Global research trends on the gut microbiota and immunotherapy for colorectal cancer: a bibliometric analysis

Introduction

Currently, surgery remains the primary treatment modality for colorectal cancer (CRC), often combined with radiotherapy, chemotherapy, targeted therapy, and immunotherapy in a comprehensive therapeutic approach. In recent years, immunotherapy has been recognized as a principal cancer therapy approach due to its minimal side effects and precise targeting of tumor cells. Major forms of tumor immunotherapy include immune checkpoint inhibitors (ICIs), Adoptive cell immunotherapy, and cancer vaccines (1-3). Among these, ICIs have become an innovative and highly effective form of cancer immunotherapy (4). Similarly, Immunotherapy has shown therapeutic effectiveness against colorectal tumors (5,6). For patients with advanced or drug-resistant CRC, monotherapy often offers limited clinical benefit. Combination therapies involving ICIs not only improve disease control rates (DCR) but also enhance patients’ health-related quality of life (7). However, some patients may experience primary or acquired resistance, accompanied by treatment-associated adverse events. The most frequently reported adverse events include infusion-associated reactions (29%), thyroid disorders (22%), and fatigue (20%), though these are generally tolerable and reflect an acceptable safety profile (8,9).

The gut microbiota (GM) plays a critical regulatory role in cancer development, therapeutic response, and host immunity, particularly in modulating antitumor immune surveillance (10,11). Evidence indicates that GM influences the antitumor immune response in CRC, thereby affecting both the efficacy and incidence of adverse events associated with immunotherapy, particularly ICIs (8,12). Furthermore, GM-derived metabolites not only mediate inflammatory processes but also regulate the course of cancer therapy, ultimately shaping treatment outcomes. These metabolites may also serve as predictive biomarkers for immunotherapy response and prognosis (13). By modulating microsatellite instability (MSI) immune function and systemic inflammation, the GM can substantially impact the therapeutic efficacy of ICIs (14).

In recent years, the relationship between GM and CRC immunotherapy stands out as a significant domain of research activity, evidenced by a marked increase in related publications. However, systematically understanding the field’s overall development and identifying key research frontiers remains a challenge. To address this, a comprehensive analysis of core topics and emerging trends within this specialized domain is essential. As an emerging method of knowledge synthesis, bibliometric analysis quantitatively evaluates publication patterns to assess both the quantitative aspects of scientific output, and to uncover significant research trends within a field (15). With the rapid growth of scientific literature, bibliometric approaches have become increasingly important (16). According to available evidence, no bibliometric research has so far been performed in the academic community on research concerning the GM and CRC immunotherapy. This study adopts bibliometric approaches via tools such as CiteSpace, VOSviewer, and Scimago Graphica to examine influential authors, journals, institutions, along with countries/regions involved in the field throughout the previous decade. This study aims to summarize current research hotspots and identify existing challenges, with the goal of providing guidance for future research on CRC immunotherapy. We present this article in accordance with the BIBLIO reporting checklist (available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0072/rc).

Methods

Research methods

Bibliometrics is a vital branch of information science. Unlike traditional literature reviews, it utilizes quantitative methods such as bibliometric indicators, citation analysis, and text data analysis to systematically reveal the internal structure, developmental trajectory, and evolutionary dynamics of a research field. By facilitating the systematic integration and efficient retrieval of published literature, bibliometric analysis supports a deeper understanding of the core elements underlying specific research topics (17,18). Bibliometric research extends beyond descriptive statistics by employing keyword analysis, textual data mining, and citation network analysis to uncover distribution patterns, relational characteristics, and clustering structures. It has become a key method for tracing thematic evolution, academic trends, and collaboration networks. Through the integration of multidimensional data analysis, this approach systematically reveals the developmental trajectory, emerging trends, and underlying connections within the knowledge networks of a given discipline (19).

VOSviewer is an open-access software for bibliometric visualization, created by Leiden University in the Netherlands. It is primarily used to construct and visualize network maps that illustrate relationships among keywords, journals, co-authors, countries/regions, and institutions (20). Unlike most bibliometric mapping tools, VOSviewer presents large-scale bibliometric maps in a clear and interpretable manner, with a particular emphasis on graphical representation (21). The software supports multiple viewing modes such as the label, density, cluster, and scatter visualizations, allowing users to explore bibliometric networks from multiple perspectives.

Authored by Professor Chaomei Chen, CiteSpace is a Java-powered visualization and analysis application. It generates scientific knowledge maps by mining and clustering bibliographic data, enabling the clear visualization of a research field’s developmental trajectory, knowledge structure, and hotspot distribution. Based on co-occurrence network analysis, this tool effectively visualizes the evolution of disciplines, research frontiers, and knowledge interconnections, offering valuable visual support for exploring the developmental patterns of a research field (21,22). This study incorporated 325 publications into CiteSpace for subsequent analysis. After duplicate removal, no duplicate records were identified. The time span was set from January 2014 to December 2024, where each time slice corresponded to one year, using a g-index with k=25; all other parameters were maintained at their default values. Visualizations were generated for institutional and national co-occurrence networks, journal dual-map overlays, co-citation clustering, citation bursts, keyword bursts, and keyword timeline views. In the visual outputs, where node size reflects the occurrence frequency or citation count, the color gradient represents the publication year, and the connecting lines between nodes signify collaboration or co-occurrence relationships.

Scimago Graphica is a free bibliometric visualization tool that generates maps at the country or region level, producing partitioned visualizations of publication distribution to intuitively illustrate scientific impact and patterns of academic collaboration (23).

Given that all data originated from open-access databases and no human participants were involved, no ethical clearance was needed.

