Bibliometric Review of Cancer-Associated Fibroblasts in Breast Cancer from 2005 to 2025

Bibliometric Review of Cancer-Associated Fibroblasts in Breast Cancer from 2005 to 2025 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bibliometric Review of Cancer-Associated Fibroblasts in Breast Cancer from 2005 to 2025 Shixue Jing, Siping Zhou, Cui Jia, Yongfeng Wang, Hui Cao, Lushun Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6881099/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Breast cancer is one of the most common and lethal heterogeneous cancers among women worldwide, posing a significant threat to female health. Cancer associated fibroblasts (CAFs) play a critical role in the initiation and progression of breast cancer, and their behavior within the tumor microenvironment profoundly influences disease development. Systematic research on the functions of CAFs in breast cancer remains relatively limited. This study employs bibliometric theories and methods to comprehensively analyze the existing knowledge framework of breast cancer CAFs research, thereby identifying and examining research hotspots and future trends in this field. Methods This study retrieved literature related to CAFs in breast cancer from the Science Core Collection (WOSCC) database, covering publications from 2005 to 2025. After rigorous screening, 377 eligible academic papers were included as research subjects. Utilizing software tools such as VOSviewer, Pajek, Scimago Graphica, and CiteSpace, we conducted an in-depth analysis across multiple dimensions, including countries, research institutions, authors, journals, and keywords, to elucidate the knowledge structure of this field and identify research hotspots and developmental trends. Results The findings indicate a general upward trend in annual publications, with particularly rapid growth between 2022 and 2024. China, the United States, and Italy were the most prolific countries, demonstrating strong academic influence. Among institutions, the University of Calabria, Chongqing Medical University, and the University of Manchester contributed the most research output. Notable researchers, including Marcello Maggiolini, Michael P. Lisanti, and Manran Liu, stood out in terms of publication volume and collaborative engagement. Regarding journals, Cancers published the highest number of articles, while Nature Communications had the greatest impact, with a 2023 impact factor of 14.7. This study systematically reviews the origin, activation mechanisms, and heterogeneity of CAFs in breast cancer. The research reveals that CAFs can be activated through various cytokines and signaling pathways and exhibit significant heterogeneity across different breast cancer subtypes. CAFs secrete multiple factors involved in angiogenesis and extracellular matrix (ECM) remodeling, thereby promoting breast cancer cell metastasis and invasion. Additionally, CAFs may suppress the host's anti-tumor immune response. Current therapeutic strategies targeting CAFs primarily focus on disrupting intercellular communication, degrading the ECM, and overcoming drug resistance. Future research may concentrate on exploring the mechanisms by which immunotherapy regulates CAF activity and the potential of combination therapies. This study provides a comprehensive overview of the current status, hotspots, and cutting-edge advancements in breast cancer CAF research. Conclusion Through rigorous bibliometric analysis, this study systematically examines research hotspots and trends in breast cancer CAF studies, establishing a solid literature-based foundation for defining future research directions and priorities. It highlights the significant potential and importance of targeting CAFs in the breast cancer stroma for therapeutic intervention and tumor progression inhibition. The findings are expected to offer scientific guidance for subsequent research and advance the development of breast cancer CAF studies. Although this study has certain limitations, it provides valuable references for future related research. Breast cancer CAFs Tumor microenvironment Bibliometric analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1.Introduction Breast cancer is a highly heterogeneous disease that predominantly affects women and is widely distributed globally. It represents the leading cause of cancer-related deaths among women, with its incidence closely associated with age and a country's economic development level. Based on the Human Development Index analysis, developed countries exhibit significantly higher breast cancer incidence rates compared to developing nations. Economic conditions influence key factors such as women's growth, nutritional status, and the regulation of menarche age, reproductive history, menopause age, and obesity rates. These factors, in conjunction with genetic determinants, significantly contribute to breast cancer incidence. Early diagnosis and detection are critical for improving patient survival rates; however, over half of the patients are already at an advanced or metastatic stage by the time clinical symptoms manifest [ 1 ] .Breast cancer can be classified into molecular subtypes, including luminal A, luminal B, HER2-positive, and triple-negative breast cancer (TNBC) [ 2 ] .Metastasis is the primary cause of mortality in breast cancer patients, and the tumor microenvironment (TME) plays a pivotal role in metastatic mechanisms. Immune and non-immune cells within the TME collaboratively regulate the cancer metastasis process. Targeted inhibition of relevant signaling pathways to suppress metastatic activation can effectively reduce the vascular invasive capacity and metastatic potential of tumor cells [ 3 ] . CAFs are activated heterogeneous stromal cells within the TME that play a pivotal role in tumor progression [ 4 ] . As the most abundant stromal component in the TME, CAFs are closely associated with tumor progression [ 5 ] . They drive tumor development by facilitating ECM deposition and remodeling [ 6 ] , mediating immune suppression, enhancing cancer cell proliferation, inducing angiogenesis, and promoting therapy resistance. In breast cancer, CAFs primarily originate from normal fibroblasts (NFs), where CD26 + NFs and CD26 − NFs differentiate into inflammatory CAFs and myofibroblastic CAFs, respectively, synergistically promoting tumor progression [ 7 ] . The activation mechanisms of CAFs are a current research focus, with transforming growth factor beta (TGF-β) family ligands serving as key signaling molecules regulating the transformation of NFs into CAFs [ 8 – 10 ] . CAFs exhibit high heterogeneity and can be classified into distinct subpopulations. A systematic analysis based on six CAF biomarkers in breast cancer (FAP, CD29 [integrin-β1], α-SMA, FSP1, PDGFRβ, and Caveolin) has identified four well-characterized CAF subtypes designated as CAF-S1 through CAF-S4 [ 11 ] . These subpopulations play critical roles in breast cancer initiation, progression, invasion, metastasis, and treatment response, with their classification providing novel perspectives and potential strategies for breast cancer diagnosis and therapy. Furthermore, CAFs mediate immunosuppression through the secretion of immunoregulatory cytokines and chemokines, thereby promoting tumor progression [ 12 ] . To date, no bibliometric studies have been reported on breast cancer or CAFs. This study employs VOSviewer and CiteSpace to conduct a visualized analysis of breast cancer CAF related literature from the Web of Science database. Through qualitative and quantitative assessments across dimensions including countries, authors, institutions, and journals, we elucidate research hotspots and developmental trends in this field. 2.Materials and methods 2.1 Data Sources and Search Strategy This study utilized the WOSCC database as the data source, with the index scope limited to SCI EXPANDED and SSCI. The search keywords were "breast cancer" and "CAFs," and the retrieval strategy was as follows:TS=(“Breast Neoplasm” OR “Breast Tumor” OR “Breast Cancer” OR “Cancer of the Breast” OR “Cancer of Breast” OR “Breast Carcinoma”) AND TS=(“Cancer Associated Fibroblast” OR “Tumor Associated Fibroblast” OR “Cancer Related Fibroblast” OR “Tumor Related Fibroblast” OR “Tumour Associated Fibroblast” OR “Tumour Related Fibroblast”). The search was conducted for publications from January 1, 2005, to March 13, 2025, with the document types limited to "Article" and "Review Article" and the language restricted to English. Based on the above search strategy, a total of 1,929 publications were retrieved. After evaluating the titles and abstracts, 377 articles highly relevant to the research topic were selected for further analysis.The complete records, including cited references, were exported in plain text format and imported into CiteSpace for bibliometric analysis. The final dataset comprised 377 publications, consisting of 334 research articles and 37 review articles . 2.2Data Analysis and Visualization This study employed bibliometric analysis on the screened 377 publications using tools including WPS Office, VOSviewer, CiteSpace (v6.3.R1), Pajek, and Scimago Graphica. Figure 1 illustrates the literature search and processing workflow. Bibliometrics applies statistical and mathematical methods to quantitatively analyze literature within a specific field, thereby revealing research trends and developmental trajectories. By systematically organizing and analyzing information such as authors, keywords, countries, institutions, and journals, a comprehensive understanding of the knowledge structure and research hotspots in the field can be achieved. VOSviewer, a freely available bibliometric visualization tool developed by Leiden University in the Netherlands, constructs network maps based on co-occurrence matrices to intuitively display the bibliometric structure of a research domain. In this study, VOSviewer was utilized to generate author collaboration networks, document co-citation networks, and keyword co-occurrence networks. In these network visualizations, node size represents element importance, line thickness indicates connection strength, and node colors distinguish different cluster modules. CiteSpace, developed by Professor Chaomei Chen's research team, specializes in collaboration networks and cluster analysis. By identifying and visualizing burst detection of key nodes, CiteSpace reveals pivotal turning points and critical advancements in a research field. Through centrality and burstness analyses, researchers can efficiently grasp the knowledge base, research hotspots, and cutting edge trends within the domain. 3.Results 3.1 Annual Publication Output and Trends Figure 2 presents the annual and cumulative publication volumes of literature in the field of breast cancer and CAFs from 2005 to 2025. The relatively low number of publications in 2010 indicates that this period had not yet formed a significant research focus. From 2010 to 2020, publications appeared annually with a steady growth rate, reaching the first peak in 2021, suggesting a breakthrough during this phase. Although the number of publications declined between 2021 and 2022, it rebounded rapidly in 2023 and reached its highest level in 2024, demonstrating important advancements during these two years. From 2005 to 2025, the cumulative number of publications in this field amounted to 377, with an average annual output of 19 papers. This trend indicates that research on breast cancer and CAFs has gradually gained attention, with significantly increased activity in recent years, establishing itself as an important direction in oncology research. 3.2 Contributions of Countries or Regions This study encompasses literature from 129 countries and 1,537 institutions. Table 1 presents the top ten countries by publication output, along with their respective publication counts, average citation counts, and centrality metrics. The top three countries by publication volume are China (n = 143), the United States (n = 76), and Italy (n = 36). The United Kingdom ranks first in average citations with 97 citations per publication. The top three countries by centrality are the United States (0.52), the United Kingdom (0.28), and China (0.20). The data indicate that the United States, the United Kingdom, and China exhibit substantial research influence in this field. Table 1 Contributions of Countries Rank Countries/Regions Count Citations Centrality Average Citations/Publications 1 China 143 5885 0.32 41.15 2 USA 76 6259 0.53 82.36 3 Italy 36 2217 0.27 61.58 4 United Kingdom 23 2231 0.23 97.00 5 Japan 18 675 0.12 37.50 6 South Korea 17 600 0.00 35.29 7 France 16 1487 0.11 92.94 8 Germany 14 649 0.12 46.36 9 Sweden 14 1198 0.24 85.57 10 Saudi Arabia 12 349 0.09 29.08 Using CiteSpace, we analyzed international collaboration networks. Figure 3 presents a geographic visualization map of 20 countries, generated using VOSviewer and Scimago Graphica. The United States occupies a central role in international collaborations, demonstrating the broadest collaborative reach, with strong partnerships established with 13 countries, including China, the United Kingdom, Italy, Spain, and Canada, positioning it as the leader in the collaboration network. Figure 4 , generated using CiteSpace, illustrates inter-country collaboration relationships. Node size represents the publication volume of each country, while the purple outer ring indicates centrality, with thickness reflecting centrality strength. The thickness of connecting lines between nodes denotes collaboration intensity. The United States exhibits the highest collaboration intensity and centrality in the network, followed by the United Kingdom and China. This underscores the United States' pivotal role in international collaborations within this field, while the United Kingdom and China also demonstrate strong collaborative influence. 3.3 Institutional Contributions Table 2 lists the top 10 institutions by publication output in the field of CAFs research.The University of Calabria (n = 20), Chongqing Medical University (n = 18), and the University of Manchester (n = 13) rank as the top three, demonstrating the highest research productivity in this field. Table 2 Contributions of Institute Rank Organization Documents Total Link Strength 1 Univ Calabria 20 25 2 Chongqing Med Univ 18 14 3 Univ Manchester 13 19 4 Thomas Jefferson Univ 12 14 5 King Faisal Specialist Hosp & Res Ctr 11 5 6 Yonsei Univ 10 5 7 Shanghai Jiao Tong Univ 9 17 8 Tel Aviv Univ 9 7 9 Lund Univ 8 16 10 Huazhong Univ Sci & Technol 8 7 Using VOSviewer, we analyzed institutional collaboration networks by setting the minimum co-occurrence threshold to 3. As shown in Fig. 5 , the collaboration network is divided into three clusters: red, green, and blue. The node size represents the volume of collaborative publications, while the connecting line thickness reflects the strength of collaboration. Within the red cluster, the University of Calabria, Chongqing Medical University, the University of Manchester, and Thomas Jefferson University exhibit larger nodes, indicating their central role in the collaboration network. These institutions maintain extensive connections, with particularly strong collaboration between the University of Manchester and Thomas Jefferson University. The University of Calabria demonstrates the highest collaboration intensity within its cluster, highlighting its pivotal position. At the periphery of the red and blue clusters, as well as between the green and blue clusters, overlapping nodes suggest potential interdisciplinary collaboration. However, the thin connecting lines indicate that these collaborations remain relatively independent. In contrast, the blue cluster shows sparse internal connections, reflecting weaker collaboration among its members. 