Pathological features and biomarkers of hepatocellular carcinoma: a bibliometric analysis from 2005 to 2025

Introduction

Background

Hepatocellular carcinoma (HCC) (1) is the most common primary liver cancer and a major cause of cancer-related death worldwide (2). Global estimates from GLOBOCAN 2020 indicate that liver cancer ranks among the leading causes of cancer mortality, with substantial burden in many regions (3). The impact is particularly high in East Asia and sub-Saharan Africa, largely driven by chronic hepatitis B virus (HBV) infection (4). In many Western countries, however, the rising prevalence of non-alcoholic fatty liver disease (NAFLD) (5) and alcohol-related liver disease has shifted the etiologic profile (6). Projections indicate that liver cancer cases and deaths will rise in coming decades (7), highlighting the need for more effective strategies in prevention, risk stratification, and early detection.

Pathologically, HCC is a highly heterogeneous tumor. Classical histologic parameters—such as tumor size, tumor number, histologic grade, and microvascular invasion (MVI)—remain central to staging (8) and are strongly associated with recurrence and survival. In particular, MVI is a major risk factor for early intrahepatic metastasis and poor outcomes. Molecular and immunophenotypic subtypes (9) have been linked to aggressive behavior and treatment resistance, reflecting substantial inter- and intra-tumoral heterogeneity. These observations highlight the need to integrate tissue-based pathology with biomarker information to better capture tumor complexity and guide personalized management.

Biomarkers play a pivotal role along the HCC care continuum, particularly in the surveillance of high-risk populations, early diagnosis, and prognostication. Alpha-fetoprotein (AFP) (10) remains the most widely used serum biomarker; however, it has suboptimal sensitivity and specificity for early-stage tumors and can miss a substantial proportion of cases. This limitation has prompted efforts to identify alternative or complementary markers such as des-γ-carboxy prothrombin [DCP; also known as protein induced by vitamin K absence or antagonist II (PIVKA-II)] (11,12) and tumor-associated proteins such as glypican-3 (GPC3) (13). These markers show improved performance when combined with AFP and may predict aggressive pathological features and post-treatment recurrence. Beyond conventional serum analytes, advances in multi-omics and liquid-biopsy technologies (14) have expanded the biomarker landscape to include genomic and transcriptomic signatures, as well as circulating tumor DNA (ctDNA). Composite panels derived from these modalities have the potential to enhance early detection and provide dynamic information on treatment response and resistance.

Rationale and knowledge gap

Despite this expanding repertoire, translation into routine clinical practice remains limited. Many markers have been evaluated in small or retrospective cohorts, have lacked robust external validation, and have relied on heterogeneous assay platforms and cut-off values (15). Differences in study design, patient populations, and clinical endpoints further hinder comprehensive synthesis across studies. As a result, only a few biomarkers—principally AFP and DCP, sometimes in combination with AFP-L3—have been incorporated into widely used scores and surveillance algorithms, and their performance is suboptimal in specific subgroups such as patients with NAFLD-related HCC (16) or non-cirrhotic liver disease. There is a clear need for an integrative, evidence-based framework that links pathology, biomarker signatures, and clinical outcomes.

Bibliometrics and science mapping provide a way to address this gap by quantitatively characterizing the structure and dynamics of a research field. Analyses of publication output, citation patterns, co-authorship networks, and keyword co-occurrence, or co-citation clusters can reveal research hotspots and underexplored niches that are not readily captured by narrative reviews. Tools such as VOSviewer, CiteSpace, and the R-based Bibliometrix package enable visualization of these networks and support comparisons of countries, institutions, and journals (17-19). In hepatology, bibliometric studies have mapped topics such as HCC immunotherapy and combinations of immunotherapy and targeted therapy, thereby highlighting the rapid evolution of treatment strategies and the growing emphasis on predictive biomarkers (20,21).

Objective

However, comprehensive bibliometric analyses specifically focused on pathology-related biomarkers in HCC—and on how biomarker-oriented research connects to histopathologic concepts and clinically relevant endpoints—are still lacking. Existing studies typically emphasize therapeutic themes, cover limited time windows, and fail to integrate pathological features, biomarker categories, and clinical outcomes into a unified framework. In this context, the primary aim of the present study is to provide a comprehensive and pathology-oriented bibliometric mapping of global research on HCC biomarkers over the past two decades (2005–2025), with particular emphasis on their histopathologic and translational relevance. To achieve this aim, we applied bibliometric and visual-analytic techniques based on literature retrieved from a major citation database and constructed a literature-mechanism-clinical-translation framework to systematically characterize temporal publication trends, identify leading contributors and collaborative networks, delineate thematic structures and emerging research hotspots, and highlight critical gaps that should be prioritized in future pathology-based and translational HCC biomarker research. We present this article in accordance with the BIBLIO reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2760/rc).

Methods

Data sources and search strategy

All bibliographic records were retrieved from the Science Citation Index Expanded (SCIE) within the Web of Science Core Collection (WoSCC), Clarivate Analytics, which is widely used for scientometric and bibliometric analyses (22). We conducted a topic search (TS) across titles, abstracts, author keywords, and Keywords Plus, restricting the time span from 1 January 2005 to 21 November 2025. To avoid bias from subsequent database updates, all searches and downloads were completed on 21 November 2025. The search formula was: TS = (“liver neoplasm*” OR “liver cancer*” OR “liver carcinoma” OR “liver tumor” OR “liver malignancy” OR “liver cell carcinoma” OR “liver metasta*” OR “cancer of the liver” OR “hepatoma” OR “hepatoblastoma*” OR “hepatic cancer*” OR “hepatic tumor*” OR “hepatic neoplasm*” OR “hepatic metasta*” OR “hepatocellular cancer*” OR “hepatocellular carcinoma*” OR “HCC” OR “cholangiocarcinoma*” OR “intrahepatic cholangiocarcinoma*” OR “intrahepatic bile duct cancer” OR “intrahepatic bile duct tumor” OR “ICC” OR “bile duct cancer” OR “bile duct tumor” OR “metastatic liver cancer” OR “neoplasm of the liver” OR “carcinoma of the liver”) AND TS = (“biomarker*” OR “biologic marker*” OR “biological marker*” OR “biochemical marker*” OR “biochemical tumor marker*” OR “biologic tumor marker*” OR “biological tumor marker*” OR “clinical marker*” OR “cancer biomarker*” OR “carcinogen marker*” OR “immune marker*” OR “immunologic marker*” OR “laboratory marker*” OR “neoplastic biomarker” OR “neoplastic marker*” OR “molecular biomarker*” OR “neoplasm metabolite marker*” OR “surrogate marker*” OR “surrogate endpoint*” OR “surrogate end point*” OR “serum biomarker*” OR “serum marker*” OR “tumor biomarker*” OR “tumour biomarker*” OR “tumor metabolite marker*” OR “tumor marker*” OR “viral marker*”) AND TS = (“patholog*” OR “histopatholog*” OR “cytopatholog*” OR “clinical patholog*” OR “surgical patholog*” OR “diagnostic patholog*” OR “molecular patholog*” OR “diagnostic molecular patholog*” OR “molecular diagnostic*”). The search period was limited from 1 January 2005 to 21 November 2025. Document types were restricted to “article” and “review”, and the language was restricted to English. Records in other languages were excluded. Under these criteria, we identified a total of 2,907 publications, including 2,322 articles and 585 reviews. According to the search strategy described earlier in WoSCC, the results were exported as plain-text txt files and csv files. All records were exported in the “full record and cited references” format for subsequent data cleaning, bibliometric analysis, and visualization.

