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在NSCLC中解码时间异质性通过机器学习和预后模型构建.

Junpeng Cheng1, Meizhu Xiao1, Qingkang Meng1

  • 1Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China.

World journal of surgical oncology
|June 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究使用单细胞分析确定了驱动非小细胞肺癌 (NSCLC) 进展的关键基因,开发了潜在个性化治疗策略的风险评分模型.

关键词:
机器学习是机器学习.非小细胞肺癌是非小细胞肺癌.时间异质性 时间异质性

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科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 非小细胞肺癌 (NSCLC) 是一种异质性疾病,在早期和晚期阶段具有明显的基因组特征.
  • 确定关键的基因和途径对于改善NSCLC诊断和治疗结果至关重要.

研究的目的:

  • 用单细胞转录组分析来表征恶性NSCLC细胞.
  • 确定驱动NSCLC进展的基因和途径,并开发风险评估工具.

主要方法:

  • 单细胞转录组分析了22名患者的93,406个NSCLC细胞.
  • 使用cNMF进行聚类以识别分子模块,并对时间基因表达进行伪时间分析.
  • 用TCGA和GEO数据验证的XGBoost和LASSO回归用于标记基因选择和风险评分模型开发.

主要成果:

  • 恶性NSCLC细胞分为代谢重编程,细胞周期和干细胞模块.
  • 代谢,细胞循环和干性被确定为NSCLC恶性进化的关键驱动因素.
  • 标记基因 (例如CHCHD2,GAPDH,CD24) 与NSCLC进展相关,通过一个强大的八基因风险评分模型进行验证.

结论:

  • 在NSCLC中确定了时间异质生物标志物,提供了对疾病进展的见解.
  • 潜在的治疗目标和有希望的临床应用工作流程,用于NSCLC风险评估和个性化治疗.