通过多个机器学习算法识别大动脉剖析和代谢综合征的共同诊断效应基因
Yang Zhang1,2, Jinwei Li3,4, Lihua Chen5
1Kunming Medical University, Kunming, 650000, Yunnan, China.
Scientific reports
|September 8, 2023
概括
这项研究确定了主动脉解剖 (AD) 和代谢综合征 (MS) 中常见的关键基因和分子通路,揭示了共享的发病因. 这些发现为了解和潜在地治疗这些相互关联的疾病提供了新的途径.
科学领域:
- 心血管生物学 心血管生物学
- 基因组学就是基因组学.
- 代谢疾病 代谢疾病
背景情况:
- 大动脉解剖 (AD) 是一种关键的血管疾病.
- 代谢综合征 (MS) 越来越多地与阿尔茨海默病有关,但潜在的机制仍然不清楚.
研究的目的:
- 确定AD和MS之间共享的枢纽基因特征和分子机制.
- 在MS的背景下开发AD的诊断生物标志物.
主要方法:
- 大量和单细胞RNA测序数据集 (AD,MS,ATAA) 的分析.
- 使用WGCNA识别差异表达基因 (DEG) 和关键模块.
- 机器学习算法 (随机森林,LASSO,XGBoost) 用于枢纽基因选择.
- 功能丰富,免疫细胞透和途径分析.
主要成果:
- 在AD和MS之间406种常见的DEG,在新陈代谢和细胞过程中富含.
- 确定了9个枢纽基因 (例如,SLC20A1),对AD和MS的诊断准确度高.
- SLC20A1涉及脂肪酸代谢,并在内皮细胞中表达.
结论:
- 在AD和MS之间存在共享的分子病原体.
- 确定了枢纽基因和通路,为AD和MS研究提供了新的见解.
- 新的诊断和治疗策略的潜力,针对共同的途径.
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