Widely embraced for its standardized, high-quality academic publication data, the Web of Science Core Collection (WoSCC) (https://www.webofscience.com/wos/) is a renowned resource in bibliometric analysis for tracking the evolution of scientific frontiers (Boudry et al., 2018; Zhang XL. et al., 2020; Pei et al., 2022; Ling et al., 2023). It provides bibliometric software with a source of general statistics (Pei et al., 2022), and the accuracy of document type labeling in the WoSCC has been demonstrated to be superior to that of other databases (Yeung, 2019). This study involved a comprehensive online search within the WoSCC, with a focus on original researches and reviews pertaining to “ferroptosis-TCM”. The search covered publications from 1 January 2012, to 30 November 2023, using both Medical Subject Heading terms and free words for data extraction. The search strategy was refined through multiple iterations by three researchers (SYC, AKL, and SL) to fortify its sensitivity and precision. The Supplementary Materials contain an extensive exposition of the search methodology.

This study strictly adhered to well-defined eligibility criteria. The scope encompassed research pertaining to “ferroptosis-TCM”, with specific attention given to original investigations and reviews issued in scientific journals using the English language. Excluded were dissertations, letters, commentaries, editorials, conference abstracts, and works with the same or similar titles published in different journals. The inclusion and exclusion criteria were finalized through discussions among team members and peer groups.

Research data were gathered from the WoSCC and processed using specific software tools: WPS Office 12.1.0 (Kingsoft Office, China) for data organization, VOSviewer 1.6.18 (Leiden University, Netherlands) and Pajek 64 5.16 (University of Ljubljana, Slovenia) for co-occurrence analysis, Citespace version 6.2.6R (developed by Chaomei Chen, China) for visual mapping, Scimago Graphica version 1.0.35 (https://www.graphica.app/, USA) for graphical analysis. Additionally, various R packages (R Studio, version 4.2.0) including chorddiag, enrichplot, ggplot2, and clusterprofiler were utilized for generating specialized graphics.

The chorddiag R package and VOSviewer were employed to generate maps depicting national/regional collaboration and publication analysis charts. For co-occurrence analysis involving institutions, authors, journal publications, keywords, and diseases, a combination of VOSviewer, Scimago Graphica, and Pajek was utilized. Citespace was used for visualized analysis and mapping of data related to countries/regions, institutions, authors, journals, co-citations, and keywords. In addition, we employed the clusterProfiler, enrichplot, and ggplot2 R packages to carry out enrichment analyses for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Data regarding disease information were obtained from the Citexs Data Analysis Platform (https://www.citexs.com). This platform facilitates the generation of relevant visual graphs, enabling an in-depth analysis of the current state, key focus areas, and emerging trends within this field of study.

Figure 1A depicts the procedure of data retrieval and collection. The quantity of scientific reports produced by a study within a given timeframe indicates its research progress (Koo, 2021; Li Z. et al., 2022; Sun G. et al., 2022). From 2012 to 2023, we compiled 557 relevant scientific reports on “ferroptosis-TCM”, comprising 458 original articles and 99 reviews, with an annual average of 50.64 publications. In the past 11 years, there has been a continuous increase in academic production within this domain. Commencing from 2021, the annual tally of pertinent publications surpassed 50, culminating at 201 in 2023. This surge stands for a more than 22-fold increase since 2012, with a yearly growth rate of 48.61%. This trend underscores the continuous rise in research activity and the significant importance of this field. An exponential equation (y = 6.2711e^0.3526x, where x is the year and y the yearly publication count, R² = 0.9845) was employed to accurately depict the annual pattern, resulting in a well-fitted curve (Figure 1B). This growth reflects a dynamic research landscape, contributing extensive knowledge that enriches both present and future studies. Consequently, we anticipate substantial advancements in “ferroptosis-TCM” research in the foreseeable future.

