| 引用本文: | 樊登峰,陈振东,时欢欢,毛艳.基于自噬相关基因构建动脉粥样硬化[]诊断模型及潜在中药预测[J].生物信息学,2026,24(1):85-94. |
| FAN Dengfeng,CHEN Zhendong,SHI Huanhuan,MAO Yan.Construction of atherosclerosis diagnostic model based on autophagy-related genes and prediction of potential chinese medicine[J].Chinese Journal of Bioinformatics,2026,24(1):85-94. |
|
| 摘要: |
| 基于单细胞转录组数据和bulk转录组数据构建动脉粥样硬化诊断模型并预测其潜在治疗中药。本研究从GEO数据库中获取了GSE159677数据集,该数据集包含51 981个细胞的单细胞转录组信息,涵盖了3个患者的动脉粥样硬化核心斑块及其匹配的颈动脉近端相邻组织,利用R软件limma包筛选差异基因。将差异基因与自噬相关基因取交集得到动脉粥样硬化自噬相关差异基因。利用R软件对差异基因进行GO和KEGG富集分析。收集了包含69个动脉粥样硬化样本和35个对照样本的转录组数据集(GSE100927),基于该数据集并利用LASSO进一步筛选关键差异基因并构建诊断模型。差异基因与自噬相关基因取交集得到25个基因。在此基础上,筛选出FOS、HIF1A、HSPA5、HSPA8、P4HB、RAB11A、RGS19、TNFSF10、VAMP 9个基因。基于这9个基因构建的诊断模型具有很好的预测能力,其曲线下面积(Area under curve, AUC)为0.99(95% CI:0.976~1.0)。此外,对9个核心基因进行靶向中药预测,共筛选出182味潜在中药。该研究成果为动脉粥样硬化的诊断和治疗提供了新思路。 |
| 关键词: 动脉粥样硬化 生物信息学 自噬 诊断模型 中药预测 |
| DOI：10.12113/202412009 |
| 分类号:Q343.1 |
| 文献标识码:A |
| 基金项目:新疆维吾尔自治区重点研发计划项目(No. 2023B03012-2). |
|
| Construction of atherosclerosis diagnostic model based on autophagy-related genes and prediction of potential chinese medicine |
|
FAN Dengfeng1,CHEN Zhendong1,SHI Huanhuan1,MAO Yan2
|
|
(1. Karamay Hospital of Integrated Traditional Chinese and Western Medicine (Karamay Peoples Hospital),Karamay 834000,Xinjiang,China; 2. Xinjiang Institute of Materia Medica,Urumqi 830004,Xinjiang,China)
|
| Abstract: |
| The purpose of study is to construct a diagnostic model for atherosclerosis and predict its potential therapeutic Traditional Chinese Medicine based on single-cell and bulk transcriptomic data. his study utilized the GSE159677 dataset from the GEO database, containing single-cell transcriptomic profiles of 1,1 cells from atherosclerotic core plaques and matched proximal adjacent carotid tissues of three patients. Differential gene expression analysis was performed using the limma package in R. The intersection of DEGs and autophagy-related genes was used to obtain atherosclerosis-related autophagy-associated DEGs. GO and KEGG enrichment analyses were performed on the DEGs using R software. Based on the transcriptomic data from GSE100927 dataset, which includes 69 atherosclerosis samples and 35 controls, comprising 69 atherosclerosis samples and 35 controls was analyzed, LASSO regression was used to further select key DEGs and construct a diagnostic model. The intersection of DEGs and autophagy-related genes resulted in 25 genes. From this, FOS, HIF1A, HSPA5, HSPA8, P4HB, RAB11A, RGS19, TNFSF10, and VAMP were selected. The diagnostic model based on these 9 genes demonstrated excellent predictive performance, achieving an area under the curve (AUC) of 0.99 (95% CI: 0.976–1.0). Additionally, 182 potential traditional Chinese medicine (TCM) candidates targeting these core genes were predicted. In conclusion, the results of this study provide novel insights into the diagnosis and treatment of atherosclerosis. |
| Key words: Atherosclerosis Bioinformatics Autophagy Diagnostic model Traditional chinese medicine prediction |