3.4 Author Contributions and Co - cited Authors Table 3 lists the top 10 authors in the field of CAFs in breast cancer based on publication volume and co-citation frequency, respectively. Table 3 Author Contributions Rank Author Documents Average Citations/Publications 1 Maggiolini, Marcello 16 152 2 Lisanti, Michael p. 13 150 3 Liu, Manran 13 144 4 Aboussekhra, Abdelilah 11 140 5 Lappano, Rosamaria 11 138 6 Pestell, Richard g. 11 133 7 Whitaker-Menezes, Diana 10 86 8 Howell, Anthony 9 52 9 Martinez-outschoorn, Ubaldo e. 9 47 10 Chiavarina, Barbara 8 26 In terms of publication count, the top three authors are Maggiolini, Marcello; Lisanti, Michael P; and Liu, Manran. When analyzed by average citations per paper, among the top 10 authors by publication volume, Whitaker-Menezes, Diana (n = 152), Pestell, Richard G. (n = 150), and Chiavarina, Barbara (n = 144) rank highest. This indicates that the research outputs of these three authors are highly cited in the field, likely appearing together in multiple key publications, reflecting their close academic connections and significant influence in breast cancer CAFs research. Using VOSviewer software, a co-authorship network analysis was conducted on 53 authors with at least four publications, revealing the largest cluster comprising 12 authors to visualize collaborative relationships (Fig. 6 ). The dense connecting lines among these 12 authors indicate frequent collaborations. Among them, Maggiolini, Marcello has the largest node, representing the highest number of collaborative publications, with the most prominent connecting lines to multiple co-authors, highlighting his central role in the collaboration network. Similarly, Lappano, Rosamaria demonstrates extensive collaborative ties with other researchers. 3.5 Journal Analysis This study involved a total of 164 journals.Table 4 presents the top 10 journals ranked by publication volume, along with their respective article counts, journal quartiles, and 2023 impact factors. Among these, Cancers ranked first with 19 publications, followed by Oncogene (N = 14) and Cancer Research (N = 13). Nature Communications had the highest impact factor in the listed journals, reaching 14.7 in 2023. Table 4 Contributions of Journal Rank Journal Count JCR IF(2023) 1 Cancers 19 Q1 4.5 2 Oncogene 14 Q1 6.9 3 Cancer Research 13 Q1 12.5 4 Frontiers In Oncology 12 Q2 3.5 5 Breast Cancer Research 11 Q1 6.1 6 Cell Cycle 10 Q3 3.4 7 International Journal Of Molecular Sciences 10 Q1 4.9 8 Theranostics 8 Q1 12.4 9 Cancer Letters 7 Q1 9.1 10 Nature Communications 7 Q1 14.7 Using a minimum publication threshold of 5 articles, this study conducted a citation analysis of 22 journals using VOSviewer, with the results illustrated in Fig. 7 . In the figure, node size represents co-publication volume, line thickness indicates collaboration strength, and differently colored node connections denote interdisciplinary journal collaborations. The analysis revealed that Cancers had the largest node and maintained dense connections with multiple journals, demonstrating its broad academic influence and collaborative reach. Key journals facilitating interdisciplinary collaboration included Cancers, Cancer Research, Frontiers in Oncology, and Cell Cycle, all of which exhibited extensive collaborative linkages with journals from other disciplinary clusters. 3.6 Keywords Through keyword extraction and analysis, this study visualized the research hotspots and frontier directions in the field. Table 5 lists the top 10 most frequently occurring keywords. The high-frequency keywords include "breast cancer" (N = 164)," microenvironment" (N = 153), "cancer-associated fibroblasts" (N = 116), "expression" (N = 110)," cell" (N = 83)," metastasis" (N = 81), "growth" (N = 79),"stromal fibroblasts" (N = 74),"progression" (N = 47), and"activation" (N = 44). The top three keywords in terms of frequency breast cancer, microenvironment, and CAFs indicate that the microenvironment plays a significant role in research related to CAFs in breast cancer. Table 5 keyword Contributions Rank Keyword Frequency 1 Breast Cancer 164 2 Microenvironment 153 3 Cancer-Associated Fibroblasts 116 4 Expression 110 5 Cells 83 6 Metastasis 81 7 Growth 79 8 Stromal Fibroblasts 74 9 Progression 47 10 activation 44 Figure 8 presents a VOSviewer based visualization of keyword co-occurrence to reveal the distribution of research themes in this field. The keywords were clustered into six distinct groups: red, light blue, blue, green, yellow, and purple clusters. Based on node size, the primary keywords in each cluster are as follows: Red cluster: TGF-β, tumor growth, stem cells, metabolism; Light blue cluster:breast cancer, prognosis, extracellular matrix༛ Blue cluster:cell, heterogeneity, resistance༛ Green cluster: growth, proliferation, tumor stroma immunotherapy༛ Yellow cluster:stromal myofibroblasts, epithelial-mesenchymal transition (EMT), microenvironment༛ Purple cluster:cancer-associated fibroblasts, metastasis. Using CiteSpace, we conducted a burst analysis on keywords and extracted the top 10 key burst terms. By examining the burst start time, duration, and end time of these keywords, we can clearly understand the evolution, frontiers, and latest trends in the research hotspots of this field. Figure 9 displays the burst start and end times of the keywords. The burst strength reflects the popularity of a keyword in academic research, with the red line segments indicating the trending period of a keyword within a specific historical interval. Light blue nodes represent periods when the keyword had not yet emerged, while dark blue nodes indicate its initial appearance and gradual rise in attention. The analysis reveals that "tumor stroma" exhibits the highest burst strength (6.97), followed by "metastasis" (5.5) and "stromal fibroblasts" (4.6). Between 2005 and 2025, the earliest emerging keywords included "stromal fibroblasts," "tumor microenvironment," "aerobic glycolysis," and "metastasis," among which "TGF-β" maintained sustained research interest over a prolonged period. In recent years, research hotspots have gradually shifted toward keywords such as "promotion," "metastasis," and "mechanism," indicating that the field has entered a new phase, focusing primarily on tumor drug resistance mechanisms and their regulation. 3.7Literature Co-citation Analysis Co-citation analysis is used to reveal the intrinsic relationships between publications. When two or more documents are simultaneously cited by another paper, a co-citation relationship is formed between them. Through co-citation analysis, researchers can gain in depth insights into the knowledge structure and developmental trajectory of a research field. Table 6 lists the top 10 most frequently co-cited references. The most highly cited publications are "The prognostic significance of tumor-associated stroma in invasive breast carcinoma" and "Stromal miR-200s contribute to breast cancer cell invasion through CAF activation and ECM remodeling" The first study demonstrates that CAFs in tumor stroma can serve as a prognostic marker for breast cancer patients, with higher CAF density in TNBC being associated with chemotherapy resistance and reduced survival. The second study reveals, from a molecular perspective, that the miR-200s-ZEB1-MMP axis is a key mechanism through which CAFs promote metastasis, highlighting its potential as a therapeutic target. Together, these two studies underscore the critical role of stromal reprogramming in breast cancer progression. Table 6 Contributions of co-cited references Rank Title Frequency Centrality Year 1 The prognostic significance of tumor-associated stroma in invasive breast carcinoma 60 0.03 2018 2 Stromal miR-200s contribute to breast cancer cell invasion through CAF activation and ECM remodeling 60 0.02 2020 3 Cancer-associated fibroblasts induce high mobility group box 1 and contribute to resistance to doxorubicin in breast cancer cells 46 0.01 2016 4 p16(INK4A) represses the paracrine tumor-promoting effects of breast stromal fibroblasts 44 0.01 2019 5 Stroma in breast development and disease 38 0.02 2018 6 Human breast cancer invasion and aggression correlates with ECM stiffening and immune cell infiltration 31 0.06 2018 7 Cancer-associated fibroblasts release exosomal microRNAs that dictate an aggressive phenotype in breast cancer 30 0.01 2021 8 Paracrine signaling by platelet-derived growth factor-CC promotes tumor growth by recruitment of cancer-associated fibroblasts 26 0.04 2020 9 Fibroblast Subtypes Regulate Responsivenessof Luminal Breast Cancer to Estrogen 26 0.01 2017 10 Autophagy in cancer associated fibroblasts promotes tumor cell survival: Role of hypoxia,HIF1 induction and NFκB activation in the tumor stromal microenvironment 23 0.04 2020 Conducting bibliographic coupling analysis using VOSviewer bibliographic coupling is used to examine the connections between scientific publications. When two documents are simultaneously cited by a third publication, they form a bibliographic coupling pair, with the coupling strength indicating the similarity of their research topics. In the visualization, each node represents a document, labeled with the first author's name and publication year. As shown in Fig. 10 , this study set a minimum threshold of 183 co-citations, meaning only document pairs that were jointly cited at least 183 times were considered. Based on this criterion, the 20 most strongly coupled documents were identified. Among them, Pelon (2020), Raz (2018), Cohen (2017), Ershaid (2019), and Martinez Outschoorn (2010) exhibited the highest coupling strengths, indicating their substantial influence and representativeness in this research field. By integrating citation frequency, cited frequency, and centrality analysis, key representative documents can be further identified. These publications are not only widely cited within the field but also demonstrate strong connections with other studies through bibliographic coupling relationships. This analysis provides valuable insights into the research hotspots and emerging directions in the discipline. 4.Discussion 4.1 General Information This study employs bibliometric methods to evaluate the contributions and influence of research on CAFs in breast cancer across dimensions such as countries, institutions, authors, and journals. Since the first publication in this field in 2005, the number of articles has increased significantly from 2020 to 2024, marking a phase of rapid development. Although there was a decline in publications between 2021 and 2022, the upward trend resumed in 2022, reflecting dynamic shifts in research activity and interest. At the national level, China, the United States, and Italy have contributed the highest number of publications, highlighting their research maturity and academic impact in this field. A total of 164 journals have published research on breast cancer CAFs, with Cancers being the most prolific, followed by Cell Cycle and Cancer Research. These journals provide scholars with the latest advancements and trends in the field. The top three most published authors are Marcello Maggiolini, Michael P. Lisanti, and Manran Liu, who have made significant contributions to breast cancer CAF research. For instance, Marcello Maggiolini uncovered the mechanism by which CAFs mediate therapy resistance in estrogen-rich microenvironments and proposed phytochemical based intervention strategies. Michael P. Lisanti and Diana Whitaker Menezes, long-term research collaborators, jointly proposed a novel mechanism for the Warburg effect, demonstrating that stromal fibroblasts undergo aerobic glycolysis to produce and secrete more pyruvate/lactate, which fuels mitochondrial TCA cycles, oxidative phosphorylation, and metabolism in adjacent epithelial cancer cells. In 2014, they further elucidated how altered signaling pathways in cancer cells drive neighboring CAFs to generate reactive oxygen species and autophagy, thereby supporting cancer growth and metastasis. Their work revealed the critical role of CAF metabolism in breast tumor progression and introduced the "metabolic coupling" model, providing a theoretical foundation for targeting CAF metabolism to improve breast cancer treatment. Among the top ten most published authors, Diana Whitaker Menezes has the highest average citation count. Her research focuses on the TME and CAFs in breast cancer, establishing the central role of CAFs in metabolic support, ECM stiffening, and therapy resistance. U. E. Martinez Outschoorn investigates the complexity of the TME, collaborating with Michael P. Lisanti and Federica Sotgia to explore metabolic interactions between cancer cells and CAFs. A. Orimo's research demonstrated that fibroblasts constitute the majority of stromal cells in breast cancer and found that stromal fibroblasts in invasive breast cancer promote tumor growth and angiogenesis through high secretion of stromal cell derived factor1. These studies provide a fundamental understanding of CAF's role in breast cancer and offer new directions for future research and therapeutic strategies. 4.2 Knowledge Base Based on the results of the literature co-citation network analysis, the research foundation of CAFs in breast cancer can be broadly delineated. Among the top 10 most cited publications, the 2nd, 7th, and 8th articles focus on the tumor-promoting role of CAFs, highlighting their facilitation of breast cancer invasion and growth through various signaling molecules. The 4th and 9th articles explore CAF heterogeneity and the functions of different subtypes, particularly the role of the CAF-S1 subset in breast cancer in modulating regulatory T lymphocytes (Tregs). The 3rd and 10th articles summarize CAF-mediated therapy resistance, including chemotherapy resistance induced by activated CAFs via HMGB1 and the protective role of CAF autophagy under hypoxic conditions in shielding tumor cells from stress-induced damage. The 5th and 6th articles propose that CAFs influence breast cancer progression through ECM remodeling, while the 1st article mentions the prognostic value of tumor-associated stroma, suggesting CAFs as potential prognostic markers for breast cancer. Ana Costa et al. investigated the heterogeneity and plasticity of CAFs in breast cancer, as well as their relationship with immune suppression. Through experiments, they identified differences in the accumulation of CAF subtypes across breast cancer subtypes and classified breast cancer CAFs into four distinct subsets. Michael Bartoschek and Nikola Oskolk et al. utilized a breast cancer mouse model to perform single-cell RNA sequencing on 768 mesenchymal cell transcriptomes, defining three distinct CAF subpopulations and thereby enhancing the cellular resolution of CAF research. Ana Costa et al. further elucidated the association between breast cancer CAF subsets and immune suppression, demonstrating that CAF-S1 attracts CD4⁺CD25⁺ Tregs via CXCL12 expression and retains them on its surface through ligands TNFSF4/OX40L and PDCD1LG2/PD-L2, as well as the adhesion molecule JAM2. Moreover, compared to CAF-S4, CAF-S1 increases Treg numbers and enhances their ability to suppress effector T cell proliferation, thereby functionally augmenting Treg activity. These mechanisms enable CAF-S1 in breast cancer to mediate immune suppression [ 13 ] . Heather M. Brechbuhl's research revealed that CAF subtypes influence the sensitivity of breast cancer receptors to estrogen, and their expression affects tumor cell responsiveness to chemotherapeutic agents. Understanding the composition of CAFs in breast cancer may aid in predicting treatment response and prognosis, positioning CAFs as potential targets for drug development [ 14 ] . Jan Kiefer et al. uncovered a positive feedback loop between immunosuppressive ECM-myCAF and TGF-β-myCAF (CAF-S1 clusters) and Tregs, which may contribute to immunotherapy resistance [ 15 ] . Erik Sahai et al. summarized the conceptual framework of CAFs, providing an overview of breast cancer CAF research: fibroblasts are defined as cells negative for epithelial, endothelial, and leukocyte markers, exhibiting an elongated morphology and lacking oncogenic mutations [ 5 ] . CAFs display phenotypic diversity, and their origin involves a process termed "stromagenesis", wherein resident fibroblasts undergo some form of tissue dysfunction [ 16 ] . While stromal fibroblasts proliferate extensively in tumor-bearing patients, this phenomenon is rarely observed [ 17 ] . The study also summarized key signals for fibroblast activation and mechanisms underlying CAF activation: heat shock factor 1 contributes to CAF generation, IL-1 promotes CAF phenotype formation, TGF-β family ligands and the lipid mediator lysophosphatidic acid activate signaling pathways, Notch signaling drives CAF phenotypes in breast cancer, and physical changes in the ECM can also activate CAFs. Additionally, other stromal cells in the tumor microenvironment participate in CAF generation. For example, macrophage-derived granulin promotes the activation of fibrotic environments in liver metastasis [ 18 , 19 ] , illustrating the interplay between cancer and inflammatory regulation. The study further outlined CAF functions, including their association with tumor angiogenesis, interactions with other stromal cells, involvement in ECM deposition and remodeling within the tumor microenvironment [ 20 , 21 ] , and their role in promoting tumor growth. 