ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Pharmacology of Anti-Cancer Drugs
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1581820
This article is part of the Research TopicInnovative Strategies for Overcoming Resistance in Tumor Angiogenesis TherapiesView all 5 articles
Emerging Trends and Knowledge Networks in Pan-Cancer Sorafenib Resistance: A 20-Year Bibliometric Investigation
Provisionally accepted
Qiong Zhou*
Rui Wang- Nanjing University, Nanjing, China
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Background: Sorafenib, a multi-kinase inhibitor, plays a crucial role in the treatment of advanced hepatocellular carcinoma (HCC), metastatic renal cell carcinoma (RCC), and radioactive iodinerefractory differentiated thyroid cancer (DTC). Despite its widespread clinical application, suboptimal therapeutic outcomes are common, driven by high rates of both innate and acquired resistance, particularly in HCC. This necessitates a comprehensive pan-cancer investigation of the mechanisms underlying drug resistance. Methods: We employed a systematic bibliometric approach utilizing the Web of Science Core Collection to conduct a structured literature search. Performance analysis and visualization were conducted using VOSviewer and CiteSpace. A triphasic screening protocol was implemented to identify publications focused on sorafenib resistance, covering a period from 2006 to 2025. Results: Our analysis identified 1,484 eligible publications, with a peak of 194 articles published in 2022. The majority of research (79.48%) centered on HCC, with significant contributions from institutions in China and the United States. Co-authorship and keyword network analyses revealed a robust interdisciplinary collaboration landscape. Key themes emerged, including dysregulation of drug transporters and clearance mechanisms, metabolic reprogramming, programmed cell death, interactions within the tumor microenvironment, and epigenetic regulatory mechanisms, highlighting critical areas contributing to resistance.This study provides a comprehensive overview of the current research landscape concerning sorafenib resistance, facilitating the identification of emerging trends and research gaps. Future research priorities should include biomarker-driven clinical trials, the development of nanoparticle delivery systems, and the clinical translation of combination therapy strategies. Additionally, the integration of deep learning algorithms in the context of big data has the potential to enhance our understanding of resistance mechanisms in silico, ultimately aiming to overcome resistance challenges and improve patient survival outcomes.
Keywords: Sorafenib resistance, Bibliometrics, Pan-cancer, knowledge network, targeted therapy
Received: 23 Feb 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Zhou and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qiong Zhou, Nanjing University, Nanjing, China
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