基于深度学习的癌症远程转移的预测和相关基因
Wei-Luo Cai1, Mo Cheng1, Yi Wang2
1Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, China.
Computers in biology and medicine
|November 24, 2023
概括
这项研究确定了与骨,肺部和肝脏癌症转移有关的关键基因. 一个新的CNN模型,MDCNN,准确地预测转移的发生,改善治疗策略.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 癌症转移是癌症死亡率和治疗耐药性的主要驱动因素.
- 基因表达显著影响瘤细胞行为和特定器官的转移模式.
- 识别转移相关基因和开发预测模型对于改善患者的治疗结果至关重要.
研究的目的:
- 识别与癌症转移到特定器官 (骨,肺,肝脏) 相关的基因.
- 开发和验证一种新的深度学习模型,用于预测癌症转移.
- 探索与骨转移相关的生物途径和免疫微环境.
主要方法:
- 利用LASSO和Pearson相关系数来识别与转移相关的基因.
- 开发了一个基于卷积神经网络 (CNN) 的模型,称为MDCNN,包含适应卷积的调制机制.
- 对已识别的与骨转移相关的基因进行了丰富分析和免疫透分析.
主要成果:
- 确定了45个与骨转移,89个与肺转移,86个与肝转移相关的基因.
- 与其他四种方法相比,MDCNN在骨,肺部和肝脏转移方面的预测准确度更高.
- 丰富分析揭示了骨转移的显著途径和GO术语;在骨转移患者中观察到巨细胞和单细胞丰度的增加.
结论:
- 鉴定的基因组为器官特异性癌症转移的分子机制提供了洞察力.
- MDCNN为预测转移提供了一个有前途的工具,可能有助于早期诊断和个性化治疗.
- 骨转移与特定的生物学途径和独特的免疫微环境有关,突出显示了潜在的治疗点.
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