ArticleDiscover oncology2025
Bibliometric analysis reveals the current status and future trends of cancer associated fibroblasts research in breast cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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7 authors.
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Abstract
backgroundBreast 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.
methodsThis 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.
resultsThe 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.
conclusionOur 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.