This study employs bibliometric analysis to map the current research landscape of traditional Chinese medicine in gastric cancer treatment and to further reveal the evolution of research hotspots.
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Research Article
This study employs bibliometric analysis to map the current research landscape of traditional Chinese medicine in gastric cancer treatment and to further reveal the evolution of research hotspots.
The study conducted a bibliometric analysis of 704 articles on traditional Chinese medicine for gastric cancer published between 2006 and 2025, using the Web of Science Core Collection WOSCC (literature database). Network analysis and visualization were performed using CiteSpace (bibliometric analysis software), VOSviewer (bibliometric mapping software), and Scimago Graphica (a free scientific data visualization application). From the perspectives of publication trends, collaborative networks, research themes, and the knowledge base, the study elucidates the current research landscape, core research forces, and the evolution of frontiers in this field. Over the past two decades, the number of publications continued to grow, reaching a historic peak of 102 in 2025. Articles published in the last decade account for over 75% of the total, indicating that this field is in an active research phase. China ranks first in publication volume (671 articles), total citations (13,447), and international collaboration centrality. Nanjing University of Chinese Medicine and Beijing University of Chinese Medicine are the core research institutions. Major publication outlets include the Journal of Ethnopharmacology and Frontiers in Pharmacology. Keyword cluster analysis identified nine major research themes: Helicobacter pylori, acupuncture, Chinese herbal injections, network pharmacology, molecular docking, metabolomics, and others. Research hotspots evolved from basic mechanistic exploration to comprehensive treatment strategies, and further to systematic studies supported by emerging technologies. Molecular docking and epithelial-mesenchymal transition represent the current research frontiers.
Gastric cancer is one of the most common malignant tumors of the digestive tract worldwide, with its incidence and mortality rates consistently ranking among the leading malignancies, posing a serious threat to human health1. The disease often presents with insidious early symptoms, and most patients are already at an advanced stage at the time of initial diagnosis, missing the optimal window for curative surgery. Even with surgery, radiotherapy, and chemotherapy, the efficacy of conventional treatment modalities remains limited, and these approaches are frequently accompanied by severe adverse reactions that not only compromise patients' quality of life but often lead to treatment discontinuation2,3,4. Traditional Chinese medicine, as an important component of comprehensive cancer therapy, has demonstrated unique value in the prevention and treatment of gastric cancer5. In recent years, the treatment model combining traditional Chinese medicine with surgery, radiotherapy, and chemotherapy has become increasingly widespread. Multiple clinical studies have confirmed that traditional Chinese medicine can effectively alleviate chemotherapy-induced adverse reactions, inhibit tumor recurrence and metastasis, and improve patients' quality of life, while also exhibiting characteristics such as multi-pathway, multi-target effects and mild action6,7,8,9,10. In China, traditional Chinese medicine permeates the entire course of gastric cancer prevention and treatment, from early intervention in precancerous lesions to palliative care in advanced stages, having become an indispensable therapeutic approach11. However, with the rapid growth in related studies, this field faces challenges such as fragmented research topics, heterogeneous evidence quality, and unclear core research questions, underscoring the urgent need for a systematic review of the current research landscape.
Bibliometrics is an interdisciplinary field that treats the literature system as its research object and employs mathematical and statistical methods to quantitatively analyze the distributional characteristics and evolutionary patterns of knowledge carriers12. Compared with traditional review methods such as systematic reviews and meta-analyses, bibliometrics focuses more on revealing patterns and frontiers at the macro and meso levels. It can compensate for potential subjective selection bias in traditional reviews and is particularly well suited to the systematic sorting of large-scale, multidimensional characteristics of the literature. Based on this, the method can provide scholars with data to grasp the discipline's development trajectory, identify core research forces, and plan future research directions13,14,15. In recent years, this method has been widely applied in knowledge mapping and frontier analysis across various fields, offering a scientific basis for systematically tracing the development of disciplines. This study, based on the literature database, employs bibliometric analysis tools, including bibliometric mapping software and a free scientific data visualization application, to visually analyze the literature on traditional Chinese medicine for the treatment of gastric cancer from 2006 to 2025. By examining multiple dimensions such as publication trends, country distribution, institutional collaboration, author networks, journal distribution, keyword clustering, and co-cited references, this study systematically elucidates the current research status, core research forces, hot topics, and frontier evolution in this field, aiming to provide a reference for clinical practice and scientific research on the prevention and treatment of gastric cancer with traditional Chinese medicine.