4.3 Research Trends and Hotspots By analyzing the 10 most frequently cited terms, the research frontiers and hotspots of CAFs in breast cancer were identified, including CAFs, tumor microenvironment, activation, expression, stromal fibroblasts, EMT, metastasis, and diagnostic/therapeutic strategies. Breast cancer CAFs are activated stromal cells, with research focus primarily on their regulatory mechanisms, exploring the origin, activation, and influencing factors of CAFs, as well as analyzing their functional characteristics and heterogeneity in breast cancer. Additionally, studies emphasize the interaction between CAFs and cancer stem cells, their synergistic effects with other stromal cells in the tumor microenvironment, and their collective role in regulating tumor progression. Research also investigates the association between CAFs and immunosuppressive regulation, where CAFs secrete cytokines to inhibit immune cell function and promote tumor development. Furthermore, strategies for targeting CAFs in breast cancer treatment are explored, such as eliminating CAFs using specific markers or blocking CAF related signaling pathways. 4.3.1 Basic Functions and Characteristics of CAFs in Breast Cancer 4.3.1.1 Origin and Activation of CAFs in Breast Cancer Fibroblasts, once activated in breast cancer CAFs primarily originate from the activation of resident fibroblasts, with other sources including mesenchymal stem cells, adipocytes, and bone marrow derived fibroblasts [ 5 ] .Resident fibroblasts can be transformed into various types of CAFs through factors such as TGF-β, CXCL12, and co-culture with cancer cells. Transforming growth TGF-β, CXCL12, Wnt7a, miR-125b, miR-9, and chemotherapy can convert resident fibroblasts into α-SMA⁺ CAFs; transforming growth factor beta and platelet derived growth factor can transform fibroblasts into FSP1⁺ CAFs; mesenchymal stem cells can be converted into CAFs through the action of tumor exosomes and transforming growth factor-beta, among other factors; adipocytes can be transformed into FSP1⁺/α-SMA⁻/FAP⁻ CAFs under the influence of Wnt3a, providing another source of CAFs in breast cancer; bone marrow derived fibroblasts can be recruited into the TME as α-SMA⁺/PDGFRα⁻ CAFs, participating in the progression of breast cancer; and pericyte derived CAFs express vascular regulatory genes [ 22 ] . The activation mechanisms of breast cancer CAFs include cytokine mediation, direct interactions between tumor cells and fibroblasts, and the influence of the tumor microenvironment. Cytokine mediation primarily involves activation through cytokine receptor binding. Studies have shown that breast cancer cells transform normal mammary fibroblasts into CAFs through autocrine signaling of transforming growth TGF-β and CXCL12, contributing to the construction of a microenvironment conducive to tumor growth and invasion [ 23 ] . Growth factors, cytokines, and their receptor binding activate CAFs through signaling pathways. CXCL12, as a CXCL12, binds to CXCR4 on the surface of fibroblasts, activating them and inducing the upregulation of α-SMA. This process also promotes the secretion of TGF-β and CXCL12 by fibroblasts, enhancing the activation effect through positive feedback [ 23 ] . After binding to its receptor, TGF-β not only activates fibroblasts and induces α-SMA upregulation but also promotes the secretion of TGF-β and CXCL12. Additionally, it participates in the regulation of physiological processes such as glycolysis and oxidative phosphorylation, influencing cellular metabolism [ 23 ] . Platelet derived growth factor binds to platelet derived growth factor receptors and synergizes with transforming growth TGF-β to influence intracellular metabolism by modulating the levels of isocitrate dehydrogenase 3α. Downregulation of IDH3α increases the levels of α-ketoglutarate and hypoxia inducible factor 1α, leading to enhanced glycolysis and reduced oxidative phosphorylation. Wnt7a, a member of the Wnt protein family, is a class of evolutionarily highly conserved secreted glycoproteins that play critical roles in biological processes such as embryonic development, cell proliferation, differentiation, polarity establishment, and tissue homeostasis maintenance. In breast cancer cells, Wnt7a activates fibroblasts by binding to TGF-β receptors, participating in the CAF activation process. Additionally, osteopontin (OPN) binds to CD44 and integrin αvβ3, activating fibroblasts through the AKT serine/threonine kinase and mitogen-activated protein kinase 1 signaling pathways, ultimately upregulating the expression of markers such as α-SMA, fibroblast-specific protein-1, and fibroblast activation protein α (FAP). Activated fibroblasts secrete increased levels of CXCL1, CXCL2, cyclooxygenase-2, IL-6, OPN, and collagen, among other substances. Transforming growth factor-beta plays a significant role in the activation mechanisms of breast cancer CAFs. In breast cancer patients, tumor cells not only recruit and activate fibroblasts into CAFs through autocrine secretion of the chemokine CXCL12 but also transport miRNAs to NFs via exosomes, transforming them into myofibroblasts (CAFs). The upregulation of miRNAs in cancer cell-derived exosomes can activate fibroblasts, inducing their transformation into CAFs. Recent studies have found that miR-370-3p derived from breast cancer cells activates fibroblasts, enhancing the stemness, migration, and invasive capabilities of cancer cells [ 24 ] . Additionally, breast cancer cells participate in the activation process by releasing exosomes containing proteins. Survivin is transferred to surrounding fibroblasts via exosomes, upregulating the expression of superoxide dismutase 1 and converting them into CAFs [ 25 ] . 4.3.1.2 Heterogeneity of CAFs in Breast Cancer Different CAF subpopulations exhibit distinct characteristics, and in breast cancer, CAFs lack a unified marker. The expression of markers and functions vary among subpopulations, reflecting the heterogeneity of CAFs. Breast cancer is classified into luminal A, luminal B, HER2-positive, and triple-negative subtypes based on the expression levels of ERα, PR, and HER2. Each subtype has different prognoses, and CAF expression also demonstrates heterogeneity. Studies have found that CAFs in HER2-positive breast cancer are significantly different from those in triple-negative and ER-positive breast cancers, particularly in genes related to the cytoskeleton and integrin signaling [ 26 ] .Different subtypes of breast cancer exhibit varying expressions of CAF-related proteins. For instance, FAP, FSP1, and platelet-derived growth factor receptor beta (PDGFRβ) are overexpressed in invasive lobular carcinoma, while in ductal carcinoma, stromal cells show higher expression levels of prolyl 4-hydroxylase, platelet-derived growth factor receptor alpha (PDGFRα), and chondroitin sulfate proteoglycan [ 27 ] . Utilizing single-cell RNA sequencing technology, breast cancer CAFs demonstrate spatial and functional heterogeneity, with at least three subpopulations identified: vCAF, mCAF, and dCAF.These subpopulations exhibit differences in cellular origin and function: mCAFs produce a wide range of stromal components; vCAFs primarily generate basement membrane products; and dCAFs mainly secrete paracrine signaling molecules [ 15 ] . The phenotypic heterogeneity of breast cancer CAFs is reflected through various biological markers, including PDGFRα/β, CD90/THY1, podoplanin (PDPN), α-SMA, fibroblast-specific protein 1 (FSP-1), FAP, fibronectin 1, vimentin (VIM), CD29, CD10, or G protein-coupled receptor 77. These markers may be expressed individually or co-expressed by different CAF subpopulations, and their expression varies across different tissues. Based on the expression levels of α-SMA and FAP, human breast cancer CAFs are classified into four subtypes (CAF-S1 to CAF-S4). Fluorescence imaging reveals that CAF-S1 and CAF-S4 are preferentially enriched within tumors, CAF-S3 accumulates significantly in the peritumoral regions, and CAF-S2 is evenly distributed in both areas. CAF-S1 is associated with an immunosuppressive environment by secreting CXCL12 and enhancing the differentiation of Tregs, whereas CAF-S4 lacks this phenotype [ 11 ] . Single-cell transcriptomic analysis has identified two additional CAF subtypes in human breast cancer patients: FSP-1⁺ CAFs and PDPN⁺ CAFs [ 28 ] . The proportions of these two subtypes are correlated with BRCA mutations and clinical outcomes in TNBC. The heterogeneity and plasticity of breast cancer CAFs significantly influence changes in the tumor microenvironment, driving cancer cell progression. The differences between CAFs and NFs stem from variations in protein expression, which can be distinguished by detecting specific biomarkers. Due to the high heterogeneity of CAFs, different biomarkers may exhibit distinct functional effects. Understanding the functions of proteins expressed by CAFs can provide a basis for developing targeted CAF therapies in breast cancer.α-SMA-positive CAFs generate lactate and pyruvate during metabolism, providing nutrients for tumor cells and promoting tumor progression [ 29 ] . VIM, a type III intermediate filament protein, is commonly used as a marker for maintaining cell structure and motility during cell migration [ 30 ] and is involved in late-stage tumor metastasis. FAP, another widely distributed biomarker of CAFs, is a serine protease that participates in ECM remodeling and fibrosis, accelerating tumor progression [ 31 ] . FAP-positive CAFs contribute to the formation of an immunosuppressive TME through multiple mechanisms [ 32 , 33 ] , promoting tumor growth and serving as a potential therapeutic target. Elevated levels of PDGFRα and PDGFRβ in the breast cancer stroma [ 34 ] are involved in CAF activation. Research targeting CAF biomarkers holds significant potential for controlling breast cancer progression and treatment. 4.3.2 CAFs Promote Breast Cancer Cell Progression TME is influenced by CAFs, which interact with the TME to modulate breast cancer cell progression. CAFs play a role in interfering with breast cancer cell metastasis, and the extensive proliferation of mammary connective tissue results in CAFs constituting up to 80% of the tumor mass, making them the most prevalent stromal cell component in the breast TME [ 35 , 36 ] . The complexity of CAF-TME interactions is mutually influential [ 37 ] , and the transformation and activation of CAFs are fundamental to cancer progression [ 38 ] . Upon activation, NFs promote cancer cell development through various mechanisms, including the secretion of growth factors, chemokines, and interleukins. Growth factors can directly activate fibroblasts, and CAFs also secrete a multitude of autocrine and paracrine cytokines, as well as other pro-tumor factors, to modulate the environment and create a microclimate conducive to tumor growth and metastasis [ 20 , 39 ] . The most representative growth factor is transforming growth TGF-β, which plays a critical role in breast cancer progression by participating in ECM mechanical sensing and myofibroblast differentiation [ 40 ] . In the TME, CAFs secrete substantial amounts of TGF-β, activating the TGF-β/Smad signaling pathway in breast cancer cells and driving tumor progression. The communication between CAFs and tumor cells is bidirectional: tumor cells can secrete TGF-β themselves or mediate the transformation of stromal fibroblasts into CAFs through paracrine signaling, altering the TME and promoting tumor development and dissemination.Studies have shown that Grem1, a bone morphogenetic protein antagonist produced by CAFs, promotes fibroblast activation and breast cancer cell infiltration and extravasation, driving the formation of micrometastases, which is the initial step in the invasion-metastasis cascade [ 41 ] . Additionally, the lack of CAV-1 in fibroblasts leads to increased TGF-β secretion, activating the TGF-β/Smad signaling pathway in breast cancer and promoting tumor metastasis [ 42 ] . Other growth factors also participate in remodeling the TME and facilitating cancer cell metastasis. For example, CAF-secreted factors such as hepatocyte growth factor (HGF), nerve growth factor, connective tissue growth factor, and basic fibroblast growth factor are all involved in breast cancer metastasis and invasion.Among these, HGF secreted by CAFs exhibits a bidirectional interaction with breast cancer cells: breast cancer cells can reprogram surrounding NFs, transforming them into CAFs or key-stage cells; meanwhile, HGF secreted by CAFs plays a significant role in breast cancer progression, with its secretion levels positively correlated with mammary tumorigenesis, cell migration, and invasive capacity [ 43 ] , thereby driving the metastatic process. Breast cancer cells induce fibroblasts to secrete HGF, further enhancing the pro-tumor effects of CAFs and establishing a mutually reinforcing relationship. Chemokines and their receptors mediate chemotaxis and are deeply involved in tumorigenesis and progression [ 44 ] . The association between CAFs and the chemokine CXCL12 is particularly significant: on one hand, chemokines secreted by CAFs regulate the cytoskeleton of mammary tumor cells, influencing their motility [ 45 ] ; on the other hand, OPN secreted by cancer cells stimulates CAFs to secrete more CXCL12. The increased CXCL12 triggers EMT in tumor cells, enhancing their invasive and metastatic potential [ 46 ] . Additionally, CXCL12 produced by CAFs acts on endothelial cells, modulates the tumor microenvironment, and stimulates tumor cells through inflammatory mechanisms, activating the Notch1 signaling pathway and increasing CXCL8 production, thereby enhancing tumor cell metastatic activity [ 47 ] IL-6 is widely present in the tumor microenvironment. Studies have shown that IL-6 reduces the expression of the tumor suppressor HIC1 and promotes breast cancer progression through paracrine or autocrine signaling [ 48 ] . CAFs mediate breast cancer metastasis through exosomes, which are enriched with miRNAs that can be taken up by tumor cells to regulate target gene expression, promoting breast cancer progression and metastasis. Additionally, CAFs interact with stromal cells such as macrophages and TANs, participating in angiogenesis and ECM remodeling. Angiogenesis is critical for the formation of premetastatic niches, while ECM remodeling is a necessary step for tumor cell invasion and metastasis. CAFs primarily promote angiogenesis through the VEGF dependent classical pathway [ 49 ] . The binding of VEGF to VEGFR activates downstream signaling pathways, prompting vascular endothelial cells to develop into mature blood vessels, which supply nutrients to the tumor and remove metabolic waste, thereby facilitating tumor growth and metastasis.The ECM, composed of structural proteins, proteoglycans, and glycoproteins, has mechanical properties closely related to CAFs. Dynamic remodeling of the ECM leads to changes in tumor cell density, tissue stiffness, and structure, causing structural alterations in the TME due to force transmission, which creates conditions for directional cancer cell migration and invasion [ 50 ] . CAFs enhance tumor invasion and metastasis by promoting ECM protein deposition, secreting growth factors, and remodeling the ECM. 4.3.3 Targeting CAFs in Breast Cancer for Therapeutic Intervention As shown in Fig. 11 , synergistic therapeutic approaches targeting CAFs involve cellular reprogramming to deactivate their pro-tumor functions, combined with natural compounds and chemotherapeutic agents. In terms of immune regulation, CAFs increase the number of regulatory T cells in the TME by secreting IL-6 and CXCL12, thereby suppressing the body's anti-tumor immune response. CAFs induce cytotoxic T cell death in an antigen-independent manner by activating apoptotic ligands and LAG-3, further weakening anti-tumor immunity [ 51 ] . Studies have identified a senescent CAF population in breast cancer that promotes tumor progression by inhibiting the anti-tumor response of NK cells. senCAFs form physical or chemical barriers that impede NK cell infiltration into tumor cells and interfere with NK cell cytotoxicity through ECM secretion. Eliminating senCAFs significantly delays tumorigenesis, suggesting their potential as a therapeutic target in breast cancer [ 52 ] . As key cells in the TME, CAFs participate in tumor immune escape by communicating with other cells and secreting ECM components, modulating the functions of myeloid and lymphoid cells. Therapeutic strategies targeting CAFs may disrupt this process [ 51 ] . TME in breast cancer influences cancer cell metastasis, with the transformation of normal stromal fibroblasts into CAFs being a critical step in the transition to a pre-cancerous microenvironment, as confirmed in studies [ 51 ] . Intervening in the activation state of CAFs is a key strategy to prevent breast cancer stroma formation, including inhibiting CAF activity to limit their functions or reprogramming active CAFs into quiescent fibroblasts. Both approaches may disrupt the formation of breast cancer stroma, offering new insights for early prevention and treatment of breast cancer [ 51 ] . Targeting CAFs for treatment can be achieved by disrupting the breast cancer tumor microenvironment. First, blocking communication between tumor cells and CAFs, such as targeting the TGF-β1/Smad or CXCL1/CXCR12 signaling axes, can inhibit tumor metastasis [ 51 ] . Disrupting the ECM, which acts as a protective barrier for tumors, is a potential strategy for anti-tumor therapy. Investigating the interactions between CAFs and immune cells, as well as the role of CAFs in anti-angiogenesis and estrogen related mechanisms particularly focusing on CAF hormone resistance in estrogen-related breast cancer subtypes, may provide new insights for blocking CAF functions. CAF mediated chemotherapy resistance is a key mechanism by which they protect tumor cells. CAFs increase IFN expression through paracrine signaling, activating the IFN pathway to induce chemotherapy resistance, while IFN-blocking antibodies can inhibit this effect. Chemotherapy-resistant breast cancer cells activate CAFs and induce resistance via the TGF-β/p44/42 MAPK signaling axis, and targeting this pathway may reverse CAF-mediated resistance. Additionally, radiotherapy resistance is associated with CAFs, and inhibiting Dll1-mediated Notch signaling post-radiotherapy can reduce CAF numbers and enhance tumor cell radiosensitivity. Therapeutic strategies targeting CAFs include using natural products to reprogram CAFs into quiescent fibroblasts and employing nanoparticle technology combined with chemotherapy drugs to improve targeting efficiency. Nanoparticles can both inhibit CAF formation and enhance therapeutic efficacy, reduce toxicity, and activate the immune microenvironment [ 53 , 54 ] . 