Analysis and visualization

Based on the WoSCC dataset described above, we assembled a visualization pipeline in which each tool plays a distinct, non-overlapping role. In this workflow, CiteSpace (23) was used to reconstruct co-citation and keyword networks year by year, allowing the intellectual backbone of HCC pathology-related biomarker research and its emerging fronts to be visualized over time. Using CiteSpace (version 6.4.R2), we analyzed records from 2005 to 2025 with a 1-year time slice in separate runs focusing on countries and regions, institutions, authors, keywords, cited journals, and cited references. Cosine similarity was used as the measure of link strength, and links were constrained within each slice. Node selection followed the g-index (k=25) or a “Top N per slice” rule. The resulting clusters were interpreted in terms of HCC, pathological concepts, biomarker categories, and clinical endpoints such as early detection, prognosis, and treatment response, while reference and keyword citation bursts highlighted rapidly intensifying topics.

Complementing this temporal and intellectual mapping, VOSviewer is tailored for dense network visualization (24). It transforms large matrices of co-authorship, co-citation, and keyword co-occurrence into clustered maps whose node sizes and colors indicate which authors or institutions collaborate and which concepts co-occur. We used VOSviewer (version 1.6.20) with full counting and association-strength normalization to construct co-authorship maps, co-citation maps, and keyword co-occurrence networks. Minimum thresholds for publications or occurrence frequency were applied to reduce noise. Terms extracted from titles and abstracts were manually cleaned so that obvious synonyms, abbreviations, or spelling variants (for example, “hepatocellular carcinoma” or “HCC”, and “biomarker” or “tumor marker”) were merged before clustering.

Bibliometrix, implemented as an R package in RStudio, provided a reproducible statistical backbone by generating core descriptive indicators and overview plots (17). The cleaned WoSCC records were imported into RStudio (version 2025.09.2+418). The Bibliometrix package, along with its Biblioshiny interface when needed, was used to calculate annual publications and citations, the productivity and impact of countries and regions, institutions, authors, and journals, and basic collaboration indices.

Finally, Microsoft Excel 2021, although not a specialized bibliometric program, served as the primary tool for inspecting, deduplicating, and relabeling raw WoSCC exports. It was also used to generate simple line and bar charts as quick checks of the trends that were revealed by more sophisticated visualizations. All figures from these tools were exported as high-quality images with a resolution of at least 300 dpi, suitable for journal publication.

Results

Temporal trends in publications and citations

Using the predefined search strategy for liver cancer, biomarkers, and pathology, we identified 2,907 English-language articles and reviews in WoSCC published between 2005 and 2025 (Figure 1). The annual number of publications increased from 9 in 2005 to 280 in 2025, representing more than a 30-fold rise and an approximate compound annual growth rate of 18–19% over the study period. The number of publications remained relatively low before 2010 (148 publications, 5.1% of the total), then experienced a phase of steady growth during 2011–2015 (463 publications, 15.9%) and a marked expansion in 2016–2020 (836 publications, 28.8%). Notably, 50.2% of all publications (1,458/2,907) were produced in the most recent five years (2021–2025), underscoring the recent intensification of research at the intersection of HCC, pathology, and biomarker discovery. The cumulative number of publications increased almost linearly on a logarithmic scale, reaching 2,625 by 2024 and 2,907 by 2025. To further analyze this trend, a third-order polynomial regression fitted to the annual publication counts showed an excellent overall fit to the data (R²=0.96), indicating a sustained long-term upward trajectory rather than short-term random fluctuations.

Figure 1 Temporal trends in annual publications and citations. Blue bars depict a steady rise in yearly publications, with slow growth before 2010, progressive acceleration thereafter, and a plateauing phase after a peak around 2022. The red line shows a parallel, continuous increase in annual citations, and the polynomial regression (dotted line) indicates a sustained long-term upward trajectory of research output and impact over the entire period.

Distribution of countries/regions

A total of 88 countries and regions contributed to the literature on pathology-related biomarkers in liver cancer, with China and the USA clearly dominating the field, producing 1,414 and 291 publications, respectively. Figure 2A illustrates the yearly publication output of each country and region from 2005 to 2024; data beyond 2024 are projections. The top 10 countries collectively produced 2,427 publications, approximately 83.5% of all 2,907 publications, indicating that global research output is highly concentrated in a small group of primarily Asian and Western nations. Table 1 presents the top 10 countries/regions in terms of publication output, citation impact and international collaboration in liver cancer-biomarker-pathology research. China is the leading contributor by volume (1,414 publications) and total citations (n=32,757), followed by the USA, which ranks second in both metrics (291 publications, 24,925 citations). Japan and Italy form a second productivity tier, with 177 and 120 publications, respectively. Several European countries—particularly Italy, Spain, Germany, and France—achieve relatively high citation counts compared with their output, indicating strong visibility and influence per publication. In the VOSviewer co-authorship maps, total link strength (TLS) represents the summed weight of a country’s collaborative links, reflecting its overall embeddedness in the international network. In the co-authorship network, the USA, China, and Italy exhibit the highest TSL (all greater than 1,100), acting as major collaboration hubs. Meanwhile, Belgium, the Netherlands, Spain, Japan, France, and Canada also serve as important connectors despite more modest publication counts.

Figure 2 Analysis of countries/regions. (A) Changes in the annual number of publications in the top ten countries and regions from 2005 to 2025. Stacked bars display the yearly output of China, the USA, Japan, Italy, Germany, the UK, France, South Korea, India, and Egypt, illustrating a sustained rise in publications over time, with China and the USA contributing the largest and fastest-growing shares and other countries forming a steadily expanding second tier. (B) Map showing country collaborations in metabolomics research on pathology-related biomarkers of HCC, with lines indicating collaborative relationships between countries. (C) The citation network visualization map of countries and regions. The thickness of the lines in the network visualization reflects the citation strength. HCC, hepatocellular carcinoma.

The country co-authorship network in Figure 2B translates these numerical patterns into a reorganized global map. China and the USA form a new bipolar core that concentrates both productivity and TLS, while Italy, Germany, the Netherlands, and Belgium emerge as secondary hubs bridging multiple regional clusters. The chord diagram in Figure 2C further reveals a pronounced hub-and-spoke architecture: thick chords radiate from China, the USA, and Italy to numerous partners. In contrast, many low- and middle-income countries in Africa, the Middle East, and parts of Asia appear as small peripheral segments linked by only one or two collaboration channels. Taken together, Table 1 and Figure 2B,2C indicate a structural turning point: research on HCC pathology biomarkers has become globally distributed in name, but knowledge production is now centered on a China-led Asian output core, whereas cross-border knowledge brokerage remains dominated by a limited set of Western and European hubs that provide the backbone of the international collaboration network.

Contributions of institutions

Analysis of the collaboration network reveals that the top institutions contributing to liver cancer research, specifically related to biomarkers, are primarily based in China. Fudan University leads the field with 107 publications and a strong collaboration network, closely followed by Sun Yat-Sen University and Zhejiang University, which have 101 and 73 publications, respectively (Table 2). These institutions dominate the collaboration network, as illustrated in the VOSviewer map (Figure 3), where central clusters—represented by larger nodes, which correspond to institutions with high publication counts—indicate strong co-authorship ties. Additionally, institutions such as Shanghai Jiao Tong University and Nanjing Medical University also feature prominently, exhibiting substantial co-authorship ties.

Figure 3 The collaborative network visualization of institutions. The figure displays institutions with more than five publications. The nodes, shown in various colors, represent institutions belonging to distinct clusters, and the size of each node corresponds to the number of publications from that institution.

Overall, the network structure indicates that institutions within China play a dominant role in this field, characterized by strong collaboration among themselves. From a global perspective, the University of Texas MD Anderson Cancer Center in the USA appears as an external collaborator, making notable contributions to the field, however, it is relatively isolated in the network compared with Chinese institutions, with fewer co-authorship ties linking it to the main clusters. This collaboration map highlights the global significance of Chinese research and the interconnectedness of its major academic institutions.