The rise in academic publications signifies a noteworthy inclination towards investigating the regulatory impacts of TCM on ferroptosis. This pattern underscores the swift expansion of this domain, drawing in a broader spectrum of researchers and accentuating its escalating prominence within the academic sphere. This anticipated expansion could also engender increased financial support, expertise, and attention from diverse stakeholders. Potential breakthroughs in understanding ferroptosis and its integration with TCM could have profound implications, such as the development of innovative treatments, diagnostic tools, or therapeutic strategies. Industry practitioners, including pharmaceutical companies and healthcare providers, should take note of the rising research activity in this area.

Global research on “ferroptosis-TCM” involves 57 countries/regions. To create maps of national collaboration networks, a minimum threshold of two publications per country/region was employed, as depicted in Figures 2A, B. Notably, China leads with 401 publications, constituting 71.99% of the total research output in this field. This highlights China’s significant role in advancing knowledge in this area. Following China, the USA and Germany contribute 7.18% (40 publications) and 3.77% (21 publications), respectively, to global research on “ferroptosis-TCM”, demonstrating the worldwide interest and involvement in this research.

The chord diagram utilizes peripheral curve segments to represent different countries and regions, with each segment’s length indicating the corresponding publication volume (Cao et al., 2023b). Moreover, the extent of connectivity among nations reflects the degree of their collaborative engagement. Notably, significant international collaboration was observed during the study period. The most frequent academic collaborations were observed between China and the USA, exhibiting a notable link strength of 13 (Figure 2B). The number of publications serves as an indicator of a nation or region’s prominence within this domain (Joshi, 2014). China and the USA, as leaders in publications related to “ferroptosis-TCM”, underscore their substantial academic influence. This robust collaboration has the potential to propel theoretical progress and tackle ongoing field-specific challenges (Dara et al., 2017). Such collaboration can facilitate the interchange of ideas, resources, and expertise, potentially accelerating theoretical advancements and addressing challenges in the field. Researchers and practitioners from different countries should consider leveraging these collaborations for mutual benefit.

Citation bursts play a vital role in the identification of publications that have witnessed notable surges in citations over a defined time frame (Cao et al., 2023b). These bursts shed light on the dynamic trends and directions within research fields. Analyzing publications experiencing rapid citation increases aids in identifying emerging trends within areas of high scholarly interest. Figure 2C illustrates the citation bursts for the leading 10 nations, with the red line denoting the burst magnitude for each country. During the period between 2013 and 2019, Iran witnessed a significant surge in publication citations (strength = 4.55), closely followed by England (strength = 3.64), indicating emerging trends and areas of high scholarly interest. Researchers and industry practitioners should pay attention to these countries and their specific research directions, as they may provide valuable insights and opportunities for further exploration and collaboration. Researchers and industry professionals can use this information to identify potential partners and track the evolution of research collaborations. This can help them gain a better understanding of the global distribution of expertise in the field.

Fundamentally, the worldwide dispersion, collaborative structures, and emerging directions within “ferroptosis-TCM” investigation imply a substantial shift towards comprehensive worldwide involvement. These discoveries underscore the necessity for expanded partnerships, pinpoint emerging research focal points, and signify prospects for researchers and industry stakeholders alike to leverage varied proficiency in promoting remedies for human ailments through TCM.

Identifying top-performing institutions and analyzing their citation bursts provides valuable insights for researchers seeking collaborators and funding opportunities. The dynamic collaborative network among institutions signifies a vibrant research ecosystem. Over the past 11 years, global research on “ferroptosis-TCM” has made significant strides, involving over 760 entities. A collaboration network, illustrated in Figure 3A, was established among these institutions, requiring a minimum of six publications for inclusion. In this network, circles and texts represent each institution; connecting lines denote collaborative occurrences; line thickness signifies collaboration strength; gradient colors represent the total collaboration strength between an institution and others; circle size correlates with the institution’s publication count. Guangzhou University of Chinese Medicine emerges as the leading contributor, accounting for approximately 5.02% (28 publications) of the total research output. Following closely, Chengdu University of Traditional Chinese Medicine and Zhejiang University contribute 2.33% (13 publications) respectively. These institutions have demonstrated a substantial contribution to the field, making them potential collaborators for future research projects. Our analysis of interinstitutional collaboration revealed remarkable enthusiasm from Guangzhou University of Chinese Medicine and Sun Yat-sen University in engaging with partnering institutions. It is worth mentioning that the vast majority of these organizations prioritize domestic cooperation rather than international collaboration. The dynamic collaborative network among institutions indicates a vibrant research ecosystem in the “ferroptosis-TCM” field, which can foster innovation and knowledge exchange. This ecosystem provides fertile ground for sharing ideas, resources, and expertise. Researchers can tap into this ecosystem to stay updated on the latest developments, access shared resources, and enhance the overall quality of research.

