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绘制关于单克隆抗体的乳腺癌研究图谱:一种使用VOSviewer、Bibliometrix和CiteSpace的数据驱动方法。

Mapping breast cancer research on monoclonal antibodies: a data-driven approach using VOSviewer, Bibliometrix, and CiteSpace.

作者信息

Abdelwahab Siddig Ibrahim, Moni Sivakumar S, Taha Manal Mohamed Elhassan, Sahli Khaled A, Alqhtani Hatem Ahmed Salem, Farasani Moath Mohamed, Qadri Marwa, Alarifi Abdulaziz, Khardali Amani, Alsaadi Khulud Hamoud, Farasani Abdullah, Khamjan Nizar A, Al-Shamsi Humaid, Moshi Jobran M, Alshahrani Saeed, Salawi Ahmed, Assiri Ahmad, Ibrahim Ayah

机构信息

Health Research Centre, Jazan University, 45142, Jazan, Saudi Arabia.

Department of Pharmaceutics, College of Pharmacy, Jazan University, 45142, Jazan, Saudi Arabia.

出版信息

Naunyn Schmiedebergs Arch Pharmacol. 2025 Jun 25. doi: 10.1007/s00210-025-04401-7.

DOI:10.1007/s00210-025-04401-7
PMID:40560396
Abstract

Monoclonal Antibodies and Breast Cancer Research (MABCR) has progressed substantially, particularly in the areas of HER2-positive and triple-negative breast cancer. However, a comprehensive synthesis of global trends, influential contributors, and emerging research areas remains lacking. This study presents a bibliometric analysis of MABCR, highlighting major research trajectories, key publications, and evolving themes. Using data from the Scopus database spanning 1972 to 2024 and applying a multistep search strategy cross-validated with MeSH terms, 14,764 English-language original research articles were identified across 2125 sources. Non-research documents, duplicates, and retractions were excluded. Quantitative analysis was conducted using Bibliometrix, VOSviewer, and CiteSpace. The annual publication growth rate was 17.8%. The USA and China were the leading contributors, while Breast Cancer Research and Treatment emerged as the most prolific journal. Influential authors such as Slamon, Baselga, and Hortobagyi were identified based on publication volume, H-index, and citation impact. Co-citation analysis revealed major thematic clusters including HER2-positive and triple-negative breast cancer, trastuzumab deruxtecan, and the integration of machine learning in MABCR. Emerging areas of interest include HER2-low subtypes and tumor microenvironment profiling using radiomics and multi-omic approaches. This analysis provides a structured overview of MABCR, offering valuable insights to guide future research and foster innovation in precision oncology.

摘要

单克隆抗体与乳腺癌研究(MABCR)已经取得了长足的进展,尤其是在HER2阳性和三阴性乳腺癌领域。然而,目前仍缺乏对全球趋势、有影响力的贡献者以及新兴研究领域的全面综合分析。本研究对MABCR进行了文献计量分析,突出了主要研究轨迹、关键出版物和不断演变的主题。利用Scopus数据库中1972年至2024年的数据,并采用与医学主题词(MeSH)交叉验证的多步骤搜索策略,从2125个来源中识别出14764篇英文原创研究文章。排除了非研究文献、重复文献和撤稿文献。使用Bibliometrix、VOSviewer和CiteSpace进行了定量分析。年发表增长率为17.8%。美国和中国是主要贡献国,而《乳腺癌研究与治疗》是发表量最多的期刊。根据发表量、H指数和引用影响力等指标,确定了Slamon、Baselga和Hortobagyi等有影响力的作者。共被引分析揭示了主要的主题聚类,包括HER2阳性和三阴性乳腺癌、曲妥珠单抗德鲁替康以及机器学习在MABCR中的整合。新兴的研究领域包括HER2低表达亚型以及使用放射组学和多组学方法对肿瘤微环境进行分析。本分析提供了MABCR的结构化概述,为指导未来研究和推动精准肿瘤学的创新提供了有价值的见解。

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本文引用的文献

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Antibody-Drug Conjugates (ADCs): current and future biopharmaceuticals.抗体药物偶联物(ADCs):当前及未来的生物制药
J Hematol Oncol. 2025 Apr 30;18(1):51. doi: 10.1186/s13045-025-01704-3.
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A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6.利用机器学习分析 HSPB6 中分子免疫浸润物的乳腺癌前瞻性诊断模型。
J Cancer Res Clin Oncol. 2024 Oct 23;150(10):475. doi: 10.1007/s00432-024-05995-w.
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Association of tumor-infiltrating lymphocytes with clinical outcomes in patients with triple-negative breast cancer receiving neoadjuvant chemotherapy: a systematic review and meta-analysis.
三阴性乳腺癌患者接受新辅助化疗时肿瘤浸润淋巴细胞与临床结局的关联:一项系统评价和荟萃分析
Clin Transl Oncol. 2025 Mar;27(3):974-987. doi: 10.1007/s12094-024-03661-8. Epub 2024 Aug 18.
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Integrating machine learning algorithms and multiple immunohistochemistry validation to unveil novel diagnostic markers based on costimulatory molecules for predicting immune microenvironment status in triple-negative breast cancer.整合机器学习算法和多重免疫组化验证,以揭示基于共刺激分子的新型诊断标志物,用于预测三阴性乳腺癌的免疫微环境状态。
Front Immunol. 2024 Jun 28;15:1424259. doi: 10.3389/fimmu.2024.1424259. eCollection 2024.
5
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Front Oncol. 2024 Jun 20;14:1364397. doi: 10.3389/fonc.2024.1364397. eCollection 2024.
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Res Synth Methods. 2024 Sep;15(5):802-817. doi: 10.1002/jrsm.1729. Epub 2024 Jun 14.
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