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Data Acquisition and Search Strategy
A topic search was conducted in the literature database using Boolean logical operators. The search strategy was designed using Medical Subject Headings (MeSH)- related concepts and free-text keywords adapted for the WOSCC database: "Stomach Neoplasms" for gastric cancer and "Medicine, Chinese Traditional" for traditional Chinese medicine, along with relevant entry terms. The search was limited to the "Topic" field. To comprehensively capture the broad literature on traditional Chinese medicine in gastric cancer research, two core research directions were prioritized, namely acupuncture and Chinese herbal medicine, as the primary retrieval axes, as these two modalities represented the most widely applied therapeutic approaches and had generated the largest body of published research in this field. The search strategy was constructed as follows: TS=(“traditional Chinese medicine*” OR “TCM” OR “Chinese herbal” OR “acupuncture” OR “herb medicine*”) AND TS=(“stomach neoplasm*” OR “gastric cancer*” OR “gastric tumor*” OR “gastric carcinoma*”). Core and extended keywords in the fields of gastric cancer and traditional Chinese medicine were selected hierarchically and combined within each group using the “OR” operator and between groups using the “AND” operator to ensure comprehensive coverage of relevant research topics. Wildcards were employed to avoid omission of related terms. The search period was limited from January 1, 2006, to December 31, 2025 (Table 1). The search was performed on March 18, 2026. The index categories included SCI-EXPANDED and SSCI.
Literature Screening Process and Criteria
The literature screening process was as follows: (1) Language filtering: only English articles were included. (2) Document type filtering: only journal articles (Article) and reviews (Review) were included; conference abstracts, editorials, letters, news items, etc., were excluded. All records retrieved from WOSCC were exported in their entirety without any manual preselection, ensuring an unbiased and reproducible screening process. When exporting records from the literature database, the export settings were as follows: record content was set to “Full Record and Cited References”; file format was set to ”Plain Text”; encoding was set to “UTF-8”; the file was named “20260318_TCM_gastric”. If disagreements arose during the screening process, they were resolved through consensus discussion between the two authors to ensure the accuracy of the screening results.
Data Import and Cleaning
After exporting the data, the first step was to convert the format and import it. The TXT files exported from WOSCC were uniformly stored in the “input” folder. Subsequently, using the bibliometric analysis tool's built-in data format conversion tool, the original literature records were converted into a format recognizable by bibliometric analysis tools; the converted files were stored in the “output” folder, and corresponding “data” and “project” folders were automatically generated, laying a structured data foundation for subsequent co-occurrence analysis, co-citation analysis, and keyword evolution analysis.
In the data cleaning step, to avoid interference from duplicate literature in the analysis results, the bibliometric analysis tools' data deduplication function for automated screening was used. This function identified and removed duplicate records using unique literature identifiers, ensuring that only one record was retained per publication. Specifically, in the data preprocessing interface of bibliometric analysis tools, the “Remove Duplicates” option was selected, and the system automatically scanned the dataset, removed redundant entries, and generated a deduplication log for verification. After cleaning, the remaining unique literature records were all included in subsequent analyses, thereby ensuring the accuracy of network construction and statistical counts. In addition, the TXT files exported from WOSCC were directly imported into a bibliometric mapping software and processed using the map-based bibliographic data function to construct collaboration and co-occurrence networks.
Analytical Tools
A bibliometric analysis tool was used to construct co-citation networks and analyze keyword evolution; bibliometric mapping software was used to construct and visualize collaborative and co-occurrence networks; a free scientific data-visualization application was used to visualize country/regional collaboration networks; and WPS Excel 2023( WPS spreadsheet) and GraphPad Prism 10.1 (scientific data analysis and graphing software) were used for basic statistical analysis and figure preparation.