Authors and co-cited authors

Authorship analysis revealed a small core of highly productive researchers in pathology-associated biomarkers for HCC (Figure 4A). Table 3 summarizes the publication output of the leading authors in this field over the specified time frame. The most prolific authors were Chapiro J. (Yale University, USA, 15 publications) and Chen G. (Guangxi Medical University, China, 15 publications), followed by predominantly China-based investigators, including Fan J., Wang W., Zhou J., Chen J., Li J., Li B., and Song B., with 10–14 papers each. Overall, nine of the eleven leading authors are affiliated with Chinese institutions, indicating that Chinese surgical and pathology teams contribute significantly to much of the recent output, whereas a small number of USA-based and UK-based investigators provide important external contributions. In terms of collaboration intensity, Vermeulen P.B., Chapiro J., and Lin M. exhibit the highest TLS, acting as international bridges between several author clusters, whereas many prolific Chinese authors are embedded in dense co-authorship networks that are predominantly national.

Figure 4 Analysis of related authors. (A) In the co-author network of metabolomics research on pathology-related biomarkers of HCC, each circle represents a different author. The connecting lines between circles reflect collaborations between authors, and different colors indicate groups of authors who frequently cooperate. (B) Co-cited author map in pathology-related biomarker research on hepatocellular carcinoma. In this map, nodes with the same color belong to the same co-citation cluster; node and label sizes correspond to co-citation frequency, while edge thickness reflects link strength. HCC, hepatocellular carcinoma.

The author co-citation network delineates a somewhat distinct set of intellectual leaders (Figure 4B). Classic hepatology and oncology experts such as Llovet J.M., Schwartz M., Bruix J., El-Serag H.B., and Bray F. are among the most frequently co-cited authors. Their seminal work on HCC epidemiology, staging, prognosis, and therapeutic strategies forms the conceptual foundation on which studies oriented toward pathology and biomarkers build. Taken together, these findings suggest that the field is driven by highly productive Chinese research groups, but is conceptually anchored by a small group of internationally recognized hepatology and oncology authorities.

Journals and co-cited journals

At the journal level, publication output in pathology-related biomarker research for HCC is concentrated in a small group of oncology and multidisciplinary journals, most of which are open access. The most productive outlets (Table 4) were Frontiers in Oncology [86 papers; impact factor (IF) 3.3; Q2] and Cancers (77 papers; IF 4.4; Q2), followed by Scientific Reports, International Journal of Molecular Sciences, World Journal of Gastroenterology, BMC Cancer, European Radiology, PLoS One, Oncology Letters, and Frontiers in Immunology. Most of these journals are Q1–Q2 and have broad scopes covering oncology, molecular medicine, and imaging. The VOSviewer source map (Figure 5A) shows that these journals cluster into several thematic groups, including general oncology, hepatology/gastroenterology, molecular and immunology journals, and radiology journals. This clustering reflects the translational nature of HCC biomarker work, which spans pathology, basic science, and clinical imaging.

Figure 5 Analysis of related journals. (A) The figure presents pathology-related biomarker research on hepatocellular carcinoma as represented in related journals. Each dot, or node, in the figure represents a journal. Nodes of the same color indicate membership in the same research cluster. The node size and link thickness reflect the frequency of collaboration between journals. (B) Co-cited journal map in pathology-related biomarker research on hepatocellular carcinoma, where node size and link thickness are proportional to co-citation frequency and connection strength. (C) The figure shows the dual-map overlay of journals. The citing papers are listed on the left, while the cited papers are placed on the right. Between them is a curve that represents the citation relationship, which illustrates the citation flow between the two sets of papers. Different colors denote journals from different academic disciplines. The length of the vertical axis of the ellipse is proportional to the number of papers published in the journal, and the horizontal length corresponds to the number of authors.

By contrast, the co-citation network (Figure 5B) reveals a different set of core knowledge sources. Hepatology and Journal of Hepatology occupy central positions with 5,094 and 3,120 co-citations, respectively (IF 51.0 and 33.0; both Q1). They are followed by PLoS One, Cancer Research, Gastroenterology, Clinical Cancer Research, Nature, Journal of Clinical Oncology, Cell, and World Journal of Gastroenterology. These highly cited hepatology, gastroenterology, oncology, and general-science journals, predominantly Q1, provide the main methodological and conceptual foundation upon which more practice-oriented publications in mid-impact oncology and molecular journals build.

In the source-journal map (Figure 5A), nodes represent journals in which the included HCC pathology-biomarker articles were published. Node size is proportional to output, and edge thickness (TLS) indicates the intensity of citation links between journals. Four major clusters delineate a stratified publication landscape. The yellow cluster is dominated by Cancers, Annals of Surgical Oncology, and Diagnostics, representing clinical oncology, surgical, and diagnostic journals. The green cluster, centered on International Journal of Molecular Sciences, PLoS One, and Biomedicine & Pharmacotherapy, reflects molecular and translational cancer research outlets. The blue cluster aggregates Frontiers in Oncology, European Radiology, and hepatobiliary imaging journals, representing a radiology- and liver-focused translational domain. The red cluster, organized around Scientific Reports, Oncotarget, Journal of Cancer, and Oncology Letters, comprises broad-scope oncology journals that connect the more specialized clusters. Overall, Figure 5A indicates that pathology-related biomarker studies in HCC are disseminated mainly through a limited set of high-volume, largely open-access oncology and molecular journals spanning clinical, imaging, and experimental research.

The co-cited-journal network (Figure 5B) shifts the perspective from publication venues to the intellectual backbone of the field: node size reflects journal co-citation frequency, while edge thickness (TLS) indicates the strength of co-citation links. A large red cluster anchored by Hepatology, Journal of Hepatology, Gastroenterology, World Journal of Gastroenterology, and Journal of Clinical Oncology defines a core of hepatology, gastroenterology, and clinical oncology journals that underpin most HCC biomarker work. The blue cluster, grouping Nature, Science, Cell, Nature Reviews Cancer, Cancer Research, represents high-impact general-science and oncology journals that bridge basic discovery with clinical translation. The green cluster encompasses Molecular Cancer, Oncogene, Cell Death & Disease, and related titles, corresponding to molecular and cell-biology mechanisms of oncogenesis and signaling. The dense, thick links between these clusters indicate that HCC pathology-biomarker research routinely integrates hepatology/gastroenterology evidence with mechanistic cancer biology and general medical oncology, relying on a relatively small set of elite journals as its conceptual scaffold.

To characterize the dynamics of cross-journal knowledge exchange, we performed an analysis of knowledge flow based on citation and co-citation patterns between citing and cited journals. The dual-map overlay of journals delineates the disciplinary distribution of topics, temporal shifts in citation trajectories, and shifts in research focus across the scholarly landscape (Figure 5C). In this visualization, labels on the left side of the map denote citing journals, while labels on the right side denote cited journals. The colored citation paths linking the two sides trace the principal routes of knowledge transfer and indicate the contextual relationships between citing and cited literature. This provides an intuitive macro-level view of how HCC pathology-biomarker research is anchored within and diffuses across disciplines. Citing journals are mainly from Molecular, Biology, Immunology; Medicine, Medical, Clinical; and Neurology, Sports, Ophthalmology. The cited journals are mainly from Molecular, Biology, Genetics; Health, Nursing, Medicine; and Dentistry, Dermatology, Surgery; which together constitute the knowledge base.

