多奥米克与先发性癌症相遇:为早期预防癌症铺平道路
Feiran Zhang1, Ziyi Zhou1, Peng Zhang1
1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China.
Research (Washington, D.C.)
|November 19, 2025
概括
了解癌前病变 (PMLs) 是早期癌症检测的关键. 多组和AI揭示了PML的演变,有助于早期诊断和预防各种癌症的策略.
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:
- 癌前病变 (PML) 是癌症早期检测和拦截的关键目标.
- 多组学和人工智能 (AI) 的进步为瘤发生提供了新的见解.
- 了解各种癌症类型的PML对于开发有效的预防策略至关重要.
研究的目的:
- 在15种癌症类型中对临床认可的PML进行目录,详细说明其流行病学和恶性转变潜力.
- 总结最近的多学科发现和理解PML演变的挑战.
- 讨论人工智能驱动的多omics集成和瘤发生轨迹推断的计算策略.
主要方法:
- 在15种癌症类型中对PML进行分类,包括流行病学数据和转化潜力.
- 审查批量,单细胞和空间奥米克研究,以了解PML的分子,细胞和空间进化.
- 讨论基于网络的计算策略和深度学习人工智能,用于多omics数据分析.
主要成果:
- 多组技术揭示了PML从癌前状态到侵袭性恶性瘤的动态演变.
- 人工智能和计算策略促进了多omics集成和推断瘤发生轨迹.
- 在15种癌症类型中确定了PML,具有独特的流行病学概况和转变潜力.
结论:
- 多学科和人工智能的融合重新定义了PML研究,用于胰腺癌早期风险分层.
- 可以开发高精度的早期诊断生物标志物和针对PML的药理预防策略.
- 这种方法使得各种癌症的早期检测和药理预防成为可能.
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