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基于细胞衰老相关基因及其潜在机制的诊断模型的构建

Xiangyao Gong^1,2, Wei Lu³, Qihua Xiao^2,4

¹Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.

Frontiers in neurology

|June 16, 2025

概括

这项研究确定了用于诊断的关键细胞衰老相关基因. 开发的诊断模型显示了个性化治疗和理解疾病机制的前景.

科学领域:

神经科学是一个神经科学.
遗传学遗传学是一个
生物技术是生物技术.

背景情况:

是一种慢性脑部疾病,其特征是神经退行和异常的神经元放电.
细胞衰老在神经退行性疾病中越来越被认可,但其在中的作用仍然不清楚.

研究的目的:

基于细胞衰老相关基因 (CSRGs) 构建的诊断模型.
分析CSRG在病原发生中的作用.
确定针对的个性化诊断和治疗的潜在目标.

主要方法:

从病数据集中选差异表达基因 (DEGs).
通过将DEG与CSRG交叉来确定与有关的CSRG.
利用基因本体学 (GO),基因和基因组的京都百科全书 (KEGG) 和基因组丰富分析 (GSEA) 进行功能丰富.
使用后勤回归,随机森林和LASSO回归构建了一个诊断模型,确定RPS6KA3,CTSD和ZNF101作为关键基因.

主要成果:

开发了一种多因素后勤回归模型来评估风险.
诊断模型在验证数据集 (GSE143272和GSE32534) 中显示了高的曲线下面面积 (AUC) 值.
mRNA-miRNA和mRNA-转录因子 (TF) 网络揭示了关键基因的潜在调节机制.

结论:

生物信息学分析阐明了细胞衰老在中的作用.
鉴定到的关键基因和诊断模型为个性化的诊断和治疗提供了潜力.
这项研究通过细胞衰老的透视提供了对病原学的洞察.

关键词:

生物信息学分析分析细胞衰老细胞衰老诊断模型诊断模型诊断模型是一种.与衰老相关的分泌表现型

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