Analysis of top co-cited references

In the reference co-citation analysis, all references cited by the WoSCC records included in the study were imported into CiteSpace, and the most influential works were identified. Among the top-cited references (Table 5), Lencioni et al.’s proposal of modified Response Evaluation Criteria in Solid Tumors (mRECIST) for HCC ranked first (3,614 citations), which provides the imaging-response framework that underpins the evaluation of locoregional and systemic therapies in HCC. Highly cited mechanistic papers included Qi et al. on the competing endogenous RNA (ceRNA) hypothesis, Han et al. on the tumor-suppressive role of circMTO1 in HCC, and Han et al.’s comprehensive review of circular RNAs (circRNAs); these together established non-coding RNA networks as a rich source of diagnostic and prognostic biomarkers. Other key contributions were Sia et al.’s definition of an immune-specific molecular class of HCC, Wurmbach et al.’s genome-wide profiling of hepatitis C virus (HCV)-induced dysplasia and HCC, Jiang et al.’s demonstration that plasma DNA fragment length can serve as a liquid-biopsy indicator of HCC, and Kumar et al.’s recent overview of extracellular vesicles (EVs) as diagnostic and therapeutic tools. Most of these landmark articles were published in high-impact Q1 journals such as Hepatology, Gastroenterology, Proceedings of the National Academy of Sciences of the United States of America (PNAS), and Signal Transduction and Targeted Therapy. This underscores the strong integration of our field with leading hepatology and oncology outlets.

Using CiteSpace, we constructed a reference co-citation network over the period 2005–2025 with a 1-year time slice and g-index (k=5) as the selection criterion. The map could be partitioned into several major clusters (Figure 6A). The largest cluster, “systemic therapy” (#0), includes guidelines, staging systems, and response-evaluation papers (including mRECIST), reflecting the clinical backbone against which biomarkers are interpreted. Surrounding this core are pathology- and prognosis-oriented clusters such as “prognostic biomarker” (#4), “microvascular invasion” (#1), “histopathological growth pattern” (#11), “intrahepatic cholangiocarcinoma” (#5), and “hepatocellular carcinoma” (#8), which link tissue architecture, invasion patterns, and molecular signatures to recurrence and survival. More recent clusters—including “circulating microRNA” (#2), “circular RNA” (#7), “extracellular vesicle” (#3), and “predictive tool” (#12)—are driven by the ceRNA, circRNA, miRNA, cell-free DNA (cfDNA), and extracellular-vesicle literature and mark the shift toward liquid biopsy and RNA-based prognostic models. Earlier clusters such as “chronic hepatitis B virus infection” (#9), “multistage hepatocarcinogenesis” (#6), and “natural history treatment” (#10) capture foundational work on viral carcinogenesis and disease evolution. The co-citation map reveals a highly stratified and hierarchically organized intellectual structure of the HCC pathology-biomarker field. At the thematic level, the 13 largest clusters follow a clear temporal trajectory. Early work focused on natural history, multistage hepatocarcinogenesis, and chronic HBV infection (clusters 6, 9, and 10; average year 2003–2008). This was followed by the first wave of circulating miRNA biomarkers (cluster 2; average year 2010) and classical serum markers such as AFP and GPC3. More recent fronts center on EVs and non-coding RNAs (clusters 3, 4, and 7; average year 2015–2018), and, ultimately, systemic and immunotherapeutic strategies, radiomics, and MVI (clusters 0, 1, and 11; average year 2016–2019). Overall, the co-citation structure shows a coherent progression: the field has evolved from studies of viral liver injury and multistage hepatocarcinogenesis, through the establishment of standardized therapeutic response criteria, to a precision medicine phase in which non-coding RNAs, EVs, and other circulating markers are integrated with histopathology and systemic therapy to refine prognosis and guide individualized management of HCC.

Figure 6 Analysis of related references. (A) CiteSpace visualization map of cluster view and timeline view of co-citation references. The literature was clustered within the bibliographic network, resulting in a total of 11 clusters. The colors, ranging from deep blue to bright yellow, indicate the years between 2005 and 2025. (B) The top 25 highly cited papers with citation bursts. The timeline is represented by the blue line, with the red segment indicating the burst period. This analysis is based on burst detection, a method used to identify sudden increases in the frequency of citations for documents and keywords.

The citation burst analysis of references (Figure 6B) further reveals that the most intense and persistent citation surges are not driven by individual biomarkers, but by successive waves of global cancer statistics and HCC management and systemic-therapy guidelines. Early citation surges are dominated by landmark epidemiology and miRNA papers (Parkin, El-Serag, Bartel, Mitchell, and Bruix). A second wave centers on updated burden estimates and clinical roadmaps (Ferlay, Torre, Chen, Jemal, and Forner). Following this, the current ongoing wave (2019–2025) is overwhelmingly anchored in Sung’s 2021 global cancer statistics and high-impact reviews and trials on systemic and immunotherapy for HCC (Villanueva, Llovet, Finn, Reig, Vogel), together with widely used pan-cancer bioinformatics resources. Collectively, these patterns indicate that contemporary HCC pathology-biomarker research is fundamentally influenced by updated global-burden data and the immunotherapy era; moreover, a very small set of epidemiologic and guideline papers functions as the dominant citation backbone of the field.

Keyword analysis of research hotspots

In the VOSviewer keyword co-occurrence map (Table 6, Figure 7A), four color-coded clusters outline the thematic architecture of pathology-related biomarker research in HCC. A central yellow cluster links “cancer”, “expression”, “prognosis”. This cluster captures the main progression from molecular assays to outcome prediction. A green cluster aggregates “Breast cancer”, “Colorectal cancer”, “Cell proliferation”, “microRNAs” and “extracellular vesicles”; it reflects mechanistic and multicancer studies that supply the molecular toolkit later applied in liver-cancer pathology. A blue cluster is enriched for “cirrhosis”, “fibrosis”, “inflammation” and “liver fibrosis”. This cluster maps the chronic liver-disease background in which HCC develops and against which biomarkers must be interpreted. The red cluster centres on explicitly clinical terms such as “alpha-fetoprotein”, “microvascular invasion”, “resection”, “liver transplantation” and “radiomics”. It indicates a practice-oriented stream that connects serum, tissue and imaging markers to treatment and prognosis. The density map (Figure 7B) confirms that activity is most concentrated along this central cancer-expression-prognosis axis, with cooler peripheral zones indicating more specialised topics (e.g., cancer, expression, prognosis, survival). Furthermore, CiteSpace burst analysis (Figure 7C) reveals a clear three-stage evolution of hotspots. The early phase (2005–2013) was dominated by classical serum markers and histologic concepts, including AFP, tumor markers, DCP, and gene expression. The transitional phase (~2010–2018) centred on vascular signalling, β-catenin, clinical prognostic factors and circulating miRNAs. The current phase (from 2020 onwards) focuses on texture analysis, immune infiltration, tumour microenvironment, liquid biopsy and EVs. This marks a shift towards radiomics-assisted, microenvironment-based and minimally invasive biomarker strategies.

Figure 7 Analysis of keyword co-occurrence. (A) Cluster diagram of keyword co-occurrence analysis network. Each keyword is represented as a node, and the node size is proportional to the frequency of keyword occurrence. The lines, or edges, between nodes indicate co-occurrence relationships. The distance and strength between nodes indicate the relevance between keywords. (B) Keyword density map of pathology-related biomarker research in hepatocellular carcinoma. The warmer colors indicate high-frequency, highly connected terms centered on cancer, expression, prognosis, and diagnosis. (C) The top 25 keywords with the strongest citation bursts. It shows along a timeline represented by the blue line, with the red segment indicating the burst period.

Discussion

This study mapped the research landscape of pathology-related biomarkers in HCC by analysing 2,907 English-language articles and reviews (2005–2025) from SCIE (WoSCC). Using CiteSpace, VOSviewer and Bibliometrix, we quantified temporal trends, country/institution and journal contributions, co-authorship and co-citation structures, and keyword co-occurrence and bursts, thereby delineating the core knowledge base and emerging frontiers most relevant to early diagnosis, prognostic stratification and therapeutic decision-making in HCC.

