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对糖尿病病的综合性转录和基因组洞察:来自多omics分析和实验验证的证据

Shengnan Chen¹, Lei Chen¹, Ruiqing Dong²

¹Department of Critical Care Nephrology and Blood Purification, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

|October 16, 2025

概括

这项研究使用多omics分析确定了糖尿病病 (DKD) 的新生物标志物. LRG1和PEX6在DKD诊断和治疗开发方面表现有前途.

科学领域:

基因组学就是基因组学.
生物标志物发现发现
翻译医学是一种翻译医学.

背景情况:

糖尿病病 (DKD) 在糖尿病管理中构成了重大挑战.
了解DKD的分子基础对于改善诊断和治疗至关重要.
需要新的生物标志物来有效管理DKD的进展.

研究的目的:

确定和验证糖尿病病 (DKD) 的新生物标志物.
阐明DKD病变发生的分子基础.
为开发有针对性的DKD治疗奠定基础.

主要方法:

综合的多omics分析,包括权重基因共同表达网络和门德尔随机化分析.
机器学习算法 (LASSO回归,SVM) 用于特征性基因识别.
使用人类外周血液和小鼠脏组织 (RT-qPCR) 进行实验验证.

主要成果:

11个基因被确定为与DKD有因果关联.
ADARB2,GOLPH3L,LRG1和PEX6被确定为具有高预测精度的特征DKD基因.
LRG1和PEX6在人类和小鼠组织中得到验证;ADARB2在小鼠脏组织中得到验证.
基因失调与DKD的炎症和免疫反应有关.

结论:

多omics分析对于揭示复杂的疾病机制和识别生物标志物的强大.
LRG1,PEX6和ADARB2显示出作为DKD可靠生物标志物的潜力.
这项研究为开发针对DKD的新型治疗提供了基础.

关键词:

大量和单细胞转录组分析.门德尔的随机化糖尿病病糖尿病病实验验证验证的实验验证.机器学习是机器学习.监管网络监管网络监管网络监管网络监管

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