Precise Electromagnetic Modulation of the Cell Cycle and Its Applications in Cancer Therapy.

Journal: International journal of molecular sciences
Published Date: May 7, 2025

Abstract

Precise modulation of the cell cycle via electromagnetic (EM) control presents a groundbreaking approach for cancer therapy, especially in the development of personalized treatment strategies. EM fields can precisely regulate key cellular homeostatic mechanisms such as proliferation, apoptosis, and repair by finely tuning parameters like frequency, intensity, and duration. This review summarizes the mechanisms through which EM fields influence cancer cell dynamics, highlighting recent developments in high-throughput electromagnetic modulation platforms that facilitate precise cell cycle regulation. Additionally, the integration of electromagnetic modulation with emerging technologies such as artificial intelligence, immunotherapy, and nanotechnology is explored, collectively enhancing targeting precision, immune activation, and therapeutic efficacy. A systematic analysis of existing clinical studies indicates that EM modulation technology significantly overcomes key challenges such as tumor heterogeneity, microenvironment complexity, and treatment-related adverse effects. This review summarizes the prospects of electromagnetic modulation in clinical translation and future research directions, emphasizing its critical potential as a core element in individualized and multimodal cancer treatment strategies.

Authors

  • Keni Shi
    School of Biomedical Engineering, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou 510275, China.
  • Xiqing Peng
    School of Biomedical Engineering, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou 510275, China.
  • Ting Xu
    Bioresources Green Transformation Collaborative Innovation Center of Hubei Province, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China.
  • Ziqi Lin
    School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China.
  • Mingyu Sun
    School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China.
  • Yiran Li
    University of California at Davis, Davis, CA, USA.
  • Qingyi Xian
    School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China.
  • Tingting Xiao
    School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China.
  • Siyuan Chen
    First author: Department of Computer Science, Columbia University in the City of New York, 10027; second, fourth, and sixth authors: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853; third author: Department of Mechanical Engineering, Columbia University; fifth author: Uber AI Labs, San Francisco 94103; seventh author: Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University; and eighth author: Department of Mechanical Engineering and Institute of Data Science, Columbia University.
  • Ying Xie
    Department of Sociology, School of Public Administration, Guangzhou University, Guangzhou, 510006, China. xysoc@gzhu.edu.cn.
  • Ruihan Zhang
    Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China. xiaoweilie@ynu.edu.cn.
  • Jincheng Zeng
    Dongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Medical University, Dongguan 523808, China.
  • Bingzhe Xu
    Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China.

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