Zhou Qiong, Wang Rui
Laboratory of Medical Oncology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Front Pharmacol. 2025 Jul 23;16:1581820. doi: 10.3389/fphar.2025.1581820. eCollection 2025.
BACKGROUND: Sorafenib, a multi-kinase inhibitor, is a key therapeutic agent in the treatment of advanced hepatocellular carcinoma (HCC), metastatic renal cell carcinoma (RCC), and radioactive iodine-refractory differentiated thyroid cancer (DTC). However, its clinical efficacy is frequently hampered by the rising prevalence of sorafenib resistance, particularly in HCC. This reality underscores the urgent need for a comprehensive pan-cancer investigation to elucidate the underlying mechanisms of 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. CONCLUSION: This study highlights 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 , ultimately aiming to overcome resistance challenges and improve patient survival outcomes.
背景:索拉非尼是一种多激酶抑制剂,是治疗晚期肝细胞癌(HCC)、转移性肾细胞癌(RCC)和放射性碘难治性分化型甲状腺癌(DTC)的关键治疗药物。然而,其临床疗效常常受到索拉非尼耐药性日益普遍的阻碍,尤其是在HCC中。这一现实凸显了迫切需要进行全面的泛癌研究以阐明耐药的潜在机制。 方法:我们采用系统的文献计量学方法,利用科学网核心合集进行结构化文献检索。使用VOSviewer和CiteSpace进行绩效分析和可视化。实施了一个三阶段筛选方案,以识别2006年至2025年期间聚焦于索拉非尼耐药性的出版物。 结果:我们的分析确定了1484篇符合条件的出版物,2022年发表的文章数量达到峰值,为194篇。大多数研究(79.48%)集中在HCC,中国和美国的机构做出了重大贡献。共同作者和关键词网络分析揭示了一个强大的跨学科合作格局。出现了关键主题,包括药物转运体和清除机制的失调、代谢重编程、程序性细胞死亡、肿瘤微环境内的相互作用以及表观遗传调控机制,突出了导致耐药性的关键领域。 结论:本研究突出了当前关于索拉非尼耐药性的研究格局,有助于识别新出现的趋势和研究差距。未来的研究重点应包括生物标志物驱动的临床试验、纳米颗粒递送系统的开发以及联合治疗策略的临床转化。此外,在大数据背景下整合深度学习算法有可能增强我们对耐药机制的理解,最终目标是克服耐药挑战并改善患者生存结果。
Cancer Lett. 2025-3-31
Front Oncol. 2024-12-20
Adv Sci (Weinh). 2024-12
Cytokine Growth Factor Rev. 2024-10
J Cancer Res Clin Oncol. 2024-6-24