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通过生物信息学和机器学习,通过生物信息学和机器学习,识别semaphorin 3C作为sarcopenia和冠状动脉疾病的生物标志物

Shu Ran¹, Zhuoqi Li², Xitong Lin²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China; Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200082, PR China.

Archives of gerontology and geriatrics

|January 19, 2025

概括

赛马福林3C (SEMA3C) 是一个关键基因,将肉类和冠状动脉疾病 (CAD) 联系起来. 针对SEMA3C可能为这些相关疾病提供新的治疗策略.

科学领域:

发现生物标志物的发现.
基因组学就是基因组学.
心血管研究的心血管研究.

背景情况:

萨科白会对生活质量产生负面影响,并可能使冠状动脉疾病 (CAD) 恶化.
识别共享的生物标志物对于结合诊断和治疗肉症和CAD至关重要.

研究的目的:

为了确定新的生物标志物,同时诊断和治疗肉症和CAD.
调查已识别的生物标志物在两种疾病的发病过程中的潜在作用.

主要方法:

使用基因表达综合 (GEO) 数据集用于肉症和CAD.
应用权重基因共同表达网络分析 (WGCNA) 和机器学习算法 (SVM-RFE,LASSO,RF) 来识别关键基因.
进行了门德尔随机化 (MR) 分析,以探索候选基因与疾病风险之间的因果关系.

主要成果:

WGCNA确定了278个与肉症和CAD相关的基因,突出了RNA代谢,信号传递和细胞应激中的作用.
机器学习和ROC曲线分析确定了semaphorin 3C (SEMA3C) 作为一个关键的诊断基因.
MR 分析证实,较高的 SEMA3C 水平与 CAD 风险增加和手握强度较低有关.

结论:

SEMA3C被确定为一个关键的基因,涉及到sarcopenia和CAD.
针对SEMA3C是一个潜在的治疗途径,用于管理同时发生的肉症和CAD.

关键词:

冠状动脉疾病是一种冠状动脉疾病.机器学习机器学习门德尔的随机化萨尔科佩尼亚是什么意思萨尔科佩尼亚在WGCNA中,WGCNA是WGCNA.

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