4.4 Future With advancements in medical technology, breast cancer treatment has developed mature strategies, including early screening, prevention, diagnosis, chemotherapy, and drug therapy. Targeting the immune regulation mediated by CAFs, the interaction signaling pathways between CAFs and tumor cells, and the metabolic mechanisms involved in breast cancer progression can lead to the development of CAF targeted drugs that block CAF driven tumor progression and overcome chemotherapy and radiotherapy resistance. Currently, CAFs have become a focal point in drug development as a therapeutic target for breast cancer. The heterogeneity of CAFs contributes to immune suppression, weakening anti-tumor immune responses and promoting breast cancer development. Future research directions include refining immunotherapy strategies by modulating CAF activity to revert them to quiescent fibroblasts, as well as exploring the combined application of CAF targeted therapies with immunotherapy, chemotherapy, and radiotherapy to provide better therapeutic outcomes for patients. 4.5 Advantages and Disadvantages The data for this study were sourced from the WOSCC, a comprehensive academic literature indexing database that covers renowned multidisciplinary journals and is updated daily. As a literature retrieval tool, WOSCC ensures the credibility and high quality of the literature data, providing a solid foundation for bibliometric analysis. In terms of analytical methods, three bibliometric tools, VOSviewer, CiteSpace, and WPS Office, were employed to conduct visual analysis of the relevant literature. This approach minimizes bias caused by subjective information filtering, ensuring the objectivity and accuracy of the research findings. Compared to traditional reviews, this study, through visual analysis and systematic review, presents a more intuitive and comprehensive overview of the hotspots and frontiers in the field of breast cancer related CAFs. However, this study has certain limitations. Due to constraints in data sources, literature from non-SCI journals or other databases may not have been included, potentially affecting the comprehensiveness of the findings. The study primarily relies on citation frequency and publication counts to assess the importance of literature, but this approach may not fully capture the multidimensional value of research, such as its innovativeness or practical application potential. Future studies could expand data sources and incorporate additional metrics for a more comprehensive evaluation to address these limitations. 5 Conclusion This study systematically evaluated the current research status of breast CAFs through bibliometric analysis, covering dimensions such as publication date, country, institution, author, and literature source, while also Summarizeing research trends, hotspots, and frontier directions. The results indicate that CAFs in breast cancer originate from diverse sources and play a crucial role in tumor development. CAFs participate in tumor microenvironment remodeling through multiple mechanisms, with angiogenesis and ECM remodeling being key prerequisites for tumor cell progression.In response to the tumor-promoting effects of CAFs, this study proposes strategies to target and block CAF related signaling pathways to inhibit cancer cell metastasis and invasion. The research also explores the interaction mechanisms between CAFs and breast cancer cells, suggesting potential methods to disrupt CAF mediated interference in the tumor microenvironment. CAFs not only promote tumor cell progression but also protect tumor cells from the effects of anti-cancer drugs by secreting cytokines and growth factors, leading to drug resistance. Therefore, therapeutic strategies targeting CAFs hold promise for overcoming this protective effect and addressing the issue of anti-cancer drug resistance. However, research on CAF targeted therapies for breast cancer remains exploratory, requiring further in-depth studies to elucidate their mechanisms of action and clinical application potential. Declarations Authors’ contributions Research conducted by Shixue Jing, Siping Zhou, Lushun Zhang; Data collection, Siping Zhou, Yongfeng Wang and Cui Jia; Visualization by Siping Zhou, Xinlian Liu and Hui Cao; Initial drafting by Siping Zhou, Shixue Jing, Yongfeng Wang and Xinlian Liu; Review and editing by Shinxue Jing, Cui Jia, Hui Cao, Lushun Zhang; Oversight by Hui Cao and Shixue Jing; Funding secured by Lushun Zhang and Xinlian Liu. The published version of the manuscript has been read and approved by all authors. Acknowledgments Not applicable. Availability of data and materials’ statement All the data generated or analyzed during this study are included in this paper. Further enquiries can be directed to the corresponding author. Funding Statement This work was supported by the Open Fund of Development and Regeneration Key Laboratory of Sichuan Province (Grant No.23LHPDZYB07; Grant No.23LHPDZZD05), the Chinese Ministry of Education Cooperative Education Project (Grant No.231100882305626), and the National College Student Innovation and Entrepreneurship Training Program Project (Grant No.202413705016). Supplementary Information The data analyzed in this study were retrieved from the Web of Science Core Collection Ethics approval and consent to participate This is a systematic review. Ethics approval was waived for this study because no patient data were reported. Competing interests The authors declare that they have no competing interests. Clinical Trial Registration Number Not applicable Consent for Publication Not applicable References Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022;95(1130):20211033. Zhang X. 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Supplementary Files Analyzedata.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 22 Jul, 2025 Editor invited by journal 25 Jun, 2025 Editor assigned by journal 16 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 12 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6881099","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488934117,"identity":"cbd9f504-a934-451c-aabe-06215dd55abc","order_by":0,"name":"Shixue Jing","email":"","orcid":"","institution":"The Third Affiliated Hospital of Chengdu Medical College, Chengdu Pidu District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shixue","middleName":"","lastName":"Jing","suffix":""},{"id":488934118,"identity":"ecf2b39c-9dcf-44ec-8e8f-cc7807c83e8b","order_by":1,"name":"Siping Zhou","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Siping","middleName":"","lastName":"Zhou","suffix":""},{"id":488934119,"identity":"6d7688ea-7438-427b-8b30-4937b1f1eb0e","order_by":2,"name":"Cui Jia","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Jia","suffix":""},{"id":488934120,"identity":"8dde274c-5e78-45cf-adc2-372f7847dac9","order_by":3,"name":"Yongfeng Wang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Chengdu Medical College, Chengdu Pidu District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Wang","suffix":""},{"id":488934121,"identity":"93c8757e-7e3a-459f-adaa-39ab8ba2b9ed","order_by":4,"name":"Hui Cao","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Cao","suffix":""},{"id":488934122,"identity":"b6f8256c-aa7e-4267-86cf-43f329310a6e","order_by":5,"name":"Lushun Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACCRBRwCDHxsx88AFEKIEYLQYMxvzsbMkGJGlJnNnPYyZBlBb52c3PHn4xqGPccJjBrOJnjh0DP3uOAcPPHbi1MM45Zm4sY3CY2eAwQ9rN3m3JDJI9bwwYe8/g1sIskWAmLWFwgA2o5dhtxm0HGAxu5BgwM7bh1sImkf4NqKWOx+AwY1sxSIs9IS08Ejlmkh8MmCUkm5nZmMG2SBDQIiGRUybNYHDYgB+oQxLoFx6JM88KDvbi0SI/I32b5I+Kuvo2/vMfP/zcZifH35688cFPPFrAQcCD7FIQcQC/BmBA/yCkYhSMglEwCkY2AAAXd0j3ZeXn6QAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":true,"prefix":"","firstName":"Lushun","middleName":"","lastName":"Zhang","suffix":""},{"id":488934123,"identity":"f848ae4e-04c8-4ca6-80e1-714e1817acf7","order_by":6,"name":"Xinlian Liu","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xinlian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-12 14:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6881099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6881099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87513424,"identity":"f1f0927b-6882-48ab-91a2-01179cb9696e","added_by":"auto","created_at":"2025-07-24 16:01:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210130,"visible":true,"origin":"","legend":"\u003cp\u003eflow diagram\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/b7617515491505e9f9347b3f.jpg"},{"id":87513422,"identity":"777c4097-7fa7-409a-b0a9-e077b186b6eb","added_by":"auto","created_at":"2025-07-24 16:01:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79985,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual publication volume and its trend\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/fa44acbf016d20f0c98542e7.jpg"},{"id":87513423,"identity":"b26fe48d-1a8e-4dbb-85e1-13c9faa32c85","added_by":"auto","created_at":"2025-07-24 16:01:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93873,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution map drawn according to the total number of publications in different ountriesregions\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/959333abca7cf2f623eae2e1.jpg"},{"id":87514383,"identity":"67f14ddb-3aed-4ba6-9bd2-f273a8e75ba2","added_by":"auto","created_at":"2025-07-24 16:09:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74463,"visible":true,"origin":"","legend":"\u003cp\u003eVisual Analytics of National Collaboration Networks\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/8fbd8342475ee46f9e9d64ad.jpg"},{"id":87514785,"identity":"9b77cea3-8f84-4978-9199-4d08e75bd88f","added_by":"auto","created_at":"2025-07-24 16:17:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119217,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization map of countriesregions for international cooperation\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/ddc9e39c0425e6bdf2750369.jpg"},{"id":87514786,"identity":"5c18a578-09c1-4faf-8b6d-379865efdfd2","added_by":"auto","created_at":"2025-07-24 16:17:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":155666,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork visualization of the relationships among different authors\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/fc6e63c52f9d571fbd2e62b5.jpg"},{"id":87514787,"identity":"2c30b857-b677-4857-838b-6727955c5501","added_by":"auto","created_at":"2025-07-24 16:17:13","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":125305,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization map of co - cited journals\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/05960e2a4f8a28ce67b4bc1e.jpg"},{"id":87514389,"identity":"7610caf4-fc9d-4ff2-8098-2afeeabf2eb5","added_by":"auto","created_at":"2025-07-24 16:09:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":199258,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization map of keywords\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/7b01778335014dd6a39cb80b.jpg"},{"id":87514391,"identity":"0edea7d7-a881-4ebd-b793-9cc7093cff0b","added_by":"auto","created_at":"2025-07-24 16:09:13","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":146997,"visible":true,"origin":"","legend":"\u003cp\u003eThe top 10 keywords with the strongest citation bursts.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/92ece8892768715185defc75.jpg"},{"id":87514388,"identity":"eb37e619-8d13-4c0d-a1b6-529016d88d07","added_by":"auto","created_at":"2025-07-24 16:09:13","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":90724,"visible":true,"origin":"","legend":"\u003cp\u003eLiterature Coupling Visualization Analysis\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/e53247a78c61adf10e7e95cc.jpg"},{"id":87513437,"identity":"36dad4a8-089c-40bf-9855-ae7093b3f85c","added_by":"auto","created_at":"2025-07-24 16:01:13","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":148923,"visible":true,"origin":"","legend":"\u003cp\u003eSynergistic Therapy for Breast Cancer via CAF-Targeting\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/10e46ba0e134cb5777298c02.jpg"},{"id":88506150,"identity":"2ed70d39-6f31-404f-8a22-86b2b210af17","added_by":"auto","created_at":"2025-08-07 07:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2615438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/73f9b5c5-f3cd-4c1d-96bb-4848096ca4ea.pdf"},{"id":87514385,"identity":"8f3cb14a-f22a-49f5-8d35-c22db091c7bc","added_by":"auto","created_at":"2025-07-24 16:09:13","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2106619,"visible":true,"origin":"","legend":"","description":"","filename":"Analyzedata.zip","url":"https://assets-eu.researchsquare.com/files/rs-6881099/v1/ff7b80de18886519f6b0480f.