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相关概念视频

The Tumor Microenvironment02:17

The Tumor Microenvironment

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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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相关实验视频

Updated: Jun 5, 2025

A Preclinical Mouse Model of Osteosarcoma to Define the Extracellular Vesicle-mediated Communication Between Tumor and Mesenchymal Stem Cells
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A Preclinical Mouse Model of Osteosarcoma to Define the Extracellular Vesicle-mediated Communication Between Tumor and Mesenchymal Stem Cells

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探索基于瘤微环境的骨髓瘤.

Ao Wu1, Zhi-Kai Yang2, Peng Kong3

  • 1The First Clinical College of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.

Frontiers in immunology
|December 10, 2024
PubMed
概括

这项研究开发了一种精确的骨髓瘤风险评估模型,识别与免疫相关的基因,以预测患者的结果并有效分析免疫检查点.

关键词:
与免疫相关的基因免疫检查站 免疫检查站免疫疗法 免疫疗法骨质肉瘤是骨质肉瘤的一种疾病.瘤微环境是一个微环境.

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Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma
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Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma

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Intratibial Osteosarcoma Cell Injection to Generate Orthotopic Osteosarcoma and Lung Metastasis Mouse Models

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相关实验视频

Last Updated: Jun 5, 2025

A Preclinical Mouse Model of Osteosarcoma to Define the Extracellular Vesicle-mediated Communication Between Tumor and Mesenchymal Stem Cells
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Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma
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Intratibial Osteosarcoma Cell Injection to Generate Orthotopic Osteosarcoma and Lung Metastasis Mouse Models
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科学领域:

  • 在瘤学瘤学.
  • 免疫学 免疫学 免疫学
  • 生物信息学是一种生物信息学.

背景情况:

  • 骨髓瘤是一种来自介质细胞的骨癌,具有转移,耐药性,残疾和死亡率的挑战.
  • 瘤微环境 (TME) 的免疫特征对于骨髓瘤的预后和治疗至关重要,需要敏感的预后签名.

研究的目的:

  • 通过分析TME内的免疫特征来确定骨髓瘤的敏感预后特征.
  • 开发和验证基于免疫相关差异表达基因 (IR-DEGs) 的风险评估模型,用于预测骨髓瘤患者的结果.

主要方法:

  • 来自UCSC Xena数据库的84个骨髓瘤样本的分析,用于免疫透和分类.
  • 使用TIMER数据库和交叉分析识别差异表达基因 (DEG) 和与免疫相关的DEG (IR-DEG).
  • 使用单变COX回归和LASSO分析构建风险模型,然后进行生存分析和免疫检查点评估.

主要成果:

  • 大多数被发现的DEGs在免疫领域中富含,突出显示了免疫在骨髓瘤中的作用.
  • 开发的风险评估模型显示了高风险和低风险评分组之间的显著预测区别.
  • 该模型的发现与之前的研究一致,并在分析免疫检查点时产生了有意义的结果.

结论:

  • 开发的风险评估模型准确且可靠地预测骨髓瘤的结果.
  • 该模型有助于分析骨髓瘤的免疫特征,为改善患者管理提供了潜力.