Additionally, understanding which institutions are experiencing citation bursts can help researchers identify institutions with active and impactful research programs that may offer collaboration opportunities or funding prospects. When contemplating collaborations, one should evaluate not only the quantity of publications but also the enduring impact and flexibility of research endeavors over an extended duration (Cao et al., 2024). Using CiteSpace analysis (Figure 3B), this study identified institutions with significant citation bursts. Bose Institute and the Department of Science & Technology (India) experienced remarkably long burst periods in citations from 2012 to 2018. However, this trend declined in the last 5 years. In contrast, Chengdu University of Traditional Chinese Medicine exhibited a delayed citation burst from 2021 to 2023. This pattern suggests a shift in focus and a delayed timeline for research output in this institution’s field.

Recognizing prolific authors and analyzing their collaboration networks can help researchers identify key contributors in the field. Authors with high citation rates and a consistent publication history offer valuable insights and can guide future research directions. Following a thorough examination of authorship within the domain of ‘ferroptosis-based TCM’, a total of 3,615 authors emerged as significant contributors. Among these, eight authors were particularly prolific, each having authored a minimum of five papers. These authors likely possess in-depth knowledge and expertise in the field, making their work essential for those seeking guidance in “ferroptosis-TCM” research. To conduct a detailed examination of co-authorship networks, we utilized VOSviewer software to generate visualization maps. These maps were created with a baseline criterion of a minimum of three publications per author. In these representations, the dimensions of the circles are proportional to the number of publications by each author. Various color coding denotes discrete author clusters. The thickness of interconnecting lines among these circles indicates the degree of collaborative interactions. Remarkably, 90 authors exceeded this publication threshold, Thomas Efferth, Victor Kuete, and Armelle T. Mbaveng demonstrated the most robust collaborative relationships, as illustrated in Figure 4A. Furthermore, Thomas Efferth and Nripendranath Mandal were recognized as high-yielding scholars in the research on “ferroptosis-TCM”, emphasizing their essential contributions to this scientific field.

CiteSpace assesses network integrity and clustering clarity using Modularity (Q value) and Mean Silhouette (S value). A Q value above 0.3 indicates robust clustering, while an S value above 0.5 suggests clear and reasonable clustering (Wang B. et al., 2022). The study recorded a cluster modularity value (Q) of 0.8341 and a mean silhouette value (S) of 0.9492, indicating highly significant and clear delineation of keyword clusters. In Figure 4B, authors are categorized into 12 groups and labeled with keywords, including: #0 colon cancer, #1 iron metabolism, #2 ferritin, #3 p53, #4 oxytosis, #5 formosanin c, #6 hydrogen peroxide, #7 gastric cancer, #8 ferroptosis, #9 transforming growth factor-β (TGF-beta), #10 kidney-reinforcing and marrow-beneficial Traditional Chinese Medicine, #11 Heme Oxygenase-1 (HO-1).

Citation burst analysis serves as a critical metric, indicating the frequency of citations received by an author in a specific research domain during a defined timeframe (Jiang et al., 2022; Li and Du, 2022). Figure 4C presents the top ten authors with the highest citation counts in the realm of “ferroptosis-TCM”. Nripendranath Mandal leads with a burst strength of 4.31, closely followed by Thomas Efferth and Sourav Panja. Researchers and industry practitioners can refer to their work as a reference framework for future research directions, ensuring that their research builds upon the foundation of influential and highly cited studies. These influential authors offer valuable guidance for researchers navigating this research domain. Moreover, their work can inform industry practitioners about key players and emerging trends, potentially influencing their strategic decisions and collaborations.