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bibliometric Review of Cancer-Associated Fibroblasts in Breast Cancer from 2005 to 2025","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eBreast cancer is a highly heterogeneous disease that predominantly affects women and is widely distributed globally. It represents the leading cause of cancer-related deaths among women, with its incidence closely associated with age and a country's economic development level. Based on the Human Development Index analysis, developed countries exhibit significantly higher breast cancer incidence rates compared to developing nations. Economic conditions influence key factors such as women's growth, nutritional status, and the regulation of menarche age, reproductive history, menopause age, and obesity rates. These factors, in conjunction with genetic determinants, significantly contribute to breast cancer incidence. Early diagnosis and detection are critical for improving patient survival rates; however, over half of the patients are already at an advanced or metastatic stage by the time clinical symptoms manifest\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.Breast cancer can be classified into molecular subtypes, including luminal A, luminal B, HER2-positive, and triple-negative breast cancer (TNBC)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.Metastasis is the primary cause of mortality in breast cancer patients, and the tumor microenvironment (TME) plays a pivotal role in metastatic mechanisms. Immune and non-immune cells within the TME collaboratively regulate the cancer metastasis process. Targeted inhibition of relevant signaling pathways to suppress metastatic activation can effectively reduce the vascular invasive capacity and metastatic potential of tumor cells\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCAFs are activated heterogeneous stromal cells within the TME that play a pivotal role in tumor progression\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. As the most abundant stromal component in the TME, CAFs are closely associated with tumor progression \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. They drive tumor development by facilitating ECM deposition and remodeling\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, mediating immune suppression, enhancing cancer cell proliferation, inducing angiogenesis, and promoting therapy resistance. In breast cancer, CAFs primarily originate from normal fibroblasts (NFs), where CD26\u0026thinsp;+\u0026thinsp;NFs and CD26\u0026thinsp;\u0026minus;\u0026thinsp;NFs differentiate into inflammatory CAFs and myofibroblastic CAFs, respectively, synergistically promoting tumor progression\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The activation mechanisms of CAFs are a current research focus, with transforming growth factor beta (TGF-β) family ligands serving as key signaling molecules regulating the transformation of NFs into CAFs\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCAFs exhibit high heterogeneity and can be classified into distinct subpopulations. A systematic analysis based on six CAF biomarkers in breast cancer (FAP, CD29 [integrin-β1], α-SMA, FSP1, PDGFRβ, and Caveolin) has identified four well-characterized CAF subtypes designated as CAF-S1 through CAF-S4\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These subpopulations play critical roles in breast cancer initiation, progression, invasion, metastasis, and treatment response, with their classification providing novel perspectives and potential strategies for breast cancer diagnosis and therapy. Furthermore, CAFs mediate immunosuppression through the secretion of immunoregulatory cytokines and chemokines, thereby promoting tumor progression\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo date, no bibliometric studies have been reported on breast cancer or CAFs. This study employs VOSviewer and CiteSpace to conduct a visualized analysis of breast cancer CAF related literature from the Web of Science database. Through qualitative and quantitative assessments across dimensions including countries, authors, institutions, and journals, we elucidate research hotspots and developmental trends in this field.\u003c/p\u003e"},{"header":"2.Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Sources and Search Strategy\u003c/h2\u003e\u003cp\u003eThis study utilized the WOSCC database as the data source, with the index scope limited to SCI EXPANDED and SSCI. The search keywords were \"breast cancer\" and \"CAFs,\" and the retrieval strategy was as follows:TS=(\u0026ldquo;Breast Neoplasm\u0026rdquo; OR \u0026ldquo;Breast Tumor\u0026rdquo; OR \u0026ldquo;Breast Cancer\u0026rdquo; OR \u0026ldquo;Cancer of the Breast\u0026rdquo; OR \u0026ldquo;Cancer of Breast\u0026rdquo; OR \u0026ldquo;Breast Carcinoma\u0026rdquo;) AND TS=(\u0026ldquo;Cancer Associated Fibroblast\u0026rdquo; OR \u0026ldquo;Tumor Associated Fibroblast\u0026rdquo; OR \u0026ldquo;Cancer Related Fibroblast\u0026rdquo; OR \u0026ldquo;Tumor Related Fibroblast\u0026rdquo; OR \u0026ldquo;Tumour Associated Fibroblast\u0026rdquo; OR \u0026ldquo;Tumour Related Fibroblast\u0026rdquo;).\u003c/p\u003e\u003cp\u003eThe search was conducted for publications from January 1, 2005, to March 13, 2025, with the document types limited to \"Article\" and \"Review Article\" and the language restricted to English. Based on the above search strategy, a total of 1,929 publications were retrieved. After evaluating the titles and abstracts, 377 articles highly relevant to the research topic were selected for further analysis.The complete records, including cited references, were exported in plain text format and imported into CiteSpace for bibliometric analysis. The final dataset comprised 377 publications, consisting of 334 research articles and 37 review articles .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2Data Analysis and Visualization\u003c/h2\u003e\u003cp\u003eThis study employed bibliometric analysis on the screened 377 publications using tools including WPS Office, VOSviewer, CiteSpace (v6.3.R1), Pajek, and Scimago Graphica. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the literature search and processing workflow.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBibliometrics applies statistical and mathematical methods to quantitatively analyze literature within a specific field, thereby revealing research trends and developmental trajectories. By systematically organizing and analyzing information such as authors, keywords, countries, institutions, and journals, a comprehensive understanding of the knowledge structure and research hotspots in the field can be achieved.\u003c/p\u003e\u003cp\u003eVOSviewer, a freely available bibliometric visualization tool developed by Leiden University in the Netherlands, constructs network maps based on co-occurrence matrices to intuitively display the bibliometric structure of a research domain. In this study, VOSviewer was utilized to generate author collaboration networks, document co-citation networks, and keyword co-occurrence networks. In these network visualizations, node size represents element importance, line thickness indicates connection strength, and node colors distinguish different cluster modules.\u003c/p\u003e\u003cp\u003eCiteSpace, developed by Professor Chaomei Chen's research team, specializes in collaboration networks and cluster analysis. By identifying and visualizing burst detection of key nodes, CiteSpace reveals pivotal turning points and critical advancements in a research field. Through centrality and burstness analyses, researchers can efficiently grasp the knowledge base, research hotspots, and cutting edge trends within the domain.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Annual Publication Output and Trends\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the annual and cumulative publication volumes of literature in the field of breast cancer and CAFs from 2005 to 2025. The relatively low number of publications in 2010 indicates that this period had not yet formed a significant research focus. From 2010 to 2020, publications appeared annually with a steady growth rate, reaching the first peak in 2021, suggesting a breakthrough during this phase. Although the number of publications declined between 2021 and 2022, it rebounded rapidly in 2023 and reached its highest level in 2024, demonstrating important advancements during these two years. From 2005 to 2025, the cumulative number of publications in this field amounted to 377, with an average annual output of 19 papers. This trend indicates that research on breast cancer and CAFs has gradually gained attention, with significantly increased activity in recent years, establishing itself as an important direction in oncology research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Contributions of Countries or Regions\u003c/h2\u003e\u003cp\u003eThis study encompasses literature from 129 countries and 1,537 institutions. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the top ten countries by publication output, along with their respective publication counts, average citation counts, and centrality metrics. The top three countries by publication volume are China (n\u0026thinsp;=\u0026thinsp;143), the United States (n\u0026thinsp;=\u0026thinsp;76), and Italy (n\u0026thinsp;=\u0026thinsp;36). The United Kingdom ranks first in average citations with 97 citations per publication. The top three countries by centrality are the United States (0.52), the United Kingdom (0.28), and China (0.20). The data indicate that the United States, the United Kingdom, and China exhibit substantial research influence in this field.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContributions of Countries\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountries/Regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCitations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentrality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage Citations/Publications\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e82.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnited Kingdom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouth Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGermany\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSweden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e85.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing CiteSpace, we analyzed international collaboration networks. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a geographic visualization map of 20 countries, generated using VOSviewer and Scimago Graphica. The United States occupies a central role in international collaborations, demonstrating the broadest collaborative reach, with strong partnerships established with 13 countries, including China, the United Kingdom, Italy, Spain, and Canada, positioning it as the leader in the collaboration network.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, generated using CiteSpace, illustrates inter-country collaboration relationships. Node size represents the publication volume of each country, while the purple outer ring indicates centrality, with thickness reflecting centrality strength. The thickness of connecting lines between nodes denotes collaboration intensity. The United States exhibits the highest collaboration intensity and centrality in the network, followed by the United Kingdom and China. This underscores the United States' pivotal role in international collaborations within this field, while the United Kingdom and China also demonstrate strong collaborative influence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Institutional Contributions\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the top 10 institutions by publication output in the field of CAFs research.The University of Calabria (n\u0026thinsp;=\u0026thinsp;20), Chongqing Medical University (n\u0026thinsp;=\u0026thinsp;18), and the University of Manchester (n\u0026thinsp;=\u0026thinsp;13) rank as the top three, demonstrating the highest research productivity in this field.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContributions of Institute\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrganization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDocuments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Link Strength\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniv Calabria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChongqing Med Univ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniv Manchester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThomas Jefferson Univ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKing Faisal Specialist Hosp \u0026amp; Res Ctr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYonsei Univ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShanghai Jiao Tong Univ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTel Aviv Univ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLund Univ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHuazhong Univ Sci \u0026amp; Technol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing VOSviewer, we analyzed institutional collaboration networks by setting the minimum co-occurrence threshold to 3. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the collaboration network is divided into three clusters: red, green, and blue. The node size represents the volume of collaborative publications, while the connecting line thickness reflects the strength of collaboration. Within the red cluster, the University of Calabria, Chongqing Medical University, the University of Manchester, and Thomas Jefferson University exhibit larger nodes, indicating their central role in the collaboration network. These institutions maintain extensive connections, with particularly strong collaboration between the University of Manchester and Thomas Jefferson University. The University of Calabria demonstrates the highest collaboration intensity within its cluster, highlighting its pivotal position. At the periphery of the red and blue clusters, as well as between the green and blue clusters, overlapping nodes suggest potential interdisciplinary collaboration. However, the thin connecting lines indicate that these collaborations remain relatively independent. In contrast, the blue cluster shows sparse internal connections, reflecting weaker collaboration among its members.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e3.4 Author Contributions and Co - cited Authors\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the top 10 authors in the field of CAFs in breast cancer based on publication volume and co-citation frequency, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAuthor Contributions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDocuments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage Citations/Publications\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaggiolini, Marcello\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLisanti, Michael p.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiu, Manran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAboussekhra, Abdelilah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLappano, Rosamaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePestell, Richard g.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhitaker-Menezes, Diana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHowell, Anthony\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMartinez-outschoorn, Ubaldo e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChiavarina, Barbara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn terms of publication count, the top three authors are Maggiolini, Marcello; Lisanti, Michael P; and Liu, Manran. When analyzed by average citations per paper, among the top 10 authors by publication volume, Whitaker-Menezes, Diana (n\u0026thinsp;=\u0026thinsp;152), Pestell, Richard G. (n\u0026thinsp;=\u0026thinsp;150), and Chiavarina, Barbara (n\u0026thinsp;=\u0026thinsp;144) rank highest. This indicates that the research outputs of these three authors are highly cited in the field, likely appearing together in multiple key publications, reflecting their close academic connections and significant influence in breast cancer CAFs research.\u003c/p\u003e\u003cp\u003eUsing VOSviewer software, a co-authorship network analysis was conducted on 53 authors with at least four publications, revealing the largest cluster comprising 12 authors to visualize collaborative relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The dense connecting lines among these 12 authors indicate frequent collaborations. Among them, Maggiolini, Marcello has the largest node, representing the highest number of collaborative publications, with the most prominent connecting lines to multiple co-authors, highlighting his central role in the collaboration network. Similarly, Lappano, Rosamaria demonstrates extensive collaborative ties with other researchers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Journal Analysis\u003c/h2\u003e\u003cp\u003eThis study involved a total of 164 journals.Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the top 10 journals ranked by publication volume, along with their respective article counts, journal quartiles, and 2023 impact factors. Among these, Cancers ranked first with 19 publications, followed by Oncogene (N\u0026thinsp;=\u0026thinsp;14) and Cancer Research (N\u0026thinsp;=\u0026thinsp;13). Nature Communications had the highest impact factor in the listed journals, reaching 14.7 in 2023.