Investigation of trends in gut microbiome associated with colorectal cancer using machine learning - Yu, Chaoran; Zhou, Zhiyuan; Liu, Bin; Yao, Danhua; Huang, Yuhua; Wang, Pengfei; Li, Yousheng (Frontiers in Oncology, 2023)
Background: Rapid growth of publications in gut microbiome and colorectal cancer (CRC) makes it feasible for text mining and bibliometric analysis. Methods: Publications were retrieved from Web of Science. Bioinformatics analysis was performed and a machine learning based Latent Dirichlet Allocation (LDA) model was used for identification of subfield research topics.. Results: A total of 5696 publications relating to gut microbiome and CRC were retrieved from Web of Science Core Collection from 2000 to 2022. China and USA were the most productive countries. Top 25 references, institutions and authors with the strongest citation bursts were identified. Abstracts from all 5696 publications were extracted for a text-mining analysis identified top 50 topics across this field with increasing interests. Colitis animal model, expression of cytokines, microbiome sequencing and 16s, microbiome composition and dysbiosis, cell growth inhibition, were increasingly noticed during the last two years. The 50 most intensively investigated topics were identified and further categorized into four clusters, including “microbiome sequencing and tumor”, “microbiome compositions, interactions and treatment”, “microbiome molecular features and mechanisms” and “microbiome and metabolism”. Conclusion: This bibliometric analysis explores the historical research tends of gut microbiome and CRC and identified specific topics with increasing interests. Developmental trajectory, along with noticeable research topics characterized by this analysis will contribute to the future direction of researches in CRC and clinical translation.