Drawing from visualized journal publication data, it becomes evident that 268 journals have disseminated articles related to “ferroptosis-TCM”. To illustrate the dispersion of these documents among journals, a thermodynamic chart, as depicted in Figure 5A, has been constructed, employing a minimum criterion of two documents per journal. The intensity of color in the chart corresponds to the quantity of journal papers issued. The journal ‘Frontiers in Pharmacology’ ranks highest in the number of published documents (n = 33, 5.92%), followed by ‘Phytomedicine’ (n = 24, 4.31%), ‘Journal of Ethnopharmacology’, and ‘Molecules’ (n = 14, 2.51%). This provides researchers with a comprehensive understanding of the publication landscape in this field, helping them select appropriate journals for their own work and ensuring that their work reaches the relevant audience. Additionally, Figure 5B lists the top 20 journals with the strongest citation bursts for “ferroptosis-TCM” publications. Researchers can assess the significance of specific journals and prioritize them when citing or referring to previous research in their own work.

The dual-map overlay technique effectively presents the interdisciplinary dispersion of journals, the development of citation trajectories, and the relocation of scientific research centers (Yuan et al., 2023). Labels on the map depict the various subject areas covered by the journals. Journals referencing others are positioned on the left side, while those being referenced are situated on the right side (Zhang T. et al., 2022). Diverse colored lines visually illustrate the citation pathways, originating from the citation map and concluding there. The breadth of these connecting pathways is closely associated with the frequency of z-score-scaled citations. Figure 5C depicts the classification of research pertaining to “ferroptosis-TCM” into three key domains: molecular biology, immunology, and genetics. Such interdisciplinary insights can inspire collaboration among researchers from diverse backgrounds, fostering the exchange of ideas and innovative approaches. The findings can assist industry professionals, including pharmaceutical companies and biotechnology firms, in pinpointing potential investment areas and opportunities for product development in the “ferroptosis-TCM” domain. Understanding the interdisciplinary nature of research can guide them in recruiting experts from relevant domains and developing innovative therapies or products related to ferroptosis and TCM.

Understanding which works have garnered the most attention and citations is essential for researchers as it helps them build upon existing knowledge and concepts, contributing to the advancement of their respective fields. Figure 6A displays a co-citation network diagram, delineating the co-citation network of literature related to “ferroptosis-TCM” from 1 January 2012, to 30 November 2023, as analyzed using CiteSpace. In this representation, the sizes of the spheres, aggregating their sizes across annual rings, are proportionate to their co-citation frequencies. The color scheme spans from purple, indicating earlier citation instances, to yellow, representing more recent citations. Spheres with overlapping colors indicate sustained citation over the specified years. The interconnecting lines between spheres illustrate the co-citation relationships among various pieces of literature. Notably, nodes highlighted in magenta signify pivotal nodes in the network, characterized by a centrality exceeding 0.1. The review titled ‘Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease’, authored by Brent Stockwell et al. and published in Cell in 2017, stands out among the most frequently cited works, with the highest co-citation count (n = 60) (Stockwell et al., 2017). The 2020 Cell Death & Disease paper ‘Ferroptosis: past, present and future’ by Li Jie et al. followed with 59 co-citations (Li et al., 2020).

CiteSpace employs metrics such as Modularity (Q value) and Mean Silhouette (S value) to assess network structures and clustering clarity. A Q value above 0.3 indicates significant clustering, while an S value higher than 0.5 indicates clear and effective clustering. In the analysis conducted, the computed values were Q = 0.8569 and S = 0.9535, confirming the presence of robust and convincing clustering structures within the network. This result emphasizes the reliability of the employed citation clustering method. The analysis identified 13 distinct clusters, labeled as #0 diabetic nephropathy, #1 redox homeostasis, #2 growth inhibition, #3 resveratrol, #4 non-small cell lung cancer, #6 anticancer, #7 galangin, #8 ha53t, #9 cytotoxicity, #10 ovarian cancer, #11 oxytosis, #12 Chinese herbal medicine, #16 condensed tannins, as illustrated in Figure 6B. Researchers can use this information to ensure their work aligns with established research clusters, increasing the likelihood of their research being recognized and cited by peers and experts.

