Sun Qi, Xie Longchuan, An He, Chen Wei, Yang Qirong, Wang Peng, Tang Yijun, Peng Chunyan
Clinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Hubei Key Laboratory of Embryonic Stem Cell Research, Hubei University of Medicine, Shiyan, Hubei, China.
Front Cardiovasc Med. 2025 May 15;12:1585030. doi: 10.3389/fcvm.2025.1585030. eCollection 2025.
Metabolic disorder and endothelial dysfunction (ED) are key events in the development and pathophysiology of atherosclerosis and are associated with an elevated risk of Cardiovascular disease (CVD). The pathophysiology remains incompletely understood.
Leftover serum samples were collected and stored at -20 °C until study. Serum specimens were mixed to obtain pooled high glucose serum (GLU group) (11.97 ± 2.09 mmol/L); pooled elevated low-density lipoprotein serum (LDL group) [3.465 (3.3275, 3.6425 mmol/L)]; pooled high triglycerides serum (1.15 ± 0.35 mmol/L) (TG group); Subsequently, Human umbilical vein endothelial cells (HUVECs) were exposed to culture media supplemented with these pooled serum or control serum for 72 h. Whole transcriptome sequencing was performed to characterize gene expression profiles and data were analyzed using GSEA, GO, KEGG. qPCR was used to validate the gene expression.
A total of 306 mRNAs and 523 lncRNAs were identified as differentially expressed in the GLU group, 335 mRNAs and 471 lncRNAs in the LDL group, and 364 mRNAs and 562 lncRNAs in the TG group, compared to the control group. These genes are primarily involved in inflammation, lipid metabolism, and EndMT pathways. By integrating differentially expressed mRNA and curated EndMT-related gene sets from the KEGG, GO, and dbEMT2.0 databases, we identified 52 differentially expressed genes associated with EndMT under metabolic stress conditions. Utilizing machine learning techniques, we established an EndMT-associated gene diagnostic signature comprising CD36, ISG15, HSPB2, and IRS2 for the diagnosis of AS, which achieved an AUC of 0.997. The model was subsequently validated across three independent external cohorts (GSE43292, GSE28829, GSE163154), in which it consistently demonstrated strong diagnostic performance, with AUC values of 0.958, 0.808, and 0.884, respectively. The ceRNA networks associated with EndMT are constructed and related lncRNAs including LINC002381, VIM-AS1, and ELF-AS1 were significantly upregulated in peripheral blood samples.
This study identified novel biomarkers for ED. These findings may provide both a potential biomarker and therapeutic target for the prevention and treatment of atherosclerosis and CAD.
代谢紊乱和内皮功能障碍（ED）是动脉粥样硬化发生发展及病理生理学过程中的关键事件，与心血管疾病（CVD）风险升高相关。其病理生理学仍未完全阐明。
收集剩余血清样本并储存于-20°C直至研究使用。将血清标本混合以获得合并的高血糖血清（GLU组）（11.97±2.09 mmol/L）；合并的低密度脂蛋白升高血清（LDL组）[3.465（3.3275，3.6425 mmol/L）]；合并的高甘油三酯血清（1.15±0.35 mmol/L）（TG组）；随后，将人脐静脉内皮细胞（HUVECs）暴露于补充有这些合并血清或对照血清的培养基中72小时。进行全转录组测序以表征基因表达谱，并使用GSEA、GO、KEGG对数据进行分析。采用qPCR验证基因表达。
与对照组相比，GLU组共鉴定出306个mRNA和523个lncRNA差异表达，LDL组有335个mRNA和471个lncRNA，TG组有364个mRNA和562个lncRNA。这些基因主要参与炎症、脂质代谢和内皮-间质转化（EndMT）途径。通过整合差异表达的mRNA以及KEGG、GO和dbEMT2.0数据库中整理的与EndMT相关的基因集，我们在代谢应激条件下鉴定出52个与EndMT相关的差异表达基因。利用机器学习技术，我们建立了一个用于诊断动脉粥样硬化（AS）的与EndMT相关的基因诊断特征，包括CD36、ISG15、HSPB2和IRS2，其曲线下面积（AUC）为0.997。该模型随后在三个独立的外部队列（GSE43292、GSE28829、GSE163154）中进行验证，在这些队列中它始终表现出强大的诊断性能，AUC值分别为0.958、0.808和0.884。构建了与EndMT相关的竞争性内源RNA（ceRNA）网络，外周血样本中包括LINC002381、VIM-AS1和ELF-AS1在内的相关lncRNA显著上调。
本研究鉴定出了ED的新型生物标志物。这些发现可能为动脉粥样硬化和冠心病的预防和治疗提供潜在的生物标志物和治疗靶点。
