使用共同表达基因模块和机器学习方法开发和验证喘风险预测模型.
Eskezeia Y Dessie1, Yadu Gautam1, Lili Ding1
1Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Scientific reports
|July 12, 2023
概括
这项研究确定了来自呼吸道和鼻上皮细胞的关键基因特征,以准确地分类喘. 这些发现为诊断喘和了解其遗传基础提供了潜在的非侵入性方法.
科学领域:
- 基因组学和生物信息学
- 呼吸系统医学 呼吸系统医学
- 分子生物学分子生物学
背景情况:
- 喘是一种复杂的呼吸系统疾病,基因基础不明.
- 基因表达造型是剖析喘等复杂疾病分子基础的强大工具.
研究的目的:
- 利用基因表达数据,识别参与喘病变的候选基因和途径.
- 开发和验证基于基因签名的机器学习模型,用于基于基因签名的喘分类和预测.
主要方法:
- 差异表达基因 (DEGs) 分析和加权基因共同表达网络分析 (WGCNA) 在气道上皮细胞 (AECs) 和鼻上皮细胞 (NECs) 上进行.
- 机器学习算法,包括最小绝对收缩和选择运算符 (LASSO) 和支持矢量机 (SVM),用于识别基因特征和构建预测模型.
- 模型使用来自支气管上皮细胞 (BEC),气道光滑肌肉 (ASM) 和全血 (WB) 的独立数据集进行了验证.
主要成果:
- 来自AEC和NEC的基因签名在将喘患者与对照患者区分开来时表现出高准确度 (AUC=1).
- 在BEC,ASM和WB的验证显示出显著的诊断性能 (AUC从0.66到0.82不等).
- 功能性注释显示了IL-13,PI3K/AKT和亡信号通路的丰富,突出了关键的分子机制.
结论:
- 表皮衍生的基因签名为喘分类和预测提供了强大的,潜在的非侵入性方法.
- 已识别的基因特征,包括SERPINB2和CTSC,为喘病原体提供了洞察力,可以作为生物标志物.
- 这种以表皮为基础的模型可以作为更难获得的组织的替代品,推进对喘的分子理解.
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