The analytical capabilities of CiteSpace were used to detect citation bursts, highlighting studies that have garnered significant scholarly attention in the field of “ferroptosis-TCM”. As depicted in Figure 6C, an analysis of the top 20 references reveals their substantial impact, evidenced by significant citation bursts. The field experienced a notable surge in citations starting from 2016, with numerous co-citation references gaining substantial citations in the following years. This trend highlights the lasting significance of research in “ferroptosis-TCM”. Notably, in 2019, 45% (9 out of 20) of these references exhibited citation bursts, marking it as the year with the highest frequency, followed by 2016 and 2020, accounting for 20% (4 out of 20) and 15% (3 out of 20) of the bursts, respectively. The study with the most prominent citation burst (strength = 11.26) was titled ‘Ferroptosis: Process and Function’, originally published in Cell Death & Differentiation (Xie et al., 2016). This was closely followed in impact by the works of Brent Stockwell et al. (Stockwell et al., 2017), and Wan Seok Yang et al. (Yang and Stockwell, 2016). Researchers can identify key studies that have gained significant scholarly attention and understand when these bursts of interest occurred. This information can help researchers stay updated on emerging trends and focus their efforts on areas of high impact. By analyzing co-citation references and identifying works that are frequently cited together, researchers can identify potential collaborators who share common research interests, enhancing their ability to conduct collaborative research projects. Collaborating with authors whose works are frequently cited alongside one’s own research can enhance the visibility and impact of their work. Industry practitioners can use the information on influential works and research trends to inform their strategic decisions, such as identifying research partnerships or investment opportunities in areas aligned with the most cited and impactful studies.

Keyword co-occurrence clustering analysis was performed using VOSviewer software, with a minimum keyword occurrence threshold set at three instances. Keywords meeting these criteria were included in the graph. A total of 131 keywords were selected from the initial pool of 1,469 keywords (after removing duplicates) to create the visualized network. Figure 7A represents a word frequency-time graph, where circles and corresponding labels collectively constitute a node. The size of each circle corresponds to the frequency of keyword occurrence, while the thickness of connections between circles indicates the strength of relationships among keywords. The color of each circle, as indicated in the lower right corner, gradually changes to represent the average year of appearance. Blue indicates earlier appearances, while yellow represents later appearances of the keywords. Based on the data presented in Figure 7A, it is evident that ferritin, phytochemicals, and thalassemia were topics of earlier research, while metabolomics, neuroinflammation, and diabetic nephropathy have emerged as recent research hotspots and directions. This information is invaluable for researchers as it highlights areas of growing interest and potential for further exploration.

The identification of keyword bursts, particularly those with substantial increases in citations, indicates areas of intense scholarly attention (Figure 7B). Figure 7B illustrates that 30% of the keywords (3 out of 10) experienced their initial citation surge in 2020, followed closely by 2014, which accounted for 20% of the surges (2 out of 10). Notably, 40% of these keywords maintained elevated citation rates over the past 3 years, indicating a sustained and growing interest in specific research domains. The keyword ‘iron overload’ displayed the most significant burst, with a burstness value of 2.78, followed by ‘iron chelation’ and ‘cell death’, with burstness values of 1.64 and 1.45, respectively. These insights bear significant implications for researchers in their respective fields.

