Fig 1: Flowchart describing the schematic overview of the current study design.We used the GSE7547 data set to identify the relationship between changes in hub genes expressed on monocytes and the degree of coronary collateral artery formation. After data processing, the light‐cyan module was identified as the most significant module through the WGCNA. DEGs in the light‐cyan module were analyzed using the limma package of R. GO enrichment analysis was performed on DEGs in the light‐cyan module. Finally, the hub gene SERPING1 was identified and validated in an independent cohort of patients. CTO indicates chronic total occlusion; DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; GO, Gene Ontology; MAD, median absolute deviation; NYHA, New York Heart Association; PCI, percutaneous coronary intervention; and WGCNA, weighted gene coexpression network analysis.
Fig 2: ROC curve analysis for detecting poor collateral growth. A, ROC curves of plasma serpinG1 for diagnosing coronary collateralization. B, Predicted probabilities derived from regression models for detecting bad arteriogenic responders. Traditional risk factors include variables of male sex, age, body mass index, hypertension, diabetes, cigarette smoking, total cholesterol/high‐density lipoprotein cholesterol ratio, glomerular filtration rate, and log‐transferred hs‐CRP. hs‐CRP indicates high‐sensitivity C‐reactive protein; and ROC, receiver operating characteristic.
Fig 3: The relationship between plasma serpinG1 levels and coronary collateralization. A, Serum levels of serpinG1 were significantly higher in patients with BARs than those in patients with GARs. B, The serum levels of serpinG1 increased stepwise according to the decrease in the Rentrop score. BARS indicates bad arteriogenic responders; and GARs, good arteriogenic responders.
Fig 4: Identification and functional enrichment analysis of hub genes. A, Heat map of the correlation between clinical traits, including GARs, BARs, and CFI. Each column corresponds to a clinical trait, and each row corresponds to a module. Each box contains the corresponding correlation coefficient and P value. Green represents negative correlation, and red represents positive correlation. B, Eigengene adjacency heat map showing extramodular connectivity among all the modules and clinical traits. Red indicates high adjacency (positive correlation), and blue indicates low adjacency (negative correlation). C, Heat map of the DEGs in the light‐cyan module. The expression level of each gene in one sample is represented in the shade of red or blue, which represents upregulation and downregulation, respectively. D, The top GO terms enriched by 14 DEGs in the light‐cyan module. E, Correlation between MM of modules of interest and GS with clinical traits. Scatterplot of GS for BARs vs MM in the light‐cyan module. F, Violin plot of the expression level of SERPING1. The orange violin represents the GAR group as control, and the blue violin represents the BAR group. BARS indicates bad arteriogenic responders; CFI, collateral flow index; DEGs, differentially expressed genes; GARs, good arteriogenic responders; GO, Gene Ontology; GS, gene significance; ME, Module eigengene E; and MM, module membership.
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