AUTHOR=Xiong Shuai , Liu Ke , Yang Fei , Dong Yuanwei , Zhang Hongcai , Wu Pengning , Zhou Yu , Zhang Lu , Wu Qin , Zhao Xiaojing , Li Wei , Yuan Lingling , Huang Biao , Yue Rensong , Feng Li , Chen Jing , Zhang Yi TITLE=Global research trends on inflammatory bowel diseases and colorectal cancer: A bibliometric and visualized study from 2012 to 2021 JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.943294 DOI=10.3389/fonc.2022.943294 ISSN=2234-943X ABSTRACT=

Inflammatory bowel disease (IBD) is a chronic non-specific inflammatory disease of intestinal tract and a common digestive system disease. Current studies have shown that IBD significantly increases the incidence of colorectal cancer (CRC), and is positively correlated with the degree and extent of inflammation of IBD. The relationship between IBD and CRC has attracted extensive attention. However, the relationship between IBD and CRC has not been systematically studied by bibliometrics and visual analysis. This study conducted bibliometric analysis based on 3528 publications from the Core Collection of Web of Science to determine the research status, research hotspots and frontiers of this field. The results show that the number of publications has increased significantly over the past 10 years. The cooperative network analysis shows that the United States, Mayo Clin and Bo Shen are the country, institution and author with the most publications respectively. Belgium, Icahn Sch Med Mt Sinai and Erik Mooiweer are the most collaborative country, institution and author respectively. Analysis of keywords and references showed that inflammation, intestinal flora, and obesity were hot topics in this field. Analysis of keyword outbreaks shows that the gut microbiome and metabolism will be an emerging new research area and a potential hot spot for future research. This study is the first to visually examine the association between IBD and CRC using bibliometrics and visual analysis, and to predict potential future research trends.