Co-occurrence analysis reveals the relationships between keywords and identifies key themes and concepts in the field. Figure 7C illustrates a co-occurrence analysis chart of keyword frequencies, utilizing CiteSpace to analyze the co-occurrence of “ferroptosis-TCM” from 1 January 2012, to 30 November 2023. The spheres’ sizes indicate the cumulative size of spheres on yearly rings, in proportion to the frequency of keyword usage. Purple represents earlier keyword appearances, while yellow indicates later appearances, and overlapping colors indicate citations in corresponding years. Lines connecting spheres represent co-citation connections among literature, and nodes highlighted in magenta represent critical nodes with a centrality above 0.1. Figure 7C shows that the most frequently co-occurring keyword is ‘oxidative stress’, followed by ‘iron overload’ and ‘traditional Chinese medicine’, which are tied for second place.

Understanding the evolving trends and hotspots in “ferroptosis-TCM” research is crucial as it can guide investment and collaboration decisions, ensuring strategic alignment with current research interests. Figure 7D depicts a temporal analysis of keyword frequency clustering within hotspots. The diagram showcases circles of varied sizes, where the cumulative diameter on each annual ring corresponds to the keyword frequency. Interconnections among keywords signify their co-occurrence. The color scheme is informative: purple represents earlier keyword emergence, yellow denotes recent appearances, and overlapping colors indicate consistent keyword presence across years. Magenta nodes highlight keywords with significant centrality, underscoring their pivotal role as network hubs. Keywords within the same cluster are horizontally aligned. The timeline orientation positions the initial keyword occurrence at the top, progressing chronologically to the right. This visualization aids in understanding keyword distribution within each cluster, with a larger quantity signifying greater cluster relevance. Additionally, it outlines the temporal duration of keywords in each cluster. The keywords are categorized into 13 distinct groups: #0 Traditional Chinese Medicine, #1 Iron Overload, #2 Oxidative Stress, #3 Lipid Peroxidation, #4 •OH-induced Damage, #5 Green Tea, #6 Diabetes, #7 Natural Products, #8 Epimedium Herb, #9 Gut Microbiota, #10 Iron-Chelating Activity, #11 Iron Load, #12 Antioxidant Activity. These clusters encapsulate primary themes and hotspots in the research domain, reflecting their significance in scientific advancements, therapeutic potential, or clinical applicability. Researchers and industry practitioners can use this information to navigate the various research directions and pinpoint areas of particular relevance, helping them focus their efforts more effectively.

It is not difficult to observe that within the multiple analyses of keywords above, ‘iron overload’ and ‘oxidative stress’ appear with the highest frequency. TCMs offer a plethora of natural antioxidant substances, encompassing polyphenols, alkaloids, flavonoids, terpenoids, etc., all of which exhibit excellent antioxidant properties. Therefore, future investigations should explore the potential of TCMs in modulating iron metabolism in the context of oxidative stress, aiming to identify potential targets for synergistic combination therapy, with the goal of achieving a “one TCM-multiple targets " approach for future interventions. To date, progress has already been made in this area with compounds such as baicalein, curcumin, and epigallocatechin gallate (Mandel et al., 2008; Perez et al., 2009; Dijiong et al., 2019; Guerrero-Hue et al., 2019; Kose et al., 2019; Rainey et al., 2019).

Utilizing VOSviewer software, we conducted a co-occurrence clustering analysis of genes related to “ferroptosis-TCM”. From a dataset of 557 articles obtained from the Citexs big data platform, we extracted a total of 1,109 genes, each appearing at least 6 times, which was then visualized in a map (Figure 8A). This visualization is categorized based on gene function similarity or their co-occurrence in research. Every node consists of a circle and a label, where the circle’s size corresponds to the gene’s frequency of occurrence, and the line thickness connecting circles indicates the strength of gene associations. Nodes of distinct colors form separate clusters, with each color symbolizing genes grouped within different domains, signifying particular biological or medical gene categories. These clusters represent specific biological or medical gene groups, enabling researchers to identify key genes and their functions within the context of ferroptosis and TCM.