General distribution

This analysis is based on 2,907 English-language articles and reviews authored by 18,992 researchers from 3,653 institutions and indexed in SCIE (WoSCC) between 1 January 2005 and 21 November 2025. Over this 20-year period, annual publications on pathology-related biomarkers in HCC increased from 9 in 2005 to 280 in 2025—more than a 30-fold rise, corresponding to an approximate compound annual growth rate of 18–19%. Nearly half of all papers were published in the last 5 years (2021–2025), indicating that pathology-oriented biomarker research has shifted from a marginal topic to a rapidly expanding and strategically prioritised area within liver cancer research.

At the country and region levels, the landscape is both highly concentrated and structurally asymmetric. Although 62 countries and regions contributed to the dataset, the top 10 account for 98.4% of all publications. Almost half of the literature originates from China alone (1,409/2,907; 48.5%), followed by the USA (16.4%), with Japan and Italy forming a second productivity tier. When betweenness centrality is considered alongside publication output, a clear division emerges between volume producers—countries with high publication counts—and network brokers—countries that serve as key connectors within the collaboration network. The USA, despite publishing fewer papers than China, exhibits the highest centrality (0.34), acting as the principal bridge linking East Asian and European clusters, and functioning as a cross-regional corridor for multinational projects. Italy (0.21), Germany (0.18), and Egypt (0.16) also display disproportionately high centrality relative to their publication output, suggesting that they serve as important regional hubs connecting otherwise weakly linked segments of the network. By contrast, high-output countries such as Japan, South Korea, and England show low centrality values (≤0.03), implying that much of their work is generated within relatively closed or bilateral collaborations rather than truly global partnerships.

Institutional contributions further reinforce this pattern of concentration. All of the top 10 most productive institutions are based in mainland of China and together account for roughly one-fifth of the global output (609/2,907). Fudan University leads with 107 publications and the highest institutional network centrality (0.22), followed by Sun Yat-Sen University (101 publications; centrality 0.12) and Zhejiang University (73 publications; centrality 0.05). Shanghai Jiao Tong University and Nanjing Medical University combine substantial productivity (67 and 56 papers) with relatively high centrality (both 0.10), indicating that they act not only as volume centers but also as key domestic connectors. These metrics depict a system in which China dominates quantitative output and much of the collaboration architecture, whereas international knowledge brokerage is concentrated in a limited subset of highly central academic hubs. This structure highlights considerable scope for broader North-South and South-South integration in future multicenter studies.

At the author level, the field is driven by a small, tightly clustered community with a clear separation between “volume producers” and “network brokers”. Among the 11 most prolific authors, eight are affiliated with Chinese surgical or pathology centers (e.g., Fan J., Wang W., Zhou J., Chen J., Li J., Li B., Song B.). Only three are based at Western institutions: Chapiro J. and Lin M. at Yale, and Vermeulen P.B. at the Institute of Cancer Research. However, when collaboration intensity is quantified by TLS, which reflects the cumulative strength of co-authorship ties in the VOSviewer networks, Western imaging- and radiology-oriented investigators emerge as key connectors. Vermeulen, Chapiro, and Lin occupy the top positions (TLS 42, 40, and 40, respectively), bridging multiple Chinese and international clusters. In contrast, many high-output Chinese authors are embedded in dense but predominantly national co-authorship communities. This asymmetry suggests that, while Chinese groups currently drive much of the quantitative expansion of HCC pathology-biomarker research, a disproportionate share of transnational knowledge exchange and methodological diffusion still flows through a small number of internationally networked investigators; this underscores the need to widen and diversify cross-continental collaborations.

In terms of journal categorization, there is a clear separation between the main publication platforms for pathology-related HCC biomarker studies and the core knowledge sources that are most frequently cited. Regarding output, articles are concentrated in specialised oncology and translational journals such as Frontiers in Oncology [86 papers, Journal Citation Reports (JCR) Q2]; Cancers (77, Q2); Scientific Reports (54, Q1); International Journal of Molecular Sciences (52, Q1); World Journal of Gastroenterology (52, Q1); and BMC Cancer (39, Q2). These are supplemented by imaging and immunology journals, including European Radiology, Oncology Letters, and Frontiers in Immunology. This pattern indicates that new pathology-oriented biomarker work is primarily disseminated through open-access or broad-scope oncology, molecular, and gastroenterology journals rather than a single ultra-high-impact venue. In contrast, the co-citation and dual-map overlay analyses—a bibliometric method that visualizes citation relationships across disciplines—reveal a compact, high-impact core of journals that provide the intellectual backbone of the field. Hepatology-focused journals dominate this core: Hepatology (5,094 co-citations) and Journal of Hepatology (3,120) are the most central and heavily linked nodes. They are supported by general oncology and gastroenterology journals such as Cancer Research, Gastroenterology, and Clinical Cancer Research, as well as multidisciplinary flagship journals including Nature, Journal of Clinical Oncology, Cell, and PNAS. Together, these findings point to a two-tier journal ecology. High-volume, largely open-access oncology and molecular journals act as the primary vehicles for disseminating new studies. Meanwhile, a relatively small cluster of elite hepatology, oncology, and general medical journals constitutes the high-centrality, heavily co-cited evidence base that integrates hepatology, cancer biology, imaging, and immunology into a coherent translational framework.

Hotspots and frontiers

Keyword co-occurrence mapping indicates that research on pathology-related biomarkers in liver cancer is organized around an integrated axis linking tumor biology, diagnostic pathology, and clinical outcomes (25,26). High-frequency terms such as HCC, cancer, biomarkers, gene expression, prognosis, survival, diagnosis, metastasis, and recurrence rank among the most frequent and strongly connected keywords simultaneously. This pattern suggests that most studies focus on molecularly defined biomarkers embedded in histopathologic assessments, which are directly tied to prognosis and treatment decision-making. The prominence of cholangiocarcinoma, resection, gene expression, proliferation, and apoptosis further indicates that the field has expanded from classic serum markers in advanced HCC to encompass the entire spectrum of primary liver malignancies, resectable disease, and mechanistic studies of tumor growth and cell death (27).

Building on these findings, temporal burst analysis refines this picture and highlights a clear shift in research frontiers. Early bursts (2005–2013) are dominated by traditional markers such as AFP, DCP, and other well-established serum tumor markers, reflecting an era centered on single serum indices for HCC detection and monitoring (28-30). A second wave (approximately 2010–2018) is characterized by terms such as gene expression, beta-catenin, prognostic factors, clinical relevance, and circulating miRNAs, pointing to the incorporation of genomic signatures and non-coding RNA into routine pathological and prognostic work-ups (31,32). The most recent bursts, extending to 2023–2025, cluster around tumor microenvironment, immune infiltration, texture analysis, liquid-biopsy platforms, and EVs. These trends indicate that current hotspots have shifted toward microenvironment-centered pathology, radiomics-assisted image analysis, and minimally invasive biomarker platforms (33,34). Collectively, these patterns suggest that the field is evolving from static, single-analyte markers toward multidimensional, systems-level biomarkers that integrate tissue morphology, molecular profiling, and dynamic liquid-biopsy signals to refine risk stratification and guide precision therapy in HCC (35).

