SerpinG1: A Novel Biomarker Associated With Poor Coronary Collateral in Patients With Stable Coronary Disease and Chronic Total Occlusion.
Shuai ChenLe-Ying LiZhi-Ming WuYong LiuFei-Fei LiKe HuangYi-Xuan WangQiu-Jing ChenXiao Qun WangWei-Feng ShenRui-Yan ZhangYing ShenLin LuFeng-Hua DingYang DaiPublished in: Journal of the American Heart Association (2022)
Background This study aimed to explore predictive biomarkers of coronary collateralization in patients with chronic total occlusion. Methods and Results By using a microarray expression profiling program downloaded from the Gene Expression Omnibus database, weighted gene coexpression network analysis was constructed to analyze the relationship between potential modules and coronary collateralization and screen out the hub genes. Then, the hub gene was identified and validated in an independent cohort of patients (including 299 patients with good arteriogenic responders and 223 patients with poor arteriogenic responders). Weighted gene coexpression network analysis showed that SERPING1 in the light-cyan module was the only gene that was highly correlated with both the gene module and the clinical traits. Serum levels of serpinG1 were significantly higher in patients with bad arteriogenic responders than in patients with good arteriogenic responders (472.53±197.16 versus 314.80±208.92 μg/mL; P <0.001) and were negatively associated with the Rentrop score (Spearman r =-0.50; P <0.001). Receiver operating characteristic curve analysis indicated that the area under the curve was 0.77 (95% CI, 0.72-0.81; P <0.001) for serum serpinG1 in prediction of bad arteriogenic responders. After adjusting for traditional cardiovascular risk factors, serum serpinG1 levels (per SD) remained an independent risk factor for bad arteriogenic responders (odds ratio, 2.20 [95% CI, 1.76-2.74]; P <0.001). Conclusions Our findings illustrate that SERPING1 screened by weighted gene coexpression network analysis was associated with poor collateralization in patients with chronic total occlusion.
Keyphrases
- network analysis
- genome wide
- genome wide identification
- copy number
- gene expression
- coronary artery
- coronary artery disease
- cardiovascular risk factors
- dna methylation
- metabolic syndrome
- end stage renal disease
- heart failure
- type diabetes
- cardiovascular disease
- newly diagnosed
- genome wide analysis
- magnetic resonance
- magnetic resonance imaging
- computed tomography
- chronic kidney disease
- quality improvement
- bioinformatics analysis
- adverse drug
- aortic valve
- drug induced
- human health