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基于机器学习算法的糖尿病代谢相关诊断模型的构建和评估

Qiong Xu¹, Yina Zhou², Jianfen Lou³

¹Department of Endocrinology, Hangzhou Ninth People's Hospital, Hangzhou, China.

Environmental toxicology

|April 29, 2024

概括

这项研究确定了诊断糖尿病 (DM) 的五个关键基因,并开发了一个准确的诊断模型. 这些发现为DM病原和患者分类提供了新的见解.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

糖尿病是一种广泛的代谢障碍,需要更好地了解其分子基础,以改善诊断和治疗.
识别特定的分子标志物对于早期检测和DM的治疗干预至关重要.

研究的目的:

利用基因表达数据开发一种针对糖尿病的强有力的诊断模型.
确定具有DM诊断价值的关键基因.
根据分子形状,探索DM患者的分类潜力.

主要方法:

利用公开的基因表达数据集 (GSE7014,GSE25724,GSE156248) 用于模型开发和 (GSE20966) 用于验证.
使用随机森林和LASSO回归来构建诊断分类器.
在PBMC中通过qRT-PCR进行了功能丰富分析和验证了关键基因表达 (CXCL12,PPP1R12B).

主要成果:

在DM患者和对照人群中鉴定出131个差异表达基因 (DEG).
发现了显著的途径改变,包括IL-12信号,JAK信号和DM中的铁.
验证了五个基因 (CXCL12,MXRA5,UCHL1,PPP1R12B,C7) 具有高诊断准确性,通过qRT-PCR证实,在DM患者中显示CXCL12和PPP1R12B的升调.

结论:

开发了一种基于五个关键基因组的DM高度准确的诊断模型.
证明了基因组对DM患者亚型的有用性,揭示了不同的功能特征.
提供了对糖尿病分子机制和病原发生的新见解.

关键词:

在糖尿病中,糖尿病是血糖性糖尿病.不同的基因表达.疾病分类的疾病分类.基因表达总体 (GEO)机器学习模型 (随机森林,LASSO)

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