Data sources

The data for this study were retrieved from the Web of Science (WoS) core collection, a widely recognized high-quality database among researchers (24). Using the advanced search mode, the following retrieval strategy was applied:

• A topic-based search was implemented with the algorithm described below TS = (“Rectal Neoplasm*” OR “Rectal Tumor*” OR “Rectal Cancer*” OR “Rectum Neoplasm*” OR “Rectum Cancer*” OR “Cancer of the Rectum” OR “Cancer of Rectum” OR “Colorectal Neoplasm*” OR “Colorectal Tumor*” OR “CRC*” OR “Colorectal Carcinoma*” OR “Colonic Neoplasm*” OR “Colon Neoplasm*” OR “Cancer of Colon” OR “Colon Cancer*” OR “ Cancer of the Colon” OR “Colonic Cancer*”) AND (TS = (“Gut Microbio*” OR “Gut Microflora” OR “Gut Flora” OR “Gut Microbial Flora” OR “Intestinal Microbio*” OR “Intestinal Microflora” OR “Intestinal Flora” OR “Intestinal Microbial Flora” OR “Gastrointestinal Microbio*” OR “Gastrointestinal Microflora” OR “Gastrointestinal Flora” OR “Gastrointestinal Microbial Flora” OR “Gastrointestinal Microbial Communit*” OR “Fecal Microbio*” OR “Fecal Microflora” OR “Fecal Flora” OR “Fecal Microbial Flora” OR “Faecal Microbio*” OR “Faecal Microflora” OR “Faecal Flora” OR “Faecal Microbial Flora” OR “Gut Bacteri*” OR “Intestinal Bacteri*” OR “Gastrointestinal Bacteri*” OR “Fecal Bacteri*” OR “Faecal Bacteri*” OR “Enteric Bacteri*” OR “microbiot*” OR “microbiome*” OR “flora” OR “microflora” OR “bacteria” OR “prebiotic” OR “probiotic” OR “antibiotic” OR “dysbiosis” OR “Saccharomyces” OR “Lactobacillus” OR “Bifidobacterium” OR “Escherichia coli”)) AND (TS = (“immunotherap*” OR “immune therapy” OR “immunization” OR “immunity therapy” OR “immunization therapy” OR “immunotherapy treatment” OR “immunological therapy” OR “immunity treatment” OR “anti-CTLA-4” OR “anti-PD-1” OR “anti-PD-L1” OR “Ipilimumab” OR “Tremelimumab” OR “Nivolumab” OR “Pembrolizumab” OR “pda001” OR “Atezolizumab” OR “Durvalumab” OR “Avelumab” OR “Immune Checkpoint Inhibit*” OR “Immune Checkpoint Block*” OR “PD-L1 Inhibit*” OR “PD-L1 Block*” OR “PD L1 Inhibit*” OR “PD L1 Block*” OR “Programmed Death-Ligand 1 (PD-L1) Inhibit*” OR “Programmed Death Ligand 1 Inhibit*” OR “PD-1 Inhibit*” OR “PD-1 Block*” OR “PD 1 Inhibit*” OR “PD 1 Block*” OR “PD-1-PD-L1 Block*” OR “PD-1-PD-L1 Inhibit*” OR “PD 1 PD L1 Block*” OR “PD 1 PD L1 Inhibit*” OR “CTLA-4 Inhibit*” OR “CTLA-4 Block*” OR “CTLA 4 Inhibit*” OR “CTLA 4 Block*” OR “Checkpoint Inhibit*” OR “Checkpoint Block*” OR “PD-L1” OR “PD-1” OR “CTLA-4” OR “PD L1” OR “PD 1”OR”CTLA 4”));
• Only English-language original and review articles were considered;
• The analysis spanned January 1, 2014, to December 31, 2024;
• To ensure the reliability of the results, literature retrieval was independently conducted by two researchers.

Data extraction was completed on April 22, 2025, to reduce the likelihood of bias stemming from the database’s ongoing updates. The criteria for exclusion were defined as follows: (I) conference abstracts, correspondence, and proceedings unrelated to the topic; (II) studies that were unpublished or lacked sufficient data for thorough evaluation; (III) duplicate publications or reports of the same study; (IV) non-English literature. The initial search yielded 640 records. To ensure data quality, systematic data cleaning and standardization were performed to eliminate duplicates and resolve inconsistencies. After a rigorous screening process, 325 eligible publications were included in the final analysis. The retrieved records were saved as TXT files labeled “full text records and references”. The extracted dataset included titles, author names, institutional affiliations (such as research institutes, universities, and hospitals), abstracts, journal names, publication dates, and bibliographies. The organized dataset was then transferred to Microsoft Excel for further analysis, as illustrated in Figure 1. Scimago Graphica was employed for geospatial visualization and the creation of regional publication distribution maps. VOSviewer was used to analyze collaborations among authors, institutions, and countries/regions. CiteSpace was employed to perform keyword and co-citation analyses. These visualization techniques enabled the construction of a comprehensive knowledge map of research on GM and CRC immunotherapy, contributing to a more comprehensive understanding of the field’s progression, forecasting future trends, and promoting international scientific collaboration.

Figure 1 Flowchart illustrating the literature screening process for studies on the GM and CRC immunotherapy. CRC, colorectal cancer; GM, gut microbiota; SCIE, Science Citation Index Expanded; SSCI, Social Sciences Citation Index.

Statistical analysis

All statistical and bibliometric analyses were conducted using Microsoft Excel, VOSviewer, CiteSpace, and Scimago Graphica. Descriptive statistics were used to summarize publication output, countries/regions, institutions, authors, journals, keywords, cited references, and co-cited journals. Frequencies, percentages, total citations, and average citations per publication were calculated where appropriate. Collaboration, co-occurrence, and co-citation networks were evaluated using link strength, total link strength, betweenness centrality, burst detection, clustering, and timeline analysis. Because this study was based on bibliographic records rather than clinical or experimental data, no hypothesis testing, regression analysis, survival analysis, or sample-size calculation was performed.

Results

Annual publication development trends

The chronological analysis of academic publications provides valuable insights into the evolving research focus within a field. The first publication on this topic was published in 2014 by Yan Xia. The yearly publication pattern for GM and CRC immunotherapy is presented in Figure 2, based on data from the WoS database between 2014 and 2024. During the initial period of 2014–2015, the number of papers (Np) was minimal (n=3, 0.92%), reflecting the nascent stage of this research area. From 2016 to 2019, increased scholarly attention led to a notable rise in publications (n=37, 11.38%). A substantial surge occurred between 2020 and 2024 (n=285, 87.69%).

Figure 2 Annual number of publications on GM and CRC immunotherapy from 2014 to 2024. CRC, colorectal cancer; GM, gut microbiota.

