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Updated: Jun 24, 2025

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计算单细胞方法用于预测癌症风险.

Andrew E Teschendorff1

  • 1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Biochemical Society transactions
|June 10, 2024
PubMed
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此摘要是机器生成的。

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预测癌症风险是一项挑战. 使用单细胞数据的新计算方法可以识别可能成为癌症的细胞,改善早期检测和预防策略.

科学领域:

  • 计算生物学是一种计算生物学.
  • 系统生物学 系统生物学
  • 癌症研究 癌症研究

背景情况:

  • 尽管生物技术取得了进展,但癌症风险预测仍是一个重大挑战.
  • 改善预测对于提高预防,早期检测和生存率至关重要.
  • 当前的计算和实验方法面临着局限性.

研究的目的:

  • 总结新出现的计算挑战和癌症风险预测方面的进展.
  • 专注于利用单细胞数据进行癌症风险评估的计算策略.
  • 引入新的自下而上的网络建模方法,用于估计癌症干和脱差.

主要方法:

  • 使用单细胞奥米克数据 (scRNA-seq,snRNA-seq).
  • 从系统生物学角度使用自下而上的网络建模.
  • 描述两种方法:扩散网络 (独立于组织/血统) 和转录因子规律 (特定于组织/血统).

主要成果:

  • 开发的计算工具成功地划分了异质的细胞间癌症风险景观.
  • 这些方法可以识别更有可能发展为癌细胞的细胞.
  • 在风险评估中,对前入侵性癌症阶段的应用证明了有效性.
关键词:
癌症的风险 癌症的风险基因监管网络 基因监管网络一个单细胞的奥米克.干细胞生物学 干细胞生物学系统生物学 系统生物学

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结论:

  • 单细胞欧米数据的自下而上的系统生物建模代表了一种新的计算范式.
  • 这种方法有望推动预防和早期癌症检测策略的发展.
  • 它可以在单细胞水平上更准确地预测癌症风险.