Exploration of the current status and trends of pancreatic cancer immune cells in the past 30 years: a bibliometric analysis

Abstract

Objective: Immune cells are pivotal in progressing and treating pancreatic cancer. Over the past three decades, the complex interactions between pancreatic cancer and immune cells have garnered much attention, as evidenced by the increasing number of publications in this domain. This bibliometric study maps the global research landscape of pancreatic cancer immune cell interactions, emphasizing evolving trends, collaborative networks, and therapeutic innovation.

Method: Using VOSviewer and CiteSpace, we analyzed 2658 articles from the Web of Science Core Collection (2000-2024) to evaluate publication trends, collaborative networks, keyword dynamics, and highly cited works.

Results: Annual publications surged from 4 (2000) to 453 (2024), with China (44.9%) and the U.S. (33.6%) dominating output. Key institutions included Fudan University (102 articles) and Zhejiang University (88 articles). Keyword evolution revealed three phases: antitumor mechanisms, clinical translation of checkpoint inhibitors, and recent emphasis on stromal-immune crosstalk. High-impact works by Brahmer (N Engl J Med 366:2455-2465, 2012) and Marabelle (J Clin Oncol 38:1-10, 2020) underscored immunotherapy milestones.

Conclusion: This bibliometric analysis highlights the dynamic nature of pancreatic cancer immune cell research, emphasizing the growing global interest and investment in this field. The findings underscore the need for ongoing monitoring of research trends to inform and propel innovative therapeutic strategies, ultimately improving patient outcomes.

Keywords: Bibliometric analysis; Citespace; Immune cells; Pancreatic cancer; VOSviewer.

Fig. 1

Flow chart of literature screening

Fig. 2

The annual number of publications and trends

Fig. 3

aWorld map showing the country scientific production and collaboration in pancreatic cancer immune cells field. b Country cooperation co-occurrence clustering map. c Chronological network of co-occurrence of countries

Fig. 4

Institution cooperation co-occurrence map. a Institution cooperation co-occurrence clustering map and b Chronological network of co-occurrence of institutions

Fig. 5

a Journal cooperation co-occurrence clustering map. b Chronological network of co-occurrence of journals. c Double-graph overlay of journals

Fig. 6

Author cooperation co-occurrence map. a Author cooperation co-occurrence clustering map and b Chronological network of co-occurrence of authors

Fig. 7

a Keyword cooperation co-occurrence clustering map. b Chronological network of co-occurrence of keywords. c Cluster diagram of relevant keywords. d Timeline chart of relevant keywords. e Keyword emergence analysis. Year: the first occurrence of the keyword; strength: the larger the value, the stronger the emergence of the keyword; begin: the start time of the keyword burst; end: the end time of the keyword burst. Red represents the time of the keyword burst, dark green represents the time interval, and light green represents the keyword does not appear

Fig. 8

Highly cited local literature collaboration network. a Co-citation network analysis of highly cited references and b Chronological network of co-occurrence of co-cited references