BIMM

地塞米松 (DEX) 在人类非小细胞肺癌 (NSCLC) 中显示出抗癌功效和抗雌激素活性; 因此，了解影响肺癌治疗方案实施和有效性的潜在机制至关重要。在这项研究中，我们结合了生物信息学和系统生物学的力量，使用scRNA-seq数据和蛋白质组学数据的生物信息学推断和多尺度建模来揭示药物治疗的功能和信号通路。在这里，我们提出了一个以TGF-β 诱导和ERBB扩增信号通路为中心的肿瘤调节的综合多尺度模型，以表征DEX疗法对肺癌细胞的动力学作用。我们还提供了不同剂量的预测，以说明DEX治疗的趋势和治疗潜力，因此可以进一步应用于肿瘤发生和肿瘤治疗的其他计算研究Dexamethasone (DEX) exhibits anticancer efficacy and antiestrogenic activity in human non-small cell lung cancer (NSCLC). Therefore, it is critical to elucidate the underlying mechanisms that affect the implementation and efficacy of lung cancer treatment regimens. In this study, we integrated the strengths of bioinformatics and systems biology, employing bioinformatic inference and multi-scale modeling based on single-cell RNA sequencing (scRNA-seq) and proteomic data to uncover the functional mechanisms and signaling pathways of drug therapies. Herein, we propose a comprehensive multi-scale model of tumor regulation centered on TGF-β-induced and ERBB-amplified signaling pathways, to characterize the kinetic effects of DEX therapy on lung cancer cells. We additionally provide predictions across varying doses to demonstrate the treatment trends and therapeutic potential of DEX, which can be further applied to other computational studies on tumorigenesis and cancer therapy.

搜集汇总

背景与挑战

背景概述

该数据集基于生物信息学与系统生物学方法，利用scRNA-seq和蛋白质组学数据构建多尺度模型，以研究地塞米松（DEX）在非小细胞肺癌中的抗癌机制，重点关注TGF-β和ERBB信号通路，并预测不同剂量下的治疗趋势。