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Bibliometric analysis reveals shifting research trends in hepatocellular carcinoma lipid metabolism toward the tumor microenvironment, highlighting emerging computational and omics directions.
This study maps the research landscape, hotspots, and emerging trends in hepatocellular carcinoma and lipid metabolism, offering a bibliometric overview to facilitate cross-disciplinary inquiry and guide future research directions. Literature related to “hepatocellular carcinoma” and “lipid metabolism” published between January 1, 2020 and March 31, 2025 were obtained from the Web of Science Core Collection and evaluated for inclusion. CiteSpace (version 6.2.R6) was used to illustrate annual publication trends, country- and institution-level collaboration networks, author collaborative relationships, keyword co-occurrence, clustering, and timeline analyses of research themes. A total of 1,112 records were included, demonstrating a consistent increase in annual publication output. China, the United States, and Japan were the three most prolific countries. The United States had the highest betweenness centrality (0.36), reflecting a bridging position in the international collaboration network. The author collaboration network revealed six primary clusters, with notable themes including metabolic reprogramming and the tumor microenvironment as well as clinically focused research. High-frequency keywords included “hepatocellular carcinoma” and “lipid metabolism”. Burst and timeline analyses indicated a current shift from initial mechanistic emphasis toward integrative themes such as the tumor microenvironment and model-based research, including computational approaches. Over the past five years, research on hepatocellular carcinoma and lipid metabolism has progressed from basic mechanisms toward more integrated themes that link biological processes with translationally relevant questions. The bibliometric findings offer a survey of key contributors and thematic evolution, which may help researchers readily identify dynamic areas and developing directions, including model-based studies and high-resolution omics approaches.
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Liu et al. (2026) studied this question.
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