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多个机器学习算法在大型和多个非酒精性脂肪肝炎队列中识别了13种类型的细胞死亡关键基因

Renao Jiang¹, Longfei Dai¹, Xinjian Xu¹

¹Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, Anhui Province, 230022, China.

Lipids in health and disease

|May 9, 2025

概括

识别关键的细胞死亡基因有助于诊断和向非酒精性脂肪肝炎 (NASH). 一个随机森林模型准确地预测了NASH,揭示了与炎症,脂质代谢和纤维化有关的四个基因.

科学领域:

肝病学肝病学是一种肝病学.
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

不调节的编程细胞死亡途径是非酒精性脂肪肝炎 (NASH) 中肝炎和纤维化的主要原因.
鉴定特定的细胞死亡基因为NASH管理提供了潜在的诊断和治疗途径.

研究的目的:

使用机器学习识别NASH中关键的失调细胞死亡相关基因.
开发NASH诊断和患者分层的预测模型.

主要方法:

整合了来自多个NASH队列的数据,并应用了12个机器学习算法.
使用随机森林算法为NASH开发了一种二进制分类模型.
与临床标记,免疫特征和疾病驱动因素相关联的基因表达;执行了GSEA和共识聚类.

主要成果:

一个随机森林模型实现了跨队列NASH的高诊断准确性.
确定了参与脂质代谢和炎症的四个关键基因.
基因表达与NAS评分,炎症,代谢失调和纤维化有显著的相关性,使患者的分层成为可能.

结论:

一个基于森林的随机分类模型准确地识别了NASH患者.
四个已识别的细胞死亡基因是NASH的潜在生物标志物和治疗点.
这些基因与免疫激活,脂质失调和肝纤维化有关,这凸显了它们在NASH病变发生过程中的作用.

关键词:

细胞死亡细胞死亡机器学习是机器学习.纳什·纳什 (Nash Nash) 是一个名为纳什的城市.预测模型的预测模型.

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Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...