Institutions and countries/regions

To elucidate the substantial influence of GM on CRC immunotherapy over the past decade, this study conducted a systematic analysis of the top 20 institutions worldwide based on publication volume. As shown in Table 1, Shanghai Jiao Tong University ranked first in Np (N=14; 471 citations, 33.64 citations per article), followed by Sun Yat-sen University (N=11; 315 citations, 28.64 citations per article), the Chinese University of Hong Kong (N=10; 595 citations, 59.50 citations per article), and Fudan University (N=10; 339 citations, 33.90 citations per article). Notably, 17 of the top 20 institutions are based in China. Visualization of the institutional collaboration network (Figure 3) indicates generally weak cooperative ties among institutions. The Chinese Academy of Sciences emerges as a central node, maintaining frequent collaborations with Tongji University, the University of Chinese Academy of Sciences, and the Changchun Institute of Applied Chemistry. In contrast, despite their high publication output, Shanghai Jiao Tong University and Sun Yat-sen University exhibit relatively limited collaboration with other institutions.

Figure 3 Visualization of institutional collaboration networks involving organizations with more than four published papers. CC, LBY, LIN, LRF.

This study further examined the national distribution of major contributors to this research field from 2014 to 2024.A systematic analysis of academic outputs from 45 countries/regions revealed a distinct pattern of regional clustering among the top 20 countries/regions (Table 2), which are spread across Asia, Europe, North America, South America, and Oceania. This pattern indicates broad international participation in this field, but it does not necessarily imply a fully integrated global collaborative network. Asia (n=6) and Europe (n=11) show particular prominence. China (n=185, 47%) and the USA (n=61, 15%) are the leading contributors, followed by Italy (n=19, 5%), France (n=15, 4%), and Germany (n=15, 4%). Notably, China, USA, Italy, and Germany each exhibit a centrality score exceeding 0.1, indicating their pivotal roles and substantial contributions to this research field. Although Belgium, England, and Sweden have produced fewer publications, their betweenness centrality values also exceed 0.1, suggesting that their research holds substantial academic influence. China has accrued 6,159 citations, far more than any other country, yet its citation per publication ratio (33.29) remains relatively low, highlighting a discrepancy between research volume and academic impact. This suggests that publication volume alone should not be equated with academic influence or clinical maturity. A visual collaboration map was generated for countries/regions that had at least two publications (Figure 4A-4D), comprising 29 countries/regions (excluding Taiwan). Within this network, China and USA act as central nodes and exhibit the closest linkage. China maintains connections with nearly all other countries/regions, whereas USA has fewer connections, notably with Japan, Australia, and Greece. Temporal analysis (Figure 4B) shows that USA, Italy, and Spain began contributing to the field as early as 2021, while increased engagement from China, Singapore, Portugal, and the Netherlands emerged after 2022.

Figure 4 Geographical distribution of research on the GM and CRC immunotherapy (A-D). CRC, colorectal cancer; GM, gut microbiota.

Author analysis

Over the past decade, 2,206 scholars have made significant contributions to advancing the study of GM and CRC immunotherapy. Notably, each of the top 10 authors has published at least four articles in this field (Table 3). Based on this dataset, an author collaboration network was constructed (Figure 5A), including all authors who have published at least two papers (a total of 168 scholars met this criterion). In the network, where each node corresponds to one author, with node size indicating the Np and connecting lines denoting collaborative relationships. Larger nodes signify more publications, while thicker lines represent stronger collaborations. Prominent contributors such as Jun Yu, Harry Cheuk-Hay Lau, and Bo Xiao are represented by larger nodes due to their significant influence within the discipline. However, given that only 168 of 2,206 authors had published at least two papers, the field appears to involve broad participation but relatively limited sustained collaboration among a large proportion of contributors. Of the ten most frequently co-cited authors, five have received over 100 citations. Routy B. ranks first with 135 citations, followed by Gopalakrishnan V. (116 citations) and Le DT (115 citations) (Table 3). To further explore scholarly connections, the co-citation network included 78 authors with a minimum of 25 citations each (Figure 5B). These co-cited authors indicate that the field is grounded in studies on GM-ICI interactions and immunotherapy in molecularly defined CRC. However, evidence from non-CRC or broader cancer settings may not be directly generalizable to CRC because of heterogeneity in MSI/mismatch repair (MMR) status, immune microenvironment, microbiota composition, and treatment sensitivity. CRC-specific validation and integrated microbiome-immune-clinical analyses are therefore needed to improve translational robustness.

Figure 5 The co-occurrence map of authors (A) and co-cited authors (B) in the field of GM and immunotherapy for CRC. Nodes are colored according to. CRC, colorectal cancer; GM, gut microbiota.

Journal analysis

In total, 173 journals have published studies in this area. Table 4 presents the top 10 journals by publication volume, nine of which have published more than five articles. Three journals have published more than 10 articles: Frontiers in Immunology (21 articles, 6.5%), Cancers (17 articles, 5.2%), and International Journal of Molecular Sciences (13 articles, 4.0%). Of these, the most frequently cited journal is Gut [impact factor (IF) =23], with 803 citations and an average of 133.8 citations per article.

As indicated in Table 5, all of the ten most cited journals received more than 400 co-citations. Science leads with 1,506 co-citations, underscoring its substantial academic impact. It is followed by Nature [821], Gut [667], Gastroenterology [635], Cell [574], Frontiers in Immunology [560], Nature Medicine [532], Nature Communications [508], Immunity [440], and PLOS One [438]. Notably, journals such as Nature and Nature Medicine, both with IFs exceeding 50, serve as key drivers in advancing this scholarly field. Eight of the top 10 journals are classified in Q1 of the Journal Citation Reports (JCR), each with an IF above 14.

VOSviewer was employed to conduct the visual analysis. With a minimum publication threshold of two articles, 173 eligible journals were identified to construct a journal co-occurrence network (Figure 6A), illustrating the interrelationships among journals within this research domain. The co-citation network encompassed 2,516 journals, from which those cited more than 70 times were selected for visualization. This process resulted in the identification of 77 core journals. The resulting co-citation network (Figure 6B) reveals the academic linkages and knowledge dissemination pathways among these core journals.

Figure 6 Visualization of the journal collaboration network in GM and CRC immunotherapy research over the past decade (A) and visualization of the journal co-citation network (B). Nodes are colored according to. CRC, colorectal cancer; GM, gut microbiota.

The journal dual-map overlay reveals the mutual knowledge connections linking citing and co-cited journals. Citing journals are positioned on the left, with co-cited journals on the right. As depicted in Figure 7, the orange trajectory indicates the primary citation path, suggesting that articles published in Molecular Biology Genetics are predominantly cited by those in Molecular Biology Immunology. Additionally, the green trajectory shows that research from Molecular Biology Genetics is frequently cited by Medicine Medical Clinical, indicating interdisciplinary knowledge flow.

