of Medicine,” “Blood” is the top five core academic journals in this field. Cancer, disease, and tumor necrosis factor are the three keywords with the highest highlighting intensity. Comprehensive analysis shows that the coauthors, co‐institutions, and co‐cited journals of the literature are mostly concentrated in the United States. The rise of immunotherapy provides a new direction for tumor treatment. From 2018 to 2019, the number of literatures on tumor immunotherapy worldwide increased sharply. Immunotherapy combined with specific diseases is the current research frontier and hotspot.

human cells, which will have serious side effects on bone marrow cells, liver cells and digestive system. 1 Although targeted therapeutic drugs have the characteristics of specific killing cancer cells without affecting normal cells, the biggest disadvantage of targeted drugs is prone to drug resistance. On the other hand, tumor immunotherapy is different from the former two treatments in that it targets normal immune cells and achieves the purpose of treating cancer by directly activating the body's own immune system. 2   by Pritchard, as "the application of mathematical and statistical methods to books and other media of communication" and by Hawkins as "the quantitative analysis of the bibliographic features of a body of literature." 3,4 Besides, bibliometrics has become increasingly popular, especially in medical research. [5][6][7] CiteSpace is a software developed and applied to literature visualization analysis by Dr Chen Chaomei, a scholar at the University of Drexel in the United States.
This software can obtain the historical development, research hotspots and trends of this research field through the analysis of a large number of literature with similar research topics. 8 In this study, the core dataset of Web of Science (WOS) was used as the data source, 2 | DATA SOURCES AND RESEARCH METHODS

| Sources and strategies
The research data come from the core data set of WOS. Formulation of search terms: Subject ("Tumor" OR "Cancer" OR "Malignant Neoplasm" OR "Benign Neoplasm") AND Subject ("Passive Immunization" OR "Passive Antibody Transfer" OR "Passive Transfer of Immunity" OR "Passive Immunotherapy" OR "Immunoglobulin Therapy"). The search time was set from 2000 to 2019, the search literature type was set to article, and the search language was set to English.

| Research method
After using EndNote X9 software to deduplicate the obtained data, CiteSpace 5.6.R2 was used to analyze the coauthors, cooperative countries and cooperative institutions, co-citation analysis of literature and journals, and keyword co-occurrence and cluster analysis.
Parameter setting: node type selection author, institution, country, keyword, literature, journal; in addition to the author's choice of "2", the remaining nodes were selected as "1" from 2000 to 2019. The   Table 1.

| Co-occurrence analysis of cooperative countries
CiteSpace 5.6.R2 software was used for co-occurrence analysis of partner countries that published literature on cancer immunotherapy.
Software parameters were set as follows: the network node was "Country", and the threshold setting remained default. The visual map of the co-occurrence of cooperation countries is shown in Figure 3,   3.5 | Journal co-citation analysis CiteSpace 5.6.R2 software was used to analyze the journal co-citation of articles published on cancer immunotherapy. Software parameters were set: the network node was "Cited Journal", the threshold was set to top 20 per slice, and the critical path algorithm was selected. The visual map of the journal co-citation is shown in Figure 5

| Literature co-citation analysis
CiteSpace 5.6.R2 software was used to analyze the co-citation of tumor immunotherapy research literature, and the software parameters were set: the network node was "Reference", and the threshold setting remained default. According to the literature cocitation visualization map, the top five co-cited articles were sorted out, as shown in Table 4. Literature published by Dakappagari NK (2003) and Dudley ME (2002) has been cited most frequently in the field of cancer immunotherapy research, with Dakappagari NK in the journal "Journal of Immunology" and Dudley ME in the journal "Science".

| Keyword co-occurrence analysis
CiteSpace 5.6.R2 software was used to analyze keyword cooccurrence in published cancer immunotherapy literature, setting software parameters: the network node is "Keyword" and the threshold setting remains the default. The keyword co-occurrence visualization map is shown in Figure 6, including 123 nodes and 335 lines.
Each node in Figure 6 represents a keyword, and the radius of the node represents the number of times the word appears. The connection between nodes represents that the same keyword has appeared F I G U R E 7 Clustering analysis of tumor immunotherapy keywords co-citation network.

| Keyword cluster analysis
Keyword clustering analysis clusters keywords with the same or similar meanings into one category, thus forming a set of common themes.
As shown in Figure 7

| Keyword evolution and mutation word analysis
According to the keyword evolution map (see Figure 8)  Growth factor receptor/monoclonal 81c6/glioblastoma multiforme/blood-brain barrier/tumor infiltrating lymphocyte/brain tumor/activated killer cell/immunotherapy At present, many tumor patients who fail to be treated with other methods can choose PD-1 antibody treatment. 15,16 However, Europe, the United States, Japan, and other developed countries approved PD-1 antibody for cancer treatment in 2014, and China approved the first PD-1 antibody drugs for the treatment of lung cancer in 2018. 17 Compared with the research on tumor immunotherapy in the past 20 years abroad, the research in this field in China is relatively lagging behind. [18][19][20] At present, China has recognized the importance of tumor immunotherapy. For example, outstanding achievements have been made in the field of CAR-T-cell therapy. 21 Recently, it was reported that relapsed T-cell malignancies have poor outcomes when treated with chemotherapy, but survival after allogeneic F I G U R E 9 Keywords with strongest citation bursts in adapted tumor immunotherapy. The most recent burst keywords were disease, children, and receptor.
bone marrow transplantation approaches 50%. 22  Zhang. All the authors read and approved the final manuscript.