Scholarlink

Background <p>Atherosclerosis is a chronic immune-metabolic inflammatory disease characterized by dysregulated lipid metabolism. Despite its recognized metabolic basis, biomarkers that reflect coordinated metabolic and immune features remain limited. This study aimed to identify diagnostic biomarkers that capture immune-metabolic features of atherosclerosis.</p> Methods <p>Transcriptomic data were obtained from the GEO database. GO and KEGG analyses were performed. WGCNA identified hub genes. ssGSEA was used to evaluate immune cell infiltration. Unsupervised clustering identified atherosclerosis subtypes. Feature genes were selected using machine learning algorithms. Their diagnostic performance was assessed using receiver operating characteristic curve analysis.</p> Results <p>Integrated transcriptomic and metabolic analyses identified 17 metabolism-related hub genes associated with atherosclerosis. Based on their expression profiles, patients in the training set GSE100927 were stratified into two subtypes. Cluster 2 showed higher immune cell infiltration than Cluster 1. <i>DGKZ</i> and <i>UAP1</i> were prioritized as feature genes and demonstrated excellent diagnostic performance in GSE100927 (<i>DGKZ</i>: AUC = 0.983, 95% CI: 0.961–1.000; <i>UAP1</i>: AUC = 0.949, 95% CI: 0.906–0.993), with validation in GSE57691 (<i>DGKZ</i>: AUC = 0.900, 95% CI: 0.780–1.000; <i>UAP1</i>: AUC = 0.822, 95% CI: 0.598–1.000) and moderate stage discrimination in GSE28829. Immune correlation analysis showed that <i>DGKZ</i> broadly positively correlated with immune infiltration (max <i>r</i> = 0.83 for MDSCs), whereas <i>UAP1</i> exhibited predominantly negative correlations (max <i>r</i> = − 0.65 for immature B cells). <i>DGKZ</i> and <i>UAP1</i> were also strongly negatively correlated in the training set (<i>r</i> = − 0.769), supporting their association with distinct immune-metabolic states.</p> Conclusion <p><i>DGKZ</i> and <i>UAP1</i> are potential diagnostic biomarkers for atherosclerosis and reflect immune-metabolic heterogeneity across molecular subtypes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification of atherosclerosis biomarkers through metabolic signatures and immune microenvironment analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月3日

查看全文
  • Hao Zhang,
  • Xue Lv,
  • Lingchuan Guo,
  • Hongli Yang,
  • Meng Yan

摘要

Background

Atherosclerosis is a chronic immune-metabolic inflammatory disease characterized by dysregulated lipid metabolism. Despite its recognized metabolic basis, biomarkers that reflect coordinated metabolic and immune features remain limited. This study aimed to identify diagnostic biomarkers that capture immune-metabolic features of atherosclerosis.

Methods

Transcriptomic data were obtained from the GEO database. GO and KEGG analyses were performed. WGCNA identified hub genes. ssGSEA was used to evaluate immune cell infiltration. Unsupervised clustering identified atherosclerosis subtypes. Feature genes were selected using machine learning algorithms. Their diagnostic performance was assessed using receiver operating characteristic curve analysis.

Results

Integrated transcriptomic and metabolic analyses identified 17 metabolism-related hub genes associated with atherosclerosis. Based on their expression profiles, patients in the training set GSE100927 were stratified into two subtypes. Cluster 2 showed higher immune cell infiltration than Cluster 1. DGKZ and UAP1 were prioritized as feature genes and demonstrated excellent diagnostic performance in GSE100927 (DGKZ: AUC = 0.983, 95% CI: 0.961–1.000; UAP1: AUC = 0.949, 95% CI: 0.906–0.993), with validation in GSE57691 (DGKZ: AUC = 0.900, 95% CI: 0.780–1.000; UAP1: AUC = 0.822, 95% CI: 0.598–1.000) and moderate stage discrimination in GSE28829. Immune correlation analysis showed that DGKZ broadly positively correlated with immune infiltration (max r = 0.83 for MDSCs), whereas UAP1 exhibited predominantly negative correlations (max r = − 0.65 for immature B cells). DGKZ and UAP1 were also strongly negatively correlated in the training set (r = − 0.769), supporting their association with distinct immune-metabolic states.

Conclusion

DGKZ and UAP1 are potential diagnostic biomarkers for atherosclerosis and reflect immune-metabolic heterogeneity across molecular subtypes.