The reference co-citation map and the most highly cited articles converge on a coherent frontier for pathology-related biomarker research in HCC. The first stream is defined by imaging and outcome-oriented response frameworks, in which mRECIST redefines viable tumour on contrast-enhanced imaging and aligns radiologic response with pathological necrosis and survival, thereby setting the standard for evaluating locoregional and systemic therapies (36). The second stream centres on non-coding RNA biology: the ceRNA hypothesis, tumour-suppressive circMTO1, and broader circRNA mechanisms collectively position messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), pseudogenes, and circRNAs as densely interconnected regulatory hubs with clear diagnostic and prognostic relevance in HCC (37-39). The third, more recent stream is driven by liquid biopsy technologies and microenvironment-focused research. Fragment-size profiling of plasma cfDNA, together with ctDNA-based liquid biopsy studies, demonstrates that circulating nucleic acids can sensitively reflect tumour burden and clonal evolution. Parallel advances in EV biology, both in HCC-specific settings and across solid tumours, highlight EV cargo as a rich, minimally invasive source of biomarkers and therapeutic targets (40-42). These developments are based on genome-wide profiling across chronic viral hepatitis, cirrhosis, dysplasia, and overt HCC, as well as immune-specific molecular subclasses and tumour microenvironment remodelling, which collectively capture the multistep and immunologically sculpted nature of hepatocarcinogenesis (43,44). Overall, the clustered citation structure indicates a decisive shift away from single serum markers such as AFP or DCP toward an integrated framework in which histopathology, molecular subclassification, immune contexture, and serial liquid biopsy readouts are combined to refine risk stratification and support genuinely personalised systemic therapy strategies in HCC.

Hotspots: pathology-based prognostic stratification

Pathology-derived features remain the fundamental basis of risk stratification in HCC. Our co-citation clusters, centered on “prognostic biomarker”, “microvascular invasion”, and “histopathological growth pattern”, highlight this axis as a major research hotspot. Contemporary guidelines recognize MVI, tumor differentiation, satellite nodules, and resection margin status as key determinants of recurrence after resection or transplantation, underscoring the central role of histology in therapeutic decision-making and trial-eligibility criteria (45-47). A large body of work confirms that MVI represents an early surrogate of intrahepatic dissemination and independently predicts early relapse and shortened survival across diverse etiologies and treatment settings (48,49).

In this context, a major research focus has been to refine MVI-based stratification and to make it accessible before surgery. Pathology-anchored prognostic nomograms have integrated routine clinicopathologic variables with MVI status to estimate recurrence risk among patients within the Milan criteria. This enables more individualized decisions on resection-margin width, bridging therapies, and transplant allocation (50). Meanwhile, parallel efforts have attempted to simulate histology by decoding radiologic correlates of MVI on gadoxetic acid-enhanced magnetic resonance imaging (MRI) or multiphasic computed tomography (CT), such as non-smooth tumor margins, peritumoral enhancement, and peritumoral hypointensity (51). Building on these qualitative features, radiomics-based models that mine high-dimensional texture patterns from CT, MRI, or positron emission tomography-computed tomography (PET/CT) have achieved area-under-the-curve values of 0.69–0.94 for preoperative MVI prediction, while deep-learning radiogenomic approaches have linked imaging signatures to gene-expression programs and clinical outcomes (52-54). Collectively, these studies move the field from purely postoperative histologic classification toward non-invasive, pathology-informed risk stratification that can shape surgical and locoregional treatment strategies.

In parallel, histopathologic subtyping and growth-pattern analysis have emerged as complementary layers of prognostic stratification. The updated World Health Organization (WHO) and expert-consensus classifications describe several special HCC subtypes—including macrotrabecular-massive (MTM), steatohepatitic, scirrhous, and lymphocyte-rich variants—that exhibit distinct molecular profiles and clinical behaviors (55-57). Among them, MTM-HCC has attracted particular attention as a highly aggressive phenotype characterized by thick trabeculae, extensive MVI, TP53 mutations, FGF19 amplification, and strong angiogenic signaling. This subtype is associated with large tumor size, high serum AFP, and early recurrence after curative therapy (56-58). Dedicated imaging-pathology correlation studies have suggested that MTM-HCC can be suspected preoperatively based on gadoxetic acid-enhanced MRI features such as substantial necrosis, irregular margins, and peritumoral enhancement. These findings raise the possibility of subtype-specific surgical planning and enrichment of clinical trials targeting adjuvant therapies (59).

Furthermore, beyond discrete subtypes, more granular analyses of histopathologic growth patterns and tumor-stroma interfaces provide additional prognostic information (60,61). Recent work has demonstrated that thick-trabecular or compact growth, infiltrative borders, and rich microvascular proliferation are closely associated with MVI and early intrahepatic relapse, whereas microtrabecular or pseudoglandular architecture with pushing margins confers a relatively indolent course (62,63). Moreover, the prognostic weight of MVI appears to be modulated by the surrounding histopathologic context: in some series, co-occurrence of extensive MVI with an inflamed, fibrotic microenvironment portends particularly poor survival, whereas limited MVI within otherwise favorable growth patterns exerts a weaker effect. These observations therefore support a shift from binary presence/absence scoring toward semi-quantitative MVI-grading schemes that incorporate the number of invaded vessels, distance from the tumor edge, and associated stromal reaction (56,64).

A further layer of complexity arises from the intersection between pathology-based classes, and molecular or immune phenotypes. Transcriptomic profiling across the spectrum from chronic viral hepatitis and dysplastic nodules, to overt HCC has mapped stepwise activation of proliferation, angiogenesis, and immune-evasion programs, identifying gene sets that correlate with histologic grade, MVI, and satellite nodules (65,66). An immune class of HCC enriched for inflammatory signaling, programmed death-ligand 1 (PD-L1) expression, and markers of cytolytic activity has been proposed (67). This immune class partially overlaps with poorly differentiated, macrotrabecular, and MVI-rich tumors and appears particularly relevant for response to immune-checkpoint inhibitors (68,69). In circulating blood, serum AFP and other conventional markers retain prognostic value. This is especially true when dynamic changes after treatment are considered. However, these markers are increasingly complemented by cfDNA fragmentomics (70) and other liquid-biopsy assays that reflect underlying histologic aggressiveness. Integration of these molecular metrics with refined pathological grading and imaging surrogates is beginning to yield composite prognostic scores with higher discriminatory power than any single modality alone.

Taken together, these findings provide a comprehensive overview of the evolving landscape of pathology-related biomarker research in HCC. Several strengths of this study should be highlighted. Several strengths of this study should be highlighted. This analysis spans a long time frame (2005–2025), enabling a comprehensive assessment of the evolution of pathology-related biomarker research in HCC. By adopting a pathology-oriented perspective and integrating multiple bibliometric tools, the study systematically maps research themes, collaboration patterns, and emerging hotspots. Importantly, the explicit alignment of bibliometric findings with histopathologic and translational contexts enhances the clinical interpretability of the results and distinguishes this work from prior bibliometric studies that primarily focused on therapeutic topics.

Limitations

Several limitations should be considered when interpreting these findings. First, all data were retrieved from the SCIE of the WoSCC and restricted to English-language articles and reviews. Studies indexed only in other databases (e.g., Scopus, Embase, journals exclusive to PubMed), non-English literature, and gray literature were not captured; this may bias rankings at the country, institutional, and journal levels—particularly in regions where high-quality HCC pathology work is preferentially published in local journals. Second, bibliographic metadata are not fully standardized: the same institution or author may appear under multiple names over time, and journal or keyword variants may not be completely merged, leading to inconsistencies in classification. Third, bibliometric indicators are inherently citation-dependent (71). Citation counts, co-citation strength, and bursts primarily reflect visibility rather than methodological quality or clinical utility. These metrics are influenced by field size and self-citation, and they tend to disadvantage very recent publications, whose long-term impact is not yet evident.

Conclusions

Pathology-related biomarker research in HCC shows clear promise for transforming risk stratification and treatment guidance; however, its clinical translation requires several critical steps. Future research should prioritize the multimodal integration of histology, digital pathology, multi-omics, and liquid-biopsy readouts into standardized, externally validated prognostic and predictive models. Large, prospectively designed, multicenter cohorts are needed to clarify the incremental value of non-coding RNAs, EVs, tumour microenvironment, and immune-infiltration signatures when evaluated alongside established pathological features such as MVI and histologic grade.