Figure 7 Dual-map overlay of journals in GM and CRC immunotherapy (on the left are the citing journals, and on the right are the cited journals. Colors represent journal classifications, while curves indicate citation links. The long axis of each ellipse reflects the number of papers cited within journals of the same subject area, whereas the short axis indicates the number of contributing authors in journals on similar topics. CRC, colorectal cancer; GM, gut microbiota.

Research hot spots and trend analysis

Co-cited references and citation burst analysis

Between January 1, 2014, and December 31, 2024, a total of 19,474 co-cited references were identified in academic literature related to the interaction between GM and CRC immunotherapy. Analysis of the core references (Table 6) reveals that all ten leading co-cited references were cited in excess of 50 times, with two cited over 100 times. Based on this, references with a minimum co-citation frequency of 25 were chosen for further analysis. A network of co-citations was constructed (Figure 8A), with node size proportional to citation frequency, visually indicating each reference’s relative influence. Notable co-citation patterns were observed in widely recognized publications, such as Routy B, 2018, Science; Sivan A, 2015, Science; and Vétizou M, 2015, Science. Co-citation clustering analysis was conducted using CiteSpace (Figure 8B), where cluster numbers are inversely proportional to cluster size (smaller numbers represent larger clusters). A total of 15 major co-citation clusters were identified, including: “k-means” (Cluster 0, n=55), “microsatellite stable” (Cluster 1, n=43), “biomimetic nanomedicine” (Cluster 2, n=35), “probiotics” (Cluster 3, n=34), “androgens” (Cluster 4, n=34), “oral liposomes” (Cluster 5, n=31), “predictive biomarkers” (Cluster 6, n=31), “chemotherapy response” (Cluster 7, n=28), “mucosal immunology” (Cluster 8, n=26), “anti-PD-1/PD-L1 therapy” (Cluster 9, n=23), “PD-1 antibody” (Cluster 10, n=23), “clinical trials” (Cluster 12, n=20), “cancer” (Cluster 13, n=19), “intestinal epithelial cells” (Cluster 14, n=17), and “anti-cancer” (Cluster 15, n=17).

Figure 8 Visualization of co-cited references in research on GM and CRC immunotherapy (A) and cluster network diagram of co-cited references (B). Nodes are colored according to. CC; CRC, colorectal cancer; GM, gut microbiota; LBY; LIN; LRF.

The term “reference bursts” refers to references that are widely cited by researchers in a specific research domain within a defined time period. When a specific group of references demonstrates high-frequency co-citation characteristics, it constitutes a conceptual cluster (25). In this study, CiteSpace identified 20 core references with significant citation burst characteristics. As illustrated in Figure 9, the references are sequenced by burst order, determined from their initial publication year, with each bar representing one year. The red line highlights high-citation references that experienced a sudden burst in a specific year. Two of these references had burst citation strengths greater than 10. One is “Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy” by Sivan et al. (26) (burst intensity =13.37), which persisted from 2016 to 2020. This study found that gut Bifidobacterium enhances antitumor immunity by activating dendritic cells and CD8 (+) T cells, and when combined with programmed death ligand-1 (PD-L1) antibodies, significantly inhibits tumor growth, suggesting that microbiota regulation can optimize cancer immunotherapy. Another study, titled “Anticancer immunotherapy by CTLA-4 blockade relies on the GM”, by Vétizou et al. (27) (burst intensity =12.7), was published in Science and covered the period from 2018 to 2020. This research demonstrated that the antitumor effect of CTLA-4 blockade therapy depends on the GM, particularly Bacteroides fragilis, which enhances immune therapy effectiveness by activating specific T-cell responses. A detailed analysis of the burst references shown in Figure 9 and Table 7 reveals that 15 of these articles concern the role of the GM in cancer therapy and its mechanisms, focusing on how the microbiota influences the effectiveness of cancer therapies, including immunotherapy [e.g., programmed death-1 (PD-1)/PD-L1 inhibitors, CTLA-4 inhibitors], chemotherapy (e.g., cyclophosphamide, platinum-based drugs), and more. The research indicates that specific microbiota (e.g., Bifidobacterium, Bacteroides, Fusobacterium) can regulate immune cell functions (e.g., T cells, dendritic cells), thereby enhancing or inhibiting antitumor immune responses. Five articles further explored the correlation between tumor molecular characteristics and therapeutic efficacy, focusing on how tumor molecular features (e.g., MSI, mismatch repair deficiency, gene mutations) affect treatment responses. For example, tumors with mismatch repair deficiencies are more sensitive to PD-1 inhibitors, and different molecular subtypes (e.g., CMS classification) may respond differently to treatments. Additionally, the application of molecular markers in diagnostics is also discussed. Data analysis demonstrates that the intensity of citation bursts for these 20 references spans 2.65 to 13.37, and their academic influence persists for periods ranging from 2 to 5 years.

Figure 9 Citation burst analysis of cited references generated using CiteSpace.

Keywords and research hotspots

The “Author Keywords” in the WoS core collection (WoSCC) database effectively reflect the research hotspots and nascent trends in the discipline. According to Table 8, of the twenty most common keywords, terms such as CRC, immunotherapy, GM, and microbiome exhibit high-frequency co-occurrence, demonstrating that these research areas form the central focus of ongoing research. Given potential variations in the results produced by different bibliometric tools, this study integrates the strengths of two tools for a more comprehensive analysis of research frontiers and directions. First, VOSviewer was applied to develop a co-occurrence clustering network derived from 711 author keywords (with a minimum occurrence threshold of 3), resulting in the identification of 65 high-frequency keywords. A cluster analysis was then conducted to generate a visualization map based on link strength (Figure 10A). In the map, each node denotes a keyword, with its size proportional to its frequency, the thickness of the connecting lines indicating co-occurrence strength, and the colors distinguishing four clusters (red, green, blue, and yellow). The red cluster, which is the largest, pertains to the mechanisms and clinical translation of CRC immunotherapy efficacy, featuring keywords such as CRC, immunotherapy, gut microbiome, anti-PD-1, and combination therapy. The green cluster is associated with gut dysbiosis, CRC treatment, and clinical interventions, including keywords like antibiotics, colon cancer, dysbiosis, fecal microbiota transplantation, and F. nucleatum. The blue cluster focuses on the role of GM and its metabolites in regulating CRC treatment, with keywords such as antitumor immunity, cancer immunotherapy, metabolites, microbiota, and CRC. The yellow cluster highlights the synergistic effects and molecular mechanisms linking GM and immunotherapy in metastatic CRC (mCRC), comprising keywords such as intestinal flora, immune checkpoint inhibitor, mCRC, oxaliplatin, and immunogenic cell death. In the overlay visualization (Figure 10B), a color gradient illustrates the temporal evolution of research themes. Keywords marked in purple indicate an average publication year of 2021 or earlier, whereas those in yellow represent more recent research trends, emerging after 2023. Earlier topics include intestinal microbiota, inflammation, diet, prebiotic, and T cells, while more recent keywords include metabolites, intestinal microbiome, diagnosis, microsatellite stable, and survival.