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContributions of Journal\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJournal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIF(2023)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOncogene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer Research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrontiers In Oncology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast Cancer Research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCell Cycle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternational Journal Of Molecular Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTheranostics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer Letters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNature Communications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing a minimum publication threshold of 5 articles, this study conducted a citation analysis of 22 journals using VOSviewer, with the results illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. In the figure, node size represents co-publication volume, line thickness indicates collaboration strength, and differently colored node connections denote interdisciplinary journal collaborations. The analysis revealed that Cancers had the largest node and maintained dense connections with multiple journals, demonstrating its broad academic influence and collaborative reach. Key journals facilitating interdisciplinary collaboration included Cancers, Cancer Research, Frontiers in Oncology, and Cell Cycle, all of which exhibited extensive collaborative linkages with journals from other disciplinary clusters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e3.6 Keywords\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThrough keyword extraction and analysis, this study visualized the research hotspots and frontier directions in the field. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e lists the top 10 most frequently occurring keywords. The high-frequency keywords include \"breast cancer\" (N\u0026thinsp;=\u0026thinsp;164),\" microenvironment\" (N\u0026thinsp;=\u0026thinsp;153), \"cancer-associated fibroblasts\" (N\u0026thinsp;=\u0026thinsp;116), \"expression\" (N\u0026thinsp;=\u0026thinsp;110),\" cell\" (N\u0026thinsp;=\u0026thinsp;83),\" metastasis\" (N\u0026thinsp;=\u0026thinsp;81), \"growth\" (N\u0026thinsp;=\u0026thinsp;79),\"stromal fibroblasts\" (N\u0026thinsp;=\u0026thinsp;74),\"progression\" (N\u0026thinsp;=\u0026thinsp;47), and\"activation\" (N\u0026thinsp;=\u0026thinsp;44). The top three keywords in terms of frequency breast cancer, microenvironment, and CAFs indicate that the microenvironment plays a significant role in research related to CAFs in breast cancer.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ekeyword Contributions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKeyword\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicroenvironment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer-Associated Fibroblasts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExpression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStromal Fibroblasts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProgression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eactivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents a VOSviewer based visualization of keyword co-occurrence to reveal the distribution of research themes in this field. The keywords were clustered into six distinct groups: red, light blue, blue, green, yellow, and purple clusters. Based on node size, the primary keywords in each cluster are as follows: Red cluster: TGF-β, tumor growth, stem cells, metabolism; Light blue cluster:breast cancer, prognosis, extracellular matrix༛ Blue cluster:cell, heterogeneity, resistance༛ Green cluster: growth, proliferation, tumor stroma immunotherapy༛ Yellow cluster:stromal myofibroblasts, epithelial-mesenchymal transition (EMT), microenvironment༛ Purple cluster:cancer-associated fibroblasts, metastasis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing CiteSpace, we conducted a burst analysis on keywords and extracted the top 10 key burst terms. By examining the burst start time, duration, and end time of these keywords, we can clearly understand the evolution, frontiers, and latest trends in the research hotspots of this field.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays the burst start and end times of the keywords. The burst strength reflects the popularity of a keyword in academic research, with the red line segments indicating the trending period of a keyword within a specific historical interval. Light blue nodes represent periods when the keyword had not yet emerged, while dark blue nodes indicate its initial appearance and gradual rise in attention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis reveals that \"tumor stroma\" exhibits the highest burst strength (6.97), followed by \"metastasis\" (5.5) and \"stromal fibroblasts\" (4.6). Between 2005 and 2025, the earliest emerging keywords included \"stromal fibroblasts,\" \"tumor microenvironment,\" \"aerobic glycolysis,\" and \"metastasis,\" among which \"TGF-β\" maintained sustained research interest over a prolonged period. In recent years, research hotspots have gradually shifted toward keywords such as \"promotion,\" \"metastasis,\" and \"mechanism,\" indicating that the field has entered a new phase, focusing primarily on tumor drug resistance mechanisms and their regulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.7Literature Co-citation Analysis\u003c/h2\u003e\u003cp\u003eCo-citation analysis is used to reveal the intrinsic relationships between publications. When two or more documents are simultaneously cited by another paper, a co-citation relationship is formed between them. Through co-citation analysis, researchers can gain in depth insights into the knowledge structure and developmental trajectory of a research field. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e lists the top 10 most frequently co-cited references. The most highly cited publications are \"The prognostic significance of tumor-associated stroma in invasive breast carcinoma\" and \"Stromal miR-200s contribute to breast cancer cell invasion through CAF activation and ECM remodeling\" The first study demonstrates that CAFs in tumor stroma can serve as a prognostic marker for breast cancer patients, with higher CAF density in TNBC being associated with chemotherapy resistance and reduced survival. The second study reveals, from a molecular perspective, that the miR-200s-ZEB1-MMP axis is a key mechanism through which CAFs promote metastasis, highlighting its potential as a therapeutic target. Together, these two studies underscore the critical role of stromal reprogramming in breast cancer progression.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContributions of co-cited references\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTitle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCentrality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe prognostic significance of tumor-associated stroma in invasive breast carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStromal miR-200s contribute to breast cancer cell invasion through CAF activation and ECM remodeling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer-associated fibroblasts induce high mobility group box 1 and contribute to resistance to doxorubicin in breast cancer cells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep16(INK4A) represses the paracrine tumor-promoting effects of breast stromal fibroblasts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStroma in breast development and disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHuman breast cancer invasion and aggression correlates with ECM stiffening and immune cell infiltration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer-associated fibroblasts release exosomal microRNAs that dictate an aggressive phenotype in breast cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParacrine signaling by platelet-derived growth factor-CC promotes tumor growth by recruitment of cancer-associated fibroblasts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibroblast Subtypes Regulate Responsivenessof Luminal Breast Cancer to Estrogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutophagy in cancer associated fibroblasts promotes tumor cell survival: Role of hypoxia,HIF1 induction and NFκB activation in the tumor stromal microenvironment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eConducting bibliographic coupling analysis using VOSviewer bibliographic coupling is used to examine the connections between scientific publications. When two documents are simultaneously cited by a third publication, they form a bibliographic coupling pair, with the coupling strength indicating the similarity of their research topics. In the visualization, each node represents a document, labeled with the first author's name and publication year.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, this study set a minimum threshold of 183 co-citations, meaning only document pairs that were jointly cited at least 183 times were considered. Based on this criterion, the 20 most strongly coupled documents were identified. Among them, Pelon (2020), Raz (2018), Cohen (2017), Ershaid (2019), and Martinez Outschoorn (2010) exhibited the highest coupling strengths, indicating their substantial influence and representativeness in this research field. By integrating citation frequency, cited frequency, and centrality analysis, key representative documents can be further identified. These publications are not only widely cited within the field but also demonstrate strong connections with other studies through bibliographic coupling relationships. This analysis provides valuable insights into the research hotspots and emerging directions in the discipline.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 General Information\u003c/h2\u003e\u003cp\u003eThis study employs bibliometric methods to evaluate the contributions and influence of research on CAFs in breast cancer across dimensions such as countries, institutions, authors, and journals. Since the first publication in this field in 2005, the number of articles has increased significantly from 2020 to 2024, marking a phase of rapid development. Although there was a decline in publications between 2021 and 2022, the upward trend resumed in 2022, reflecting dynamic shifts in research activity and interest. At the national level, China, the United States, and Italy have contributed the highest number of publications, highlighting their research maturity and academic impact in this field.\u003c/p\u003e\u003cp\u003eA total of 164 journals have published research on breast cancer CAFs, with Cancers being the most prolific, followed by Cell Cycle and Cancer Research. These journals provide scholars with the latest advancements and trends in the field. The top three most published authors are Marcello Maggiolini, Michael P. Lisanti, and Manran Liu, who have made significant contributions to breast cancer CAF research. For instance, Marcello Maggiolini uncovered the mechanism by which CAFs mediate therapy resistance in estrogen-rich microenvironments and proposed phytochemical based intervention strategies. Michael P. Lisanti and Diana Whitaker Menezes, long-term research collaborators, jointly proposed a novel mechanism for the Warburg effect, demonstrating that stromal fibroblasts undergo aerobic glycolysis to produce and secrete more pyruvate/lactate, which fuels mitochondrial TCA cycles, oxidative phosphorylation, and metabolism in adjacent epithelial cancer cells. In 2014, they further elucidated how altered signaling pathways in cancer cells drive neighboring CAFs to generate reactive oxygen species and autophagy, thereby supporting cancer growth and metastasis. Their work revealed the critical role of CAF metabolism in breast tumor progression and introduced the \"metabolic coupling\" model, providing a theoretical foundation for targeting CAF metabolism to improve breast cancer treatment.\u003c/p\u003e\u003cp\u003eAmong the top ten most published authors, Diana Whitaker Menezes has the highest average citation count. Her research focuses on the TME and CAFs in breast cancer, establishing the central role of CAFs in metabolic support, ECM stiffening, and therapy resistance. U. E. Martinez Outschoorn investigates the complexity of the TME, collaborating with Michael P. Lisanti and Federica Sotgia to explore metabolic interactions between cancer cells and CAFs. A. Orimo's research demonstrated that fibroblasts constitute the majority of stromal cells in breast cancer and found that stromal fibroblasts in invasive breast cancer promote tumor growth and angiogenesis through high secretion of stromal cell derived factor1. These studies provide a fundamental understanding of CAF's role in breast cancer and offer new directions for future research and therapeutic strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Knowledge Base\u003c/h2\u003e\u003cp\u003eBased on the results of the literature co-citation network analysis, the research foundation of CAFs in breast cancer can be broadly delineated. Among the top 10 most cited publications, the 2nd, 7th, and 8th articles focus on the tumor-promoting role of CAFs, highlighting their facilitation of breast cancer invasion and growth through various signaling molecules. The 4th and 9th articles explore CAF heterogeneity and the functions of different subtypes, particularly the role of the CAF-S1 subset in breast cancer in modulating regulatory T lymphocytes (Tregs). The 3rd and 10th articles summarize CAF-mediated therapy resistance, including chemotherapy resistance induced by activated CAFs via HMGB1 and the protective role of CAF autophagy under hypoxic conditions in shielding tumor cells from stress-induced damage. The 5th and 6th articles propose that CAFs influence breast cancer progression through ECM remodeling, while the 1st article mentions the prognostic value of tumor-associated stroma, suggesting CAFs as potential prognostic markers for breast cancer.\u003c/p\u003e\u003cp\u003eAna Costa et al. investigated the heterogeneity and plasticity of CAFs in breast cancer, as well as their relationship with immune suppression. Through experiments, they identified differences in the accumulation of CAF subtypes across breast cancer subtypes and classified breast cancer CAFs into four distinct subsets. Michael Bartoschek and Nikola Oskolk et al. utilized a breast cancer mouse model to perform single-cell RNA sequencing on 768 mesenchymal cell transcriptomes, defining three distinct CAF subpopulations and thereby enhancing the cellular resolution of CAF research. Ana Costa et al. further elucidated the association between breast cancer CAF subsets and immune suppression, demonstrating that CAF-S1 attracts CD4⁺CD25⁺ Tregs via CXCL12 expression and retains them on its surface through ligands TNFSF4/OX40L and PDCD1LG2/PD-L2, as well as the adhesion molecule JAM2. Moreover, compared to CAF-S4, CAF-S1 increases Treg numbers and enhances their ability to suppress effector T cell proliferation, thereby functionally augmenting Treg activity. These mechanisms enable CAF-S1 in breast cancer to mediate immune suppression\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHeather M. Brechbuhl's research revealed that CAF subtypes influence the sensitivity of breast cancer receptors to estrogen, and their expression affects tumor cell responsiveness to chemotherapeutic agents. Understanding the composition of CAFs in breast cancer may aid in predicting treatment response and prognosis, positioning CAFs as potential targets for drug development\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eJan Kiefer et al. uncovered a positive feedback loop between immunosuppressive ECM-myCAF and TGF-β-myCAF (CAF-S1 clusters) and Tregs, which may contribute to immunotherapy resistance\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Erik Sahai et al. summarized the conceptual framework of CAFs, providing an overview of breast cancer CAF research: fibroblasts are defined as cells negative for epithelial, endothelial, and leukocyte markers, exhibiting an elongated morphology and lacking oncogenic mutations\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. CAFs display phenotypic diversity, and their origin involves a process termed \"stromagenesis\", wherein resident fibroblasts undergo some form of tissue dysfunction\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. While stromal fibroblasts proliferate extensively in tumor-bearing patients, this phenomenon is rarely observed\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The study also summarized key signals for fibroblast activation and mechanisms underlying CAF activation: heat shock factor 1 contributes to CAF generation, IL-1 promotes CAF phenotype formation, TGF-β family ligands and the lipid mediator lysophosphatidic acid activate signaling pathways, Notch signaling drives CAF phenotypes in breast cancer, and physical changes in the ECM can also activate CAFs. Additionally, other stromal cells in the tumor microenvironment participate in CAF generation. For example, macrophage-derived granulin promotes the activation of fibrotic environments in liver metastasis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, illustrating the interplay between cancer and inflammatory regulation. The study further outlined CAF functions, including their association with tumor angiogenesis, interactions with other stromal cells, involvement in ECM deposition and remodeling within the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, and their role in promoting tumor growth.