Genes associated with “ferroptosis-TCM” form the core components of this research focus, effectively acting as dual indicators: one for recognizing shifting research patterns and the other for identifying changes in research orientation and the deepening of investigations. Illustrated in Figure 8A, the top three genes with the highest frequency were GPx4, NFE2L2, and SLC7A11. The sensitivity of ferroptosis correlates directly with the expression level of nuclear factor erythroid 2 like 2 (Nrf2), also known as nuclear factor erythroid 2-related factor 2 (NFE2L2). An increase in Nrf2 expression hinders ferroptosis, while a decrease in expression promotes it (Sun et al., 2016; Dodson et al., 2019). The mechanisms through which Nrf2 regulates ferroptosis encompass two primary aspects. Firstly, Nrf2 amplifies the antioxidant system by upregulating the expression of GSH and glutathione peroxidase 4 (GPx4), thereby enhancing antioxidant functionality. Secondly, Nrf2 stimulates the expression of ferritin and ferroportin, aiding in the storage or export of free iron. Consequently, this reduction in iron accumulation averts ferroptosis (Yang et al., 2017). Solute carrier family 7 member 11 (SLC7A11) functions as a cystine/glutamate antiporter (System Xc⁻), playing a crucial role in importing cystine for GSH biosynthesis and antioxidant defense (Koppula et al., 2021). In response, employing TCMs to target the specified pivotal genes for modulating ferroptosis could potentially offer an innovative strategy for preventing and managing human illnesses. This encompasses, without restriction, enhancers of iron metabolism, controllers of Nrf2-associated routes, and substances that inhibit or activate system Xc⁻ and GPx4.

Moreover, we performed GO and KEGG enrichment assessments on genes linked to “ferroptosis-TCM”, with a focus on genes referenced in the articles two or more times (Figures 8B,C). Researchers can use this information to pinpoint the specific functions and pathways influenced by these genes, guiding their experimental design and hypothesis generation. The identification of key genes and pathways can also facilitate collaborations between researchers and industry professionals, as it allows for the targeted leveraging of academic expertise in specific genes and pathways, potentially accelerating the development of novel treatments or interventions. The bubble chart for GO enrichment analysis illustrates each bubble as a GO term. Bubble size corresponds to the quantity of genes linked to that function, while color indicates the level of enrichment. The X-axis displays the GeneRatio, signifying the proportion of genes associated with the GO term in relation to the total gene count in the background gene set. A higher GeneRatio suggests a greater number of genes associated with the GO term, potentially indicating greater significance. The Y-axis categorizes GO terms into various biological processes, molecular functions, or cellular components.

Figure 8B illustrates the GO functional enrichment results, covering biological processes (BP), molecular functions (MF), and cellular components (CC). In terms of BP, the genes display enrichment in functions associated with responses to oxidative stress and cellular responses to chemical stress. Regarding CC, the genes exhibit enrichment in functions related to the vesicle lumen, outer membrane, and organelle outer membrane. Concerning MF, the genes show enrichment in functions such as DNA-binding transcription factor binding and protein serine/threonine kinase activity. Identifying specific cellular components and molecular functions enriched in these genes can assist researchers in pinpointing the organelles and activities critical to ferroptosis, thus facilitating the development of targeted interventions or therapies.

Furthermore, we performed KEGG pathway enrichment analysis to identify the top 10 signaling pathways, which is presented as a histogram. The X-axis represents the number of genes significantly enriched in each pathway, while the Y-axis displays different signaling pathways. The height of each column in the histogram signifies the gene count within the pathway and its level of significance after enrichment. As shown in Figure 8C, this research area is predominantly linked to signaling pathways such as ‘Lipid and Atherosclerosis’ and ‘PI3K/AKT signaling pathway’. This information can be of particular interest to industry practitioners, including pharmaceutical companies, as it suggests potential therapeutic targets or areas for drug development. Understanding the signaling pathways enriched in this context can guide industry professionals in designing interventions or treatments related to ferroptosis and its implications for various health conditions.

