Mapping the knowledge domains of literature on hepatocellular carcinoma and liver failure: a bibliometric approach

Abstract

Background: Hepatocellular carcinoma (HCC) accounts for 75-85% of primary liver cancers, with its incidence continually rising, posing a threat to socio-economic development. Currently, liver resection is the standard treatment for HCC. However, post-hepatectomy liver failure (PHLF) is a severe and formidable postoperative complication that increases patients' medical expenses and mortality risk. Additionally, liver failure can occur at any stage of HCC development, severely affecting patients' quality of life and prognosis.

Method: Using the Web of Science Core Collection, this bibliometric study analyzed English articles and reviews on HCC and liver failure from 2003 to 2023. Bibliometric tools like CiteSpace, VOSviewer, and R-studio were employed for data visualization and analysis, focusing on publication trends, citation metrics, explosive intensity, and collaborative networks. Use the Comparative Toxicogenomics and Genecards databases to screen for genes related to liver failure, and perform enrichment analyses using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and PubMed on the identified differentially expressed genes.

Results: The study identified a significant increase in publications on HCC and liver failure, with key contributions from journals such as the World Journal of Gastroenterology and the Journal of Hepatology. The United States, China, and Japan were the leading countries in research output. Prominent authors and institutions, including Kudo Masatoshi and Sun Yat-sen University, were identified. Enrichment analysis showed drug metabolism, oxidative stress, lipid metabolism, and other pathways are closely related to this field. Research hotspots included risk prediction models and novel therapies.

Conclusion: This bibliometric analysis highlights the growing research interest and advancements in HCC and liver failure. Future research should focus on improving risk prediction, developing new therapies, and enhancing international collaboration to address these critical health issues.

Keywords: CiteSpace; VOSviewer; bibliometrics; hepatocellular carcinoma; liver failure.

Figure 1

Flowchart of hepatocellular carcinoma and liver failure.

Figure 2

Publication and citations of hepatocellular carcinoma and liver failure. The citation trend line of the publication volume shows an exponential growth pattern. The line graph illustrates a year-on-year increase in the number of citations.

Figure 3

Network visualization of hepatocellular carcinoma and liver failure. (A) Collaborative network map of journals with ≥9 publications. Each node represents a journal, with the node’s size indicating the number of publications, and the connections between nodes representing collaborative relationships. (B) Map of core journals in hepatocellular carcinoma and liver failure. (C) Map of co-cited journals in hepatocellular carcinoma and liver failure. The nodes with darker colors represent greater importance in the field. (D) Map of emerging journals in hepatocellular carcinoma and liver failure. The color variation reflects the spatial mapping of nodes based on the average publication year of co-cited journals.

Figure 4

The journal dual overlay view. The citing journal is shown on the left, and the cited journal is on the right. The z-value represents the standardized citation count. Citation paths, colored in green and yellow, indicate the connections between the journals. The vertical axis of the ellipses represents the number of published papers, and the horizontal axis represents the number of authors.

Figure 5

Visualization of the author network for hepatocellular carcinoma and liver failure. Each node represents an author or a co-authored author, with the size of the node indicating the number of publications, and the connections between the nodes representing collaborative relationships. (A) Collaborative network map of authors with ≥7 publications. (B) Core authors in hepatocellular carcinoma and liver failure. The nodes with darker colors represent greater importance in the field. (C) Emerging scholars in hepatocellular carcinoma and liver failure. The colors vary in accordance with the respective years. (D) Author’s production over time. Each line illustrates the annual publication and citation trends of each author. The node size represents the publication volume, while the color intensity indicates the citation count.

Figure 6

Visualization of Institutional Networks. Each node represents an institution, while the connections between nodes indicate collaborative relationships between institutions. (A) The institutional co-occurrence network map. (B) Emerging institutions in hepatocellular carcinoma and liver failure. The color represents a significant contribution to the field in the corresponding year.

Figure 7

Collaborative Network Map of Counties-Regions. (A) Countries/Regions Cooperation Network Map. Each node represents a country, with the thickness of the connecting lines indicating the strength of the relationship. (B) Core Countries in hepatocellular carcinoma and liver failure. The darker the node color, the more significant the country. (C) Emerging Countries in hepatocellular carcinoma and liver failure. The color variations in the overlay view reflect the spatial mapping of the nodes based on the average publication year of each country. (D) Collaborative network map of countries. The size of each node corresponds to the volume of publications, and the thicker the line, the closer the collaboration between countries.

Figure 8

Network visualization of co-occurring keywords in hepatocellular carcinoma and liver failure. The nodes represent keywords, with the size indicating the frequency of their occurrence. The outer purple transparent circle represents the intermediary centrality of the node. A visible purple circle signifies that the keyword is highly important in the field. The connecting lines indicate that the keywords are cited within the same article.

Figure 9

Clustering of Nine Key Terms in hepatocellular carcinoma and liver failure. Generate appropriate cluster titles based on the keywords within each cluster.

Figure 10

Most explosive keywords for hepatocellular carcinoma and liver failure. The blue bars show the time intervals, whereas the red bars highlight the burst periods. The beginning and ending years of these bursts are also provided.

Figure 11

Timeline mapping of hepatocellular carcinoma and liver failure domain keywords. (A) keyword year evolution analysis. Node size represents the frequency of occurrence. (B) clustering timeline analysis. (C) keyword time hotspot graph. Each grid represents the popularity of keywords for the corresponding year, with lighter colors indicating a higher intensity of the surge, closer to the 1. (D) keyword timeline graph. Each line represents the evolution of a keyword in the past twenty years.

Figure 12

Co-citation visualization analysis of hepatocellular carcinoma and liver failure. (A) Visualization of citations. Each node represents an article, and the connection represents being cited together by the same article. (B) Visualization of literature clustering. The main clustering based on the content of the cited literature.

Figure 13

Evolution analysis of co-cited literature for hepatocellular carcinoma and liver failure. Each node represents a cited paper, with the connecting lines indicating co-citation with the same article.

Figure 14

The enrichment analysis results of hepatocellular carcinoma and liver failure associated genes. (A) Overlap of common genes between liver failure-related genes from the CTD and GeneCards databases. (B) Principal component analysis. (C) Volcano plot illustrating differentially expressed genes. Up-regulated genes (n=144) are highlighted in red, while down-regulated genes (n=44) are shown in blue. (D) GO and KEGG enrichment analysis of the intersecting genes. (E) Enrichment analysis of differential gene-related PubMed database. FDR is a correction method for multiple hypothesis testing, which is used to control the proportion of false positive results. Signal usually refers to the correlation strength score between terms and target gene sets in the literature.