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Research Trends and Hotspots\u003c/h2\u003e\u003cp\u003eBy analyzing the 10 most frequently cited terms, the research frontiers and hotspots of CAFs in breast cancer were identified, including CAFs, tumor microenvironment, activation, expression, stromal fibroblasts, EMT, metastasis, and diagnostic/therapeutic strategies. Breast cancer CAFs are activated stromal cells, with research focus primarily on their regulatory mechanisms, exploring the origin, activation, and influencing factors of CAFs, as well as analyzing their functional characteristics and heterogeneity in breast cancer.\u003c/p\u003e\u003cp\u003eAdditionally, studies emphasize the interaction between CAFs and cancer stem cells, their synergistic effects with other stromal cells in the tumor microenvironment, and their collective role in regulating tumor progression. Research also investigates the association between CAFs and immunosuppressive regulation, where CAFs secrete cytokines to inhibit immune cell function and promote tumor development. Furthermore, strategies for targeting CAFs in breast cancer treatment are explored, such as eliminating CAFs using specific markers or blocking CAF related signaling pathways.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Basic Functions and Characteristics of CAFs in Breast Cancer\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section4\"\u003e\u003ch2\u003e4.3.1.1 Origin and Activation of CAFs in Breast Cancer\u003c/h2\u003e\u003cp\u003eFibroblasts, once activated in breast cancer CAFs primarily originate from the activation of resident fibroblasts, with other sources including mesenchymal stem cells, adipocytes, and bone marrow derived fibroblasts\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.Resident fibroblasts can be transformed into various types of CAFs through factors such as TGF-β, CXCL12, and co-culture with cancer cells. Transforming growth TGF-β, CXCL12, Wnt7a, miR-125b, miR-9, and chemotherapy can convert resident fibroblasts into α-SMA⁺ CAFs; transforming growth factor beta and platelet derived growth factor can transform fibroblasts into FSP1⁺ CAFs; mesenchymal stem cells can be converted into CAFs through the action of tumor exosomes and transforming growth factor-beta, among other factors; adipocytes can be transformed into FSP1⁺/α-SMA⁻/FAP⁻ CAFs under the influence of Wnt3a, providing another source of CAFs in breast cancer; bone marrow derived fibroblasts can be recruited into the TME as α-SMA⁺/PDGFRα⁻ CAFs, participating in the progression of breast cancer; and pericyte derived CAFs express vascular regulatory genes\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe activation mechanisms of breast cancer CAFs include cytokine mediation, direct interactions between tumor cells and fibroblasts, and the influence of the tumor microenvironment. Cytokine mediation primarily involves activation through cytokine receptor binding. Studies have shown that breast cancer cells transform normal mammary fibroblasts into CAFs through autocrine signaling of transforming growth TGF-β and CXCL12, contributing to the construction of a microenvironment conducive to tumor growth and invasion\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Growth factors, cytokines, and their receptor binding activate CAFs through signaling pathways. CXCL12, as a CXCL12, binds to CXCR4 on the surface of fibroblasts, activating them and inducing the upregulation of α-SMA. This process also promotes the secretion of TGF-β and CXCL12 by fibroblasts, enhancing the activation effect through positive feedback\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. After binding to its receptor, TGF-β not only activates fibroblasts and induces α-SMA upregulation but also promotes the secretion of TGF-β and CXCL12. Additionally, it participates in the regulation of physiological processes such as glycolysis and oxidative phosphorylation, influencing cellular metabolism\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Platelet derived growth factor binds to platelet derived growth factor receptors and synergizes with transforming growth TGF-β to influence intracellular metabolism by modulating the levels of isocitrate dehydrogenase 3α. Downregulation of IDH3α increases the levels of α-ketoglutarate and hypoxia inducible factor 1α, leading to enhanced glycolysis and reduced oxidative phosphorylation. Wnt7a, a member of the Wnt protein family, is a class of evolutionarily highly conserved secreted glycoproteins that play critical roles in biological processes such as embryonic development, cell proliferation, differentiation, polarity establishment, and tissue homeostasis maintenance. In breast cancer cells, Wnt7a activates fibroblasts by binding to TGF-β receptors, participating in the CAF activation process. Additionally, osteopontin (OPN) binds to CD44 and integrin αvβ3, activating fibroblasts through the AKT serine/threonine kinase and mitogen-activated protein kinase 1 signaling pathways, ultimately upregulating the expression of markers such as α-SMA, fibroblast-specific protein-1, and fibroblast activation protein α (FAP). Activated fibroblasts secrete increased levels of CXCL1, CXCL2, cyclooxygenase-2, IL-6, OPN, and collagen, among other substances. Transforming growth factor-beta plays a significant role in the activation mechanisms of breast cancer CAFs.\u003c/p\u003e\u003cp\u003eIn breast cancer patients, tumor cells not only recruit and activate fibroblasts into CAFs through autocrine secretion of the chemokine CXCL12 but also transport miRNAs to NFs via exosomes, transforming them into myofibroblasts (CAFs). The upregulation of miRNAs in cancer cell-derived exosomes can activate fibroblasts, inducing their transformation into CAFs. Recent studies have found that miR-370-3p derived from breast cancer cells activates fibroblasts, enhancing the stemness, migration, and invasive capabilities of cancer cells\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Additionally, breast cancer cells participate in the activation process by releasing exosomes containing proteins. Survivin is transferred to surrounding fibroblasts via exosomes, upregulating the expression of superoxide dismutase 1 and converting them into CAFs\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section4\"\u003e\u003ch2\u003e4.3.1.2 Heterogeneity of CAFs in Breast Cancer\u003c/h2\u003e\u003cp\u003eDifferent CAF subpopulations exhibit distinct characteristics, and in breast cancer, CAFs lack a unified marker. The expression of markers and functions vary among subpopulations, reflecting the heterogeneity of CAFs. Breast cancer is classified into luminal A, luminal B, HER2-positive, and triple-negative subtypes based on the expression levels of ERα, PR, and HER2. Each subtype has different prognoses, and CAF expression also demonstrates heterogeneity. Studies have found that CAFs in HER2-positive breast cancer are significantly different from those in triple-negative and ER-positive breast cancers, particularly in genes related to the cytoskeleton and integrin signaling\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.Different subtypes of breast cancer exhibit varying expressions of CAF-related proteins. For instance, FAP, FSP1, and platelet-derived growth factor receptor beta (PDGFRβ) are overexpressed in invasive lobular carcinoma, while in ductal carcinoma, stromal cells show higher expression levels of prolyl 4-hydroxylase, platelet-derived growth factor receptor alpha (PDGFRα), and chondroitin sulfate proteoglycan\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Utilizing single-cell RNA sequencing technology, breast cancer CAFs demonstrate spatial and functional heterogeneity, with at least three subpopulations identified: vCAF, mCAF, and dCAF.These subpopulations exhibit differences in cellular origin and function: mCAFs produce a wide range of stromal components; vCAFs primarily generate basement membrane products; and dCAFs mainly secrete paracrine signaling molecules\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The phenotypic heterogeneity of breast cancer CAFs is reflected through various biological markers, including PDGFRα/β, CD90/THY1, podoplanin (PDPN), α-SMA, fibroblast-specific protein 1 (FSP-1), FAP, fibronectin 1, vimentin (VIM), CD29, CD10, or G protein-coupled receptor 77. These markers may be expressed individually or co-expressed by different CAF subpopulations, and their expression varies across different tissues.\u003c/p\u003e\u003cp\u003eBased on the expression levels of α-SMA and FAP, human breast cancer CAFs are classified into four subtypes (CAF-S1 to CAF-S4). Fluorescence imaging reveals that CAF-S1 and CAF-S4 are preferentially enriched within tumors, CAF-S3 accumulates significantly in the peritumoral regions, and CAF-S2 is evenly distributed in both areas. CAF-S1 is associated with an immunosuppressive environment by secreting CXCL12 and enhancing the differentiation of Tregs, whereas CAF-S4 lacks this phenotype\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Single-cell transcriptomic analysis has identified two additional CAF subtypes in human breast cancer patients: FSP-1⁺ CAFs and PDPN⁺ CAFs\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The proportions of these two subtypes are correlated with BRCA mutations and clinical outcomes in TNBC.\u003c/p\u003e\u003cp\u003eThe heterogeneity and plasticity of breast cancer CAFs significantly influence changes in the tumor microenvironment, driving cancer cell progression. The differences between CAFs and NFs stem from variations in protein expression, which can be distinguished by detecting specific biomarkers. Due to the high heterogeneity of CAFs, different biomarkers may exhibit distinct functional effects. Understanding the functions of proteins expressed by CAFs can provide a basis for developing targeted CAF therapies in breast cancer.α-SMA-positive CAFs generate lactate and pyruvate during metabolism, providing nutrients for tumor cells and promoting tumor progression\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. VIM, a type III intermediate filament protein, is commonly used as a marker for maintaining cell structure and motility during cell migration\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e and is involved in late-stage tumor metastasis. FAP, another widely distributed biomarker of CAFs, is a serine protease that participates in ECM remodeling and fibrosis, accelerating tumor progression\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. FAP-positive CAFs contribute to the formation of an immunosuppressive TME through multiple mechanisms\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, promoting tumor growth and serving as a potential therapeutic target.\u003c/p\u003e\u003cp\u003eElevated levels of PDGFRα and PDGFRβ in the breast cancer stroma\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e are involved in CAF activation. Research targeting CAF biomarkers holds significant potential for controlling breast cancer progression and treatment.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 CAFs Promote Breast Cancer Cell Progression\u003c/h2\u003e\u003cp\u003eTME is influenced by CAFs, which interact with the TME to modulate breast cancer cell progression. CAFs play a role in interfering with breast cancer cell metastasis, and the extensive proliferation of mammary connective tissue results in CAFs constituting up to 80% of the tumor mass, making them the most prevalent stromal cell component in the breast TME\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The complexity of CAF-TME interactions is mutually influential\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, and the transformation and activation of CAFs are fundamental to cancer progression\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Upon activation, NFs promote cancer cell development through various mechanisms, including the secretion of growth factors, chemokines, and interleukins. Growth factors can directly activate fibroblasts, and CAFs also secrete a multitude of autocrine and paracrine cytokines, as well as other pro-tumor factors, to modulate the environment and create a microclimate conducive to tumor growth and metastasis\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe most representative growth factor is transforming growth TGF-β, which plays a critical role in breast cancer progression by participating in ECM mechanical sensing and myofibroblast differentiation\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. In the TME, CAFs secrete substantial amounts of TGF-β, activating the TGF-β/Smad signaling pathway in breast cancer cells and driving tumor progression. The communication between CAFs and tumor cells is bidirectional: tumor cells can secrete TGF-β themselves or mediate the transformation of stromal fibroblasts into CAFs through paracrine signaling, altering the TME and promoting tumor development and dissemination.Studies have shown that Grem1, a bone morphogenetic protein antagonist produced by CAFs, promotes fibroblast activation and breast cancer cell infiltration and extravasation, driving the formation of micrometastases, which is the initial step in the invasion-metastasis cascade\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Additionally, the lack of CAV-1 in fibroblasts leads to increased TGF-β secretion, activating the TGF-β/Smad signaling pathway in breast cancer and promoting tumor metastasis\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Other growth factors also participate in remodeling the TME and facilitating cancer cell metastasis. For example, CAF-secreted factors such as hepatocyte growth factor (HGF), nerve growth factor, connective tissue growth factor, and basic fibroblast growth factor are all involved in breast cancer metastasis and invasion.Among these, HGF secreted by CAFs exhibits a bidirectional interaction with breast cancer cells: breast cancer cells can reprogram surrounding NFs, transforming them into CAFs or key-stage cells; meanwhile, HGF secreted by CAFs plays a significant role in breast cancer progression, with its secretion levels positively correlated with mammary tumorigenesis, cell migration, and invasive capacity\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, thereby driving the metastatic process. Breast cancer cells induce fibroblasts to secrete HGF, further enhancing the pro-tumor effects of CAFs and establishing a mutually reinforcing relationship.