在冠状动脉疾病患者中使用机器学习和生物信息分析识别枢纽生物标志物
Xindi Chang1, Liyu Tao2, Lulu Tian3
1Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Scientific reports
|May 18, 2025
概括
这项研究确定了冠状动脉疾病 (CAD) 患者血液中的11个关键生物标志物,其中ITM2B显示为这种炎症状况的诊断标志物最有前途.
科学领域:
- 心血管研究研究心血管研究
- 分子生物学分子生物学
- 免疫学 免疫学 免疫学
背景情况:
- 了解冠状动脉疾病 (CAD) 的分子基础对于推进治疗策略至关重要.
- 在周围血液中识别可靠的生物标志物可以帮助早期诊断和治疗CAD.
研究的目的:
- 在CAD患者的外周血液中识别和分析差异表达的枢纽生物标志物.
- 探索这些生物标志物在免疫细胞表达和信号通路中的功能性作用.
主要方法:
- 利用了来自基因表达综合 (GEO) 数据库的RNA测序数据集.
- 应用机器学习算法 (LASSO,随机森林,SVM-递归特征消除) 来识别枢纽生物标志物.
- 进行了基因本体学 (GO),KEGG,基因组变异分析 (GSVA) 和基因组丰富分析 (GSEA) 进行功能丰富.
- 通过体内实验验验证的枢纽生物标志物.
主要成果:
- 确定了十一个枢纽生物标志物:ITM2B,GNA15,PLAU,GNG11,HIST1H2BH,SLC11A1,RPS7,DDIT4,CD83,GNLY,以及S100A12. 这些生物标志物包括:
- 这些生物标志物与CD8+ T细胞和自然杀手 (NK) 细胞有关.
- 丰富分析显示,它涉及免疫反应,心肌收缩,氧化酸化和亡信号通路.
- ITM2B成为CAD最重要的生物标志物.
结论:
- ITM2B显示出作为冠状动脉疾病炎症病原体的生物标志物的潜力.
- 鉴定的生物标志物为CAD的治疗干预提供了新的途径.
- 这项研究为CAD提供了分子洞察力,突出了免疫系统的参与.
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