在个性化瘤学中发现和应用生物标志物的多omics策略
Ziming Jiang1, Haoxuan Zhang1, Yibo Gao2,3,4,5
1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Molecular biomedicine
|November 21, 2025
概括
多omics集成,结合基因组学和其他基因组学.
科学领域:
- 生物医学研究的研究.
- 癌症基因组学 癌症基因组学
- 翻译性瘤学是指翻译性瘤学.
背景情况:
- 多omics策略正在彻底改变个性化瘤学的生物标志物发现.
- 需要对多学科整合策略和应用进行全面的综合.
研究的目的:
- 为多学科整合提供一个框架,包括工作流程,分析技术和计算工具.
- 强调机器学习和数据解释的深度学习.
- 讨论多学科研究中的挑战和未来方向.
主要方法:
- 审查当前的多主题整合策略 (水平和垂直).
- 强调用于数据分析的机器学习和深度学习.
- 突出了单细胞和空间多态的进步.
主要成果:
- 多omics方法为癌症诊断,预后和治疗决策提供了有希望的生物标志物面板.
- 挑战包括数据异质性,可重复性和临床验证.
- 单细胞和空间多组学增强生物标志物的发现和对瘤异质性的理解.
结论:
- 多组学在个性化瘤学中发挥着至关重要的作用,预测药物反应和优化治疗.
- 将技术创新与临床应用联系起来是关键.
- 本综述是研究人员和临床医生在多omics生物标志物发现和癌症护理方面的资源.
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