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Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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生态进化引导病理学分析预测DCIS升级阶段

Yujie Xiao1, Manal Elmasry2,3, Ji Dong K Bai2

  • 1Department of Applied Mathematics and Statistics, Stony Brook University, NY, USA.

bioRxiv : the preprint server for biology
|July 9, 2024
PubMed
概括
此摘要是机器生成的。

对缺氧和酸化生物标志物的生态分析改善了早期乳腺癌 (DCIS) 进展的预测. 这种方法可以识别瘤微环境的息地和,增强用于疾病分期的生物标志物发现.

关键词:
在DCIS中,我们可以使用DCIS数字病理学数字病理学生态进化的生物标志物息地 居住地 居住地机器学习 机器学习代谢表型的代谢表型利基市场是一个利基.病理学是一种病理学.瘤生态学和进化过程

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科学领域:

  • 在瘤学瘤学.
  • 癌症生物学 癌症生物学
  • 瘤微环境 生态学 生态学

背景情况:

  • 癌症在动态生态系统中进化,因此需要对它们的生态动态进行表征,以了解进化和发现预测生物标志物.
  • 管道癌 in situ (DCIS) 是一种早期乳腺癌,其表皮细胞生长异常,局限于乳道,在那里预测进展仍然具有挑战性.

研究的目的:

  • 调查低氧和酸性疾病生物标志物的生态分析是否可以改善DCIS升级的预测.
  • 开发和应用生态进化方法来识别瘤微环境息地和生物标志物发现的利基.

主要方法:

  • 开发了一种生态进化方法,根据氧气扩散距离来定义瘤息地.
  • 使用生物标志物CA9 (低氧) 和LAMP2b (酸化) 鉴定出癌细胞代谢表型.
  • 分析了生物标志物的空间模式,以定义不同的瘤,并使用随机森林分类器预测患者的升级,并进行5倍验证.

主要成果:

  • 与传统方法相比,生态分析显著提高了DCIS上升阶段的缺氧和酸化生物标志物的预测能力.
  • 通过特定的生物标志物空间模式特征的独特瘤位被确定.
  • 一个随机的森林分类器在预测基于利基分布的患者升级时获得了0.74的AUC.

结论:

  • 瘤生态特征对于生态进化设计方法在新生物标志物发现方面至关重要.
  • 这项研究表明,生物标志物的生态分析有潜力改善DCIS进展的预测.
  • 这些发现强调了考虑瘤微环境的空间和代谢异质性的重要性.