Global landscape of hepatic organoid research: A bibliometric and visual study.

Abstract

Background: Hepatic organoid-based modelling, through the elucidation of a range of in vivo biological processes and the recreation of the intricate liver microenvironment, is yielding groundbreaking insights into the pathophysiology and personalized medicine approaches for liver diseases.

Aim: This study was designed to analyse the global scientific output of hepatic organoid research and assess current achievements and future trends through bibliometric analysis.

Methods: Articles were retrieved from the Web of Science Core Collection, and CiteSpace 6.3.R1 was employed to analyse the literature, including outputs, journals, and countries, among others.

Results: Between 2010 and 2024, a total of 991 articles pertaining to hepatic organoid research were published. The journal Hepatology published the greatest number of papers, and journals with an impact factor greater than 10 constituted 60% of the top 10 journals. The United States and Utrecht University were identified as the most prolific country and institution, respectively. Clevers H emerged as the most prolific author, whereas Huch M had the highest number of cocitations, suggesting that both are ideal candidates for academic collaboration. Research on hepatic organoids has exhibited a progressive shift in focus, evolving from initial investigations into model building, differentiation research in stem cells, bile ducts, and progenitor cells, to a broader spectrum encompassing lipid metabolism, single-cell RNA sequencing, and therapeutic applications. The phrases exhibiting citation bursts from 2022 to 2024 include "drug resistance", "disease model", and "patient-derived tumor organoids".

Conclusion: Research on hepatic organoids has increased over the past decade and is expected to continue to grow. Key research areas include applications for liver diseases and drug development. Future trends likely to gain focus include patient-derived tumour organoids, disease modelling, and personalized medicine.