Figure 10 Visualization of the keyword co-occurrence network in research on GM and immunotherapy for CRC (nodes are colored according to) (A) and overlay visualization of keywords (B). CRC, colorectal cancer; GM, gut microbiota.

To further validate the analytical findings and track the dynamic evolution of the field, CiteSpace was utilized for the analysis of keyword burst detection. Burst detection is a methodological approach used to reveal sudden growth in emerging concepts within a defined time period (28). This technique relies on two core indicators, namely burst strength and burst duration, to effectively detect significant shifts in research focus, thereby capturing emerging research frontiers with greater precision (29). Of the top 25 keywords in terms of burst strength, particular attention was given to those of academic relevance, as they illustrate the evolutionary trajectory and developmental trends of research hotspots in the domain of GM and CRC immunotherapy (Figure 11). From 2014 to 2024, T cells exhibited the highest burst strength (3.46), followed by intestinal microbiota (3.22) and dendritic cells (2.57). The burst periods of these top 25 keywords span the entire decade under review. Intestinal microbiota emerged as a research focus after 2016, followed by cancer therapy and checkpoint blockade, which gained prominence in 2017 and 2018, respectively. After 2019, terms such as blockade and PD-1 blockade became central, signifying a new phase in CRC immunotherapy research. Notably, since 2022, keywords such as risk, combination, host, and checkpoint inhibitors have risen to prominence, indicating a shift toward precision risk stratification and combination immunotherapy strategies in the field.

Figure 11 Top 25 keywords with the strongest citation.

The timeline view effectively illustrates the temporal evolution of research hotspots through horizontally arranged keyword clusters. Extending from left to right, the timeline indicates the progression from past to present and intuitively reflects the emergence, development, and succession of various research domains. Figure 12 presents a comprehensive trajectory of knowledge evolution in this field, spanning from early foundational studies to recent clinical applications, and delineates distinct stages of development. It identifies thirteen distinct clusters: biomarkers, dendritic cells, mCRC, CRC, aspirin use, efficacy, tumor microenvironment (TME), microbiota, activation, expression, ileum, cancer immunotherapy, intestinal flora. Each cluster comprises a single set and is labeled with a numeric identifier (e.g., #0, #1, #2). A wider cluster range indicates a larger number of constituent members (30).

Figure 12 Timeline visualization of keywords in research on GM and CRC immunotherapy. CRC, colorectal cancer; GM, gut microbiota.

Discussion

Currently, the therapeutic benefits of chemotherapy and targeted therapy for CRC have plateaued, while immunotherapy is playing an increasingly pivotal role. Among immunotherapeutic approaches, ICIs have garnered the most attention (31). However, their use is related to unique immune-related adverse events (IRAEs), including toxicities affecting the skin, liver, gastrointestinal tract, and lungs, which pose significant clinical challenges (32). Over the past decade, accumulating evidence has shown that modulation of the GM not only enhances the response to ICIs but also improves the overall efficacy of CRC immunotherapy through immune-regulatory mechanisms, prompting a surge in related studies (33). In light of the above reasons, this study presents the first bibliometric analysis of the role of GM in CRC immunotherapy, aiming to offer researchers a foundational understanding of the current state and emerging trends in this research domain.

Trend of publications

The quantity and trend of publications in a given field are closely associated with its stage of development. In terms of annual publication output (Np), the years 2014 and 2015, which saw only three publications, represent the nascent phase of research on GM and CRC immunotherapy. From 2016 to 2019, publication numbers increased steadily, followed by a marked acceleration from 2020 to 2024 (n=285). In 1998, Handelsman introduced the concept of “metagenomics”, defined as the direct extraction and cloning of total DNA from environmental microbial communities for genetic analysis, which marked a breakthrough that revolutionized microbiome research (34). In 2010, researchers employed metagenomic sequencing to decode the human GM gene map, establishing a new paradigm in the field (35). In 2011, the U.S. Food and Drug Administration (FDA) approved the first immune checkpoint inhibitor, Ipilimumab (anti-CTLA-4), for the second-line treatment of advanced melanoma, marking a pivotal advance in cancer immunotherapy. ICIs reinstate the antitumor activity of T cells by lifting tumor-induced immune suppression, offering a transformative strategy in oncology (36). In 2024, a study employing single-cell sequencing analyzed the dynamic characteristics of the CRC immune microenvironment, developed a predictive model for PD-1 blockade therapy response, and proposed a novel synergistic therapeutic paradigm combining TME remodeling with peripheral immune modulation (37).

National publication trends and research collaboration

Research on GM and CRC immunotherapy has involved 45 countries and regions, suggesting an increasingly globalized research landscape. China, the USA, Italy, Germany, and France were the leading contributors, with China accounting for 47% of total publications and ranking first in total citations (6,159 citations; Table 2). However, China’s average citation count per article remained comparatively low (33.29), indicating that its quantitative advantage has not yet been fully translated into proportional academic influence. More importantly, the concentration of publications in a limited number of countries/regions suggests that the current evidence base may be influenced by regional research priorities, population characteristics, dietary patterns, antibiotic exposure, and healthcare contexts. Given the sensitivity of microbiota findings to ethnicity, diet, sampling procedures, sequencing platforms, and analytical pipelines, insufficient cross-regional collaboration may limit reproducibility and generalizability. Future studies should therefore emphasize multicenter cohorts, standardized microbiota assessment protocols, and cross-regional validation to enhance the translational relevance of GM-based strategies in CRC immunotherapy.

