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根据生物信息学,机器学习和贝叶斯网络来解读心力衰竭的细胞因子代码

Yiding Yu¹, Xiujuan Liu², Wenwen Liu³

¹Shandong University of Traditional Chinese Medicine, Jinan 250014, China.

Biochimica et biophysica acta. Molecular basis of disease

|February 5, 2025

概括

这项研究确定了参与心力衰竭 (HF) 进展的关键细胞因子基因,揭示了潜在的治疗点和药物. 这些发现为HF病理生理学和未来治疗的免疫调节策略提供了新的见解.

科学领域:

心血管研究研究心血管研究
免疫学免疫学免疫学
遗传学是一个遗传学.

背景情况:

尽管目前的治疗方法,心力衰竭 (HF) 仍然是一个重大的健康负担,预后不佳.
需要新的病理和心脏保护途径来有效管理HF.
细胞因子在HF进展中的作用需要进一步阐明.

研究的目的:

为了确定与心力衰竭相关的细胞因子相关的基因和途径.
探索细胞因子作为HF治疗点的潜力.
为了发现新的诊断生物标志物和治疗性药物HF.

主要方法:

使用GEO数据库用于HF数据集和WGCNA用于基因识别.
进行了GO,KEGG和Reactome通路丰富分析.
使用LASSO回归,射频算法和贝叶斯网络进行基因和途径分析.

主要成果:

确定了13个与HF相关的细胞因子相关的基因,调解炎症反应.
发现了两个关键的细胞因子调节链:IL34-CCL5-CCL4和IL34-CCL5-CXCL12.
确定了五个关键基因 (CCL4,CCL5,CXCL12,CXCL14,IL34) 并预测了47种潜在药物,包括Proscillaridin.

结论:

提供了对HF病理生理学中细胞因子机制的新见解.
突出了潜在的免疫调节策略和HF的基因疗法.
建议候选药物,并强调需要对发现进行临床验证.

关键词:

贝叶斯网络是一个贝叶斯网络.细胞因子细胞因子心脏衰竭是因为心脏衰竭.免疫透分析机器学习是机器学习.

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