IQGAP1: cross-disease target via receptor-pathway networks.

IF 3.5 3区 医学 Q2 ONCOLOGY
Frontiers in Oncology Pub Date : 2025-09-16 eCollection Date: 2025-01-01 DOI:10.3389/fonc.2025.1632060
Shaopeng Zhu, Yunpeng Zou, Jie Guo, Wenqi Ma, Laitong Lu, Ronghan Liu, Jianning Kang, Kai Zhao, Jiangbo Zhong
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引用次数: 0

Abstract

IQGAP1, a versatile scaffolding protein, critically regulates cytoskeletal organization, cell motility, proliferation, and signaling cascades. Beyond coordinating these cellular functions, it is increasingly recognized as a key driver in malignancies, immune dysfunction, metabolic dysregulation, and cardiovascular pathologies. By binding receptor tyrosine kinases, small GTPases, and downstream effectors, IQGAP1 modulates oncogenesis, immune evasion, and metabolic imbalance, while contributing to chemoresistance. This review synthesizes advances in IQGAP1's structural domains, disease-specific signaling networks, and therapeutic targeting strategies, emphasizing its translational potential in developing precision therapies for cancer, metabolic syndromes, and immune disorders.

IQGAP1:通过受体通路网络的跨疾病靶点。
IQGAP1是一种多功能支架蛋白,对细胞骨架组织、细胞运动、增殖和信号级联反应起关键调控作用。除了协调这些细胞功能外,它越来越被认为是恶性肿瘤、免疫功能障碍、代谢失调和心血管疾病的关键驱动因素。通过结合受体酪氨酸激酶、小gtpase和下游效应物,IQGAP1调节肿瘤发生、免疫逃避和代谢失衡,同时促进化学耐药。本文综述了IQGAP1在结构域、疾病特异性信号网络和治疗靶向策略方面的研究进展,强调了其在开发癌症、代谢综合征和免疫疾病的精确治疗方面的转化潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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