Institutional publication trends and research collaboration

Among the twenty most prolific institutions, 17 are located in China. This pattern may reflect growing research investment and clinical interest in microbiota-related oncology and immunotherapy in China, but it also suggests that the current evidence landscape may be influenced by regional research priorities and population characteristics. Institutional collaboration in this field remains largely regionally clustered rather than globally integrated. Both domestic and international institutions tend to collaborate mainly with organizations within their own countries/regions, as reflected by the collaboration networks of the Chinese Academy of Sciences and Assistance Publique-Hôpitaux de Paris (APHP). Such fragmented collaboration may limit the reproducibility and generalizability of microbiota-related findings, which are highly sensitive to population background, diet, sampling procedures, sequencing platforms, and analytical pipelines (38). Future studies should strengthen multicenter cohorts, standardized protocols, and cross-regional validation to improve the reliability and translational relevance of GM-based strategies in CRC immunotherapy.

Journal output and scholarly impact

The journal distribution and co-citation patterns further indicate the interdisciplinary nature of GM and CRC immunotherapy research. Publications were distributed across JCR Q1–Q3 journals, with Frontiers in Immunology contributing the largest number of articles, suggesting that immunology-oriented journals serve as an important publication venue for this field. Meanwhile, highly co-cited journals such as Science, Nature, Gut, and Nature Medicine indicate that the intellectual foundation of this field is shaped by landmark advances in microbiome biology, cancer immunology, and gastroenterology-specific evidence. This pattern suggests that mechanistic studies are mainly anchored in immunology-related research, whereas CRC and gastroenterology-focused studies provide an important clinical and citation foundation. However, the distribution across journals of varying impact also suggests that the academic influence of this field remains uneven. Overall, the journal and co-citation structure reflects a field positioned at the interface of mechanistic discovery and clinical translation, although standardized microbiota assessment and CRC-specific clinical validation remain insufficient.

Research foundations and hotspots

Tracing the research foundations via co-cited literature

Co-citation analysis uncovers the foundational knowledge and research interconnections in the field of GM and CRC immunotherapy by identifying publications frequently cited together. In this study, VOSviewer was employed to group highly co-cited references into three separate clusters. The green cluster highlights the pivotal role of the GM, including commensal bacteria, specific bacterial species, and bacterial consortia, in modulating various cancer immunotherapies, such as PD-1/PD-L1 and CTLA-4 blockade. These microbial components influence treatment efficacy and enhance antitumor immune responses (26,27,39), suggesting that the GM represents a promising target for therapeutic intervention in cancer immunotherapy.

References within the red cluster primarily investigate the relationship between GM and cancer. Studies using mouse models have demonstrated that GM can modulate tumor responses to immune checkpoint blockade therapy, a mechanism that has also been validated in human cancer patients. These findings highlight the significant role of the GM, including both the overall microbial community and specific taxa such as F. nucleatum, in influencing cancer development (e.g., intestinal tumorigenesis) and treatment outcomes (e.g., melanoma immunotherapy) (40).

The blue cluster highlights the role of commensal bacteria in regulating the TME and influencing responses to cancer treatment. Disruption of the microbiota impairs the efficacy of both immunotherapy and chemotherapy, suggesting that an intact commensal microbiota is essential for achieving optimal therapeutic outcomes (41). Additionally, the GM can modulate the immunological effects of anticancer agents such as cyclophosphamide, thereby shaping the development of antitumor immune responses. These results emphasize the essential role of the microbiota in determining the success of cancer therapies (42).

In 2013, Aleksandar D. Kostic and colleagues published a study in Cell Host & Microbe (IF =18.7) involving both mouse models and clinical data, demonstrating that the GM can modulate tumor immunotherapy. This publication has become one of the ten most frequently co-cited core studies in the field. The research reveals a significant association between the GM and CRC, with multiple studies confirming a strong correlation between F. nucleatum and human CRC. Clinical investigations have shown a markedly increased abundance of F. nucleatum in fecal samples from CRC patients and individuals with colorectal adenomas. Mouse model studies further demonstrate that this bacterium promotes tumor progression by inducing pro-inflammatory gene expression, without aggravating intestinal inflammatory lesions. Mechanistically, F. nucleatum may facilitate CRC development by recruiting immune cells and establishing a pro-inflammatory microenvironment. These results stress the key role of GM in immune regulation and CRC pathogenesis (40).

Discovering research hotspots from highly cited literature

A large-scale co-citation analysis was conducted to elucidate research hotspots and evolutionary trends in GM and CRC immunotherapy. The results indicate that highly cited publications primarily focus on the interplay between the GM and cancer immunotherapy. As a key regulatory component of the TME, the GM has emerged as a critical target in tumor immunotherapy research. Drawing on the bibliometric analysis, four major research directions have been identified: (I) investigating the influence of GM on the efficacy of CRC immunotherapy; (II) evaluating F. nucleatum as a prospective biomarker for CRC screening and diagnosis; (III) conducting metagenomic and metabolomic analyses to characterize microbiome and metabolite alterations, thereby facilitating early diagnosis and etiological research of CRC; (IV) evaluating the role of specific gut microbes [e.g., Bifidobacterium pseudolongum (B. pseudolongum)] in enhancing the therapeutic efficacy of ICIs (e.g., anti-CTLA-4 and anti-PD-L1 antibodies) in the treatment of CRC.

In 2013, Iida and colleagues systematically investigated the regulatory role of the TME in CRC treatment by establishing a subcutaneous tumor model using the MC38 colon cancer cell line. Their study demonstrated that the GM directly influences the efficacy of CpG oligonucleotide-based immunotherapy and platinum-based chemotherapy by regulating myeloid cell activity, specifically through the secretion of tumor necrosis factor/interleukin-12 (TNF/IL-12) and the generation of reactive oxygen species (ROS). Key findings include:

• Antibiotic treatment or maintenance of germ-free conditions significantly diminished therapeutic efficacy.
• Specific bacterial species, such as Alistipes shahii (which activates the TLR4/MYD88 signaling pathway) and Lactobacillus (which exerts an inhibitory effect), play critical regulatory roles.
• An intact GM is essential for achieving optimal anticancer efficacy (41). This study elucidated a mechanistic link among the GM, immune function, and cancer therapy, thereby identifying new potential targets for CRC treatment.

