AUTHOR=Zha Bowen , He Jiahui , Li Chunguang 

TITLE=A bibliometric analysis of immune escape in colorectal cancer: research trends, key contributors, and future directions

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1614613

DOI=10.3389/fimmu.2025.1614613

ISSN=1664-3224

ABSTRACT=BackgroundColorectal cancer (CRC) has brought a serious disease burden to the whole world. Immune escape not only promotes the growth and metastasis of CRC, but also limits the effect of immunotherapy. The purpose of this study is to clarify the research status of immune escape in CRC through bibliometrics.MethodsThis analysis examined publications on immune escape in CRC from the Web of Science Core Collection. The time limit is 2015-2024. After searching and screening by two researchers, data were collected and various analysis were conducted using tools such as VOSviewer, CiteSpace, and bibliometrix. By analyzing the large-scale existing literature data and using the quantitative method of bibliometric analysis, the research trends and emerging topics can be effectively identified.ResultsA total of 573 articles and reviews were included. From 2015-2024, the annual growth rate of 15.93%. The research from China is the most (50.09%), but the research from the United States and Germany is cited more times. Frontiers in Immunology has published the most articles (6.46%). Lei Wang and Peter J.K. Kuppen have made notable contributions, with substantial international collaboration. Keyword analysis highlights research hotspots such as tumor microenvironment and immune-related signaling pathways.ConclusionThe latest research status of immune escape in CRC is shown. Understanding the immune escape mechanism is very important for understanding the occurrence and development of CRC and developing effective immunotherapy strategies. Future research directions include integrating multiple databases to reduce biases inherent in single-database analyses and employing machine learning methods to predict emerging research hotspots, thus providing actionable insights into the dynamic landscape of immune escape research in CRC.