In relation to signaling pathways, as illustrated in Figure 8C, this subject is primarily associated with pathways such as lipid metabolism and atherosclerosis. It is noteworthy that various age-related diseases are frequently observed in the middle-aged and elderly population. Furthermore, these individuals are susceptible to concurrent chronic conditions, including cardiovascular and cerebrovascular diseases (atherosclerosis). To comprehensively grasp the implications of these changes in the context of a globally aging population, it is imperative to explore the capacity of TCMs in modulating ferroptosis and the aforementioned signaling pathways. The ultimate objective is to identify potential targets for a synergistic combination therapy.

In recent investigations, it has been discovered that the regulatory role of ferroptosis is pivotal in the initiation and advancement of various illnesses. This has positioned ferroptosis as a central and extensively researched area, aiming to improve the management and prognostication of associated conditions (Figure 9) (Li et al., 2020; Stockwell, 2022; Wang X. et al., 2023). Understanding the diseases most closely associated with “ferroptosis-TCM” research allows researchers to focus their efforts on specific medical conditions. This targeted approach can accelerate the development of drugs and therapies tailored to address these diseases, ultimately increasing the potential for more effective treatments. The Citexs Data Platform identified 765 diseases from 557 articles, with a minimum requirement of five articles mentioning each disease for inclusion. These diseases that met the criterion were visualized in a heatmap, created using VOSviewer, illustrating the frequency or relationships of diseases related to “ferroptosis-TCM” research (Figure 10A). The top five most frequently mentioned diseases include hepatocellular carcinoma, chemical and drug-induced liver injury, mitochondrial diseases, acute kidney injury, and liver failure. Additionally, a co-occurrence cluster analysis was performed, also requiring a minimum of five occurrences for each disease, using VOSviewer (Figure 10B). In this visualization, nodes—represented by circles and labels—are sized according to disease frequency. The thickness of lines connecting circles indicates the strength of relationships between diseases, and various colors represent distinct clusters corresponding to specific disease categories.

The identification of diseases associated with “ferroptosis-TCM” research can also stimulate efforts to discover biomarkers. Researchers can explore potential biomarkers associated with both ferroptosis and these specific diseases, which may have implications for diagnosis, prognosis, or therapy. In summary, these findings provide valuable insights into the diseases most closely linked to “ferroptosis-TCM” research, guiding researchers and industry practitioners toward targeted research, drug development, interdisciplinary collaborations, and potential clinical applications. Understanding the disease landscape in this context can pave the way for advancements in the treatment and management of related medical conditions.

TCM modulates ferroptosis by promoting it in tumor cells (addressing excess) and inhibiting it in other diseases (addressing deficiencies), aligning with the treatment principle of balancing Yin and Yang.

Despite significant advancements in “ferroptosis-TCM” research, ongoing critical challenges demand immediate attention.

In contrast to previous investigations primarily limited to systematic or narrative reviews, the combination of scientometric and visualized analysis provides readers with a clearer representation of research focal points and trends across various dimensions (Nguyen et al., 2021; Zhang J. et al., 2022). In its role as the inaugural bibliometric analysis endeavoring to chart the knowledge landscape of “ferroptosis-TCM” during the last 11 years. This research provides a relatively exhaustive and unbiased point of reference, notwithstanding inherent limitations.

This study has quite a few limitations: 1) The use of CiteSpace, which is limited to WoSCC publications, may introduce selection bias due to the software’s inherent limitations (Ling et al., 2023). 2) Citation bursts, influenced by factors like publication date and journal quality, may not accurately reflect a paper’s influence. 3) The inability to comprehensively review and analyze all papers and their subfields necessitated equal attention to both high and low-quality publications, potentially impacting the study’s credibility. 4) Scientometric methods, reliant on natural language processing, may manifest biases, as evidenced in previous studies (Zhang et al., 2018; Yan et al., 2021; Zhang J. et al., 2022; Cao et al., 2023b; Cao et al., 2024). 5) Limiting the research to English-language documents may introduce publication bias. 6) Incomplete retrieval of recent literature and keywords may impact the results due to gaps in literature collection. 7) VOSviewer automatically extracts author names, which may not always be extracted accurately. Some authors may use different name spellings or multiple names, potentially leading to inaccuracies in research results for these authors (Peng et al., 2022).