In 2017, Sunny H. Wong and colleagues employed quantitative (PD) to identify biomarkers and found that F. nucleatum was markedly enriched in the fecal specimens of CRC patients and advanced adenomas, suggesting its potential as a novel biomarker. When F. nucleatum was combined with fecal immunochemical testing (FIT), the detection sensitivity for CRC increased from 73.1% to 92.3%, while the sensitivity for advanced adenomas rose from 15.5% to 38.6%. This approach is simple, cost-effective, and can effectively compensate for false negatives from FIT, making it well-suited for large-scale screening programs (43).

In 2019, Shinichi Yachida and colleagues conducted integrated metagenomic and metabolomic analyses on patients with CRC and discovered that both microbial composition and metabolite profiles underwent dynamic changes from early adenomas to advanced cancer. For instance, F. nucleatum showed a continuous increase throughout cancer progression, whereas certain bacteria, such as Atopobium parvulum, were significantly elevated only in the early stages. Additionally, metabolites such as branched-chain amino acids and bile acids were found to be elevated in early lesions. These findings highlight stage-specific alterations in the GM and metabolome during CRC development. The presence of such early-stage changes may offer valuable etiological insights and diagnostic potential for CRC (44).

In 2020, Lukas F. Mager and colleagues discovered that the clinical effectiveness of ICIs, such as anti-CTLA-4 and antibodies against PD-L1, in CRC is dependent on the presence of specific gut bacteria. The study demonstrated that certain microbes, including B. pseudolongum, enhance the antitumor efficacy of these therapies by promoting immune responses. This effect is mediated through the microbial metabolite inosine, which activates the adenosine A2A receptor (A2AR) signaling pathway in T cells. These findings offer a novel mechanistic insight into microbe-assisted immunotherapy and highlight the potential of targeting host-microbiota interactions in cancer treatment (45).

In summary, the GM plays diverse and critical roles in diagnosing and treating CRC. F. nucleatum has been identified as a potential screening biomarker, and its combination with fecal FIT significantly improves detection rates. Dynamic changes in microbial composition and metabolite levels, such as increased abundance of F. nucleatum and elevated concentrations of branched-chain amino acids, highlight the potential for early diagnosis. Moreover, the microbiota’s regulation of immune cell function significantly influences the therapeutic efficacy of CRC treatments. Specific bacterial species, including B. pseudolongum, have been shown to enhance the effectiveness of immunotherapy via the inosine-adenosine A2AR signaling pathway. Collectively, these findings offer novel insights into the comprehensive management of CRC across the disease continuum.

Analyzing research hotspots through literature burst detection

Burst detection analysis allows for the real-time identification of emerging research frontiers and the evolution of scientific hotspots, offering a panoramic and systematic perspective on developments within a given field. In the context of CRC, the integration of microbiome-based interventions with immunotherapy targeting specific molecular subtypes represents a cutting-edge direction in precision oncology research.

The research team led by Susan Bullman discovered that F. nucleatum persists not only in primary CRC tumors but also in distant metastatic lesions, where it cooperates with other anaerobic bacteria in the TME to promote cancer progression. Animal model studies confirmed that treatment with the antibiotic metronidazole significantly reduces the burden of F. nucleatum, thereby inhibiting tumor cell proliferation and growth (46). These findings suggest that antimicrobial therapy targeting F. nucleatum may represent a promising therapeutic strategy for CRC.

Michael J. Overman and colleagues found that nivolumab, a PD-1 immune checkpoint inhibitor, displayed significant therapeutic efficacy and safety in managing patients with mCRC characterized by mismatch repair deficiency and high MSI (dMMR/MSI-H). A multicenter, open-label phase II clinical trial (CheckMate 142) confirmed that nivolumab monotherapy achieved an objective response rate (ORR) of 31.1% and a DCR of 68.9% in previously treated patients, with durable responses and prolonged survival observed in some cases. Moreover, nivolumab was well tolerated, with adverse events that were generally manageable and consistent with prior observations (47). These findings support nivolumab as a promising therapeutic option for those with dMMR/MSI-H mCRC.

In summary, the study by Susan Bullman and colleagues revealed that F. nucleatum persists in both primary colorectal tumors and distant metastases, and that treatment with the antibiotic metronidazole can suppress tumor growth by reducing the bacterial load, underscoring the therapeutic potential of antimicrobial strategies. Meanwhile, the research led by Michael J. Overman and colleagues demonstrated that the immune checkpoint inhibitor nivolumab exhibits significant efficacy, achieving an ORR of 31.1%, and a favorable safety profile in patients with mCRC characterized by mismatch repair deficiency and dMMR/MSI-H. Collectively, these studies expand the landscape of precision treatment in CRC: the former through microbiome-targeted intervention, and the latter via immunotherapy tailored to specific molecular subtypes.

Exploring research hotspots through keyword co-occurrence and burst analysis

Similarly, the application of keyword co-occurrence networks and burst detection analysis enables the identification of research hotspots. Based on keyword frequency, the principal research foci within the domain of GM and CRC immunotherapy include terms such as CRC, immunotherapy, GM, microbiome, and microbiota. Keyword analysis indicates that high-frequency clusters reflect a current emphasis on the synergistic mechanisms linking GM regulation and immunotherapy in CRC. In the network visualization, all keywords are organized into the following four clusters: (I) mechanisms underlying CRC immunotherapy efficacy and clinical translation; (II) interactions between GM dysbiosis and CRC treatment, along with clinical intervention strategies; (III) modulation of CRC treatment by GM and its metabolites; (IV) synergistic effects and molecular mechanisms of GM and immunotherapy in mCRC. These four clusters represent the major research directions in the field. The overlay visualization reveals a temporal trend: keywords related to etiology and prevention appeared earlier, while those concerning metabolic regulation and survival prognosis emerged more recently. This shift underscores a transition in research focus, from fundamental mechanistic exploration to clinical application, highlighting the growing translational relevance of current studies.

