Current trends, knowledge structure, and evidence landscape of phosphorylation-related research in organoid models: a bibliometric analysis

Research on phosphorylation in organoid models has experienced rapid expansion; however, the knowledge structure, academic impact, and depth of evidence in this field remain inadequately characterized. Publications indexed in the Web of Science Core Collection (SCI-EXPANDED) from 2005 to 2025 were retrieved on April 2, 2026. Following document-type restriction, deduplication, and manual screening, a total of 236 studies were included. CiteSpace was employed to analyze publication trends, collaboration patterns, co-citation structures, highly cited studies, keyword evolution, and citation-based impact. The included studies were further classified according to evidence type. Publication output experienced a significant increase after 2017, with the United States and China emerging as the primary contributors. However, the levels of productivity did not consistently correlate with citation impact or collaborative centrality. Co-citation analysis indicated that the field predominantly focuses on the establishment of stem cell-derived organoids, translational oncology, cerebral organoid disease models, and the signaling pathways that regulate organoid growth and differentiation. The most highly cited studies primarily centered on mechanistic phospho-signaling research, while more recent influential works have expanded into disease modeling and phosphoproteomics. Notably, 184 out of 236 studies (77.97%) were classified as providing signaling evidence, whereas only 24 studies (10.27%) offered direct evidence of phosphorylation. Furthermore, intestinal and brain organoids comprised the majority of the studies included, highlighting a pronounced tissue bias within the research. Research on phosphorylation within organoid models is a rapidly expanding yet heterogeneous field, predominantly characterized by studies focused on signaling pathways, while direct evidence of phosphorylation remains scarce. These findings underscore significant disparities in impact, model utilization, and the depth of evidence, which should guide future mechanistic and translational research endeavors.