In addition, assay harmonization, reproducible scoring systems (particularly for MVI and growth patterns), and transparent reporting of cut-offs and analytical platforms are essential prerequisites for routine clinical adoption. Particularly promising future directions include artificial intelligence (AI)-assisted image analysis, radiomics-pathomics fusion, cfDNA fragmentomics, and EV-based liquid biopsy embedded in longitudinal clinical trials. With methodologically robust validation and more equitable international collaboration, integrated pathology-biomarker frameworks have the potential to improve early detection, refine prognostic stratification, and ultimately enhance outcomes for patients with HCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2760/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2760/prf

Funding: This work was supported by Science and Technology Program of the Joint Fund of Scientific Research for the Public Hospitals of Inner Mongolia Academy of Medical Sciences (grant No. 2024GLLH0632), China Society for Metals, Metallurgical Safety and Health Branch, Health Research Project (grant No. jkws202433), Aerospace Medical and Health Technology Group Co., Ltd. Research Project (grant Nos. 2024YK10 and 2025YK17), Natural Science Foundation of Inner Mongolia Autonomous Region (grant Nos. 2024MS08058 and 2025QN08075), and Inner Mongolia Medical University Joint Project (grant No. YKD2024LH011).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2760/coif). All authors report receiving support from Science and Technology Program of the Joint Fund of Scientific Research for the Public Hospitals of Inner Mongolia Academy of Medical Sciences (grant No. 2024GLLH0632), China Society for Metals, Metallurgical Safety and Health Branch, Health Research Project (grant No. jkws202433), Aerospace Medical and Health Technology Group Co., Ltd. Research Project (grant Nos. 2024YK10 and 2025YK17), Natural Science Foundation of Inner Mongolia Autonomous Region (grant Nos. 2024MS08058 and 2025QN08075), and Inner Mongolia Medical University Joint Project (grant No. YKD2024LH011). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Youness RA, Hassan HA, Abaza T, et al. A Comprehensive Insight and In Silico Analysis of CircRNAs in Hepatocellular Carcinoma: A Step toward ncRNA-Based Precision Medicine. Cells 2024;13:1245. [Crossref] [PubMed]
2. Chakraborty E, Sarkar D. Emerging Therapies for Hepatocellular Carcinoma (HCC). Cancers (Basel) 2022;14:2798. [Crossref] [PubMed]
3. Wu Z, Xia F, Wang W, et al. Worldwide burden of liver cancer across childhood and adolescence, 2000-2021: a systematic analysis of the Global Burden of Disease Study 2021. EClinicalMedicine 2024;75:102765. [Crossref] [PubMed]
4. Rossi C, Shrier I, Marshall L, et al. Seroprevalence of chronic hepatitis B virus infection and prior immunity in immigrants and refugees: a systematic review and meta-analysis. PLoS One 2012;7:e44611. [Crossref] [PubMed]
5. Swellam M, Hamdy N. Association of nonalcoholic fatty liver disease with a single nucleotide polymorphism on the gene encoding leptin receptor. IUBMB Life 2012;64:180-6. [Crossref] [PubMed]
6. Asgharpour A, Dinani A, Friedman SL. Basic science to clinical trials in non-alcoholic fatty liver disease and alcohol-related liver disease: collaboration with industry. Transl Gastroenterol Hepatol 2021;6:5. [Crossref] [PubMed]
7. McMahon B, Cohen C, Brown RS Jr, et al. Opportunities to address gaps in early detection and improve outcomes of liver cancer. JNCI Cancer Spectr 2023;7:pkad034. [Crossref] [PubMed]
8. Lin WP, Xing KL, Fu JC, et al. Development and Validation of a Model Including Distinct Vascular Patterns to Estimate Survival in Hepatocellular Carcinoma. JAMA Netw Open 2021;4:e2125055. [Crossref] [PubMed]
9. Mahadevan A, Rao CR, Shanmugham M, et al. Primary central nervous system diffuse large B-cell lymphoma in the immunocompetent: Immunophenotypic subtypes and Epstein-Barr virus association. J Neurosci Rural Pract 2015;6:8-14. [Crossref] [PubMed]
10. El-Mesallamy HO, Hamdy NM, Rizk HH, et al. Apelin serum level in Egyptian patients with chronic hepatitis C. Mediators Inflamm 2011;2011:703031. [Crossref] [PubMed]
11. Miura M, Fujinami N, Shimizu Y, et al. Usefulness of plasma full-length glypican-3 as a predictive marker of hepatocellular carcinoma recurrence after radial surgery. Oncol Lett 2020;19:2657-66. [Crossref] [PubMed]
12. Dong L, Qiu X, Gao F, et al. Protein induced by vitamin K absence or antagonist II: Experience to date and future directions. Biochim Biophys Acta Rev Cancer 2023;1878:189016. [Crossref] [PubMed]
13. Fu Y, Urban DJ, Nani RR, et al. Glypican-3-Specific Antibody Drug Conjugates Targeting Hepatocellular Carcinoma. Hepatology 2019;70:563-76. [Crossref] [PubMed]
14. Myrou A, Barmpagiannos K, Ioakimidou A, et al. Molecular Biomarkers in Neurological Diseases: Advances in Diagnosis and Prognosis. Int J Mol Sci 2025;26:2231. [Crossref] [PubMed]
15. Beaufrère A, Paisley S, Ba I, et al. Differential diagnosis of small hepatocellular nodules in cirrhosis: surrogate histological criteria of TERT promoter mutations. Histopathology 2024;84:473-81. [Crossref] [PubMed]
16. Li H, Liu H, Yan LJ, et al. Performance of GALAD score and serum biomarkers for detecting NAFLD-related HCC: a systematic review and network meta-analysis. Expert Rev Gastroenterol Hepatol 2023;17:1159-67. [Crossref] [PubMed]
17. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr 2017;11:959-75. [Crossref]
18. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010;84:523-38. [Crossref] [PubMed]
19. Chen C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 2006;57:359-77. [Crossref]
20. Li Z, Zhang Y, Zhang B, et al. Bibliometric study of immunotherapy for hepatocellular carcinoma. Front Immunol 2023;14:1210802. [Crossref] [PubMed]
21. Gan B, Wu L, Zhou S, et al. Comprehensive analysis of publications concerning combinations of immunotherapy and targeted therapies for hepatocellular carcinoma: a bibliometric study. Front Immunol 2025;16:1476146. [Crossref] [PubMed]
22. Falagas ME, Pitsouni EI, Malietzis GA, et al. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J 2008;22:338-42. [Crossref] [PubMed]
23. Chen C, Song M. Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS One 2019;14:e0223994. [Crossref] [PubMed]
24. van Eck NJ, Waltman L. Visualizing Bibliometric Networks. In: Ding Y, Rousseau R, Wolfram D, editors. Measuring Scholarly Impact: Methods and Practice. Cham: Springer International Publishing; 2014:285-320.
25. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2021;7:6. [Crossref] [PubMed]
26. Villanueva A. Hepatocellular Carcinoma. N Engl J Med 2019;380:1450-62. [Crossref] [PubMed]
27. Craig AJ, von Felden J, Garcia-Lezana T, et al. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2020;17:139-52. [Crossref] [PubMed]
28. Trevisani F, D'Intino PE, Morselli-Labate AM, et al. Serum alpha-fetoprotein for diagnosis of hepatocellular carcinoma in patients with chronic liver disease: influence of HBsAg and anti-HCV status. J Hepatol 2001;34:570-5. [Crossref] [PubMed]
