OVsignGenes:基于基因表达的神经网络模型估计高度血清卵巢癌的分子亚型
Anastasiya Kobelyatskaya1,2, Anna Tregubova2, Andrea Palicelli3
1Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.
Cancers
|December 17, 2024
概括
高度血清性癌 (HGSCs) 呈现出分子异质性. 一个新的深度神经网络模型,OVsignGenes,准确地分类HGSC亚型,识别分化亚型作为潜在的瘤发起者.
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:
- 高度血清性癌 (HGSCs) 是分子异质的,影响患者的治疗结果和治疗反应.
- 之前定义的四种亚型 (分化,免疫反应,介质细胞,增殖) 导致了这种复杂性.
- 准确的亚型化对于个性化药物和HGSC的向治疗开发至关重要.
研究的目的:
- 开发一种强大的方法来分类HGSC分子亚型.
- 识别与每个亚型相关的关键差异表达基因.
- 调查差异化亚型在瘤进展中的潜在作用.
主要方法:
- 分析了来自六个队列 (535个样本) 的大量RNA-seq,scRNA-seq和空间转录组数据.
- 使用edgeR和通路丰富分析 (KEGG,GSVA) 的差异基因表达分析.
- 使用Keras和TensorFlow进行子类型预测的深度神经网络 (OVsignGenes) 的开发.
主要成果:
- 鉴定了357个亚型特定的差异表达基因 (96个差异化,33个免疫反应,91个介酶,137个增殖).
- OVsignGenes模型表现出高准确度 (AUC = 0.969) 和平台阻力.
- 在多个队列中成功验证模型,包括单细胞和空间转录组数据.
结论:
- 这种差异化亚型位于其他亚型的交叉点,缺乏独特的基因表达和通路配置文件.
- 差异化亚型被提议作为一个开始的亚型,进化为其他高高质量的亚型.
- 这一发现对理解高高的异质性和制定有针对性的治疗策略有影响.
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