Yao Mengchao, Zhu Xiaomei, Chen Yong-Cong, Yang Guo-Hong, Ao Ping
Shanghai Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai 200444, China.
Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200444, China.
Int J Mol Sci. 2025 Apr 1;26(7):3283. doi: 10.3390/ijms26073283.
Medical treatment of glioblastoma presents a significant challenge. A conventional medication has limited effectiveness, and a single-target therapy is usually effective only in the early stage of the treatment. Recently, there has been increasing focus on multi-target therapies, but the vast range of possible combinations makes clinical experimentation and implementation difficult. From the perspective of systems biology, this study conducted simulations for multi-target glioblastoma therapy based on dynamic analysis of previously established endogenous networks, validated with glioblastoma single-cell RNA sequencing data. Several potentially effective target combinations were identified. The findings also highlight the necessity of multi-target rather than single-target intervention strategies in cancer treatment, as well as the promise in clinical applications and personalized therapies.
胶质母细胞瘤的医学治疗面临重大挑战。传统药物疗效有限，单靶点治疗通常仅在治疗早期有效。最近，人们越来越关注多靶点治疗，但大量可能的组合使得临床实验和实施变得困难。从系统生物学的角度来看，本研究基于对先前建立的内源性网络的动态分析，对多靶点胶质母细胞瘤治疗进行了模拟，并通过胶质母细胞瘤单细胞RNA测序数据进行了验证。确定了几种潜在有效的靶点组合。研究结果还强调了在癌症治疗中采用多靶点而非单靶点干预策略的必要性，以及在临床应用和个性化治疗方面的前景。