29. Marrero JA, Feng Z, Wang Y, et al. Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectin-bound alpha-fetoprotein in early hepatocellular carcinoma. Gastroenterology 2009;137:110-8. [Crossref] [PubMed]
30. Lok AS, Sterling RK, Everhart JE, et al. Des-gamma-carboxy prothrombin and alpha-fetoprotein as biomarkers for the early detection of hepatocellular carcinoma. Gastroenterology 2010;138:493-502. [Crossref] [PubMed]
31. Calderaro J, Couchy G, Imbeaud S, et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J Hepatol 2017;67:727-38. [Crossref] [PubMed]
32. Ji J, Yamashita T, Budhu A, et al. Identification of microRNA-181 by genome-wide screening as a critical player in EpCAM-positive hepatic cancer stem cells. Hepatology 2009;50:472-80. [Crossref] [PubMed]
33. Labgaa I, Villanueva A. Liquid biopsy in liver cancer. Discov Med 2015;19:263-73. [PubMed]
34. Melo SA, Luecke LB, Kahlert C, et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 2015;523:177-82. [Crossref] [PubMed]
35. Kim SH. Transforming Liver Cancer Therapy: Integrating Molecular Profiling with Precision and Transplant-Based Care. Cancers (Basel) 2025;17:3671. [Crossref] [PubMed]
36. Lencioni R, Llovet JM. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 2010;30:52-60. [Crossref] [PubMed]
37. Qi X, Zhang DH, Wu N, et al. ceRNA in cancer: possible functions and clinical implications. J Med Genet 2015;52:710-8. [Crossref] [PubMed]
38. Han B, Chao J, Yao H. Circular RNA and its mechanisms in disease: From the bench to the clinic. Pharmacol Ther 2018;187:31-44. [Crossref] [PubMed]
39. Han D, Li J, Wang H, et al. Circular RNA circMTO1 acts as the sponge of microRNA-9 to suppress hepatocellular carcinoma progression. Hepatology 2017;66:1151-64. [Crossref] [PubMed]
40. Ye Q, Ling S, Zheng S, et al. Liquid biopsy in hepatocellular carcinoma: circulating tumor cells and circulating tumor DNA. Mol Cancer 2019;18:114. [Crossref] [PubMed]
41. Wang F, Li L, Piontek K, et al. Exosome miR-335 as a novel therapeutic strategy in hepatocellular carcinoma. Hepatology 2018;67:940-54. [Crossref] [PubMed]
42. Kumar MA, Baba SK, Sadida HQ, et al. Extracellular vesicles as tools and targets in therapy for diseases. Signal Transduct Target Ther 2024;9:27. [Crossref] [PubMed]
43. Sia D, Jiao Y, Martinez-Quetglas I, et al. Identification of an Immune-specific Class of Hepatocellular Carcinoma, Based on Molecular Features. Gastroenterology 2017;153:812-26. [Crossref] [PubMed]
44. Ringelhan M, Pfister D, O'Connor T, et al. The immunology of hepatocellular carcinoma. Nat Immunol 2018;19:222-32. [Crossref] [PubMed]
45. Vogel A, Chan SL, Dawson LA, et al. Hepatocellular carcinoma: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 2025;36:491-506. [Crossref] [PubMed]
46. Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology 2018;68:723-50. [Crossref] [PubMed]
47. Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J Hepatol 2022;76:681-93. [Crossref] [PubMed]
48. Zheng Z, Guan R, Jianxi W, et al. Microvascular Invasion in Hepatocellular Carcinoma: A Review of Its Definition, Clinical Significance, and Comprehensive Management. J Oncol 2022;2022:9567041. [Crossref] [PubMed]
49. Hu LS, Weiss M, Popescu I, et al. Impact of microvascular invasion on clinical outcomes after curative-intent resection for intrahepatic cholangiocarcinoma. J Surg Oncol 2019;119:21-9. [Crossref] [PubMed]
50. Lei Z, Li J, Wu D, et al. Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatitis B Virus-Related Hepatocellular Carcinoma Within the Milan Criteria. JAMA Surg 2016;151:356-63. [Crossref] [PubMed]
51. Renzulli M, Brocchi S, Cucchetti A, et al. Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma? Radiology 2016;279:432-42. [Crossref] [PubMed]
52. Lee S, Kim SH, Lee JE, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol 2017;67:526-34. [Crossref] [PubMed]
53. Xu T, Ren L, Liao M, et al. Preoperative Radiomics Analysis of Contrast-Enhanced CT for Microvascular Invasion and Prognosis Stratification in Hepatocellular Carcinoma. J Hepatocell Carcinoma 2022;9:189-201. [Crossref] [PubMed]
54. Wang F, Zhan G, Chen QQ, et al. Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images. Liver Int 2024;44:1351-62. [Crossref] [PubMed]
55. Nagtegaal ID, Odze RD, Klimstra D, et al. The 2019 WHO classification of tumours of the digestive system. Histopathology 2020;76:182-8. [Crossref] [PubMed]
56. Ziol M, Poté N, Amaddeo G, et al. Macrotrabecular-massive hepatocellular carcinoma: A distinctive histological subtype with clinical relevance. Hepatology 2018;68:103-12. [Crossref] [PubMed]
57. Calderaro J, Ziol M, Paradis V, et al. Molecular and histological correlations in liver cancer. J Hepatol 2019;71:616-30. [Crossref] [PubMed]
58. Pan Y, Chen H, Yu J. Biomarkers in Hepatocellular Carcinoma: Current Status and Future Perspectives. Biomedicines 2020;8:576. [Crossref] [PubMed]
59. Zhang K, He K, Zhang L, et al. Gadoxetic Acid-enhanced MRI Scoring Model to Predict Pathologic Features of Hepatocellular Carcinoma. Radiology 2025;314:e233229. [Crossref] [PubMed]
60. Ünal E, İdilman İS, Akata D, et al. Microvascular invasion in hepatocellular carcinoma. Diagn Interv Radiol 2016;22:125-32. [Crossref] [PubMed]
61. Celotti A, Bonafede M, Baiocchi G, et al. Microvascular Invasion in Hepatocellular Carcinoma (Hcc): Implications and Strategy. Curr Surg Rep 2025;13:46. [Crossref]
62. Dudek M, Pfister D, Donakonda S, et al. Auto-aggressive CXCR6(+) CD8 T cells cause liver immune pathology in NASH. Nature 2021;592:444-9. [Crossref] [PubMed]
63. Meyer YM, Beumer BR, Höppener DJ, et al. Histopathological growth patterns modify the prognostic impact of microvascular invasion in non-cirrhotic hepatocellular carcinoma. HPB (Oxford) 2022;24:507-15. [Crossref] [PubMed]
64. Poté N, Cauchy F, Albuquerque M, et al. Contribution of virtual biopsy to the screening of microvascular invasion in hepatocellular carcinoma: A pilot study. Liver Int 2018;38:687-94. [Crossref] [PubMed]
65. Wurmbach E, Chen YB, Khitrov G, et al. Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology 2007;45:938-47. [Crossref] [PubMed]
66. Hoshida Y, Nijman SM, Kobayashi M, et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res 2009;69:7385-92. [Crossref] [PubMed]
67. Shen Q, He Y, Qian J, et al. Identifying tumor immunity-associated molecular features in liver hepatocellular carcinoma by multi-omics analysis. Front Mol Biosci 2022;9:960457. [Crossref] [PubMed]
68. Morita M, Nishida N, Aoki T, et al. Role of β-Catenin Activation in the Tumor Immune Microenvironment and Immunotherapy of Hepatocellular Carcinoma. Cancers (Basel) 2023;15:2311. [Crossref] [PubMed]
69. Pelizzaro F, Farinati F, Trevisani F. Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Current Strategies and Biomarkers Predicting Response and/or Resistance. Biomedicines 2023;11:1020. [Crossref] [PubMed]
70. Ding SC, Lo YMD. Cell-Free DNA Fragmentomics in Liquid Biopsy. Diagnostics (Basel) 2022;12:978. [Crossref] [PubMed]
71. Alter N, Daiem M, Pontell ME, et al. Limitations of Academic Bibliometric Indices: The Need for More Comprehensive Metrics. Ann Plast Surg 2025;95:603-6. [Crossref] [PubMed]