Determining the biomarkers and pathogenesis of myocardial infarction combined with ankylosing spondylitis via a systems biology approach.
10.1007/s11684-025-1132-8
- Author:
Chunying LIU
1
;
Chengfei PENG
2
;
Xiaodong JIA
2
;
Chenghui YAN
2
;
Dan LIU
2
;
Xiaolin ZHANG
2
;
Haixu SONG
3
,
4
;
Yaling HAN
5
,
6
Author Information
1. Beifang Hospital of China Medical University, Shenyang, 110016, China.
2. State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Cardiovascular Research Institute and Department of Cardiology, Shenyang, 110016, China.
3. State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Cardiovascular Research Institute and Department of Cardiology, Shenyang, 110016, China. haixu0215@
4. com.
5. Beifang Hospital of China Medical University, Shenyang, 110016, China. hanyaling@
6. net.
- Publication Type:Journal Article
- Keywords:
ankylosing spondylitis;
bioinformatics analysis;
inflammation;
myocardial infarction
- MeSH:
Humans;
Spondylitis, Ankylosing/complications*;
Systems Biology/methods*;
Myocardial Infarction/diagnosis*;
Biomarkers/metabolism*;
MicroRNAs/genetics*;
Gene Regulatory Networks;
Gene Expression Profiling;
Machine Learning
- From:
Frontiers of Medicine
2025;19(3):507-522
- CountryChina
- Language:English
-
Abstract:
Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.