Bibliometric Parameter Settings (bibliometric analysis tools)
In the bibliometric analysis, time slicing was set from 2006 to 2025 in 1-year intervals, and node selection was performed using the g-index (k = 5). Network simplification was conducted using the Pathfinder pruning algorithm, applied once to each time-slice network and once again after network merging. In the visualizations, node size was scaled proportionally to occurrence frequency, and link thickness reflected the strength of co-occurrence relationships16. Keywords extracted from the exported records were merged and standardized, based on which a co-occurrence matrix was generated. Subsequently, cluster analysis, timeline visualization, and burst detection were performed to identify thematic evolution and emerging research fronts. The analysis output included co-occurrence network files, cluster reports, and burst term lists for further visualization and interpretation.
Collaborative and Co-occurrence Network Analysis Workflow (bibliometric mapping software)
In the bibliometric mapping software analysis, thresholds were set based on the distribution of publication counts for each entity: a minimum of 4 publications for authors, 6 for institutions, 2 for countries, and 3 for journals. Under these thresholds, co-authorship networks for authors, institutions, countries, and journals, as well as a keyword co-occurrence network, were constructed separately. The network layout was optimized using the Linlog/modularity-based algorithm, and node sizes were weighted by publication counts or total citation counts, depending on the analysis objective17. The analysis generated node/edge statistics files, two-dimensional network maps, and density views for each network, which were used to interpret collaboration patterns and hotspot distributions.
Visualization of Country Collaboration Networks (a free scientific data visualization application)
A free scientific data-visualization application was used primarily to analyze and visualize country collaboration networks. The workflow involved importing GML-format country collaboration data exported from bibliometric mapping software into a free scientific data-visualization application, with the following parameters: (1) Node labels set to “Country”. (2) Node clustering based on the country name string. In the resulting country collaboration network, node diameter reflected the number of publications per country, and link thickness reflected the frequency of collaboration between countries18,19.
Basic Statistical Analysis and Visualization (WPS spreadsheet and a scientific data analysis and graphing software)
WPS spreadsheet and scientific data analysis and graphing software were used to perform descriptive statistical analyses and to convert analytical data into visual representations, including pie, bar, and column charts.
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Annual Publication Output
An initial search yielded 724 records, which were refined to include only English-language articles and review articles, resulting in 704 publications: 548 articles and 156 review articles. From 2006 to 2025, the annual number of publications in traditional Chinese medicine for gastric cancer showed a fluctuating upward trend, reflecting a steady increase in research interest. The temporal evolution of research output can be categorized into three dis...
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This study employs bibliometric methods to systematically delineate the research landscape of traditional Chinese medicine in the treatment of gastric cancer over the past two decades. This analysis not only outlines the global research landscape but also identifies key contributors, emerging themes, and potential future research directions in this field.
Macro-Level Development Trends and Research Power Distribution
Over the past two decades, resea...
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The authors declare no conflicts of interest.
This work was supported by the Elite Research Fund of the Beijing Micro Love Charity Foundation under Grant No. JYKY2024-0100319019.
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| CiteSpace | Chaomei Chen, Drexel University | 6.4.R1 (https://citespace.podia.com/) | Used for constructing co-citation networks and analyzing keyword evolution. |
| VOSviewer | Centre for Science and Technology Studies (CWTS), Leiden University | 1.6.19 (https://www.vosviewer.com/) | Used for constructing and visualizing collaborative and co-occurrence networks. |
| Scimago Graphica | SCImago Lab | 1.0.25 (https://graphica.scimagojr.com/) | Used for visualizing country/regional collaboration networks. |
| GraphPad Prism | GraphPad Software | 10.1 (https://www.graphpad.com/) | Used for basic statistical analysis and figure preparation. |
| WPS Excel | Kingsoft Corporation | 2023 (https://www.wps.com/) | Used for descriptive statistical analysis and data management. |
| Web of Science Core Collection | Clarivate Analytics | https://www.webofscience.com/ | Literature database used for data acquisition and search strategy. |
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