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The Tumor Microenvironment

The Tumor Microenvironment

Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...

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Updated: Jun 24, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

从空间解析的转录学数据中剖析瘤微环境,采用异质图形学习方法.

Chunman Zuo^1,2, Junjie Xia^3,4, Luonan Chen^5,6,7

¹Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China. cmzuo@dhu.edu.cn.

Nature communications

|June 13, 2024

概括

此摘要是机器生成的。

stKeep是一种新的计算方法,使用异质图来分析空间转录组学数据. 它揭示了复杂的瘤微环境,识别了细胞状态,并预测了癌症的进展,以改善诊断和免疫治疗.

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Last Updated: Jun 24, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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科学领域:

计算生物学是一种计算生物学.
癌症研究癌症研究
基因组学就是基因组学.

背景情况:

空间解析转录学 (SRT) 通过分析分子网络和细胞-细胞通信 (CCC) 提供了对瘤微环境 (TME) 的洞察.
解释复杂的TME结构是具有挑战性的,因为在计算上探索细胞,基因和组织学区域之间的复杂关系方面存在局限性.

研究的目的:

介绍stKeep,一种新的异质图 (HG) 学习方法,用于从SRT数据中解开TME复杂性.
整合多种模式和基因-基因相互作用,以便对TME进行全面分析.

主要方法:

stKeep利用HG学习细胞模块和基因模块,通过结合多种节点 (基因,细胞,组织学区域).
它识别了TME内的更细微的细胞状态和细胞状态特定的基因-基因关系.
使用对比学习推断每个细胞的CCC,确保跨细胞状态的可比性.

主要成果:

在多种癌症样本中剖析TME时,stKeep的性能优于现有的工具.
它准确地检测特定的细胞群,如双强基底细胞,瘤肌皮细胞和转移性细胞.
确定了与疾病进展相关的关键转录因子,配体和受体.

结论:

stKeep从SRT数据中增强了对TME结构和细胞间通信的解释.
识别的生物标志物通过临床数据分析得到验证,证明了预后和免疫治疗潜力.
该方法在癌症诊断和治疗策略中提供了重要的临床应用.