\u003c/p\u003e\u003cp\u003eChemokines and their receptors mediate chemotaxis and are deeply involved in tumorigenesis and progression\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The association between CAFs and the chemokine CXCL12 is particularly significant: on one hand, chemokines secreted by CAFs regulate the cytoskeleton of mammary tumor cells, influencing their motility\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e; on the other hand, OPN secreted by cancer cells stimulates CAFs to secrete more CXCL12. The increased CXCL12 triggers EMT in tumor cells, enhancing their invasive and metastatic potential\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Additionally, CXCL12 produced by CAFs acts on endothelial cells, modulates the tumor microenvironment, and stimulates tumor cells through inflammatory mechanisms, activating the Notch1 signaling pathway and increasing CXCL8 production, thereby enhancing tumor cell metastatic activity\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e IL-6 is widely present in the tumor microenvironment. Studies have shown that IL-6 reduces the expression of the tumor suppressor HIC1 and promotes breast cancer progression through paracrine or autocrine signaling\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCAFs mediate breast cancer metastasis through exosomes, which are enriched with miRNAs that can be taken up by tumor cells to regulate target gene expression, promoting breast cancer progression and metastasis. Additionally, CAFs interact with stromal cells such as macrophages and TANs, participating in angiogenesis and ECM remodeling. Angiogenesis is critical for the formation of premetastatic niches, while ECM remodeling is a necessary step for tumor cell invasion and metastasis. CAFs primarily promote angiogenesis through the VEGF dependent classical pathway\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. The binding of VEGF to VEGFR activates downstream signaling pathways, prompting vascular endothelial cells to develop into mature blood vessels, which supply nutrients to the tumor and remove metabolic waste, thereby facilitating tumor growth and metastasis.The ECM, composed of structural proteins, proteoglycans, and glycoproteins, has mechanical properties closely related to CAFs. Dynamic remodeling of the ECM leads to changes in tumor cell density, tissue stiffness, and structure, causing structural alterations in the TME due to force transmission, which creates conditions for directional cancer cell migration and invasion\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. CAFs enhance tumor invasion and metastasis by promoting ECM protein deposition, secreting growth factors, and remodeling the ECM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Targeting CAFs in Breast Cancer for Therapeutic Intervention\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, synergistic therapeutic approaches targeting CAFs involve cellular reprogramming to deactivate their pro-tumor functions, combined with natural compounds and chemotherapeutic agents. In terms of immune regulation, CAFs increase the number of regulatory T cells in the TME by secreting IL-6 and CXCL12, thereby suppressing the body's anti-tumor immune response. CAFs induce cytotoxic T cell death in an antigen-independent manner by activating apoptotic ligands and LAG-3, further weakening anti-tumor immunity\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Studies have identified a senescent CAF population in breast cancer that promotes tumor progression by inhibiting the anti-tumor response of NK cells. senCAFs form physical or chemical barriers that impede NK cell infiltration into tumor cells and interfere with NK cell cytotoxicity through ECM secretion. Eliminating senCAFs significantly delays tumorigenesis, suggesting their potential as a therapeutic target in breast cancer\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. As key cells in the TME, CAFs participate in tumor immune escape by communicating with other cells and secreting ECM components, modulating the functions of myeloid and lymphoid cells. Therapeutic strategies targeting CAFs may disrupt this process\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTME in breast cancer influences cancer cell metastasis, with the transformation of normal stromal fibroblasts into CAFs being a critical step in the transition to a pre-cancerous microenvironment, as confirmed in studies\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Intervening in the activation state of CAFs is a key strategy to prevent breast cancer stroma formation, including inhibiting CAF activity to limit their functions or reprogramming active CAFs into quiescent fibroblasts. Both approaches may disrupt the formation of breast cancer stroma, offering new insights for early prevention and treatment of breast cancer\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTargeting CAFs for treatment can be achieved by disrupting the breast cancer tumor microenvironment. First, blocking communication between tumor cells and CAFs, such as targeting the TGF-β1/Smad or CXCL1/CXCR12 signaling axes, can inhibit tumor metastasis\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Disrupting the ECM, which acts as a protective barrier for tumors, is a potential strategy for anti-tumor therapy. Investigating the interactions between CAFs and immune cells, as well as the role of CAFs in anti-angiogenesis and estrogen related mechanisms particularly focusing on CAF hormone resistance in estrogen-related breast cancer subtypes, may provide new insights for blocking CAF functions.\u003c/p\u003e\u003cp\u003eCAF mediated chemotherapy resistance is a key mechanism by which they protect tumor cells. CAFs increase IFN expression through paracrine signaling, activating the IFN pathway to induce chemotherapy resistance, while IFN-blocking antibodies can inhibit this effect. Chemotherapy-resistant breast cancer cells activate CAFs and induce resistance via the TGF-β/p44/42 MAPK signaling axis, and targeting this pathway may reverse CAF-mediated resistance. Additionally, radiotherapy resistance is associated with CAFs, and inhibiting Dll1-mediated Notch signaling post-radiotherapy can reduce CAF numbers and enhance tumor cell radiosensitivity. Therapeutic strategies targeting CAFs include using natural products to reprogram CAFs into quiescent fibroblasts and employing nanoparticle technology combined with chemotherapy drugs to improve targeting efficiency. Nanoparticles can both inhibit CAF formation and enhance therapeutic efficacy, reduce toxicity, and activate the immune microenvironment\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Future\u003c/h2\u003e\u003cp\u003eWith advancements in medical technology, breast cancer treatment has developed mature strategies, including early screening, prevention, diagnosis, chemotherapy, and drug therapy. Targeting the immune regulation mediated by CAFs, the interaction signaling pathways between CAFs and tumor cells, and the metabolic mechanisms involved in breast cancer progression can lead to the development of CAF targeted drugs that block CAF driven tumor progression and overcome chemotherapy and radiotherapy resistance. Currently, CAFs have become a focal point in drug development as a therapeutic target for breast cancer. The heterogeneity of CAFs contributes to immune suppression, weakening anti-tumor immune responses and promoting breast cancer development.\u003c/p\u003e\u003cp\u003eFuture research directions include refining immunotherapy strategies by modulating CAF activity to revert them to quiescent fibroblasts, as well as exploring the combined application of CAF targeted therapies with immunotherapy, chemotherapy, and radiotherapy to provide better therapeutic outcomes for patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Advantages and Disadvantages\u003c/h2\u003e\u003cp\u003eThe data for this study were sourced from the WOSCC, a comprehensive academic literature indexing database that covers renowned multidisciplinary journals and is updated daily. As a literature retrieval tool, WOSCC ensures the credibility and high quality of the literature data, providing a solid foundation for bibliometric analysis. In terms of analytical methods, three bibliometric tools, VOSviewer, CiteSpace, and WPS Office, were employed to conduct visual analysis of the relevant literature. This approach minimizes bias caused by subjective information filtering, ensuring the objectivity and accuracy of the research findings. Compared to traditional reviews, this study, through visual analysis and systematic review, presents a more intuitive and comprehensive overview of the hotspots and frontiers in the field of breast cancer related CAFs.\u003c/p\u003e\u003cp\u003eHowever, this study has certain limitations. Due to constraints in data sources, literature from non-SCI journals or other databases may not have been included, potentially affecting the comprehensiveness of the findings. The study primarily relies on citation frequency and publication counts to assess the importance of literature, but this approach may not fully capture the multidimensional value of research, such as its innovativeness or practical application potential. Future studies could expand data sources and incorporate additional metrics for a more comprehensive evaluation to address these limitations.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study systematically evaluated the current research status of breast CAFs through bibliometric analysis, covering dimensions such as publication date, country, institution, author, and literature source, while also Summarizeing research trends, hotspots, and frontier directions. The results indicate that CAFs in breast cancer originate from diverse sources and play a crucial role in tumor development. CAFs participate in tumor microenvironment remodeling through multiple mechanisms, with angiogenesis and ECM remodeling being key prerequisites for tumor cell progression.In response to the tumor-promoting effects of CAFs, this study proposes strategies to target and block CAF related signaling pathways to inhibit cancer cell metastasis and invasion. The research also explores the interaction mechanisms between CAFs and breast cancer cells, suggesting potential methods to disrupt CAF mediated interference in the tumor microenvironment.\u003c/p\u003e\u003cp\u003eCAFs not only promote tumor cell progression but also protect tumor cells from the effects of anti-cancer drugs by secreting cytokines and growth factors, leading to drug resistance. Therefore, therapeutic strategies targeting CAFs hold promise for overcoming this protective effect and addressing the issue of anti-cancer drug resistance. However, research on CAF targeted therapies for breast cancer remains exploratory, requiring further in-depth studies to elucidate their mechanisms of action and clinical application potential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eResearch conducted by Shixue Jing, Siping Zhou, Lushun Zhang; Data collection, Siping Zhou, Yongfeng Wang and Cui Jia; Visualization by Siping Zhou, Xinlian Liu and Hui Cao; Initial drafting by Siping Zhou, Shixue Jing, Yongfeng Wang and Xinlian Liu; Review and editing by Shinxue Jing, Cui Jia, Hui Cao, Lushun Zhang; Oversight by Hui Cao and Shixue Jing; Funding secured by Lushun Zhang and Xinlian Liu. The published version of the manuscript has been read and approved by all authors.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026rsquo; statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this paper. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003eFunding Statement\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Open Fund of Development and\u003c/p\u003e\n\u003cp\u003eRegeneration Key Laboratory of Sichuan Province (Grant No.23LHPDZYB07; Grant No.23LHPDZZD05), the Chinese Ministry of Education Cooperative Education Project (Grant No.231100882305626), and the National College Student Innovation and Entrepreneurship Training Program Project (Grant No.202413705016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study were retrieved from the Web of Science Core Collection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a systematic review. Ethics approval was waived for this study because no patient data were reported.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eClinical Trial Registration Number\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022;95(1130):20211033.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X. Molecular Classification of Breast Cancer: Relevance and Challenges. Arch Pathol Lab Med. 2023;147(1):46\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark M, Kim D, Ko S, Kim A, Mo K, Yoon H. Breast Cancer Metastasis: Mechanisms and Therapeutic Implications. 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ACS Appl Mater Interfaces. 2021;13(2):2256\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu Y, Yu F, Tan Y, Hong Y, Meng T, Liu Y, et al. Reversing activity of cancer associated fibroblast for staged glycolipid micelles against internal breast tumor cells. Theranostics. 2019;9(23):6764\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, CAFs, Tumor microenvironment, Bibliometric analysis","lastPublishedDoi":"10.21203/rs.3.rs-6881099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6881099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBreast cancer is one of the most common and lethal heterogeneous cancers among women worldwide, posing a significant threat to female health. Cancer associated fibroblasts (CAFs) play a critical role in the initiation and progression of breast cancer, and their behavior within the tumor microenvironment profoundly influences disease development. Systematic research on the functions of CAFs in breast cancer remains relatively limited. This study employs bibliometric theories and methods to comprehensively analyze the existing knowledge framework of breast cancer CAFs research, thereby identifying and examining research hotspots and future trends in this field.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study retrieved literature related to CAFs in breast cancer from the Science Core Collection (WOSCC) database, covering publications from 2005 to 2025. After rigorous screening, 377 eligible academic papers were included as research subjects. Utilizing software tools such as VOSviewer, Pajek, Scimago Graphica, and CiteSpace, we conducted an in-depth analysis across multiple dimensions, including countries, research institutions, authors, journals, and keywords, to elucidate the knowledge structure of this field and identify research hotspots and developmental trends.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe findings indicate a general upward trend in annual publications, with particularly rapid growth between 2022 and 2024. China, the United States, and Italy were the most prolific countries, demonstrating strong academic influence. Among institutions, the University of Calabria, Chongqing Medical University, and the University of Manchester contributed the most research output. Notable researchers, including Marcello Maggiolini, Michael P. Lisanti, and Manran Liu, stood out in terms of publication volume and collaborative engagement. Regarding journals, Cancers published the highest number of articles, while Nature Communications had the greatest impact, with a 2023 impact factor of 14.7. This study systematically reviews the origin, activation mechanisms, and heterogeneity of CAFs in breast cancer. The research reveals that CAFs can be activated through various cytokines and signaling pathways and exhibit significant heterogeneity across different breast cancer subtypes. CAFs secrete multiple factors involved in angiogenesis and extracellular matrix (ECM) remodeling, thereby promoting breast cancer cell metastasis and invasion. Additionally, CAFs may suppress the host's anti-tumor immune response. Current therapeutic strategies targeting CAFs primarily focus on disrupting intercellular communication, degrading the ECM, and overcoming drug resistance. Future research may concentrate on exploring the mechanisms by which immunotherapy regulates CAF activity and the potential of combination therapies. This study provides a comprehensive overview of the current status, hotspots, and cutting-edge advancements in breast cancer CAF research.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThrough rigorous bibliometric analysis, this study systematically examines research hotspots and trends in breast cancer CAF studies, establishing a solid literature-based foundation for defining future research directions and priorities. It highlights the significant potential and importance of targeting CAFs in the breast cancer stroma for therapeutic intervention and tumor progression inhibition. The findings are expected to offer scientific guidance for subsequent research and advance the development of breast cancer CAF studies. Although this study has certain limitations, it provides valuable references for future related research.\u003c/p\u003e","manuscriptTitle":"Bibliometric Review of Cancer-Associated Fibroblasts in Breast Cancer from 2005 to 2025","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 16:01:08","doi":"10.21203/rs.3.rs-6881099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-11T08:10:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T16:38:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97501424000032880909322120157848006774","date":"2025-08-25T11:36:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T21:34:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144919251523755692108324744634298656040","date":"2025-08-06T02:49:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T05:22:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-26T03:57:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-16T09:37:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T09:35:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-06-12T13:55:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"622335d4-d017-47c3-9cce-e16d7e1ae6b0","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-26T19:51:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 16:01:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6881099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6881099","identity":"rs-6881099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}