Searching the latest literature to confirm and discover new research hotspots

To assess the alignment between recent studies and historical research patterns, we reviewed articles on GM and CRC immunotherapy published up until December 31, 2025. After screening, 125 articles and reviews were selected (Table 9). To further characterize the evidence structure of recent studies, we classified the articles listed in Table 9 according to study design type, and the distribution is summarized in Table 10. The results showed that the literature in this field was dominated by review and perspective articles and preclinical experimental studies, accounting for 42.1% and 44.4%, respectively. In contrast, clinical studies accounted for only 7.1%, most of which were retrospective or early exploratory studies. Bioinformatics/multi-omics/database analyses and bibliometric studies accounted for 4.8% and 1.6%, respectively. These findings indicate that recent research on the GM and CRC immunotherapy remains largely concentrated on evidence synthesis, mechanistic exploration, and preclinical validation. Recent studies on GM and CRC immunotherapy have increasingly shifted from descriptive microbiome profiling toward the translational application of microbiota-based strategies. A major research direction is the use of GM modulation to enhance antitumor immune responses and improve the efficacy of ICIs, as reflected by studies on PD-1/PD-L1 blockade, probiotics, fecal microbiota-related interventions, and specific beneficial bacteria such as Bifidobacterium, Akkermansia muciniphila, Clostridium butyricum, and Alistipes onderdonkii (48-51). Another prominent hotspot is the development of engineered microbial platforms and bacteria-mediated delivery systems, including yeast-based oral therapeutics, engineered Escherichia coli, engineered probiotic consortia, bacterial outer membrane vesicles, bacteriophage-based delivery, and tumor-colonizing engineered bacteria (52-56). In parallel, increasing attention has been paid to microbiota-derived metabolites, including short-chain fatty acids, bile acids, selenium-containing metabolites, butyrate, propionate, sphingosine-1-phosphate, and other microbial metabolic products, which may regulate T-cell function, immune escape, and treatment sensitivity (57-60). Moreover, nanomedicine, photothermal therapy, and microbiota-responsive nanoplatforms have emerged as interdisciplinary strategies for targeted delivery and tumor immune microenvironment remodeling (61,62).

From research hotspots to clinical translation: current progress and remaining gaps

Although keyword co-occurrence and burst analyses identified GM, immunotherapy, probiotics, microbial metabolites, and tumor immune microenvironment as major research hotspots, the 2025 literature suggests that these hotspots have reached different stages of clinical translation. Some directions have already moved toward clinical application or early clinical evaluation, including the use of microbiota-related features to assess clinicopathological characteristics and treatment efficacy (63,64), oral bacterial supplementation combined with ICIs (38), perioperative probiotic administration (65), and real-world analyses of immunotherapy outcomes in mCRC (66). In addition, studies focusing on probiotics, oral bacterial supplementation, and specific beneficial bacteria such as Bifidobacterium (50), Akkermansia muciniphila (51), Clostridium butyricum (51), and Lactobacillus paracasei (67) indicate that microbiota modulation is becoming a plausible adjunctive strategy for enhancing antitumor immunity and improving therapeutic response (38,67). However, several emerging hotspots remain largely at the basic or preclinical stage. These include engineered probiotics (68), bacteria-mediated drug delivery and bacteriophage-based PD-1 delivery (69), nanomedicine (70), photothermal therapy (71), and microbiota-derived metabolite regulation of T-cell exhaustion, macrophage polarization, and immune escape (72). Therefore, current research is transitioning from microbiota-CRC association studies and mechanistic immune regulation toward clinical translation, but most novel microbiota-engineered and nanotechnology-integrated strategies still require further validation before they can be considered clinically applicable. Taken together, these findings highlight both key scientific controversies and translational bottlenecks in GM and CRC immunotherapy research. The major scientific controversies concern whether microbiota signatures are causal determinants or merely correlates of immunotherapy response, and whether microbiota-based biomarkers are reproducible across heterogeneous CRC cohorts and analytical platforms (63,72). In contrast, the main translational bottlenecks include insufficient clinical validation of microbiota-targeted strategies, limited standardization of microbiota assessment, safety and manufacturing challenges, and the lack of well-designed CRC-specific clinical trials with clinically meaningful endpoints (71).

Limitations of the research

Compared with traditional reviews, bibliometric analysis offers valuable insights into research frontiers and evolutionary trends within a research sphere, but it also presents certain limitations. First, this study was restricted to English-language publications indexed in the WoSCC [Science Citation Index Expanded (SCIE)]. Although this reflects the mainstream of international academic output, excluding literature in other languages may lead to the omission of significant findings from specific regions. Additionally, literature from non-Science Citation Index (SCI) databases was not considered, which may result in underrepresentation of innovative studies or clinical practices published elsewhere. This selection bias renders the analysis more representative of research trends within the English-speaking academic community, rather than providing a comprehensive global perspective. Second, in contrast to systematic reviews, bibliometric analysis lacks rigorous criteria for evaluating the quality of individual publications, which may limit the accuracy of academic value assessments. Although we added a supplementary classification of study design types, recent literature remains dominated by reviews, perspective articles, and preclinical studies, whereas clinical evidence is limited and mostly retrospective or exploratory. Therefore, the observed trends should not be interpreted as mature clinical evidence. Third, this study remains subject to the inherent timeliness limitations of bibliometric analysis. Although the literature search was updated to December 31, 2025, database indexing and citation accumulation require time. As a result, very recent high-quality studies may not yet have accumulated sufficient citations, leading to an underestimation of their academic impact. In addition, studies published after the search cutoff were not captured. Thus, the results should be interpreted as reflecting the research landscape up to the end of 2025.

Conclusions

This study employs bibliometric and knowledge mapping methods to systematically uncover research hotspots and developmental trends in GM-based immunotherapy for CRC, offering novel perspectives for optimizing treatment strategies. Over the past decade, the field has witnessed rapid advancement, with Chinese scholars contributing significantly. Analysis of highly cited literature and keywords reveals that specific microbiota and their metabolites serve a pivotal function in modulating immune responses, functioning as both biomarkers and potential therapeutic targets. The integration of ICIs with microbiota-based interventions is emerging as a prominent research focus. Burst detection and timeline analyses demonstrate a paradigm shift from fundamental mechanistic research to clinical application. Cutting-edge technologies, such as engineered probiotics and nanodelivery systems, continue to advance the frontier of precision therapy. Despite limitations related to language and database selection, the findings offer important perspectives on the present research landscape and principal directions of development, providing important guidance for future translational studies at the intersection of microbiome science and immunotherapy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0072/rc

Peer Review File: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0072/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0072/coif). Z.L. is an employee of Ansteel Group Mining Corporation Limited and has received consulting fees from AMCA. The other authors have no 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/.

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